cuda : poc for norm quants (only -b 1 works)

* gguf : add workflow for Pypi publishing

* gguf : add workflow for Pypi publishing

* fix trailing whitespace

.devops/full-rocm.Dockerfile

@@ -0,0 +1,44 @@
+ARG UBUNTU_VERSION=22.04
+
+# This needs to generally match the container host's environment.
+ARG ROCM_VERSION=5.6
+
+# Target the CUDA build image
+ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
+
+FROM ${BASE_ROCM_DEV_CONTAINER} as build
+
+# Unless otherwise specified, we make a fat build.
+# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
+# This is mostly tied to rocBLAS supported archs.
+ARG ROCM_DOCKER_ARCH=\
+    gfx803 \
+    gfx900 \
+    gfx906 \
+    gfx908 \
+    gfx90a \
+    gfx1010 \
+    gfx1030 \
+    gfx1100 \
+    gfx1101 \
+    gfx1102
+
+COPY requirements.txt requirements.txt
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+# Set nvcc architecture
+ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
+# Enable ROCm
+ENV LLAMA_HIPBLAS=1
+ENV CC=/opt/rocm/llvm/bin/clang
+ENV CXX=/opt/rocm/llvm/bin/clang++
+
+RUN make
+
+ENTRYPOINT ["/app/.devops/tools.sh"]

.devops/llama-cpp-clblast.srpm.spec

@@ -0,0 +1,84 @@
+# SRPM for building from source and packaging an RPM for RPM-based distros.
+# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# Built and maintained by John Boero - boeroboy@gmail.com
+# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
+
+# Notes for llama.cpp:
+# 1. Tags are currently based on hash - which will not sort asciibetically.
+#    We need to declare standard versioning if people want to sort latest releases.
+# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.
+# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.
+#    Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
+# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.
+#    It is up to the user to install the correct vendor-specific support.
+
+Name:           llama.cpp-clblast
+Version:        %( date "+%%Y%%m%%d" )
+Release:        1%{?dist}
+Summary:        OpenCL Inference of LLaMA model in C/C++
+License:        MIT
+Source0:        https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
+BuildRequires:  coreutils make gcc-c++ git mesa-libOpenCL-devel clblast-devel
+Requires:       clblast
+URL:            https://github.com/ggerganov/llama.cpp
+
+%define debug_package %{nil}
+%define source_date_epoch_from_changelog 0
+
+%description
+CPU inference for Meta's Lllama2 models using default options.
+
+%prep
+%setup -n llama.cpp-master
+
+%build
+make -j LLAMA_CLBLAST=1
+
+%install
+mkdir -p %{buildroot}%{_bindir}/
+cp -p main %{buildroot}%{_bindir}/llamaclblast
+cp -p server %{buildroot}%{_bindir}/llamaclblastserver
+cp -p simple %{buildroot}%{_bindir}/llamaclblastsimple
+
+mkdir -p %{buildroot}/usr/lib/systemd/system
+%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llamaclblast.service
+[Unit]
+Description=Llama.cpp server, CPU only (no GPU support in this build).
+After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
+
+[Service]
+Type=simple
+EnvironmentFile=/etc/sysconfig/llama
+ExecStart=/usr/bin/llamaclblastserver $LLAMA_ARGS
+ExecReload=/bin/kill -s HUP $MAINPID
+Restart=never
+
+[Install]
+WantedBy=default.target
+EOF
+
+mkdir -p %{buildroot}/etc/sysconfig
+%{__cat} <<EOF  > %{buildroot}/etc/sysconfig/llama
+LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
+EOF
+
+%clean
+rm -rf %{buildroot}
+rm -rf %{_builddir}/*
+
+%files
+%{_bindir}/llamaclblast
+%{_bindir}/llamaclblastserver
+%{_bindir}/llamaclblastsimple
+/usr/lib/systemd/system/llamaclblast.service
+%config /etc/sysconfig/llama
+
+
+%pre
+
+%post
+
+%preun
+%postun
+
+%changelog

.devops/llama-cpp-cublas.srpm.spec

@@ -0,0 +1,83 @@
+# SRPM for building from source and packaging an RPM for RPM-based distros.
+# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# Built and maintained by John Boero - boeroboy@gmail.com
+# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
+
+# Notes for llama.cpp:
+# 1. Tags are currently based on hash - which will not sort asciibetically.
+#    We need to declare standard versioning if people want to sort latest releases.
+# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.
+# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.
+#    Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
+# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.
+#    It is up to the user to install the correct vendor-specific support.
+
+Name:           llama.cpp-cublas
+Version:        %( date "+%%Y%%m%%d" )
+Release:        1%{?dist}
+Summary:        CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
+License:        MIT
+Source0:        https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
+BuildRequires:  coreutils make gcc-c++ git cuda-toolkit
+Requires:       cuda-toolkit
+URL:            https://github.com/ggerganov/llama.cpp
+
+%define debug_package %{nil}
+%define source_date_epoch_from_changelog 0
+
+%description
+CPU inference for Meta's Lllama2 models using default options.
+
+%prep
+%setup -n llama.cpp-master
+
+%build
+make -j LLAMA_CUBLAS=1
+
+%install
+mkdir -p %{buildroot}%{_bindir}/
+cp -p main %{buildroot}%{_bindir}/llamacppcublas
+cp -p server %{buildroot}%{_bindir}/llamacppcublasserver
+cp -p simple %{buildroot}%{_bindir}/llamacppcublassimple
+
+mkdir -p %{buildroot}/usr/lib/systemd/system
+%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llamacublas.service
+[Unit]
+Description=Llama.cpp server, CPU only (no GPU support in this build).
+After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
+
+[Service]
+Type=simple
+EnvironmentFile=/etc/sysconfig/llama
+ExecStart=/usr/bin/llamacppcublasserver $LLAMA_ARGS
+ExecReload=/bin/kill -s HUP $MAINPID
+Restart=never
+
+[Install]
+WantedBy=default.target
+EOF
+
+mkdir -p %{buildroot}/etc/sysconfig
+%{__cat} <<EOF  > %{buildroot}/etc/sysconfig/llama
+LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
+EOF
+
+%clean
+rm -rf %{buildroot}
+rm -rf %{_builddir}/*
+
+%files
+%{_bindir}/llamacppcublas
+%{_bindir}/llamacppcublasserver
+%{_bindir}/llamacppcublassimple
+/usr/lib/systemd/system/llamacublas.service
+%config /etc/sysconfig/llama
+
+%pre
+
+%post
+
+%preun
+%postun
+
+%changelog

.devops/llama-cpp.srpm.spec

@@ -0,0 +1,85 @@
+# SRPM for building from source and packaging an RPM for RPM-based distros.
+# https://fedoraproject.org/wiki/How_to_create_an_RPM_package
+# Built and maintained by John Boero - boeroboy@gmail.com
+# In honor of Seth Vidal https://www.redhat.com/it/blog/thank-you-seth-vidal
+
+# Notes for llama.cpp:
+# 1. Tags are currently based on hash - which will not sort asciibetically.
+#    We need to declare standard versioning if people want to sort latest releases.
+#    In the meantime, YYYYMMDD format will be used.
+# 2. Builds for CUDA/OpenCL support are separate, with different depenedencies.
+# 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed.
+#    Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo
+# 4. OpenCL/CLBLAST support simply requires the ICD loader and basic opencl libraries.
+#    It is up to the user to install the correct vendor-specific support.
+
+Name:           llama.cpp
+Version:        %( date "+%%Y%%m%%d" )
+Release:        1%{?dist}
+Summary:        CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL)
+License:        MIT
+Source0:        https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz
+BuildRequires:  coreutils make gcc-c++ git libstdc++-devel
+Requires:       libstdc++
+URL:            https://github.com/ggerganov/llama.cpp
+
+%define debug_package %{nil}
+%define source_date_epoch_from_changelog 0
+
+%description
+CPU inference for Meta's Lllama2 models using default options.
+Models are not included in this package and must be downloaded separately.
+
+%prep
+%setup -n llama.cpp-master
+
+%build
+make -j
+
+%install
+mkdir -p %{buildroot}%{_bindir}/
+cp -p main %{buildroot}%{_bindir}/llama
+cp -p server %{buildroot}%{_bindir}/llamaserver
+cp -p simple %{buildroot}%{_bindir}/llamasimple
+
+mkdir -p %{buildroot}/usr/lib/systemd/system
+%{__cat} <<EOF  > %{buildroot}/usr/lib/systemd/system/llama.service
+[Unit]
+Description=Llama.cpp server, CPU only (no GPU support in this build).
+After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target
+
+[Service]
+Type=simple
+EnvironmentFile=/etc/sysconfig/llama
+ExecStart=/usr/bin/llamaserver $LLAMA_ARGS
+ExecReload=/bin/kill -s HUP $MAINPID
+Restart=never
+
+[Install]
+WantedBy=default.target
+EOF
+
+mkdir -p %{buildroot}/etc/sysconfig
+%{__cat} <<EOF  > %{buildroot}/etc/sysconfig/llama
+LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin"
+EOF
+
+%clean
+rm -rf %{buildroot}
+rm -rf %{_builddir}/*
+
+%files
+%{_bindir}/llama
+%{_bindir}/llamaserver
+%{_bindir}/llamasimple
+/usr/lib/systemd/system/llama.service
+%config /etc/sysconfig/llama
+
+%pre
+
+%post
+
+%preun
+%postun
+
+%changelog

.devops/main-rocm.Dockerfile

@@ -0,0 +1,44 @@
+ARG UBUNTU_VERSION=22.04
+
+# This needs to generally match the container host's environment.
+ARG ROCM_VERSION=5.6
+
+# Target the CUDA build image
+ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete
+
+FROM ${BASE_ROCM_DEV_CONTAINER} as build
+
+# Unless otherwise specified, we make a fat build.
+# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878
+# This is mostly tied to rocBLAS supported archs.
+ARG ROCM_DOCKER_ARCH=\
+    gfx803 \
+    gfx900 \
+    gfx906 \
+    gfx908 \
+    gfx90a \
+    gfx1010 \
+    gfx1030 \
+    gfx1100 \
+    gfx1101 \
+    gfx1102
+
+COPY requirements.txt requirements.txt
+
+RUN pip install --upgrade pip setuptools wheel \
+    && pip install -r requirements.txt
+
+WORKDIR /app
+
+COPY . .
+
+# Set nvcc architecture
+ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
+# Enable ROCm
+ENV LLAMA_HIPBLAS=1
+ENV CC=/opt/rocm/llvm/bin/clang
+ENV CXX=/opt/rocm/llvm/bin/clang++
+
+RUN make
+
+ENTRYPOINT [ "/app/main" ]

.dockerignore

@@ -5,14 +5,7 @@
 .vscode/
 .DS_Store
 
-build/
-build-em/
-build-debug/
-build-release/
-build-static/
-build-no-accel/
-build-sanitize-addr/
-build-sanitize-thread/
+build*/
 
 models/*
 

.github/workflows/build.yml

@@ -41,6 +41,12 @@ jobs:
         run: |
           CC=gcc-8 make
 
+      - name: Test
+        id: make_test
+        run: |
+          CC=gcc-8 make tests
+          make test
+
   ubuntu-latest-cmake:
     runs-on: ubuntu-latest
 
@@ -157,6 +163,12 @@ jobs:
         run: |
           make
 
+      - name: Test
+        id: make_test
+        run: |
+          make tests
+          make test
+
   macOS-latest-cmake:
     runs-on: macos-latest
 
@@ -291,24 +303,32 @@ jobs:
           cd build
           ctest -C Release --verbose --timeout 900
 
-      - name: Get commit hash
-        id: commit
-        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(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: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         run: |
           Copy-Item LICENSE .\build\bin\Release\llama.cpp.txt
-          7z a llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip .\build\bin\Release\*
+          7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip .\build\bin\Release\*
 
       - name: Upload artifacts
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         uses: actions/upload-artifact@v3
         with:
           path: |
-            llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-x64.zip
+            llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip
 
   windows-latest-cmake-cublas:
     runs-on: windows-latest
@@ -338,23 +358,31 @@ jobs:
           cmake .. -DLLAMA_BUILD_SERVER=ON -DLLAMA_CUBLAS=ON
           cmake --build . --config Release
 
-      - name: Get commit hash
-        id: commit
-        if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
-        uses: pr-mpt/actions-commit-hash@v2
+      - name: Determine tag name
+        id: tag
+        shell: bash
+        run: |
+          BUILD_NUMBER="$(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: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         run: |
-          7z a llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip .\build\bin\Release\*
+          7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip .\build\bin\Release\*
 
       - name: Upload artifacts
         if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
         uses: actions/upload-artifact@v3
         with:
           path: |
-            llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip
+            llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-cu${{ matrix.cuda }}-x64.zip
 
       - name: Copy and pack Cuda runtime
         if: ${{ matrix.cuda == '12.1.0' }}
@@ -400,21 +428,34 @@ jobs:
       - windows-latest-cmake-cublas
 
     steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v1
+
+      - 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: Download artifacts
         id: download-artifact
         uses: actions/download-artifact@v3
 
-      - name: Get commit hash
-        id: commit
-        uses: pr-mpt/actions-commit-hash@v2
-
       - name: Create release
         id: create_release
         uses: anzz1/action-create-release@v1
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
         with:
-          tag_name: ${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}
+          tag_name: ${{ steps.tag.outputs.name }}
 
       - name: Upload release
         id: upload_release

.github/workflows/gguf-publish.yml

@@ -0,0 +1,43 @@
+# This workflow will upload a Python Package using Twine when a GGUF release is created
+# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
+
+# See `gguf-py/README.md` for how to make a release.
+
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Upload Python Package
+
+on:
+  workflow_dispatch:
+  push:
+    # Pattern matched against refs/tags
+    tags:
+      - 'gguf-v*'           # Push events to every version tag
+
+
+jobs:
+  deploy:
+
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python
+      uses: actions/setup-python@v2
+      with:
+        python-version: '3.9.x'
+    - name: Install dependencies
+      run: |
+        cd gguf-py
+        python -m pip install poetry
+        poetry install
+
+    - name: Build package
+      run: poetry build
+    - name: Publish package
+      uses: pypa/gh-action-pypi-publish@release/v1
+      with:
+        password: ${{ secrets.PYPI_API_TOKEN }}

.gitignore

@@ -3,6 +3,9 @@
 *.so
 *.gguf
 *.bin
+*.exe
+*.dll
+*.log
 .DS_Store
 .build/
 .cache/
@@ -14,20 +17,7 @@
 .vs/
 .vscode/
 
-build/
-build-em/
-build-debug/
-build-release/
-build-ci-debug/
-build-ci-release/
-build-static/
-build-cublas/
-build-opencl/
-build-metal/
-build-mpi/
-build-no-accel/
-build-sanitize-addr/
-build-sanitize-thread/
+build*/
 out/
 tmp/
 
@@ -52,12 +42,16 @@ models-mnt
 /gguf-llama-simple
 /libllama.so
 /llama-bench
+/baby-llama
+/beam-search
+/save-load-state
 build-info.h
 arm_neon.h
 compile_commands.json
 CMakeSettings.json
 
 __pycache__
+dist
 
 zig-out/
 zig-cache/
@@ -68,17 +62,18 @@ perf-*.txt
 
 examples/jeopardy/results.txt
 
-pyproject.toml
 poetry.lock
 poetry.toml
 
 # 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-0-llama
+tests/test-tokenizer-0-falcon
+tests/test-tokenizer-1

CMakeLists.txt

@@ -74,6 +74,7 @@ set(LLAMA_CUDA_DMMV_X      "32" CACHE STRING "llama: x stride for dmmv CUDA kern
 set(LLAMA_CUDA_MMV_Y        "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
 option(LLAMA_CUDA_F16                        "llama: use 16 bit floats for some calculations"   OFF)
 set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
+option(LLAMA_HIPBLAS                         "llama: use hipBLAS"                               OFF)
 option(LLAMA_CLBLAST                         "llama: use CLBlast"                               OFF)
 option(LLAMA_METAL                           "llama: use Metal"                                 OFF)
 option(LLAMA_MPI                             "llama: use MPI"                                   OFF)
@@ -300,7 +301,7 @@ if (LLAMA_METAL)
     set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
 
     add_compile_definitions(GGML_USE_METAL)
-    add_compile_definitions(GGML_METAL_NDEBUG)
+    #add_compile_definitions(GGML_METAL_NDEBUG)
 
     # get full path to the file
     #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
@@ -352,6 +353,43 @@ if (LLAMA_CLBLAST)
     endif()
 endif()
 
+if (LLAMA_HIPBLAS)
+    list(APPEND CMAKE_PREFIX_PATH /opt/rocm)
+
+    if (NOT ${CMAKE_C_COMPILER_ID} MATCHES "Clang")
+        message(WARNING "Only LLVM is supported for HIP, hint: CC=/opt/rocm/llvm/bin/clang")
+    endif()
+    if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang")
+        message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++")
+    endif()
+
+    find_package(hip)
+    find_package(hipblas)
+    find_package(rocblas)
+
+    if (${hipblas_FOUND} AND ${hip_FOUND})
+        message(STATUS "HIP and hipBLAS found")
+        add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUBLAS)
+        add_library(ggml-rocm OBJECT ggml-cuda.cu ggml-cuda.h)
+        if (LLAMA_CUDA_FORCE_DMMV)
+            target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_FORCE_DMMV)
+        endif()
+        target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
+        target_compile_definitions(ggml-rocm PRIVATE GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
+        target_compile_definitions(ggml-rocm PRIVATE K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
+        target_compile_definitions(ggml-rocm PRIVATE CC_TURING=1000000000)
+        set_source_files_properties(ggml-cuda.cu PROPERTIES LANGUAGE CXX)
+        target_link_libraries(ggml-rocm PRIVATE hip::device PUBLIC hip::host roc::rocblas roc::hipblas)
+
+        if (LLAMA_STATIC)
+            message(FATAL_ERROR "Static linking not supported for HIP/ROCm")
+        endif()
+        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-rocm)
+    else()
+        message(WARNING "hipBLAS or HIP not found. Try setting CMAKE_PREFIX_PATH=/opt/rocm")
+    endif()
+endif()
+
 if (LLAMA_ALL_WARNINGS)
     if (NOT MSVC)
         set(c_flags
@@ -364,6 +402,7 @@ if (LLAMA_ALL_WARNINGS)
             -Wstrict-prototypes
             -Wpointer-arith
             -Wmissing-prototypes
+            -Werror=implicit-int
         )
         set(cxx_flags
             -Wall

Makefile

@@ -1,11 +1,28 @@
 # Define the default target now so that it is always the first target
-BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf llama-bench
+BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search tests/test-c.o
 
 # 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
+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
 
 default: $(BUILD_TARGETS)
 
+test:
+	@echo "Running tests..."
+	@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 [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \
+			continue; \
+		elif [ "$$test_target" = "tests/test-tokenizer-1" ]; then \
+			continue; \
+		else \
+			./$$test_target; \
+		fi; \
+	done
+	@echo "All tests have been run."
+
+all: $(BUILD_TARGETS) $(TEST_TARGETS)
+
 ifndef UNAME_S
 UNAME_S := $(shell uname -s)
 endif
@@ -64,7 +81,7 @@ endif
 
 # warnings
 CFLAGS   += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \
-			-Wmissing-prototypes
+			-Wmissing-prototypes -Werror=implicit-int
 CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar
 
 # OS specific
@@ -280,8 +297,32 @@ ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 endif # LLAMA_CLBLAST
 
+ifdef LLAMA_HIPBLAS
+	ROCM_PATH	?= /opt/rocm
+	HIPCC	    ?= $(ROCM_PATH)/bin/hipcc
+	GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch)
+	LLAMA_CUDA_DMMV_X       ?= 32
+	LLAMA_CUDA_MMV_Y        ?= 1
+	LLAMA_CUDA_KQUANTS_ITER ?= 2
+	CFLAGS      += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS
+	CXXFLAGS    += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS
+	LDFLAGS     += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib
+	LDFLAGS		+= -lhipblas -lamdhip64 -lrocblas
+	HIPFLAGS    += $(addprefix --offload-arch=,$(GPU_TARGETS))
+	HIPFLAGS    += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
+	HIPFLAGS    += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
+	HIPFLAGS    += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
+	HIPFLAGS    += -DCC_TURING=1000000000
+ifdef LLAMA_CUDA_FORCE_DMMV
+	HIPFLAGS 	+= -DGGML_CUDA_FORCE_DMMV
+endif # LLAMA_CUDA_FORCE_DMMV
+	OBJS        += ggml-cuda.o
+ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
+	$(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $<
+endif # LLAMA_HIPBLAS
+
 ifdef LLAMA_METAL
-	CFLAGS   += -DGGML_USE_METAL -DGGML_METAL_NDEBUG
+	CFLAGS   += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG
 	CXXFLAGS += -DGGML_USE_METAL
 	LDFLAGS  += -framework Foundation -framework Metal -framework MetalKit
 	OBJS     += ggml-metal.o
@@ -302,6 +343,11 @@ k_quants.o: k_quants.c k_quants.h
 	$(CC) $(CFLAGS) -c $< -o $@
 endif # LLAMA_NO_K_QUANTS
 
+ifdef LLAMA_DISABLE_LOGS
+	CFLAGS   += -DLOG_DISABLE_LOGS
+	CXXFLAGS += -DLOG_DISABLE_LOGS
+endif # LLAMA_DISABLE_LOGS
+
 #
 # Print build information
 #
@@ -332,7 +378,7 @@ OBJS += ggml-alloc.o
 llama.o: llama.cpp ggml.h ggml-alloc.h ggml-cuda.h ggml-metal.h llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
-common.o: common/common.cpp common/common.h
+common.o: common/common.cpp common/common.h build-info.h common/log.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 console.o: common/console.cpp common/console.h
@@ -345,7 +391,7 @@ libllama.so: llama.o ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
 
 clean:
-	rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c-to-ggml embd-input-test gguf llama-bench build-info.h $(TEST_TARGETS)
+	rm -vf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h $(BUILD_TARGETS) $(TEST_TARGETS)
 
 #
 # Examples
@@ -385,18 +431,33 @@ $(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-in
 embd-input-test: $(LIB_PRE)embdinput$(DSO_EXT) examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o common.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %$(DSO_EXT),$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput
 
-gguf: examples/gguf/gguf.cpp                                  build-info.h ggml.o llama.o $(OBJS)
+gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp    build-info.h ggml.o llama.o common.o $(OBJS)
+train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o common.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp    build-info.h ggml.o llama.o $(OBJS)
+convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 llama-bench: examples/llama-bench/llama-bench.cpp build-info.h ggml.o llama.o common.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
+baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
+beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
+ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))'
+BUILD_TARGETS += metal
+endif
+
+ifdef LLAMA_METAL
+metal: examples/metal/metal.cpp ggml.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
+endif
+
 build-info.h: $(wildcard .git/index) scripts/build-info.sh
 	@sh scripts/build-info.sh > $@.tmp
 	@if ! cmp -s $@.tmp $@; then \
@@ -418,29 +479,38 @@ benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o
 vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 
-tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o common.o grammar-parser.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 tests/test-grad0: tests/test-grad0.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 tests/test-opt: tests/test-opt.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 tests/test-quantize-fns: tests/test-quantize-fns.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
-tests/test-tokenizer-0: tests/test-tokenizer-0.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
+tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
+tests/test-tokenizer-1: tests/test-tokenizer-1.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
+
+tests/test-c.o: tests/test-c.c llama.h
+	$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@

README.md

@@ -11,15 +11,21 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
 
 ### Hot topics
 
-A new file format has been introduced: [GGUF](https://github.com/ggerganov/llama.cpp/pull/2398)
+- #### IMPORTANT: Tokenizer fixes and API change (developers and projects using `llama.cpp` built-in tokenization must read): https://github.com/ggerganov/llama.cpp/pull/2810
 
-Last revision compatible with the old format: [dadbed9](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa)
+- GGUFv2 adds support for 64-bit sizes + backwards compatible: https://github.com/ggerganov/llama.cpp/pull/2821
 
-### Current `master` should be considered in Beta - expect some issues for a few days!
+- Added support for Falcon models: https://github.com/ggerganov/llama.cpp/pull/2717
 
-### Be prepared to re-convert and / or re-quantize your GGUF models while this notice is up!
+- A new file format has been introduced: [GGUF](https://github.com/ggerganov/llama.cpp/pull/2398)
 
-### Issues with non-GGUF models will be considered with low priority!
+  Last revision compatible with the old format: [dadbed9](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa)
+
+  ### Current `master` should be considered in Beta - expect some issues for a few days!
+
+  ### Be prepared to re-convert and / or re-quantize your GGUF models while this notice is up!
+
+  ### Issues with non-GGUF models will be considered with low priority!
 
 ----
 
@@ -39,6 +45,7 @@ Last revision compatible with the old format: [dadbed9](https://github.com/ggerg
         <li><a href="#memorydisk-requirements">Memory/Disk Requirements</a></li>
         <li><a href="#quantization">Quantization</a></li>
         <li><a href="#interactive-mode">Interactive mode</a></li>
+        <li><a href="#constrained-output-with-grammars">Constrained output with grammars</a></li>
         <li><a href="#instruction-mode-with-alpaca">Instruction mode with Alpaca</a></li>
         <li><a href="#using-openllama">Using OpenLLaMA</a></li>
         <li><a href="#using-gpt4all">Using GPT4All</a></li>
@@ -65,12 +72,11 @@ The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quant
 - Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
 - AVX, AVX2 and AVX512 support for x86 architectures
 - Mixed F16 / F32 precision
-- 4-bit, 5-bit and 8-bit integer quantization support
-- Supports OpenBLAS/Apple BLAS/ARM Performance Lib/ATLAS/BLIS/Intel MKL/NVHPC/ACML/SCSL/SGIMATH and [more](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) in BLAS
-- cuBLAS and CLBlast support
+- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support
+- CUDA, Metal and OpenCL GPU backend support
 
 The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022).
-Since then, the project has improved significantly thanks to many contributions. This project is for educational purposes and serves
+Since then, the project has improved significantly thanks to many contributions. This project is mainly for educational purposes and serves
 as the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library.
 
 **Supported platforms:**
@@ -84,6 +90,7 @@ as the main playground for developing new features for the [ggml](https://github
 
 - [X] LLaMA 🦙
 - [x] LLaMA 2 🦙🦙
+- [X] Falcon
 - [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
 - [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all)
 - [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)
@@ -100,12 +107,13 @@ as the main playground for developing new features for the [ggml](https://github
 
 - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
 - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
-- Node.js: [hlhr202/llama-node](https://github.com/hlhr202/llama-node)
+- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp), [hlhr202/llama-node](https://github.com/hlhr202/llama-node)
 - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
 - Rust: [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
 - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
 - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
 - Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
+- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
 
 **UI:**
 
@@ -114,90 +122,84 @@ as the main playground for developing new features for the [ggml](https://github
 
 ---
 
-Here is a typical run using LLaMA-7B:
+Here is a typical run using LLaMA v2 13B on M2 Ultra:
 
 ```java
-make -j && ./main -m ./models/7B/ggml-model-q4_0.bin -p "Building a website can be done in 10 simple steps:" -n 512
+$ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e
 I llama.cpp build info:
 I UNAME_S:  Darwin
 I UNAME_P:  arm
 I UNAME_M:  arm64
-I CFLAGS:   -I.              -O3 -DNDEBUG -std=c11   -fPIC -pthread -DGGML_USE_ACCELERATE
-I CXXFLAGS: -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC -pthread
+I CFLAGS:   -I.            -O3 -std=c11   -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes -pthread -DGGML_USE_K_QUANTS -DGGML_USE_ACCELERATE
+I CXXFLAGS: -I. -I./common -O3 -std=c++11 -fPIC -DNDEBUG -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar -pthread -DGGML_USE_K_QUANTS
 I LDFLAGS:   -framework Accelerate
-I CC:       Apple clang version 14.0.0 (clang-1400.0.29.202)
-I CXX:      Apple clang version 14.0.0 (clang-1400.0.29.202)
+I CC:       Apple clang version 14.0.3 (clang-1403.0.22.14.1)
+I CXX:      Apple clang version 14.0.3 (clang-1403.0.22.14.1)
 
 make: Nothing to be done for `default'.
-main: seed = 1678486056
-llama_model_load: loading model from './models/7B/ggml-model-q4_0.bin' - please wait ...
-llama_model_load: n_vocab = 32000
-llama_model_load: n_ctx   = 512
-llama_model_load: n_embd  = 4096
-llama_model_load: n_mult  = 256
-llama_model_load: n_head  = 32
-llama_model_load: n_layer = 32
-llama_model_load: n_rot   = 128
-llama_model_load: f16     = 2
-llama_model_load: n_ff    = 11008
-llama_model_load: ggml ctx size = 4529.34 MB
-llama_model_load: memory_size =   512.00 MB, n_mem = 16384
-llama_model_load: .................................... done
-llama_model_load: model size =  4017.27 MB / num tensors = 291
+main: build = 1041 (cf658ad)
+main: seed  = 1692823051
+llama_model_loader: loaded meta data with 16 key-value pairs and 363 tensors from models/llama-13b-v2/ggml-model-q4_0.gguf (version GGUF V1 (latest))
+llama_model_loader: - type  f32:   81 tensors
+llama_model_loader: - type q4_0:  281 tensors
+llama_model_loader: - type q6_K:    1 tensors
+llm_load_print_meta: format         = GGUF V1 (latest)
+llm_load_print_meta: arch           = llama
+llm_load_print_meta: vocab type     = SPM
+llm_load_print_meta: n_vocab        = 32000
+llm_load_print_meta: n_merges       = 0
+llm_load_print_meta: n_ctx_train    = 4096
+llm_load_print_meta: n_ctx          = 512
+llm_load_print_meta: n_embd         = 5120
+llm_load_print_meta: n_head         = 40
+llm_load_print_meta: n_head_kv      = 40
+llm_load_print_meta: n_layer        = 40
+llm_load_print_meta: n_rot          = 128
+llm_load_print_meta: n_gqa          = 1
+llm_load_print_meta: f_norm_eps     = 1.0e-05
+llm_load_print_meta: f_norm_rms_eps = 1.0e-05
+llm_load_print_meta: n_ff           = 13824
+llm_load_print_meta: freq_base      = 10000.0
+llm_load_print_meta: freq_scale     = 1
+llm_load_print_meta: model type     = 13B
+llm_load_print_meta: model ftype    = mostly Q4_0
+llm_load_print_meta: model size     = 13.02 B
+llm_load_print_meta: general.name   = LLaMA v2
+llm_load_print_meta: BOS token = 1 '<s>'
+llm_load_print_meta: EOS token = 2 '</s>'
+llm_load_print_meta: UNK token = 0 '<unk>'
+llm_load_print_meta: LF token  = 13 '<0x0A>'
+llm_load_tensors: ggml ctx size =    0.11 MB
+llm_load_tensors: mem required  = 7024.01 MB (+  400.00 MB per state)
+...................................................................................................
+llama_new_context_with_model: kv self size  =  400.00 MB
+llama_new_context_with_model: compute buffer total size =   75.41 MB
 
-main: prompt: 'Building a website can be done in 10 simple steps:'
-main: number of tokens in prompt = 15
-     1 -> ''
-  8893 -> 'Build'
-   292 -> 'ing'
-   263 -> ' a'
-  4700 -> ' website'
-   508 -> ' can'
-   367 -> ' be'
-  2309 -> ' done'
-   297 -> ' in'
- 29871 -> ' '
- 29896 -> '1'
- 29900 -> '0'
-  2560 -> ' simple'
-  6576 -> ' steps'
- 29901 -> ':'
-
-sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000
+system_info: n_threads = 16 / 24 | AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | VSX = 0 |
+sampling: repeat_last_n = 64, repeat_penalty = 1.100000, presence_penalty = 0.000000, frequency_penalty = 0.000000, top_k = 40, tfs_z = 1.000000, top_p = 0.950000, typical_p = 1.000000, temp = 0.800000, mirostat = 0, mirostat_lr = 0.100000, mirostat_ent = 5.000000
+generate: n_ctx = 512, n_batch = 512, n_predict = 400, n_keep = 0
 
 
-Building a website can be done in 10 simple steps:
-1) Select a domain name and web hosting plan
-2) Complete a sitemap
-3) List your products
-4) Write product descriptions
-5) Create a user account
-6) Build the template
-7) Start building the website
-8) Advertise the website
-9) Provide email support
-10) Submit the website to search engines
-A website is a collection of web pages that are formatted with HTML. HTML is the code that defines what the website looks like and how it behaves.
-The HTML code is formatted into a template or a format. Once this is done, it is displayed on the user's browser.
-The web pages are stored in a web server. The web server is also called a host. When the website is accessed, it is retrieved from the server and displayed on the user's computer.
-A website is known as a website when it is hosted. This means that it is displayed on a host. The host is usually a web server.
-A website can be displayed on different browsers. The browsers are basically the software that renders the website on the user's screen.
-A website can also be viewed on different devices such as desktops, tablets and smartphones.
-Hence, to have a website displayed on a browser, the website must be hosted.
-A domain name is an address of a website. It is the name of the website.
-The website is known as a website when it is hosted. This means that it is displayed on a host. The host is usually a web server.
-A website can be displayed on different browsers. The browsers are basically the software that renders the website on the user’s screen.
-A website can also be viewed on different devices such as desktops, tablets and smartphones. Hence, to have a website displayed on a browser, the website must be hosted.
-A domain name is an address of a website. It is the name of the website.
-A website is an address of a website. It is a collection of web pages that are formatted with HTML. HTML is the code that defines what the website looks like and how it behaves.
-The HTML code is formatted into a template or a format. Once this is done, it is displayed on the user’s browser.
-A website is known as a website when it is hosted
-
-main: mem per token = 14434244 bytes
-main:     load time =  1332.48 ms
-main:   sample time =  1081.40 ms
-main:  predict time = 31378.77 ms / 61.41 ms per token
-main:    total time = 34036.74 ms
+ Building a website can be done in 10 simple steps:
+Step 1: Find the right website platform.
+Step 2: Choose your domain name and hosting plan.
+Step 3: Design your website layout.
+Step 4: Write your website content and add images.
+Step 5: Install security features to protect your site from hackers or spammers
+Step 6: Test your website on multiple browsers, mobile devices, operating systems etc…
+Step 7: Test it again with people who are not related to you personally – friends or family members will work just fine!
+Step 8: Start marketing and promoting the website via social media channels or paid ads
+Step 9: Analyze how many visitors have come to your site so far, what type of people visit more often than others (e.g., men vs women) etc…
+Step 10: Continue to improve upon all aspects mentioned above by following trends in web design and staying up-to-date on new technologies that can enhance user experience even further!
+How does a Website Work?
+A website works by having pages, which are made of HTML code. This code tells your computer how to display the content on each page you visit – whether it’s an image or text file (like PDFs). In order for someone else’s browser not only be able but also want those same results when accessing any given URL; some additional steps need taken by way of programming scripts that will add functionality such as making links clickable!
+The most common type is called static HTML pages because they remain unchanged over time unless modified manually (either through editing files directly or using an interface such as WordPress). They are usually served up via HTTP protocols – this means anyone can access them without having any special privileges like being part of a group who is allowed into restricted areas online; however, there may still exist some limitations depending upon where one lives geographically speaking.
+How to
+llama_print_timings:        load time =   576.45 ms
+llama_print_timings:      sample time =   283.10 ms /   400 runs   (    0.71 ms per token,  1412.91 tokens per second)
+llama_print_timings: prompt eval time =   599.83 ms /    19 tokens (   31.57 ms per token,    31.68 tokens per second)
+llama_print_timings:        eval time = 24513.59 ms /   399 runs   (   61.44 ms per token,    16.28 tokens per second)
+llama_print_timings:       total time = 25431.49 ms
 ```
 
 And here is another demo of running both LLaMA-7B and [whisper.cpp](https://github.com/ggerganov/whisper.cpp) on a single M1 Pro MacBook:
@@ -425,6 +427,35 @@ Building the program with BLAS support may lead to some performance improvements
   | LLAMA_CUDA_F16          | Boolean                |   false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. |
   | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 |       2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
 
+- #### hipBLAS
+
+  This provide BLAS acceleation on HIP supported GPU like AMD GPU.
+  Make sure to have ROCm installed.
+  You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html).
+  Windows support is coming soon...
+
+  - Using `make`:
+    ```bash
+    make LLAMA_HIPBLAS=1
+    ```
+  - Using `CMake`:
+    ```bash
+    mkdir build
+    cd build
+    CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ cmake .. -DLLAMA_HIPBLAS=ON
+    cmake --build .
+    ```
+
+  The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
+  If your GPU is not officialy supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3.
+  The following compilation options are also available to tweak performance (yes, they refer to CUDA, not HIP, because it uses the same code as the cuBLAS version above):
+
+  | Option                  | Legal values           | Default | Description |
+  |-------------------------|------------------------|---------|-------------|
+  | LLAMA_CUDA_DMMV_X       | Positive integer >= 32 |      32 | Number of values in x direction processed by the HIP dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
+  | LLAMA_CUDA_MMV_Y        | Positive integer       |       1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
+  | LLAMA_CUDA_KQUANTS_ITER | 1 or 2                 |       2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
+
 - #### CLBlast
 
   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.
@@ -542,6 +573,8 @@ As the models are currently fully loaded into memory, you will need adequate dis
 
 Several quantization methods are supported. They differ in the resulting model disk size and inference speed.
 
+*(outdated)*
+
 | Model | Measure      | F16    | Q4_0   | Q4_1   | Q5_0   | Q5_1   | Q8_0   |
 |------:|--------------|-------:|-------:|-------:|-------:|-------:|-------:|
 |    7B | perplexity   | 5.9066 | 6.1565 | 6.0912 | 5.9862 | 5.9481 | 5.9070 |
@@ -604,6 +637,16 @@ PROMPT_TEMPLATE=./prompts/chat-with-bob.txt PROMPT_CACHE_FILE=bob.prompt.bin \
     CHAT_SAVE_DIR=./chat/bob ./examples/chat-persistent.sh
 ```
 
+### Constrained output with grammars
+
+`llama.cpp` supports grammars to constrain model output. For example, you can force the model to output JSON only:
+
+```bash
+./main -m ./models/13B/ggml-model-q4_0.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
+```
+
+The `grammars/` folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](./grammars/README.md).
+
 ### Instruction mode with Alpaca
 
 1. First, download the `ggml` Alpaca model into the `./models` folder
@@ -686,8 +729,6 @@ python3 convert.py pygmalion-7b/ --outtype q4_1
   - [LLaMA 2 7B chat](https://huggingface.co/TheBloke/Llama-2-7B-chat-GGML)
   - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML)
   - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGML)
-- Specify `-eps 1e-5` for best generation quality
-- Specify `-gqa 8` for 70B models to work
 
 ### Verifying the model files
 
@@ -885,3 +926,4 @@ docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /
 - [BLIS](./docs/BLIS.md)
 - [Performance troubleshooting](./docs/token_generation_performance_tips.md)
 - [GGML tips & tricks](https://github.com/ggerganov/llama.cpp/wiki/GGML-Tips-&-Tricks)
+- [GBNF grammars](./grammars/README.md)

ci/run.sh

@@ -196,17 +196,17 @@ function gg_run_open_llama_3b_v2 {
     (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
     (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
-    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
     function check_ppl {
         qnt="$1"
@@ -233,6 +233,48 @@ function gg_run_open_llama_3b_v2 {
     check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
 
+    # lora
+    function compare_ppl {
+        qnt="$1"
+        ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+        ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2"
+            return 20
+        fi
+
+        printf '  - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2"
+        return 0
+    }
+
+    path_lora="../models-mnt/open-llama/3B-v2/lora"
+    path_shakespeare="../models-mnt/shakespeare"
+
+    shakespeare="${path_shakespeare}/shakespeare.txt"
+    lora_shakespeare="${path_lora}/ggml-adapter-model.bin"
+
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_config.json
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_model.bin
+    gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/shakespeare.txt
+
+    python3 ../convert-lora-to-ggml.py ${path_lora}
+
+    # f16
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare}                            -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
+    compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # q8_0
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare}                            -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
+    compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # q8_0 + f16 lora-base
+    (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
+    compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+
     set +e
 }
 
@@ -242,6 +284,7 @@ function gg_sum_open_llama_3b_v2 {
     gg_printf 'OpenLLaMA 3B-v2:\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
     gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
     gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
     gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
@@ -253,6 +296,11 @@ function gg_sum_open_llama_3b_v2 {
     gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
     gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
     gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
+    gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
+    gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
+    gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)"
+    gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)"
 }
 
 # open_llama_7b_v2
@@ -310,17 +358,17 @@ function gg_run_open_llama_7b_v2 {
     ./bin/quantize ${model_f16} ${model_q5_k} q5_k
     ./bin/quantize ${model_f16} ${model_q6_k} q6_k
 
-    (time ./bin/main --model ${model_f16}  -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/main --model ${model_q8_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/main --model ${model_q4_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/main --model ${model_q4_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/main --model ${model_q5_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/main --model ${model_q5_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/main --model ${model_q2_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/main --model ${model_q3_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/main --model ${model_q4_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/main --model ${model_q5_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/main --model ${model_q6_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+    (time ./bin/main --model ${model_f16}  -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/main --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
     (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
     (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
@@ -359,6 +407,48 @@ function gg_run_open_llama_7b_v2 {
     check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
 
+    # lora
+    function compare_ppl {
+        qnt="$1"
+        ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+        ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2"
+            return 20
+        fi
+
+        printf '  - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2"
+        return 0
+    }
+
+    path_lora="../models-mnt/open-llama/7B-v2/lora"
+    path_shakespeare="../models-mnt/shakespeare"
+
+    shakespeare="${path_shakespeare}/shakespeare.txt"
+    lora_shakespeare="${path_lora}/ggml-adapter-model.bin"
+
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_config.json
+    gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_model.bin
+    gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/shakespeare.txt
+
+    python3 ../convert-lora-to-ggml.py ${path_lora}
+
+    # f16
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare}                            -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log
+    (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log
+    compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # currently not supported by the CUDA backend
+    # q8_0
+    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare}                            -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log
+    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log
+    #compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
+    # q8_0 + f16 lora-base
+    #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log
+    #compare_ppl "q8_0 / f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log
+
     set +e
 }
 
@@ -368,6 +458,7 @@ function gg_sum_open_llama_7b_v2 {
     gg_printf 'OpenLLaMA 7B-v2:\n'
     gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
     gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)"
     gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
     gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
     gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
@@ -379,6 +470,11 @@ function gg_sum_open_llama_7b_v2 {
     gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
     gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
     gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+    gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)"
+    gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)"
+    #gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)"
+    #gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)"
+    #gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)"
 }
 
 ## main
@@ -391,6 +487,7 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
     ln -sfn ${mnt_models} ${SRC}/models-mnt
 
     python3 -m pip install -r ${SRC}/requirements.txt
+    python3 -m pip install --editable gguf-py
 fi
 
 ret=0

common/common.cpp

@@ -1,15 +1,21 @@
 #include "common.h"
+#include "build-info.h"
+#include "llama.h"
 
-#include <cassert>
-#include <iostream>
-#include <cstring>
-#include <fstream>
-#include <string>
-#include <iterator>
 #include <algorithm>
-#include <sstream>
-#include <unordered_set>
+#include <cassert>
+#include <cmath>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <iterator>
+#include <iostream>
 #include <regex>
+#include <sstream>
+#include <string>
+#include <unordered_set>
+#include <vector>
+#include <cinttypes>
 
 #if defined(__APPLE__) && defined(__MACH__)
 #include <sys/types.h>
@@ -19,11 +25,14 @@
 #if defined(_WIN32)
 #define WIN32_LEAN_AND_MEAN
 #define NOMINMAX
+#include <codecvt>
+#include <locale>
 #include <windows.h>
 #include <fcntl.h>
 #include <io.h>
 #else
 #include <sys/ioctl.h>
+#include <sys/stat.h>
 #include <unistd.h>
 #endif
 
@@ -93,7 +102,6 @@ void process_escapes(std::string& input) {
 
 bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
     bool invalid_param = false;
-    bool escape_prompt = false;
     std::string arg;
     gpt_params default_params;
     const std::string arg_prefix = "--";
@@ -125,8 +133,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.prompt = argv[i];
-        } else if (arg == "-e") {
-            escape_prompt = true;
+        } else if (arg == "-e" || arg == "--escape") {
+            params.escape = true;
         } else if (arg == "--prompt-cache") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -289,7 +297,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.n_batch = std::stoi(argv[i]);
-            params.n_batch = std::min(512, params.n_batch);
         } else if (arg == "--keep") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -388,11 +395,11 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
 #else
             fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
 #endif // GGML_USE_CUBLAS
-        } else if (arg == "--mul-mat-q" || arg == "-mmq") {
+        } else if (arg == "--no-mul-mat-q" || arg == "-nommq") {
 #ifdef GGML_USE_CUBLAS
-            params.mul_mat_q = true;
+            params.mul_mat_q = false;
 #else
-            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n");
+            fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n");
 #endif // GGML_USE_CUBLAS
         } else if (arg == "--low-vram" || arg == "-lv") {
 #ifdef GGML_USE_CUBLAS
@@ -416,8 +423,30 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.antiprompt.push_back(argv[i]);
+        } else if (arg == "-ld" || arg == "--logdir") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.logdir = argv[i];
+
+            if (params.logdir.back() != DIRECTORY_SEPARATOR) {
+                params.logdir += DIRECTORY_SEPARATOR;
+            }
         } else if (arg == "--perplexity") {
             params.perplexity = true;
+        } else if (arg == "--ppl-stride") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.ppl_stride = std::stoi(argv[i]);
+        } else if (arg == "--ppl-output-type") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.ppl_output_type = std::stoi(argv[i]);
         } else if (arg == "--hellaswag") {
             params.hellaswag = true;
         } else if (arg == "--hellaswag-tasks") {
@@ -451,6 +480,9 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             }
         } else if (arg == "-h" || arg == "--help") {
             gpt_print_usage(argc, argv, default_params);
+#ifndef LOG_DISABLE_LOGS
+            log_print_usage();
+#endif // LOG_DISABLE_LOGS
             exit(0);
         } else if (arg == "--random-prompt") {
             params.random_prompt = true;
@@ -490,6 +522,25 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 std::istreambuf_iterator<char>(),
                 std::back_inserter(params.grammar)
             );
+#ifndef LOG_DISABLE_LOGS
+        // Parse args for logging parameters
+        } else if ( log_param_single_parse( argv[i] ) ) {
+            // Do nothing, log_param_single_parse automatically does it's thing
+            //  and returns if a match was found and parsed.
+        } else if ( log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i] ) ) {
+            // We have a matching known parameter requiring an argument,
+            //  now we need to check if there is anything after this argv
+            //  and flag invalid_param or parse it.
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            if( !log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i-1], argv[i]) ) {
+                invalid_param = true;
+                break;
+            }
+        // End of Parse args for logging parameters
+#endif // LOG_DISABLE_LOGS
         } else {
             fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
             gpt_print_usage(argc, argv, default_params);
@@ -509,7 +560,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
         exit(1);
     }
 
-    if (escape_prompt) {
+    if (params.escape) {
         process_escapes(params.prompt);
         process_escapes(params.input_prefix);
         process_escapes(params.input_suffix);
@@ -535,7 +586,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stdout, "  -t N, --threads N     number of threads to use during computation (default: %d)\n", params.n_threads);
     fprintf(stdout, "  -p PROMPT, --prompt PROMPT\n");
     fprintf(stdout, "                        prompt to start generation with (default: empty)\n");
-    fprintf(stdout, "  -e                    process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
+    fprintf(stdout, "  -e, --escape          process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n");
     fprintf(stdout, "  --prompt-cache FNAME  file to cache prompt state for faster startup (default: none)\n");
     fprintf(stdout, "  --prompt-cache-all    if specified, saves user input and generations to cache as well.\n");
     fprintf(stdout, "                        not supported with --interactive or other interactive options\n");
@@ -600,11 +651,13 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stdout, "                        number of layers to store in VRAM\n");
     fprintf(stdout, "  -ts SPLIT --tensor-split SPLIT\n");
     fprintf(stdout, "                        how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
-    fprintf(stdout, "  -mg i, --main-gpu i   the GPU to use for scratch and small tensors\n" );
-    fprintf(stdout, "  -lv, --low-vram       don't allocate VRAM scratch buffer\n" );
-    fprintf(stdout, "  -mmq, --mul-mat-q     use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
-    fprintf(stdout, "                        Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
-    fprintf(stdout, "                        is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
+    fprintf(stdout, "  -mg i, --main-gpu i   the GPU to use for scratch and small tensors\n");
+    fprintf(stdout, "  -lv, --low-vram       don't allocate VRAM scratch buffer\n");
+#ifdef GGML_USE_CUBLAS
+    fprintf(stdout, "  -nommq, --no-mul-mat-q\n");
+    fprintf(stdout, "                        use " GGML_CUBLAS_NAME " instead of custom mul_mat_q " GGML_CUDA_NAME " kernels.\n");
+    fprintf(stdout, "                        Not recommended since this is both slower and uses more VRAM.\n");
+#endif // GGML_USE_CUBLAS
 #endif
     fprintf(stdout, "  --mtest               compute maximum memory usage\n");
     fprintf(stdout, "  --export              export the computation graph to 'llama.ggml'\n");
@@ -614,6 +667,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stdout, "  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
     fprintf(stdout, "  -m FNAME, --model FNAME\n");
     fprintf(stdout, "                        model path (default: %s)\n", params.model.c_str());
+    fprintf(stdout, "  -ld LOGDIR, --logdir LOGDIR\n");
+    fprintf(stdout, "                        path under which to save YAML logs (no logging if unset)\n");
     fprintf(stdout, "\n");
 }
 
@@ -720,12 +775,12 @@ std::vector<llama_token> llama_tokenize(
     return result;
 }
 
-std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) {
+std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) {
     std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size());
+    const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size());
     if (n_tokens < 0) {
         result.resize(-n_tokens);
-        int check = llama_token_to_str(ctx, token, result.data(), result.size());
+        int check = llama_token_to_piece(ctx, token, result.data(), result.size());
         GGML_ASSERT(check == -n_tokens);
     } else {
         result.resize(n_tokens);
@@ -734,34 +789,321 @@ std::string llama_token_to_str(const struct llama_context * ctx, llama_token tok
     return std::string(result.data(), result.size());
 }
 
-std::vector<llama_token> llama_tokenize_bpe(
-        struct llama_context * ctx,
-           const std::string & text,
-                        bool   add_bos) {
-    int n_tokens = text.length() + add_bos;
-    std::vector<llama_token> result(n_tokens);
-    n_tokens = llama_tokenize_bpe(ctx, text.c_str(), result.data(), result.size(), add_bos);
-    if (n_tokens < 0) {
-        result.resize(-n_tokens);
-        int check = llama_tokenize_bpe(ctx, text.c_str(), result.data(), result.size(), add_bos);
-        GGML_ASSERT(check == -n_tokens);
-    } else {
-        result.resize(n_tokens);
+std::string llama_detokenize_spm(llama_context * ctx, const std::vector<llama_token> & tokens) {
+    const llama_token bos_id = llama_token_bos(ctx);
+
+    std::string piece;
+    std::string result;
+
+    for (size_t i = 0; i < tokens.size(); ++i) {
+        piece = llama_token_to_piece(ctx, tokens[i]);
+
+        // remove the leading space of the first non-BOS token
+        if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') {
+            piece = piece.substr(1);
+        }
+
+        result += piece;
     }
+
     return result;
 }
 
-std::string llama_token_to_str_bpe(const struct llama_context * ctx, llama_token token) {
-    std::vector<char> result(8, 0);
-    const int n_tokens = llama_token_to_str_bpe(ctx, token, result.data(), result.size());
-    if (n_tokens < 0) {
-        result.resize(-n_tokens);
-        const int check = llama_token_to_str_bpe(ctx, token, result.data(), result.size());
-        GGML_ASSERT(check == -n_tokens);
-    } else {
-        result.resize(n_tokens);
+std::string llama_detokenize_bpe(llama_context * ctx, const std::vector<llama_token> & tokens) {
+    std::string piece;
+    std::string result;
+
+    for (size_t i = 0; i < tokens.size(); ++i) {
+        piece = llama_token_to_piece(ctx, tokens[i]);
+
+        result += piece;
     }
 
-    return std::string(result.data(), result.size());
+    return result;
 }
 
+// returns true if successful, false otherwise
+bool create_directory_with_parents(const std::string & path) {
+#ifdef _WIN32
+    std::wstring_convert<std::codecvt_utf8<wchar_t>> converter;
+    std::wstring wpath = converter.from_bytes(path);
+
+    // if the path already exists, check whether it's a directory
+    const DWORD attributes = GetFileAttributesW(wpath.c_str());
+    if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) {
+        return true;
+    }
+
+    size_t pos_slash = 0;
+
+    // process path from front to back, procedurally creating directories
+    while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) {
+        const std::wstring subpath = wpath.substr(0, pos_slash);
+        const wchar_t * test = subpath.c_str();
+
+        const bool success = CreateDirectoryW(test, NULL);
+        if (!success) {
+            const DWORD error = GetLastError();
+
+            // if the path already exists, ensure that it's a directory
+            if (error == ERROR_ALREADY_EXISTS) {
+                const DWORD attributes = GetFileAttributesW(subpath.c_str());
+                if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) {
+                    return false;
+                }
+            } else {
+                return false;
+            }
+        }
+
+        pos_slash += 1;
+    }
+
+    return true;
+#else
+    // if the path already exists, check whether it's a directory
+    struct stat info;
+    if (stat(path.c_str(), &info) == 0) {
+        return S_ISDIR(info.st_mode);
+    }
+
+    size_t pos_slash = 1; // skip leading slashes for directory creation
+
+    // process path from front to back, procedurally creating directories
+    while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) {
+        const std::string subpath = path.substr(0, pos_slash);
+        struct stat info;
+
+        // if the path already exists, ensure that it's a directory
+        if (stat(subpath.c_str(), &info) == 0) {
+            if (!S_ISDIR(info.st_mode)) {
+                return false;
+            }
+        } else {
+            // create parent directories
+            const int ret = mkdir(subpath.c_str(), 0755);
+            if (ret != 0) {
+                return false;
+            }
+        }
+
+        pos_slash += 1;
+    }
+
+    return true;
+#endif // _WIN32
+}
+
+void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data) {
+    if (data.empty()) {
+        fprintf(stream, "%s:\n", prop_name);
+        return;
+    }
+
+    fprintf(stream, "%s: [", prop_name);
+    for (size_t i = 0; i < data.size() - 1; ++i) {
+        fprintf(stream, "%e, ", data[i]);
+    }
+    fprintf(stream, "%e]\n", data.back());
+}
+
+void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data) {
+    if (data.empty()) {
+        fprintf(stream, "%s:\n", prop_name);
+        return;
+    }
+
+    fprintf(stream, "%s: [", prop_name);
+    for (size_t i = 0; i < data.size() - 1; ++i) {
+        fprintf(stream, "%d, ", data[i]);
+    }
+    fprintf(stream, "%d]\n", data.back());
+}
+
+void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data) {
+    std::string data_str(data == NULL ? "" : data);
+
+    if (data_str.empty()) {
+        fprintf(stream, "%s:\n", prop_name);
+        return;
+    }
+
+    size_t pos_start = 0;
+    size_t pos_found = 0;
+
+    if (!data_str.empty() && (std::isspace(data_str[0]) || std::isspace(data_str.back()))) {
+        data_str = std::regex_replace(data_str, std::regex("\n"), "\\n");
+        data_str = std::regex_replace(data_str, std::regex("\""), "\\\"");
+        data_str = "\"" + data_str + "\"";
+        fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
+        return;
+    }
+
+    if (data_str.find('\n') == std::string::npos) {
+        fprintf(stream, "%s: %s\n", prop_name, data_str.c_str());
+        return;
+    }
+
+    fprintf(stream, "%s: |\n", prop_name);
+    while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) {
+        fprintf(stream, "  %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str());
+        pos_start = pos_found + 1;
+    }
+}
+
+std::string get_sortable_timestamp() {
+    using clock = std::chrono::system_clock;
+
+    const clock::time_point current_time = clock::now();
+    const time_t as_time_t = clock::to_time_t(current_time);
+    char timestamp_no_ns[100];
+    std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t));
+
+    const int64_t ns = std::chrono::duration_cast<std::chrono::nanoseconds>(
+        current_time.time_since_epoch() % 1000000000).count();
+    char timestamp_ns[11];
+    snprintf(timestamp_ns, 11, "%09" PRId64, ns);
+
+    return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns);
+}
+
+void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx,
+                               const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc) {
+    fprintf(stream, "build_commit: %s\n", BUILD_COMMIT);
+    fprintf(stream, "build_number: %d\n", BUILD_NUMBER);
+    fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false");
+    fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false");
+    fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
+    fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false");
+    fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false");
+    fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false");
+    fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false");
+    fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false");
+    fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false");
+    fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false");
+    fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false");
+    fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false");
+    fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false");
+    fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false");
+
+#ifdef NDEBUG
+    fprintf(stream, "debug: false\n");
+#else
+    fprintf(stream, "debug: true\n");
+#endif // NDEBUG
+
+    fprintf(stream, "model_desc: %s\n", model_desc);
+    fprintf(stream, "n_vocab: %d  # output size of the final layer, 32001 for some models\n", llama_n_vocab(lctx));
+
+#ifdef __OPTIMIZE__
+    fprintf(stream, "optimize: true\n");
+#else
+    fprintf(stream, "optimize: false\n");
+#endif // __OPTIMIZE__
+
+    fprintf(stream, "time: %s\n", timestamp.c_str());
+
+    fprintf(stream, "\n");
+    fprintf(stream, "###############\n");
+    fprintf(stream, "# User Inputs #\n");
+    fprintf(stream, "###############\n");
+    fprintf(stream, "\n");
+
+    fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str());
+    fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch);
+    dump_string_yaml_multiline(stream, "cfg_negative_prompt", params.cfg_negative_prompt.c_str());
+    fprintf(stream, "cfg_scale: %f # default: 1.0\n", params.cfg_scale);
+    fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks);
+    fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false");
+    fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx);
+    fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false");
+    fprintf(stream, "export: %s # default: false\n", params.export_cgraph ? "true" : "false");
+    fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n");
+    fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", params.frequency_penalty);
+    dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str());
+    fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n");
+    fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false");
+    fprintf(stream, "hellaswag_tasks: %ld # default: 400\n", params.hellaswag_tasks);
+
+    const auto logit_bias_eos = params.logit_bias.find(llama_token_eos(lctx));
+    const bool ignore_eos = logit_bias_eos != params.logit_bias.end() && logit_bias_eos->second == -INFINITY;
+    fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false");
+
+    dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str());
+    fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false");
+    dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str());
+    fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false");
+    fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false");
+    fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false");
+    fprintf(stream, "keep: %d # default: 0\n", params.n_keep);
+    fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str());
+
+    fprintf(stream, "logit_bias:\n");
+    for (std::pair<llama_token, float> lb : params.logit_bias) {
+        if (ignore_eos && lb.first == logit_bias_eos->first) {
+            continue;
+        }
+        fprintf(stream, "  %d: %f", lb.first, lb.second);
+    }
+
+    fprintf(stream, "lora: %s\n", params.lora_adapter.c_str());
+    fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
+    fprintf(stream, "low_vram: %s # default: false\n", params.low_vram ? "true" : "false");
+    fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
+    fprintf(stream, "memory_f32: %s # default: false\n", !params.memory_f16 ? "true" : "false");
+    fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", params.mirostat);
+    fprintf(stream, "mirostat_ent: %f # default: 5.0\n", params.mirostat_tau);
+    fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.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, "mtest: %s # default: false\n", params.mem_test ? "true" : "false");
+    fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
+    fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers);
+    fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
+    fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs);
+    fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
+    fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false");
+    fprintf(stream, "no_penalize_nl: %s # default: false\n", !params.penalize_nl ? "true" : "false");
+    fprintf(stream, "numa: %s # default: false\n", params.numa ? "true" : "false");
+    fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type);
+    fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride);
+    fprintf(stream, "presence_penalty: %f # default: 0.0\n", params.presence_penalty);
+    dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str());
+    fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str());
+    fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false");
+    fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false");
+    dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens);
+    fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false");
+    fprintf(stream, "repeat_penalty: %f # default: 1.1\n", params.repeat_penalty);
+
+    fprintf(stream, "reverse_prompt:\n");
+    for (std::string ap : params.antiprompt) {
+        size_t pos = 0;
+        while ((pos = ap.find('\n', pos)) != std::string::npos) {
+            ap.replace(pos, 1, "\\n");
+            pos += 1;
+        }
+
+        fprintf(stream, "  - %s\n", ap.c_str());
+    }
+
+    fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base);
+    fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale);
+    fprintf(stream, "seed: %d # default: -1 (random seed)\n", params.seed);
+    fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false");
+    fprintf(stream, "temp: %f # default: 0.8\n", params.temp);
+
+    const std::vector<float> tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES);
+    dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector);
+
+    fprintf(stream, "tfs: %f # default: 1.0\n", params.tfs_z);
+    fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency());
+    fprintf(stream, "top_k: %d # default: 40\n", params.top_k);
+    fprintf(stream, "top_p: %f # default: 0.95\n", params.top_p);
+    fprintf(stream, "typical_p: %f # default: 1.0\n", params.typical_p);
+    fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
+}

common/common.h

@@ -4,6 +4,9 @@
 
 #include "llama.h"
 
+#define LOG_NO_FILE_LINE_FUNCTION
+#include "log.h"
+
 #include <string>
 #include <vector>
 #include <random>
@@ -11,6 +14,12 @@
 #include <unordered_map>
 #include <tuple>
 
+#ifdef _WIN32
+#define DIRECTORY_SEPARATOR '\\'
+#else
+#define DIRECTORY_SEPARATOR '/'
+#endif // _WIN32
+
 //
 // CLI argument parsing
 //
@@ -28,6 +37,7 @@ struct gpt_params {
     int32_t main_gpu                        = 0;    // the GPU that is used for scratch and small tensors
     float   tensor_split[LLAMA_MAX_DEVICES] = {0};  // how split tensors should be distributed across GPUs
     int32_t n_probs                         = 0;    // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t n_beams                         = 0;    // if non-zero then use beam search of given width.
     float   rope_freq_base                  = 10000.0f; // RoPE base frequency
     float   rope_freq_scale                 = 1.0f;     // RoPE frequency scaling factor
 
@@ -60,15 +70,20 @@ struct gpt_params {
     std::string input_suffix      = "";  // string to suffix user inputs with
     std::string grammar           = "";  // optional BNF-like grammar to constrain sampling
     std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
+    std::string logdir            = "";  // directory in which to save YAML log files
 
     std::string lora_adapter = "";  // lora adapter path
     std::string lora_base    = "";  // base model path for the lora adapter
 
+    int  ppl_stride        = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
+    int  ppl_output_type   = 0;     // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
+                                    //                                       (which is more convenient to use for plotting)
+                                    //
     bool hellaswag         = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
     size_t hellaswag_tasks = 400;   // number of tasks to use when computing the HellaSwag score
 
     bool low_vram          = false; // if true, reduce VRAM usage at the cost of performance
-    bool mul_mat_q         = false; // if true, use experimental mul_mat_q kernels
+    bool mul_mat_q         = true;  // if true, use mul_mat_q kernels instead of cuBLAS
     bool memory_f16        = true;  // use f16 instead of f32 for memory kv
     bool random_prompt     = false; // do not randomize prompt if none provided
     bool use_color         = false; // use color to distinguish generations and inputs
@@ -77,6 +92,7 @@ struct gpt_params {
     bool prompt_cache_ro   = false; // open the prompt cache read-only and do not update it
 
     bool embedding         = false; // get only sentence embedding
+    bool escape            = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
     bool interactive_first = false; // wait for user input immediately
     bool multiline_input   = false; // reverse the usage of `\`
     bool simple_io         = false; // improves compatibility with subprocesses and limited consoles
@@ -111,20 +127,41 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
 // Vocab utils
 //
 
+// tokenizes a string into a vector of tokens
+// should work similar to Python's `tokenizer.encode`
 std::vector<llama_token> llama_tokenize(
         struct llama_context * ctx,
            const std::string & text,
                         bool   add_bos);
 
-std::vector<llama_token> llama_tokenize_bpe(
-        struct llama_context * ctx,
-           const std::string & text,
-                        bool   add_bos);
-
-std::string llama_token_to_str(
+// tokenizes a token into a piece
+// should work similar to Python's `tokenizer.id_to_piece`
+std::string llama_token_to_piece(
         const struct llama_context * ctx,
                        llama_token   token);
 
-std::string llama_token_to_str_bpe(
-    const struct llama_context * ctx,
-                   llama_token   token);
+// 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
+//
+// detokenizes a vector of tokens into a string
+// should work similar to Python's `tokenizer.decode`
+// removes the leading space from the first non-BOS token
+std::string llama_detokenize_spm(
+                         llama_context * ctx,
+        const std::vector<llama_token> & tokens);
+
+// detokenizes a vector of tokens into a string
+// should work similar to Python's `tokenizer.decode`
+std::string llama_detokenize_bpe(
+                         llama_context * ctx,
+        const std::vector<llama_token> & tokens);
+
+bool create_directory_with_parents(const std::string & path);
+void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector<float> & data);
+void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector<int> & data);
+void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data);
+std::string get_sortable_timestamp();
+
+void dump_non_result_info_yaml(
+    FILE * stream, const gpt_params & params, const llama_context * lctx,
+    const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);

common/log.h

@@ -0,0 +1,643 @@
+#pragma once
+
+#include <chrono>
+#include <cstring>
+#include <sstream>
+#include <iostream>
+#include <thread>
+#include <vector>
+#include <algorithm>
+#include <cinttypes>
+
+// --------------------------------
+//
+// Basic usage:
+//
+// --------
+//
+//  The LOG() and LOG_TEE() macros are ready to go by default
+//   they do not require any initialization.
+//
+//  LOGLN() and LOG_TEELN() are variants which automatically
+//   include \n character at the end of the log string.
+//
+//  LOG() behaves exactly like printf, by default writing to a logfile.
+//  LOG_TEE() additionally, prints to the screen too ( mimics Unix tee command ).
+//
+//  Default logfile is named
+//   "llama.<threadID>.log"
+//  Default LOG_TEE() secondary output target is
+//   stderr
+//
+//  Logs can be dynamically disabled or enabled using functions:
+//   log_disable()
+//  and
+//   log_enable()
+//
+//  A log target can be changed with:
+//   log_set_target( string )
+//    creating and opening, or re-opening a file by string filename
+//  or
+//   log_set_target( FILE* )
+//    allowing to point at stderr, stdout, or any valid FILE* file handler.
+//
+// --------
+//
+// End of Basic usage.
+//
+// --------------------------------
+
+// Specifies a log target.
+//  default uses log_handler() with "llama.log" log file
+//  this can be changed, by defining LOG_TARGET
+//  like so:
+//
+//  #define LOG_TARGET (a valid FILE*)
+//  #include "log.h"
+//
+//  or it can be simply redirected to stdout or stderr
+//  like so:
+//
+//  #define LOG_TARGET stderr
+//  #include "log.h"
+//
+//  The log target can also be redirected to a diffrent function
+//  like so:
+//
+//  #define LOG_TARGET log_handler_diffrent()
+//  #include "log.h"
+//
+//  FILE* log_handler_diffrent()
+//  {
+//      return stderr;
+//  }
+//
+//  or:
+//
+//  #define LOG_TARGET log_handler_another_one("somelog.log")
+//  #include "log.h"
+//
+//  FILE* log_handler_another_one(char*filename)
+//  {
+//      static FILE* logfile = nullptr;
+//      (...)
+//      if( !logfile )
+//      {
+//          fopen(...)
+//      }
+//      (...)
+//      return logfile
+//  }
+//
+#ifndef LOG_TARGET
+    #define LOG_TARGET log_handler()
+#endif
+
+#ifndef LOG_TEE_TARGET
+    #define LOG_TEE_TARGET stderr
+#endif
+
+// Utility to obtain "pid" like unique process id and use it when creating log files.
+inline std::string log_get_pid()
+{
+    static std::string pid;
+    if (pid.empty())
+    {
+        // std::this_thread::get_id() is the most portable way of obtaining a "process id"
+        //  it's not the same as "pid" but is unique enough to solve multiple instances
+        //  trying to write to the same log.
+        std::stringstream ss;
+        ss << std::this_thread::get_id();
+        pid = ss.str();
+    }
+
+    return pid;
+}
+
+// Utility function for generating log file names with unique id based on thread id.
+//  invocation with log_filename_generator( "llama", "log" ) creates a string "llama.<number>.log"
+//  where the number is a runtime id of the current thread.
+
+#define log_filename_generator(log_file_basename, log_file_extension) log_filename_generator_impl(log_file_basename, log_file_extension)
+
+// INTERNAL, DO NOT USE
+inline std::string log_filename_generator_impl(const std::string & log_file_basename, const std::string & log_file_extension)
+{
+    std::stringstream buf;
+
+    buf << log_file_basename;
+    buf << ".";
+    buf << log_get_pid();
+    buf << ".";
+    buf << log_file_extension;
+
+    return buf.str();
+}
+
+#ifndef LOG_DEFAULT_FILE_NAME
+    #define LOG_DEFAULT_FILE_NAME log_filename_generator("llama", "log")
+#endif
+
+// Utility for turning #define values into string literals
+//  so we can have a define for stderr and
+//  we can print "stderr" instead of literal stderr, etc.
+#define LOG_STRINGIZE1(s) #s
+#define LOG_STRINGIZE(s) LOG_STRINGIZE1(s)
+
+#define LOG_TEE_TARGET_STRING LOG_STRINGIZE(LOG_TEE_TARGET)
+
+// Allows disabling timestamps.
+//  in order to disable, define LOG_NO_TIMESTAMPS
+//  like so:
+//
+//  #define LOG_NO_TIMESTAMPS
+//  #include "log.h"
+//
+#ifndef LOG_NO_TIMESTAMPS
+    #ifndef _WIN32
+        #define LOG_TIMESTAMP_FMT "[%" PRIu64 "] "
+        #define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
+    #else
+        #define LOG_TIMESTAMP_FMT "[%" PRIu64 "] "
+        #define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
+    #endif
+#else
+    #define LOG_TIMESTAMP_FMT "%s"
+    #define LOG_TIMESTAMP_VAL ,""
+#endif
+
+#ifdef LOG_TEE_TIMESTAMPS
+    #ifndef _WIN32
+        #define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] "
+        #define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
+    #else
+        #define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] "
+        #define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast<std::chrono::duration<std::uint64_t>>(std::chrono::system_clock::now().time_since_epoch())).count()
+    #endif
+#else
+    #define LOG_TEE_TIMESTAMP_FMT "%s"
+    #define LOG_TEE_TIMESTAMP_VAL ,""
+#endif
+
+// Allows disabling file/line/function prefix
+//  in order to disable, define LOG_NO_FILE_LINE_FUNCTION
+//  like so:
+//
+//  #define LOG_NO_FILE_LINE_FUNCTION
+//  #include "log.h"
+//
+#ifndef LOG_NO_FILE_LINE_FUNCTION
+    #ifndef _WIN32
+        #define LOG_FLF_FMT "[%24s:%5d][%24s] "
+        #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
+    #else
+        #define LOG_FLF_FMT "[%24s:%5ld][%24s] "
+        #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
+    #endif
+#else
+    #define LOG_FLF_FMT "%s"
+    #define LOG_FLF_VAL ,""
+#endif
+
+#ifdef LOG_TEE_FILE_LINE_FUNCTION
+    #ifndef _WIN32
+        #define LOG_TEE_FLF_FMT "[%24s:%5d][%24s] "
+        #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
+    #else
+        #define LOG_TEE_FLF_FMT "[%24s:%5ld][%24s] "
+        #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
+    #endif
+#else
+    #define LOG_TEE_FLF_FMT "%s"
+    #define LOG_TEE_FLF_VAL ,""
+#endif
+
+// Utility for synchronizing log configuration state
+//  since std::optional was introduced only in c++17
+enum LogTriState
+{
+    LogTriStateSame,
+    LogTriStateFalse,
+    LogTriStateTrue
+};
+
+// INTERNAL, DO NOT USE
+//  USE LOG() INSTEAD
+//
+#ifndef _WIN32
+    #define LOG_IMPL(str, ...)                                                                                          \
+    {                                                                                                               \
+        if (LOG_TARGET != nullptr)                                                                                  \
+        {                                                                                                           \
+            fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__); \
+            fflush(LOG_TARGET);                                                                                     \
+        }                                                                                                           \
+    }
+#else
+    #define LOG_IMPL(str, ...)                                                                                               \
+    {                                                                                                                    \
+        if (LOG_TARGET != nullptr)                                                                                       \
+        {                                                                                                                \
+            fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL "", ##__VA_ARGS__); \
+            fflush(LOG_TARGET);                                                                                          \
+        }                                                                                                                \
+    }
+#endif
+
+// INTERNAL, DO NOT USE
+//  USE LOG_TEE() INSTEAD
+//
+#ifndef _WIN32
+    #define LOG_TEE_IMPL(str, ...)                                                                                                          \
+    {                                                                                                                                   \
+        if (LOG_TARGET != nullptr)                                                                                                      \
+        {                                                                                                                               \
+            fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__);                     \
+            fflush(LOG_TARGET);                                                                                                         \
+        }                                                                                                                               \
+        if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr)                         \
+        {                                                                                                                               \
+            fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str "%s" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL, __VA_ARGS__); \
+            fflush(LOG_TEE_TARGET);                                                                                                     \
+        }                                                                                                                               \
+    }
+#else
+    #define LOG_TEE_IMPL(str, ...)                                                                                                               \
+    {                                                                                                                                        \
+        if (LOG_TARGET != nullptr)                                                                                                           \
+        {                                                                                                                                    \
+            fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL "", ##__VA_ARGS__);                     \
+            fflush(LOG_TARGET);                                                                                                              \
+        }                                                                                                                                    \
+        if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr)                              \
+        {                                                                                                                                    \
+            fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str "%s" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL "", ##__VA_ARGS__); \
+            fflush(LOG_TEE_TARGET);                                                                                                          \
+        }                                                                                                                                    \
+    }
+#endif
+
+// The '\0' as a last argument, is a trick to bypass the silly
+//  "warning: ISO C++11 requires at least one argument for the "..." in a variadic macro"
+//  so we can have a single macro which can be called just like printf.
+
+// Main LOG macro.
+//  behaves like printf, and supports arguments the exact same way.
+//
+#ifndef _WIN32
+    #define LOG(...) LOG_IMPL(__VA_ARGS__, "")
+#else
+    #define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "")
+#endif
+
+// Main TEE macro.
+//  does the same as LOG
+//  and
+//  simultaneously writes stderr.
+//
+// Secondary target can be changed just like LOG_TARGET
+//  by defining LOG_TEE_TARGET
+//
+#ifndef _WIN32
+    #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "")
+#else
+    #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "")
+#endif
+
+// LOG macro variants with auto endline.
+#ifndef _WIN32
+    #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n")
+    #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n")
+#else
+    #define LOGLN(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "\n")
+    #define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "\n")
+#endif
+
+// INTERNAL, DO NOT USE
+inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTriStateSame, const std::string & filename = LOG_DEFAULT_FILE_NAME, FILE *target = nullptr)
+{
+    static bool _initialized{false};
+    static bool _disabled{(filename.empty() && target == nullptr)};
+    static std::string log_current_filename{filename};
+    static FILE *log_current_target{target};
+    static FILE *logfile = nullptr;
+
+    if (change)
+    {
+        if (disable == LogTriStateTrue)
+        {
+            // Disable primary target
+            _disabled = true;
+        }
+        // If previously disabled, only enable, and keep previous target
+        else if (disable == LogTriStateFalse)
+        {
+            _disabled = false;
+        }
+        // Otherwise, process the arguments
+        else if (log_current_filename != filename || log_current_target != target)
+        {
+            _initialized = false;
+        }
+    }
+
+    if (_initialized)
+    {
+        if (_disabled)
+        {
+            // Log is disabled
+            return nullptr;
+        }
+
+        // with fallback in case something went wrong
+        return logfile ? logfile : stderr;
+    }
+
+    // do the (re)initialization
+    if (target != nullptr)
+    {
+        if (logfile != nullptr && logfile != stdout && logfile != stderr)
+        {
+            fclose(logfile);
+        }
+
+        log_current_filename = LOG_DEFAULT_FILE_NAME;
+        log_current_target = target;
+
+        logfile = target;
+    }
+    else
+    {
+        if (log_current_filename != filename)
+        {
+            if (logfile != nullptr && logfile != stdout && logfile != stderr)
+            {
+                fclose(logfile);
+            }
+        }
+
+        logfile = fopen(filename.c_str(), "w");
+    }
+
+    if (!logfile)
+    {
+        //  Verify whether the file was opened, otherwise fallback to stderr
+        logfile = stderr;
+
+        fprintf(stderr, "Failed to open logfile '%s' with error '%s'\n", filename.c_str(), std::strerror(errno));
+        fflush(stderr);
+
+        // At this point we let the init flag be to true below, and let the target fallback to stderr
+        //  otherwise we would repeatedly fopen() which was already unsuccessful
+    }
+
+    _initialized = true;
+
+    return logfile ? logfile : stderr;
+}
+
+// INTERNAL, DO NOT USE
+inline FILE *log_handler2_impl(bool change = false, LogTriState disable = LogTriStateSame, FILE *target = nullptr, const std::string & filename = LOG_DEFAULT_FILE_NAME)
+{
+    return log_handler1_impl(change, disable, filename, target);
+}
+
+// Disables logs entirely at runtime.
+//  Makes LOG() and LOG_TEE() produce no output,
+//  untill enabled back.
+#define log_disable() log_disable_impl()
+
+// INTERNAL, DO NOT USE
+inline FILE *log_disable_impl()
+{
+    return log_handler1_impl(true, LogTriStateTrue);
+}
+
+// Enables logs at runtime.
+#define log_enable() log_enable_impl()
+
+// INTERNAL, DO NOT USE
+inline FILE *log_enable_impl()
+{
+    return log_handler1_impl(true, LogTriStateFalse);
+}
+
+// Sets target fir logs, either by a file name or FILE* pointer (stdout, stderr, or any valid FILE*)
+#define log_set_target(target) log_set_target_impl(target)
+
+// INTERNAL, DO NOT USE
+inline FILE *log_set_target_impl(const std::string & filename) { return log_handler1_impl(true, LogTriStateSame, filename); }
+inline FILE *log_set_target_impl(FILE *target) { return log_handler2_impl(true, LogTriStateSame, target); }
+
+// INTERNAL, DO NOT USE
+inline FILE *log_handler() { return log_handler1_impl(); }
+
+inline void log_test()
+{
+    log_disable();
+    LOG("01 Hello World to nobody, because logs are disabled!\n")
+    log_enable();
+    LOG("02 Hello World to default output, which is \"%s\" ( Yaaay, arguments! )!\n", LOG_STRINGIZE(LOG_TARGET))
+    LOG_TEE("03 Hello World to **both** default output and " LOG_TEE_TARGET_STRING "!\n")
+    log_set_target(stderr);
+    LOG("04 Hello World to stderr!\n")
+    LOG_TEE("05 Hello World TEE with double printing to stderr prevented!\n")
+    log_set_target(LOG_DEFAULT_FILE_NAME);
+    LOG("06 Hello World to default log file!\n")
+    log_set_target(stdout);
+    LOG("07 Hello World to stdout!\n")
+    log_set_target(LOG_DEFAULT_FILE_NAME);
+    LOG("08 Hello World to default log file again!\n")
+    log_disable();
+    LOG("09 Hello World _1_ into the void!\n")
+    log_enable();
+    LOG("10 Hello World back from the void ( you should not see _1_ in the log or the output )!\n")
+    log_disable();
+    log_set_target("llama.anotherlog.log");
+    LOG("11 Hello World _2_ to nobody, new target was selected but logs are still disabled!\n")
+    log_enable();
+    LOG("12 Hello World this time in a new file ( you should not see _2_ in the log or the output )?\n")
+    log_set_target("llama.yetanotherlog.log");
+    LOG("13 Hello World this time in yet new file?\n")
+    log_set_target(log_filename_generator("llama_autonamed", "log"));
+    LOG("14 Hello World in log with generated filename!\n")
+#ifdef _WIN32
+    LOG_TEE("15 Hello msvc TEE without arguments\n")
+    LOG_TEE("16 Hello msvc TEE with (%d)(%s) arguments\n", 1, "test")
+    LOG_TEELN("17 Hello msvc TEELN without arguments\n")
+    LOG_TEELN("18 Hello msvc TEELN with (%d)(%s) arguments\n", 1, "test")
+    LOG("19 Hello msvc LOG without arguments\n")
+    LOG("20 Hello msvc LOG with (%d)(%s) arguments\n", 1, "test")
+    LOGLN("21 Hello msvc LOGLN without arguments\n")
+    LOGLN("22 Hello msvc LOGLN with (%d)(%s) arguments\n", 1, "test")
+#endif
+}
+
+inline bool log_param_single_parse(const std::string & param)
+{
+    if ( param == "--log-test")
+    {
+        log_test();
+        return true;
+    }
+
+    if ( param == "--log-disable")
+    {
+        log_disable();
+        return true;
+    }
+
+    if ( param == "--log-enable")
+    {
+        log_enable();
+        return true;
+    }
+
+    return false;
+}
+
+inline bool log_param_pair_parse(bool check_but_dont_parse, const std::string & param, const std::string & next = std::string())
+{
+    if ( param == "--log-file")
+    {
+        if (!check_but_dont_parse)
+        {
+            log_set_target(log_filename_generator(next.empty() ? "unnamed" : next, "log"));
+        }
+
+        return true;
+    }
+
+    return false;
+}
+
+inline void log_print_usage()
+{
+    fprintf(stdout, "log options:\n");
+    /* format
+    fprintf(stdout, "  -h, --help            show this help message and exit\n");*/
+    /* spacing
+    fprintf(stdout, "__-param----------------Description\n");*/
+    fprintf(stdout, "  --log-test            Run simple logging test\n");
+    fprintf(stdout, "  --log-disable         Disable trace logs\n");
+    fprintf(stdout, "  --log-enable          Enable trace logs\n");
+    fprintf(stdout, "  --log-file            Specify a log filename (without extension)\n");
+    fprintf(stdout, "                        Log file will be tagged with unique ID and written as \"<name>.<ID>.log\"\n"); /*  */
+}
+
+#define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv)
+
+// INTERNAL, DO NOT USE
+inline void log_dump_cmdline_impl(int argc, char **argv)
+{
+    std::stringstream buf;
+    for (int i = 0; i < argc; ++i)
+    {
+        if (std::string(argv[i]).find(' ') != std::string::npos)
+        {
+            buf << " \"" << argv[i] <<"\"";
+        }
+        else
+        {
+            buf << " " << argv[i];
+        }
+    }
+    LOGLN("Cmd:%s", buf.str().c_str())
+}
+
+#define log_tostr(var) log_var_to_string_impl(var).c_str()
+
+inline std::string log_var_to_string_impl(bool var)
+{
+    return var ? "true" : "false";
+}
+
+inline std::string log_var_to_string_impl(std::string var)
+{
+    return var;
+}
+
+inline std::string log_var_to_string_impl(const std::vector<int> & var)
+{
+    std::stringstream buf;
+    buf << "[ ";
+    bool first = true;
+    for (auto e : var)
+    {
+        if (first)
+        {
+            first = false;
+        }
+        else
+        {
+            buf << ", ";
+        }
+        buf << std::to_string(e);
+    }
+    buf << " ]";
+
+    return buf.str();
+}
+
+#define LOG_TOKENS_TOSTR_PRETTY(ctx, tokens)                                 \
+    [&tokens, &ctx]()                                                        \
+    {                                                                        \
+        std::stringstream buf;                                               \
+        buf << "[ ";                                                         \
+                                                                             \
+        bool first = true;                                                   \
+        for (const auto &token : tokens)                                     \
+        {                                                                    \
+            if (!first)                                                      \
+                buf << ", ";                                                 \
+            else                                                             \
+                first = false;                                               \
+                                                                             \
+            auto detokenized = llama_token_to_piece(ctx, token);             \
+                                                                             \
+            detokenized.erase(                                               \
+                std::remove_if(                                              \
+                    detokenized.begin(),                                     \
+                    detokenized.end(),                                       \
+                    [](const unsigned char c) { return !std::isprint(c); }), \
+                detokenized.end());                                          \
+                                                                             \
+            buf                                                              \
+                << "'" << detokenized << "'"                                 \
+                << ":" << std::to_string(token);                             \
+        }                                                                    \
+        buf << " ]";                                                         \
+                                                                             \
+        return buf.str();                                                    \
+    }()                                                                      \
+        .c_str()
+
+#ifdef LOG_DISABLE_LOGS
+
+#undef LOG
+#define LOG(...) // dummy stub
+#undef LOGLN
+#define LOGLN(...) // dummy stub
+
+#undef LOG_TEE
+#define LOG_TEE(...) fprintf(stderr, __VA_ARGS__); // convert to normal fprintf
+
+#undef LOG_TEELN
+#define LOG_TEELN(...) fprintf(stderr, __VA_ARGS__); // convert to normal fprintf
+
+#undef LOG_DISABLE
+#define LOG_DISABLE() // dummy stub
+
+#undef LOG_ENABLE
+#define LOG_ENABLE() // dummy stub
+
+#undef LOG_ENABLE
+#define LOG_ENABLE() // dummy stub
+
+#undef LOG_SET_TARGET
+#define LOG_SET_TARGET(...) // dummy stub
+
+#undef LOG_DUMP_CMDLINE
+#define LOG_DUMP_CMDLINE(...) // dummy stub
+
+#endif // LOG_DISABLE_LOGS

convert-falcon-hf-to-gguf.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 # HF falcon--> gguf conversion
 
 import gguf
@@ -7,6 +8,7 @@ import struct
 import json
 import numpy as np
 import torch
+import argparse
 
 from typing import Any, List
 from pathlib import Path
@@ -31,11 +33,10 @@ def bytes_to_unicode():
             bs.append(b)
             cs.append(2**8+n)
             n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
+    return dict(zip(bs, (chr(n) for n in cs)))
 
 
-def count_model_parts(dir_model: str) -> int:
+def count_model_parts(dir_model: Path) -> int:
     num_parts = 0
     for filename in os.listdir(dir_model):
         if filename.startswith("pytorch_model-"):
@@ -46,17 +47,22 @@ def count_model_parts(dir_model: str) -> int:
     return num_parts
 
 
-if len(sys.argv) < 3:
-    print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
-    print("  ftype == 0 -> float32")
-    print("  ftype == 1 -> float16")
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert a Falcon model to a GGML compatible file")
+    parser.add_argument("--vocab-only",  action="store_true",    help="extract only the vocab")
+    parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
+    parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.bin)")
+    parser.add_argument("ftype",     type=int, choices=[0, 1],   help="output format - use 0 for float32, 1 for float16", default = 1)
+    return parser.parse_args()
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f'Error: {args.model} is not a directory', file = sys.stderr)
     sys.exit(1)
 
-
-# output in the same directory as the model
-dir_model = sys.argv[1]
-last_dir = os.path.basename(os.path.normpath(dir_model))
-
 # possible tensor data types
 #   ftype == 0 -> float32
 #   ftype == 1 -> float16
@@ -64,25 +70,21 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
 # map from ftype to string
 ftype_str = ["f32", "f16"]
 
-ftype = 1
-if len(sys.argv) > 2:
-    ftype = int(sys.argv[2])
-    if ftype < 0 or ftype > 1:
-        print("Invalid ftype: " + str(ftype))
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
 
-        sys.exit(1)
+print("gguf: loading model "+dir_model.name)
 
-fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
-
-print("gguf: loading model "+last_dir)
-
-with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
     hparams = json.load(f)
 
 if hparams["architectures"][0] != "RWForCausalLM":
     print("Model architecture not supported: " + hparams["architectures"][0])
 
-    sys.exit()
+    sys.exit(1)
 
 # get number of model parts
 num_parts = count_model_parts(dir_model)
@@ -94,123 +96,102 @@ print("gguf: get model metadata")
 
 block_count = hparams["n_layer"]
 
-gguf_writer.add_name(last_dir)
+gguf_writer.add_name("Falcon")
 gguf_writer.add_context_length(2048) # not in config.json
 gguf_writer.add_tensor_data_layout("jploski") # qkv tensor transform
 gguf_writer.add_embedding_length(hparams["hidden_size"])
 gguf_writer.add_feed_forward_length(4 * hparams["hidden_size"])
 gguf_writer.add_block_count(block_count)
 gguf_writer.add_head_count(hparams["n_head"])
-if "n_head_kv" in hparams: gguf_writer.add_head_count_kv(hparams["n_head_kv"])
+if "n_head_kv" in hparams:
+    gguf_writer.add_head_count_kv(hparams["n_head_kv"])
+else:
+    gguf_writer.add_head_count_kv(1)
 gguf_writer.add_layer_norm_eps(hparams["layer_norm_epsilon"])
+gguf_writer.add_file_type(ftype)
 
 # TOKENIZATION
 
 print("gguf: get tokenizer metadata")
 
-tokens: List[str] = []
-merges: List[str] = []
+tokens: List[bytearray] = []
+scores: List[float] = []
+toktypes: List[int] = []
 
+tokenizer_json_file = dir_model / 'tokenizer.json'
+if not tokenizer_json_file.is_file():
+    print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
+    sys.exit(1)
 
-if Path(dir_model + "/tokenizer.json").is_file():
-    # gpt2 tokenizer
-    gguf_writer.add_tokenizer_model("gpt2")
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
 
-    print("gguf: get gpt2 tokenizer merges")
+with open(tokenizer_json_file, "r", encoding="utf-8") as f:
+    tokenizer_json = json.load(f)
 
-    with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
-        tokenizer_json = json.load(f)
-    merges = tokenizer_json["model"]["merges"]
+print("gguf: get gpt2 tokenizer vocab")
 
-    gguf_writer.add_token_merges(merges)
+vocab_size = len(tokenizer_json["model"]["vocab"])
 
-    print("gguf: get gpt2 tokenizer vocab")
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
 
-    vocab_size = len(tokenizer_json["model"]["vocab"])
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+byte_encoder = bytes_to_unicode()
+byte_decoder = {v: k for k, v in byte_encoder.items()}
 
-    # ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
-    tokenizer = AutoTokenizer.from_pretrained(dir_model)
+for i in range(vocab_size):
+    if i in reverse_vocab:
+        try:
+            text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
+        except KeyError:
+            text = bytearray()
+            for c in reverse_vocab[i]:
+                if ord(c) < 256:  # single byte character
+                    text.append(byte_decoder[ord(c)])
+                else:  # multibyte special token character
+                    text.extend(c.encode('utf-8'))
+    else:
+        print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
+        pad_token = f"[PAD{i}]".encode("utf8")
+        text = bytearray(pad_token)
 
-    reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
-    byte_encoder = bytes_to_unicode()
-    byte_decoder = {v: k for k, v in byte_encoder.items()}
+    tokens.append(text)
+    scores.append(0.0)                      # dymmy
+    toktypes.append(gguf.TokenType.NORMAL)  # dummy
 
-    for i in range(vocab_size):
-        if i in reverse_vocab:
-            try:
-                text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
-            except KeyError:
-                text = bytearray()
-                for c in reverse_vocab[i]:
-                    if ord(c) < 256:  # single byte character
-                        text.append(byte_decoder[ord(c)])
-                    else:  # multibyte special token character
-                        text.extend(c.encode('utf-8'))
-        else:
-            print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
-            pad_token = f"[PAD{i}]".encode("utf8")
-            text = bytearray(pad_token)
-
-        tokens.append(text)
-
-    gguf_writer.add_token_list(tokens)
-
-    if "added_tokens" in tokenizer_json and Path(dir_model + "/tokenizer_config.json").is_file():
-        print("gguf: get special token ids")
-
-        with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
-            tokenizer_config = json.load(f)
-
-        # find special token ids
-
-        if "bos_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["bos_token"]:
-                    gguf_writer.add_bos_token_id(key["id"])
-
-        if "eos_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["eos_token"]:
-                    gguf_writer.add_eos_token_id(key["id"])
-
-        if "unk_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["unk_token"]:
-                    gguf_writer.add_unk_token_id(key["id"])
-
-        if "sep_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["sep_token"]:
-                    gguf_writer.add_sep_token_id(key["id"])
-
-        if "pad_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["pad_token"]:
-                    gguf_writer.add_pad_token_id(key["id"])
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_scores(scores)
+gguf_writer.add_token_types(toktypes)
 
+special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
+special_vocab.add_to_gguf(gguf_writer)
 
 # TENSORS
 
 tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
 
 # params for qkv transform
-n_head = hparams["n_head"]
+n_head    = hparams["n_head"]
 n_head_kv = hparams["n_head_kv"] if "n_head_kv" in hparams else 1
+
 head_dim = hparams["hidden_size"] // n_head
 
 # tensor info
 print("gguf: get tensor metadata")
 
 if num_parts == 0:
-    part_names = ("pytorch_model.bin",)
+    part_names = iter(("pytorch_model.bin",))
 else:
     part_names = (
         f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
     )
 
 for part_name in part_names:
+    if args.vocab_only:
+        break
     print("gguf: loading model part '" + part_name + "'")
-    model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
+    model_part = torch.load(dir_model / part_name, map_location="cpu")
 
     for name in model_part.keys():
         data = model_part[name]
@@ -241,11 +222,8 @@ for part_name in part_names:
         data = data.squeeze().numpy()
 
         # map tensor names
-        if name.endswith(".weight") and name[:-7] in tensor_map:
-            name = tensor_map[name[:-7]] + ".weight"
-        elif name.endswith(".bias") and name[:-5] in tensor_map:
-            name = tensor_map[name[:-5]] + ".bias"
-        else:
+        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+        if new_name is None:
             print("Can not map tensor '" + name + "'")
             sys.exit()
 
@@ -264,19 +242,20 @@ for part_name in part_names:
         if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
             data = data.astype(np.float16)
 
-        print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
 
-        gguf_writer.add_tensor(name, data)
+        gguf_writer.add_tensor(new_name, data)
 
 
 print("gguf: write header")
 gguf_writer.write_header_to_file()
 print("gguf: write metadata")
 gguf_writer.write_kv_data_to_file()
-print("gguf: write tensors")
-gguf_writer.write_tensors_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
 
 gguf_writer.close()
 
-print("gguf: model successfully exported to '" + fname_out + "'")
+print(f"gguf: model successfully exported to '{fname_out}'")
 print("")

convert-gptneox-hf-to-gguf.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 # HF gptneox--> gguf conversion
 
 import gguf
@@ -7,6 +8,7 @@ import struct
 import json
 import numpy as np
 import torch
+import argparse
 
 from typing import Any, List
 from pathlib import Path
@@ -33,11 +35,10 @@ def bytes_to_unicode():
             bs.append(b)
             cs.append(2**8+n)
             n += 1
-    cs = [chr(n) for n in cs]
-    return dict(zip(bs, cs))
+    return dict(zip(bs, (chr(n) for n in cs)))
 
 
-def count_model_parts(dir_model: str) -> int:
+def count_model_parts(dir_model: Path) -> int:
     num_parts = 0
     for filename in os.listdir(dir_model):
         if filename.startswith("pytorch_model-"):
@@ -48,17 +49,22 @@ def count_model_parts(dir_model: str) -> int:
     return num_parts
 
 
-if len(sys.argv) < 3:
-    print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
-    print("  ftype == 0 -> float32")
-    print("  ftype == 1 -> float16")
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert a GPT-NeoX model to a GGML compatible file")
+    parser.add_argument("--vocab-only",  action="store_true",    help="extract only the vocab")
+    parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
+    parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.bin)")
+    parser.add_argument("ftype",     type=int, choices=[0, 1],   help="output format - use 0 for float32, 1 for float16", default = 1)
+    return parser.parse_args()
+
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f'Error: {args.model} is not a directory', file = sys.stderr)
     sys.exit(1)
 
-
-# output in the same directory as the model
-dir_model = sys.argv[1]
-last_dir = os.path.basename(os.path.normpath(dir_model))
-
 # possible tensor data types
 #   ftype == 0 -> float32
 #   ftype == 1 -> float16
@@ -66,19 +72,15 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
 # map from ftype to string
 ftype_str = ["f32", "f16"]
 
-ftype = 1
-if len(sys.argv) > 2:
-    ftype = int(sys.argv[2])
-    if ftype < 0 or ftype > 1:
-        print("Invalid ftype: " + str(ftype))
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
 
-        sys.exit(1)
+print("gguf: loading model "+dir_model.name)
 
-fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
-
-print("gguf: loading model "+last_dir)
-
-with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
     hparams = json.load(f)
 
 if hparams["architectures"][0] != "GPTNeoXForCausalLM":
@@ -96,7 +98,7 @@ print("gguf: get model metadata")
 
 block_count = hparams["num_hidden_layers"]
 
-gguf_writer.add_name(last_dir)
+gguf_writer.add_name(dir_model.name)
 gguf_writer.add_context_length(hparams["max_position_embeddings"])
 gguf_writer.add_embedding_length(hparams["hidden_size"])
 gguf_writer.add_block_count(block_count)
@@ -110,86 +112,52 @@ gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"])
 
 print("gguf: get tokenizer metadata")
 
-tokens: List[str] = []
-merges: List[str] = []
+tokens: List[bytearray] = []
 
+tokenizer_json_file = dir_model / 'tokenizer.json'
+if not tokenizer_json_file.is_file():
+    print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr)
+    sys.exit(1)
 
-if Path(dir_model + "/tokenizer.json").is_file():
-    # gpt2 tokenizer
-    gguf_writer.add_tokenizer_model("gpt2")
+# gpt2 tokenizer
+gguf_writer.add_tokenizer_model("gpt2")
 
-    print("gguf: get gpt2 tokenizer merges")
+with open(tokenizer_json_file, "r", encoding="utf-8") as f:
+    tokenizer_json = json.load(f)
 
-    with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
-        tokenizer_json = json.load(f)
-    merges = tokenizer_json["model"]["merges"]
+print("gguf: get gpt2 tokenizer vocab")
 
-    gguf_writer.add_token_merges(merges)
+vocab_size = len(tokenizer_json["model"]["vocab"])
 
-    print("gguf: get gpt2 tokenizer vocab")
+# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
+tokenizer = AutoTokenizer.from_pretrained(dir_model)
 
-    vocab_size = len(tokenizer_json["model"]["vocab"])
+reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
+byte_encoder = bytes_to_unicode()
+byte_decoder = {v: k for k, v in byte_encoder.items()}
 
-    # ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py
-    tokenizer = AutoTokenizer.from_pretrained(dir_model)
+for i in range(vocab_size):
+    if i in reverse_vocab:
+        try:
+            text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
+        except KeyError:
+            text = bytearray()
+            for c in reverse_vocab[i]:
+                if ord(c) < 256:  # single byte character
+                    text.append(byte_decoder[ord(c)])
+                else:  # multibyte special token character
+                    text.extend(c.encode('utf-8'))
+    else:
+        print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
+        pad_token = f"[PAD{i}]".encode("utf8")
+        text = bytearray(pad_token)
 
-    reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()}
-    byte_encoder = bytes_to_unicode()
-    byte_decoder = {v: k for k, v in byte_encoder.items()}
+    tokens.append(text)
 
-    for i in range(vocab_size):
-        if i in reverse_vocab:
-            try:
-                text = bytearray([byte_decoder[c] for c in reverse_vocab[i]])
-            except KeyError:
-                text = bytearray()
-                for c in reverse_vocab[i]:
-                    if ord(c) < 256:  # single byte character
-                        text.append(byte_decoder[ord(c)])
-                    else:  # multibyte special token character
-                        text.extend(c.encode('utf-8'))
-        else:
-            print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.")
-            pad_token = f"[PAD{i}]".encode("utf8")
-            text = bytearray(pad_token)
-
-        tokens.append(text)
-
-    gguf_writer.add_token_list(tokens)
-
-    if "added_tokens" in tokenizer_json and Path(dir_model + "/tokenizer_config.json").is_file():
-        print("gguf: get special token ids")
-
-        with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
-            tokenizer_config = json.load(f)
-
-        # find special token ids
-
-        if "bos_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["bos_token"]:
-                    gguf_writer.add_bos_token_id(key["id"])
-
-        if "eos_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["eos_token"]:
-                    gguf_writer.add_eos_token_id(key["id"])
-
-        if "unk_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["unk_token"]:
-                    gguf_writer.add_unk_token_id(key["id"])
-
-        if "sep_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["sep_token"]:
-                    gguf_writer.add_sep_token_id(key["id"])
-
-        if "pad_token" in tokenizer_config:
-            for key in tokenizer_json["added_tokens"]:
-                if key["content"] == tokenizer_config["pad_token"]:
-                    gguf_writer.add_pad_token_id(key["id"])
+gguf_writer.add_token_list(tokens)
 
+special_vocab = gguf.SpecialVocab(dir_model, load_merges = True)
+special_vocab.add_to_gguf(gguf_writer)
 
 # TENSORS
 
@@ -199,13 +167,15 @@ tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
 print("gguf: get tensor metadata")
 
 if num_parts == 0:
-    part_names = ("pytorch_model.bin",)
+    part_names = iter(("pytorch_model.bin",))
 else:
     part_names = (
         f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
     )
 
 for part_name in part_names:
+    if args.vocab_only:
+        break
     print("gguf: loading model part '" + part_name + "'")
     model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
 
@@ -225,11 +195,8 @@ for part_name in part_names:
         data = data.squeeze().numpy()
 
         # map tensor names
-        if name.endswith(".weight") and name[:-7] in tensor_map:
-            name = tensor_map[name[:-7]] + ".weight"
-        elif name.endswith(".bias") and name[:-5] in tensor_map:
-            name = tensor_map[name[:-5]] + ".bias"
-        else:
+        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+        if new_name is None:
             print("Can not map tensor '" + name + "'")
             sys.exit()
 
@@ -248,19 +215,20 @@ for part_name in part_names:
         if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
             data = data.astype(np.float16)
 
-        print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
 
-        gguf_writer.add_tensor(name, data)
+        gguf_writer.add_tensor(new_name, data)
 
 
 print("gguf: write header")
 gguf_writer.write_header_to_file()
 print("gguf: write metadata")
 gguf_writer.write_kv_data_to_file()
-print("gguf: write tensors")
-gguf_writer.write_tensors_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
 
 gguf_writer.close()
 
-print("gguf: model successfully exported to '" + fname_out + "'")
+print(f"gguf: model successfully exported to '{fname_out}'")
 print("")

convert-llama-7b-pth-to-gguf.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 # 7b pth llama --> gguf conversion
 # Only models with a single datafile are supported, like 7B
 # HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model
@@ -9,8 +10,9 @@ import struct
 import json
 import numpy as np
 import torch
+import argparse
 
-from typing import Any, List
+from typing import Any, List, TypeAlias
 from pathlib import Path
 from sentencepiece import SentencePieceProcessor
 
@@ -19,7 +21,7 @@ from sentencepiece import SentencePieceProcessor
 NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
 
 
-def count_model_parts(dir_model: str) -> int:
+def count_model_parts(dir_model: Path) -> int:
     num_parts = 0
     for filename in os.listdir(dir_model):
         if filename.startswith("consolidated."):
@@ -30,19 +32,22 @@ def count_model_parts(dir_model: str) -> int:
     return num_parts
 
 
-if len(sys.argv) < 3:
-    print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
-    print("  ftype == 0 -> float32")
-    print("  ftype == 1 -> float16")
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert a PyTorch 7B LLaMA model to a GGML compatible file")
+    parser.add_argument("--vocab-only",  action="store_true",    help="extract only the vocab")
+    parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
+    parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.bin)")
+    parser.add_argument("ftype",     type=int, choices=[0, 1],   help="output format - use 0 for float32, 1 for float16", default = 1)
+    return parser.parse_args()
 
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f'Error: {args.model} is not a directory', file = sys.stderr)
     sys.exit(1)
 
-
-# output in the same directory as the model
-dir_model = sys.argv[1]
-last_dir = os.path.basename(os.path.normpath(dir_model))
-
-
 # possible tensor data types
 #   ftype == 0 -> float32
 #   ftype == 1 -> float16
@@ -50,19 +55,15 @@ last_dir = os.path.basename(os.path.normpath(dir_model))
 # map from ftype to string
 ftype_str = ["f32", "f16"]
 
-ftype = 1
-if len(sys.argv) > 2:
-    ftype = int(sys.argv[2])
-    if ftype < 0 or ftype > 1:
-        print("Invalid ftype: " + str(ftype))
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
 
-        sys.exit(1)
+print("gguf: loading model "+dir_model.name)
 
-fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
-
-print("gguf: loading model "+last_dir)
-
-with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
     hparams = json.load(f)
 
 if hparams["architectures"][0] != "LlamaForCausalLM":
@@ -106,7 +107,7 @@ else:
     sys.exit()
 
 
-gguf_writer.add_name(last_dir)
+gguf_writer.add_name(dir_model.name)
 gguf_writer.add_source_hf_repo(hf_repo)
 gguf_writer.add_tensor_data_layout("Meta AI original pth")
 gguf_writer.add_context_length(ctx_length)
@@ -132,109 +133,60 @@ tokens: List[bytes] = []
 scores: List[float] = []
 toktypes: List[int] = []
 
-if Path(dir_model + "/tokenizer.model").is_file():
-    # vocab type sentencepiece
-    print("gguf: get sentencepiece tokenizer vocab and scores")
+tokenizer_model_file = dir_model / 'tokenizer.model'
+if not tokenizer_model_file.is_file():
+    print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr)
+    sys.exit(1)
 
-    tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model")
+# vocab type sentencepiece
+print("gguf: get sentencepiece tokenizer vocab and scores")
 
-    for i in range(tokenizer.vocab_size()):
-        text: bytes
-        score: float
+tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
 
-        piece = tokenizer.id_to_piece(i)
-        text = piece.encode("utf-8")
-        score = tokenizer.get_score(i)
+for i in range(tokenizer.vocab_size()):
+    text: bytes
+    score: float
 
-        toktype = 1  # defualt to normal token type
-        if tokenizer.is_unknown(i):
-            toktype = 2
-        if tokenizer.is_control(i):
-            toktype = 3
+    piece = tokenizer.id_to_piece(i)
+    text = piece.encode("utf-8")
+    score = tokenizer.get_score(i)
 
-        # toktype = 4 is user-defined = tokens from added_tokens.json
+    toktype = 1  # defualt to normal token type
+    if tokenizer.is_unknown(i):
+        toktype = 2
+    if tokenizer.is_control(i):
+        toktype = 3
 
-        if tokenizer.is_unused(i):
-            toktype = 5
-        if tokenizer.is_byte(i):
-            toktype = 6
+    # toktype = 4 is user-defined = tokens from added_tokens.json
 
-        tokens.append(text)
-        scores.append(score)
-        toktypes.append(toktype)
+    if tokenizer.is_unused(i):
+        toktype = 5
+    if tokenizer.is_byte(i):
+        toktype = 6
 
-    if Path(dir_model + "/added_tokens.json").is_file():
-        with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f:
-            addtokens_json = json.load(f)
+    tokens.append(text)
+    scores.append(score)
+    toktypes.append(toktype)
 
-            print("gguf: get added tokens")
+added_tokens_file = dir_model / 'added_tokens.json'
+if added_tokens_file.is_file():
+    with open(added_tokens_file, "r", encoding="utf-8") as f:
+        addtokens_json = json.load(f)
 
-            for key in addtokens_json:
-                tokens.append( key.encode("utf-8") )
-                scores.append(-1000.0)
-                toktypes.append(4) # user-defined token type
+        print("gguf: get added tokens")
 
-    gguf_writer.add_tokenizer_model("llama")
-    gguf_writer.add_token_list(tokens)
-    gguf_writer.add_token_scores(scores)
-    gguf_writer.add_token_types(toktypes)
+        for key in addtokens_json:
+            tokens.append( key.encode("utf-8") )
+            scores.append(-1000.0)
+            toktypes.append(4) # user-defined token type
 
+gguf_writer.add_tokenizer_model("llama")
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_scores(scores)
+gguf_writer.add_token_types(toktypes)
 
-print("gguf: get special token ids")
-
-if Path(dir_model + "/tokenizer.json").is_file():
-    # Look for special tokens in tokenizer.json if it exists
-
-    with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
-        tokenizer = json.load(f)
-
-    if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file():
-
-        with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
-            tokenizer_config = json.load(f)
-
-        if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None:
-            for key in tokenizer["added_tokens"]:
-                if key["content"] == tokenizer_config["bos_token"]["content"]:
-                    gguf_writer.add_bos_token_id(key["id"])
-
-        if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None:
-            for key in tokenizer["added_tokens"]:
-                if key["content"] == tokenizer_config["eos_token"]["content"]:
-                    gguf_writer.add_eos_token_id(key["id"])
-
-        if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None:
-            for key in tokenizer["added_tokens"]:
-                if key["content"] == tokenizer_config["unk_token"]["content"]:
-                    gguf_writer.add_unk_token_id(key["id"])
-
-        if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None:
-            for key in tokenizer["added_tokens"]:
-                if key["content"] == tokenizer_config["sep_token"]["content"]:
-                    gguf_writer.add_sep_token_id(key["id"])
-
-        if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None:
-            for key in tokenizer["added_tokens"]:
-                if key["content"] == tokenizer_config["pad_token"]["content"]:
-                    gguf_writer.add_pad_token_id(key["id"])
-else:
-    # If no tokenizer.json: Look for special tokens in config.json
-
-    if "bos_token_id" in hparams and hparams["bos_token_id"] != None:
-        gguf_writer.add_bos_token_id(hparams["bos_token_id"])
-
-    if "eos_token_id" in hparams and hparams["eos_token_id"] != None:
-        gguf_writer.add_eos_token_id(hparams["eos_token_id"])
-
-    if "unk_token_id" in hparams and hparams["unk_token_id"] != None:
-        gguf_writer.add_unk_token_id(hparams["unk_token_id"])
-
-    if "sep_token_id" in hparams and hparams["sep_token_id"] != None:
-        gguf_writer.add_sep_token_id(hparams["sep_token_id"])
-
-    if "pad_token_id" in hparams and hparams["pad_token_id"] != None:
-        gguf_writer.add_pad_token_id(hparams["pad_token_id"])
-
+special_vocab = gguf.SpecialVocab(dir_model)
+special_vocab.add_to_gguf(gguf_writer)
 
 # TENSORS
 
@@ -246,6 +198,8 @@ print("gguf: get tensor metadata")
 part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts))
 
 for part_name in part_names:
+    if args.vocab_only:
+        break
     print("gguf: loading model part '" + part_name + "'")
     model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
 
@@ -265,11 +219,8 @@ for part_name in part_names:
         data = data.squeeze().numpy()
 
         # map tensor names
-        if name.endswith(".weight") and name[:-7] in tensor_map:
-            name = tensor_map[name[:-7]] + ".weight"
-        elif name.endswith(".bias") and name[:-5] in tensor_map:
-            name = tensor_map[name[:-5]] + ".bias"
-        else:
+        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+        if new_name is None:
             print("Can not map tensor '" + name + "'")
             sys.exit()
 
@@ -288,20 +239,20 @@ for part_name in part_names:
         if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
             data = data.astype(np.float16)
 
-        print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
 
-        gguf_writer.add_tensor(name, data)
+        gguf_writer.add_tensor(new_name, data)
 
 
 print("gguf: write header")
 gguf_writer.write_header_to_file()
 print("gguf: write metadata")
 gguf_writer.write_kv_data_to_file()
-print("gguf: write tensors")
-gguf_writer.write_tensors_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
 
 gguf_writer.close()
 
-
-print("gguf: model successfully exported to '" + fname_out + "'")
+print(f"gguf: model successfully exported to '{fname_out}'")
 print("")

convert-llama-ggmlv3-to-gguf.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import sys, struct, math, argparse
 from pathlib import Path
 
@@ -74,7 +75,7 @@ class Tensor:
         self.dims = ()
         self.dtype = None
         self.start_offset = 0
-        self.len_bytes = 0
+        self.len_bytes = np.int64(0)
 
     def load(self, data, offset):
         orig_offset = offset
@@ -93,7 +94,7 @@ class Tensor:
         pad = ((offset + 31) & ~31) - offset
         offset += pad
         n_elems = np.prod(self.dims)
-        n_bytes = (n_elems * tysize) // blksize
+        n_bytes = np.int64(np.int64(n_elems) * np.int64(tysize)) // np.int64(blksize)
         self.start_offset = offset
         self.len_bytes = n_bytes
         offset += n_bytes
@@ -133,13 +134,14 @@ class GGMLV3Model:
         return offset
 
 class GGMLToGGUF:
-    def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None):
+    def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None, special_vocab = None):
         hp = ggml_model.hyperparameters
         self.model = ggml_model
         self.data = data
         self.cfg = cfg
         self.params_override = params_override
         self.vocab_override = vocab_override
+        self.special_vocab = special_vocab
         if params_override is not None:
             n_kv_head = params_override.n_head_kv
         else:
@@ -161,6 +163,8 @@ class GGMLToGGUF:
         gguf_writer = gguf.GGUFWriter(self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
         self.add_params(gguf_writer)
         self.add_vocab(gguf_writer)
+        if self.special_vocab is not None:
+            self.special_vocab.add_to_gguf(gguf_writer)
         self.add_tensors(gguf_writer)
         print("    gguf: write header")
         gguf_writer.write_header_to_file()
@@ -215,15 +219,10 @@ class GGMLToGGUF:
         if self.vocab_override is not None:
             vo = self.vocab_override
             print('* Adding vocab item(s)')
-            for (idx, vitem) in enumerate(vo.all_tokens()):
-                if len(vitem) == 3:
-                    tokens.append(vitem[0])
-                    scores.append(vitem[1])
-                    toktypes.append(vitem[2])
-                else:
-                    # Maybe try to guess the token type here?
-                    tokens.append(vitem[0])
-                    scores.append(vitem[1])
+            for (idx, (vbytes, score, ttype)) in enumerate(vo.all_tokens()):
+                tokens.append(vbytes)
+                scores.append(score)
+                toktypes.append(ttype)
             assert len(tokens) == hp.n_vocab, f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}'
             gguf_writer.add_token_list(tokens)
             gguf_writer.add_token_scores(scores)
@@ -231,13 +230,24 @@ class GGMLToGGUF:
                 gguf_writer.add_token_types(toktypes)
             return
         print(f'* Adding {hp.n_vocab} vocab item(s)')
+        assert len(self.model.vocab.items) >= 3, 'Cannot handle unexpectedly short model vocab'
         for (tokid, (vbytes, vscore)) in enumerate(self.model.vocab.items):
             tt = 1 # Normal
-            if len(vbytes) == 0:
+            # Special handling for UNK, BOS, EOS tokens.
+            if tokid <= 2:
+                if tokid == 0:
+                    vbytes = b'<unk>'
+                    tt = 2
+                elif tokid == 1:
+                    vbytes = b'<s>'
+                    tt = 3
+                else:
+                    vbytes = b'</s>'
+                    tt = 3
+            elif len(vbytes) == 0:
                 tt = 3 # Control
             elif tokid >= 3 and tokid <= 258 and len(vbytes) == 1:
-                hv = hex(vbytes[0])[2:].upper()
-                vbytes = bytes(f'<0x{hv}>', encoding = 'UTF-8')
+                vbytes = bytes(f'<0x{vbytes[0]:02X}>', encoding = 'UTF-8')
                 tt = 6 # Byte
             else:
                 vbytes = vbytes.replace(b' ', b'\xe2\x96\x81')
@@ -247,22 +257,18 @@ class GGMLToGGUF:
         gguf_writer.add_token_list(tokens)
         gguf_writer.add_token_scores(scores)
         gguf_writer.add_token_types(toktypes)
+        gguf_writer.add_unk_token_id(0)
+        gguf_writer.add_bos_token_id(1)
+        gguf_writer.add_eos_token_id(2)
 
     def add_tensors(self, gguf_writer):
-        nm = self.name_map
+        tensor_map = self.name_map
         data = self.data
         print(f'* Adding {len(self.model.tensors)} tensor(s)')
         for tensor in self.model.tensors:
             name = str(tensor.name, 'UTF-8')
-            if name.endswith('.weight'):
-                name = name[:-7]
-                suffix = '.weight'
-            elif name.endswith('.bias'):
-                name = name[:-5]
-                suffix = '.bias'
-            mapped_name = nm.get(name)
+            mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
             assert mapped_name is not None, f'Bad name {name}'
-            mapped_name += suffix
             tempdims = list(tensor.dims[:])
             if len(tempdims) > 1:
                 temp = tempdims[1]
@@ -292,8 +298,10 @@ def handle_metadata(cfg, hp):
     else:
         raise ValueError('Unable to load metadata')
     vocab = convert.load_vocab(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype)
+    # FIXME: Respect cfg.vocab_dir?
+    svocab = gguf.SpecialVocab(cfg.model_metadata_dir)
     convert.check_vocab_size(params, vocab)
-    return (params, vocab)
+    return (params, vocab, svocab)
 
 def handle_args():
     parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF')
@@ -320,15 +328,18 @@ def main():
     print(f'* GGML model hyperparameters: {model.hyperparameters}')
     vocab_override = None
     params_override = None
+    special_vocab = None
     if cfg.model_metadata_dir is not None:
-        (params_override, vocab_override) = handle_metadata(cfg, model.hyperparameters)
+        (params_override, vocab_override, special_vocab) = handle_metadata(cfg, model.hyperparameters)
         print('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.')
         print(f'* Overriding params: {params_override}')
         print(f'* Overriding vocab: {vocab_override}')
+        print(f'* Special vocab: {special_vocab}')
     else:
         print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
-    converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override)
+    converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override, special_vocab = special_vocab)
     converter.save()
     print(f'* Successful completion. Output saved to: {cfg.output}')
 
-main()
+if __name__ == '__main__':
+    main()

convert-llama-hf-to-gguf.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 # HF llama --> gguf conversion
 
 import gguf
@@ -7,8 +8,9 @@ import struct
 import json
 import numpy as np
 import torch
+import argparse
 
-from typing import Any, List, Optional
+from typing import Any, List, Optional, TypeAlias
 from pathlib import Path
 from sentencepiece import SentencePieceProcessor
 
@@ -42,40 +44,38 @@ def count_model_parts(dir_model: str) -> int:
     return num_parts
 
 
-if len(sys.argv) < 3:
-    print("Usage: convert-h5-to-ggml.py dir-model ftype\n")
-    print("  ftype == 0 -> float32")
-    print("  ftype == 1 -> float16")
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Convert a HuggingFace LLaMA model to a GGML compatible file")
+    parser.add_argument("--vocab-only",  action="store_true",    help="extract only the vocab")
+    parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
+    parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.bin)")
+    parser.add_argument("ftype",     type=int, choices=[0, 1],   help="output format - use 0 for float32, 1 for float16", default = 1)
+    return parser.parse_args()
 
+args = parse_args()
+
+dir_model = args.model
+ftype = args.ftype
+if not dir_model.is_dir():
+    print(f'Error: {args.model} is not a directory', file = sys.stderr)
     sys.exit(1)
 
-
-# output in the same directory as the model
-dir_model = sys.argv[1]
-last_dir = os.path.basename(os.path.normpath(dir_model))
-
-
 # possible tensor data types
 #   ftype == 0 -> float32
 #   ftype == 1 -> float16
 
-
 # map from ftype to string
 ftype_str = ["f32", "f16"]
 
-ftype = 1
-if len(sys.argv) > 2:
-    ftype = int(sys.argv[2])
-    if ftype < 0 or ftype > 1:
-        print("Invalid ftype: " + str(ftype))
+if args.outfile is not None:
+    fname_out = args.outfile
+else:
+    # output in the same directory as the model by default
+    fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
 
-        sys.exit(1)
+print("gguf: loading model "+dir_model.name)
 
-fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf"
-
-print("gguf: loading model "+last_dir)
-
-with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
+with open(dir_model / "config.json", "r", encoding="utf-8") as f:
     hparams = json.load(f)
 
 if hparams["architectures"][0] != "LlamaForCausalLM":
@@ -114,7 +114,7 @@ else:
     sys.exit()
 
 
-gguf_writer.add_name(last_dir)
+gguf_writer.add_name(dir_model.name)
 gguf_writer.add_source_hf_repo(hf_repo)
 gguf_writer.add_tensor_data_layout("Meta AI original pth")
 gguf_writer.add_context_length(ctx_length)
@@ -140,110 +140,61 @@ tokens: List[bytes] = []
 scores: List[float] = []
 toktypes: List[int] = []
 
-if Path(dir_model + "/tokenizer.model").is_file():
-    # vocab type sentencepiece
-    print("gguf: get sentencepiece tokenizer vocab, scores and token types")
+tokenizer_model_file = dir_model / 'tokenizer.model'
+if not tokenizer_model_file.is_file():
+    print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr)
+    sys.exit(1)
 
-    tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model")
+# vocab type sentencepiece
+print("gguf: get sentencepiece tokenizer vocab, scores and token types")
 
-    for i in range(tokenizer.vocab_size()):
-        text: bytes
-        score: float
+tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
 
-        piece = tokenizer.id_to_piece(i)
-        text = piece.encode("utf-8")
-        score = tokenizer.get_score(i)
+for i in range(tokenizer.vocab_size()):
+    text: bytes
+    score: float
 
-        toktype = 1  # defualt to normal token type
-        if tokenizer.is_unknown(i):
-            toktype = 2
-        if tokenizer.is_control(i):
-            toktype = 3
+    piece = tokenizer.id_to_piece(i)
+    text = piece.encode("utf-8")
+    score = tokenizer.get_score(i)
 
-        # toktype = 4 is user-defined = tokens from added_tokens.json
+    toktype = 1  # defualt to normal token type
+    if tokenizer.is_unknown(i):
+        toktype = 2
+    if tokenizer.is_control(i):
+        toktype = 3
 
-        if tokenizer.is_unused(i):
-            toktype = 5
-        if tokenizer.is_byte(i):
-            toktype = 6
+    # toktype = 4 is user-defined = tokens from added_tokens.json
 
-        tokens.append(text)
-        scores.append(score)
-        toktypes.append(toktype)
+    if tokenizer.is_unused(i):
+        toktype = 5
+    if tokenizer.is_byte(i):
+        toktype = 6
 
-    if Path(dir_model + "/added_tokens.json").is_file():
-        with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f:
-            addtokens_json = json.load(f)
+    tokens.append(text)
+    scores.append(score)
+    toktypes.append(toktype)
 
-            print("gguf: get added tokens")
+added_tokens_file = dir_model / 'added_tokens.json'
+if added_tokens_file.is_file():
+    with open(added_tokens_file, "r", encoding="utf-8") as f:
+        addtokens_json = json.load(f)
 
-            for key in addtokens_json:
-                tokens.append( key.encode("utf-8") )
-                scores.append(-1000.0)
-                toktypes.append(4) # user-defined token type
+        print("gguf: get added tokens")
+
+        for key in addtokens_json:
+            tokens.append( key.encode("utf-8") )
+            scores.append(-1000.0)
+            toktypes.append(4) # user-defined token type
 
 
-    gguf_writer.add_tokenizer_model("llama")
-    gguf_writer.add_token_list(tokens)
-    gguf_writer.add_token_scores(scores)
-    gguf_writer.add_token_types(toktypes)
-
-
-print("gguf: get special token ids")
-
-if Path(dir_model + "/tokenizer.json").is_file():
-    # Look for special tokens in tokenizer.json if it exists
-
-    with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f:
-        tokenizer = json.load(f)
-
-    if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file():
-
-        with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f:
-            tokenizer_config = json.load(f)
-
-        if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None:
-            for key in tokenizer["added_tokens"]:
-                if key["content"] == tokenizer_config["bos_token"]["content"]:
-                    gguf_writer.add_bos_token_id(key["id"])
-
-        if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None:
-            for key in tokenizer["added_tokens"]:
-                if key["content"] == tokenizer_config["eos_token"]["content"]:
-                    gguf_writer.add_eos_token_id(key["id"])
-
-        if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None:
-            for key in tokenizer["added_tokens"]:
-                if key["content"] == tokenizer_config["unk_token"]["content"]:
-                    gguf_writer.add_unk_token_id(key["id"])
-
-        if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None:
-            for key in tokenizer["added_tokens"]:
-                if key["content"] == tokenizer_config["sep_token"]["content"]:
-                    gguf_writer.add_sep_token_id(key["id"])
-
-        if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None:
-            for key in tokenizer["added_tokens"]:
-                if key["content"] == tokenizer_config["pad_token"]["content"]:
-                    gguf_writer.add_pad_token_id(key["id"])
-else:
-    # If no tokenizer.json: Look for special tokens in config.json
-
-    if "bos_token_id" in hparams and hparams["bos_token_id"] != None:
-        gguf_writer.add_bos_token_id(hparams["bos_token_id"])
-
-    if "eos_token_id" in hparams and hparams["eos_token_id"] != None:
-        gguf_writer.add_eos_token_id(hparams["eos_token_id"])
-
-    if "unk_token_id" in hparams and hparams["unk_token_id"] != None:
-        gguf_writer.add_unk_token_id(hparams["unk_token_id"])
-
-    if "sep_token_id" in hparams and hparams["sep_token_id"] != None:
-        gguf_writer.add_sep_token_id(hparams["sep_token_id"])
-
-    if "pad_token_id" in hparams and hparams["pad_token_id"] != None:
-        gguf_writer.add_pad_token_id(hparams["pad_token_id"])
+gguf_writer.add_tokenizer_model("llama")
+gguf_writer.add_token_list(tokens)
+gguf_writer.add_token_scores(scores)
+gguf_writer.add_token_types(toktypes)
 
+special_vocab = gguf.SpecialVocab(dir_model)
+special_vocab.add_to_gguf(gguf_writer)
 
 # TENSORS
 
@@ -253,13 +204,15 @@ tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
 print("gguf: get tensor metadata")
 
 if num_parts == 0:
-    part_names = ("pytorch_model.bin",)
+    part_names = iter(("pytorch_model.bin",))
 else:
     part_names = (
         f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
     )
 
 for part_name in part_names:
+    if args.vocab_only:
+        break
     print("gguf: loading model part '" + part_name + "'")
     model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
 
@@ -285,11 +238,8 @@ for part_name in part_names:
             data = reverse_hf_permute(data, head_count, head_count_kv)
 
         # map tensor names
-        if name.endswith(".weight") and name[:-7] in tensor_map:
-            name = tensor_map[name[:-7]] + ".weight"
-        elif name.endswith(".bias") and name[:-5] in tensor_map:
-            name = tensor_map[name[:-5]] + ".bias"
-        else:
+        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+        if new_name is None:
             print("Can not map tensor '" + name + "'")
             sys.exit()
 
@@ -308,20 +258,20 @@ for part_name in part_names:
         if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
             data = data.astype(np.float16)
 
-        print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
 
-        gguf_writer.add_tensor(name, data)
+        gguf_writer.add_tensor(new_name, data)
 
 
 print("gguf: write header")
 gguf_writer.write_header_to_file()
 print("gguf: write metadata")
 gguf_writer.write_kv_data_to_file()
-print("gguf: write tensors")
-gguf_writer.write_tensors_to_file()
+if not args.vocab_only:
+    print("gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
 
 gguf_writer.close()
 
-
-print("gguf: model successfully exported to '" + fname_out + "'")
+print(f"gguf: model successfully exported to '{fname_out}'")
 print("")

convert-lora-to-ggml.py

@@ -1,28 +1,27 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 import json
 import os
 import re
 import struct
 import sys
-from typing import Any, Dict, Sequence, TextIO
+from typing import Any, Dict, Sequence, BinaryIO
 
+import numpy as np
 import torch
 
-from convert import DATA_TYPE_TO_FTYPE, NUMPY_TYPE_TO_DATA_TYPE, DataType
+NUMPY_TYPE_TO_FTYPE: Dict[str, int] = {"float32": 0, "float16": 1}
+
 
 HF_SUBLAYER_TO_GGML = {
-    "self_attn.q_proj": "attention.wq",
-    "self_attn.k_proj": "attention.wk",
-    "self_attn.v_proj": "attention.wv",
-    "self_attn.o_proj": "attention.wo",
-    "mlp.gate_proj": "feed_forward.w1",
-    "mlp.down_proj": "feed_forward.w2",
-    "mlp.up_proj": "feed_forward.w3",
-    "input_layernorm": "attention_norm",
+    "self_attn.q_proj": "attn_q",
+    "self_attn.k_proj": "attn_k",
+    "self_attn.v_proj": "attn_v",
+    "self_attn.o_proj": "attn_output",
+    "mlp.gate_proj": "ffn_gate",
+    "mlp.down_proj": "ffn_down",
+    "mlp.up_proj": "ffn_up",
+    "input_layernorm": "attn_norm",
     "post_attention_layernorm": "ffn_norm",
-    # "norm": "norm",
-    # "embed_tokens": "tok_embeddings",
-    # "lm_head": "output",
 }
 
 
@@ -39,7 +38,7 @@ def translate_tensor_name(t: str) -> str:
             sys.exit(1)
 
         output_string = (
-            f"layers.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
+            f"blk.{nn}.{HF_SUBLAYER_TO_GGML[sub_layer]}.weight.lora{lora_type}"
         )
         return output_string
     else:
@@ -47,19 +46,21 @@ def translate_tensor_name(t: str) -> str:
         sys.exit(1)
 
 
-def write_file_header(fout: TextIO, params: Dict[str, Any]) -> None:
+def write_file_header(fout: BinaryIO, params: Dict[str, Any]) -> None:
     fout.write(b"ggla"[::-1])  # magic (ggml lora)
     fout.write(struct.pack("i", 1))  # file version
     fout.write(struct.pack("i", params["r"]))
     # https://opendelta.readthedocs.io/en/latest/modules/deltas.html says that `lora_alpha` is an int
     # but some models ship a float value instead
     # let's convert to int, but fail if lossless conversion is not possible
-    assert int(params["lora_alpha"]) == params["lora_alpha"], "cannot convert float to int losslessly"
+    assert (
+        int(params["lora_alpha"]) == params["lora_alpha"]
+    ), "cannot convert float to int losslessly"
     fout.write(struct.pack("i", int(params["lora_alpha"])))
 
 
 def write_tensor_header(
-    self, name: str, shape: Sequence[int], data_type: DataType
+    self, name: str, shape: Sequence[int], data_type: np.dtype[Any]
 ) -> None:
     sname = name.encode("utf-8")
     fout.write(
@@ -67,7 +68,7 @@ def write_tensor_header(
             "iii",
             len(shape),
             len(sname),
-            DATA_TYPE_TO_FTYPE[NUMPY_TYPE_TO_DATA_TYPE[data_type]],
+            NUMPY_TYPE_TO_FTYPE[data_type.name],
         )
     )
     fout.write(struct.pack("i" * len(shape), *shape[::-1]))

convert-pth-to-ggml.py

@@ -1,13 +0,0 @@
-# Compatibility stub
-
-import argparse
-
-import convert
-
-parser = argparse.ArgumentParser(
-    description="""[DEPRECATED - use `convert.py` instead]
-    Convert a LLaMA model checkpoint to a ggml compatible file""")
-parser.add_argument('dir_model',  help='directory containing the model checkpoint')
-parser.add_argument('ftype',      help='file type (0: float32, 1: float16)', type=int, choices=[0, 1], default=1)
-args = parser.parse_args()
-convert.main(['--outtype', 'f16' if args.ftype == 1 else 'f32', '--', args.dir_model])

convert.py

@@ -1,8 +1,9 @@
-#!/usr/bin/env python
+#!/usr/bin/env python3
 
 import gguf
 import argparse
 import concurrent.futures
+from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor
 import copy
 import enum
 import faulthandler
@@ -17,13 +18,14 @@ import re
 import signal
 import struct
 import sys
+import time
 import zipfile
 import numpy as np
 
 from abc import ABCMeta, abstractmethod
 from dataclasses import dataclass
 from pathlib import Path
-from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, TypeVar, Union)
+from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, Type, TypeVar, Union)
 from sentencepiece import SentencePieceProcessor  # type: ignore
 
 if TYPE_CHECKING:
@@ -37,30 +39,70 @@ NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]'
 ARCH=gguf.MODEL_ARCH.LLAMA
 NAMES=gguf.MODEL_TENSOR_NAMES[ARCH]
 
+DEFAULT_CONCURRENCY = 8
 #
 # data types
 #
 
 @dataclass(frozen=True)
-class UnquantizedDataType:
+class DataType:
     name: str
+    dtype: 'np.dtype[Any]'
+    valid_conversions: List[str]
 
-DT_F16  = UnquantizedDataType('F16')
-DT_F32  = UnquantizedDataType('F32')
-DT_I32  = UnquantizedDataType('I32')
-DT_BF16 = UnquantizedDataType('BF16')
+    def elements_to_bytes(self, n_elements: int) -> int:
+        return n_elements * self.dtype.itemsize
 
-DataType = Union[UnquantizedDataType]
+@dataclass(frozen=True)
+class UnquantizedDataType(DataType):
+    pass
 
-DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = {
-    DT_BF16: np.dtype(np.uint16),
-    DT_F16:  np.dtype(np.float16),
-    DT_F32:  np.dtype(np.float32),
-    DT_I32:  np.dtype(np.int32),
-}
+DT_F16  = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
+DT_F32  = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
+DT_I32  = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
+DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
 
-NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \
-    {dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()}
+@dataclass(frozen=True)
+class QuantizedDataType(DataType):
+    block_size: int
+    quantized_dtype: 'np.dtype[Any]'
+    ggml_type: gguf.GGMLQuantizationType
+
+    def quantize(self, arr: NDArray) -> NDArray:
+        raise NotImplementedError(f'Quantization for {self.name} not implemented')
+
+    def elements_to_bytes(self, n_elements: int) -> int:
+        assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'
+        return self.quantized_dtype.itemsize * (n_elements // self.block_size)
+
+@dataclass(frozen=True)
+class Q8_0QuantizedDataType(QuantizedDataType):
+    # Mini Q8_0 quantization in Python!
+    def quantize(self, arr: NDArray) -> NDArray:
+        assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}'
+        assert arr.dtype == np.float32, f'Bad array type {arr.dtype}'
+        n_blocks = arr.size // self.block_size
+        blocks = arr.reshape((n_blocks, self.block_size))
+        # Much faster implementation of block quantization contributed by @Cebtenzzre
+        def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[Tuple[Any, Any]]:
+            d = abs(blocks).max(axis = 1) / np.float32(127)
+            with np.errstate(divide = 'ignore'):
+                qs = (blocks / d[:, None]).round()
+            qs[d == 0] = 0
+            yield from zip(d, qs)
+        return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)
+
+DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
+    dtype = np.dtype(np.float32), valid_conversions = [],
+    ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
+    quantized_dtype = np.dtype([('d', '<f2'), ('qs', 'i1', (32,))]))
+
+# Quantized types skipped here because they may also map to np.float32
+NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = {}
+for dt in (DT_BF16, DT_F16, DT_F32, DT_I32):
+    if dt.dtype in NUMPY_TYPE_TO_DATA_TYPE:
+        raise ValueError(f'Invalid duplicate data type {dt}')
+    NUMPY_TYPE_TO_DATA_TYPE[dt.dtype] = dt
 
 SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
     'BF16': DT_BF16,
@@ -69,21 +111,26 @@ SAFETENSORS_DATA_TYPES: Dict[str, DataType] = {
     'I32': DT_I32,
 }
 
-class GGMLFileType(enum.Enum):
-    AllF32    = 0
-    MostlyF16 = 1  # except 1d tensors
+# TODO: match this with `llama_ftype`
+# TODO: rename to LLAMAFileType
+# TODO: move to `gguf.py`
+class GGMLFileType(enum.IntEnum):
+    AllF32     = 0
+    MostlyF16  = 1  # except 1d tensors
+    MostlyQ8_0 = 7  # except 1d tensors
 
     def type_for_tensor(self, name: str, tensor: 'LazyTensor') -> DataType:
-        if len(tensor.shape) == 1:
-            # 1D tensors are always F32.
-            return DT_F32
-        elif self == GGMLFileType.AllF32:
-            return DT_F32
-        elif self == GGMLFileType.MostlyF16:
-            return DT_F16
-        else:
+        dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self)
+        if dt is None:
             raise ValueError(self)
+        # 1D tensors are always F32.
+        return dt if len(tensor.shape) > 1 else DT_F32
 
+GGML_FILE_TYPE_TO_DATA_TYPE: Dict[GGMLFileType, DataType] = {
+    GGMLFileType.AllF32    : DT_F32,
+    GGMLFileType.MostlyF16 : DT_F16,
+    GGMLFileType.MostlyQ8_0: DT_Q8_0,
+}
 
 #
 # hparams loading
@@ -101,6 +148,14 @@ class Params:
     n_head_kv:  int
     f_norm_eps: float
 
+    f_rope_freq_base: Optional[float] = None
+    f_rope_scale: Optional[float] = None
+
+    ftype: Optional[GGMLFileType] = None
+
+    # path to the directory containing the model files
+    path_model: Optional['Path'] = None
+
     @staticmethod
     def find_n_mult(n_ff: int, n_embd: int) -> int:
         # hardcoded magic range
@@ -150,13 +205,20 @@ class Params:
     def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
         config = json.load(open(config_path))
 
-        n_vocab    = config["vocab_size"]
-        n_embd     = config["hidden_size"]
-        n_layer    = config["num_hidden_layers"]
-        n_ff       = config["intermediate_size"]
-        n_head     = config["num_attention_heads"]
-        n_head_kv  = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head
-        f_norm_eps = config["rms_norm_eps"]
+        n_vocab          = config["vocab_size"]
+        n_embd           = config["hidden_size"]
+        n_layer          = config["num_hidden_layers"]
+        n_ff             = config["intermediate_size"]
+        n_head           = config["num_attention_heads"]
+        n_head_kv        = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head
+        f_norm_eps       = config["rms_norm_eps"]
+        f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
+
+        rope_scaling = config.get("rope_scaling")
+        if isinstance(rope_scaling, dict) and rope_scaling.get("type") == "linear":
+            f_rope_scale = config["rope_scaling"].get("factor")
+        else:
+            f_rope_scale = None
 
         n_mult = Params.find_n_mult(n_ff, n_embd)
 
@@ -169,15 +231,17 @@ class Params:
                             "Suggestion: provide 'config.json' of the model in the same directory containing model files.")
 
         return Params(
-            n_vocab    = n_vocab,
-            n_embd     = n_embd,
-            n_mult     = n_mult,
-            n_layer    = n_layer,
-            n_ctx      = n_ctx,
-            n_ff       = n_ff,
-            n_head     = n_head,
-            n_head_kv  = n_head_kv,
-            f_norm_eps = f_norm_eps,
+            n_vocab          = n_vocab,
+            n_embd           = n_embd,
+            n_mult           = n_mult,
+            n_layer          = n_layer,
+            n_ctx            = n_ctx,
+            n_ff             = n_ff,
+            n_head           = n_head,
+            n_head_kv        = n_head_kv,
+            f_norm_eps       = f_norm_eps,
+            f_rope_freq_base = f_rope_freq_base,
+            f_rope_scale     = f_rope_scale,
         )
 
     # LLaMA v2 70B params.json
@@ -186,15 +250,26 @@ class Params:
     def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
         config = json.load(open(config_path))
 
-        n_vocab    = config["vocab_size"]
-        n_embd     = config["dim"]
-        n_layer    = config["n_layers"]
-        n_mult     = config["multiple_of"]
-        n_ctx      = 2048 if config["norm_eps"] == 1e-06 else 4096 # hack to determine LLaMA v1 vs v2
-        n_ff       = -1
-        n_head     = config["n_heads"]
-        n_head_kv  = config["n_kv_heads"] if "n_kv_heads" in config else n_head
-        f_norm_eps = config["norm_eps"]
+        n_vocab          = config["vocab_size"] if "vocab_size" in config else -1
+        n_embd           = config["dim"]
+        n_layer          = config["n_layers"]
+        n_mult           = config["multiple_of"]
+        n_ff             = -1
+        n_head           = config["n_heads"]
+        n_head_kv        = config["n_kv_heads"] if "n_kv_heads" in config else n_head
+        f_norm_eps       = config["norm_eps"]
+        f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None
+
+        # hack to determine LLaMA v1 vs v2 vs CodeLlama
+        if f_rope_freq_base and f_rope_freq_base == 1000000:
+            # CodeLlama
+            n_ctx = 16384
+        elif config["norm_eps"] == 1e-05:
+            # LLaMA v2
+            n_ctx = 4096
+        else:
+            # LLaMA v1
+            n_ctx = 2048
 
         if n_vocab == -1:
             n_vocab = model["tok_embeddings.weight"].shape[0]
@@ -203,15 +278,16 @@ class Params:
             n_ff = model["layers.0.feed_forward.w1.weight"].shape[0]
 
         return Params(
-            n_vocab    = n_vocab,
-            n_embd     = n_embd,
-            n_mult     = n_mult,
-            n_layer    = n_layer,
-            n_ctx      = n_ctx,
-            n_ff       = n_ff,
-            n_head     = n_head,
-            n_head_kv  = n_head_kv,
-            f_norm_eps = f_norm_eps,
+            n_vocab          = n_vocab,
+            n_embd           = n_embd,
+            n_mult           = n_mult,
+            n_layer          = n_layer,
+            n_ctx            = n_ctx,
+            n_ff             = n_ff,
+            n_head           = n_head,
+            n_head_kv        = n_head_kv,
+            f_norm_eps       = f_norm_eps,
+            f_rope_freq_base = f_rope_freq_base,
         )
 
     @staticmethod
@@ -223,8 +299,12 @@ class Params:
             params = Params.loadHFTransformerJson(model_plus.model, hf_config_path)
         elif orig_config_path.exists():
             params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path)
-        else:
+        elif model_plus.format != 'none':
             params = Params.guessed(model_plus.model)
+        else:
+            raise ValueError('Cannot guess params when model format is none')
+
+        params.path_model = model_plus.paths[0].parent
 
         return params
 
@@ -275,7 +355,7 @@ class BpeVocab:
         yield from self.added_tokens()
 
     def __repr__(self) -> str:
-        return f"BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
+        return f"<BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
 
 
 class SentencePieceVocab:
@@ -338,7 +418,6 @@ class SentencePieceVocab:
 
 Vocab = Union[BpeVocab, SentencePieceVocab]
 
-
 #
 # data loading
 # TODO: reuse (probably move to gguf.py?)
@@ -361,14 +440,14 @@ class Tensor(metaclass=ABCMeta):
     @abstractmethod
     def permute(self, n_head: int, n_head_kv: int) -> 'Tensor': ...
     @abstractmethod
-    def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
+    def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> 'UnquantizedTensor': ...
     @abstractmethod
     def part(self, n_part: int) -> 'UnquantizedTensor': ...
     @abstractmethod
     def to_ggml(self) -> 'GGMLCompatibleTensor': ...
 
 
-def bf16_to_fp32(bf16_arr: np.ndarray) -> np.ndarray:
+def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray:
     assert bf16_arr.dtype == np.uint16, f"Input array should be of dtype uint16, but got {bf16_arr.dtype}"
     fp32_arr = bf16_arr.astype(np.uint32) << 16
     return fp32_arr.view(np.float32)
@@ -381,7 +460,7 @@ class UnquantizedTensor(Tensor):
         self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype]
 
     def astype(self, data_type: DataType) -> Tensor:
-        dtype = DATA_TYPE_TO_NUMPY[data_type]
+        dtype = data_type.dtype
         if self.data_type == DT_BF16:
             self.ndarray = bf16_to_fp32(self.ndarray)
         return UnquantizedTensor(self.ndarray.astype(dtype))
@@ -389,9 +468,9 @@ class UnquantizedTensor(Tensor):
     def to_ggml(self) -> 'UnquantizedTensor':
         return self
 
-    def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor':
+    def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> 'UnquantizedTensor':
         r = self.ndarray.shape[0] // 3
-        return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head))
+        return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv))
 
     def part(self, n_part: int) -> 'UnquantizedTensor':
         r = self.ndarray.shape[0] // 3
@@ -420,22 +499,6 @@ def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, conv
 GGMLCompatibleTensor = Union[UnquantizedTensor]
 
 
-class DeferredPermutedTensor(Tensor):
-    def __init__(self, base: Tensor, n_head: int, n_head_kv: int) -> None:
-        self.base = base
-        self.n_head = n_head
-        self.data_type = self.base.data_type
-
-    def astype(self, data_type: DataType) -> Tensor:
-        return self.base.astype(data_type).permute(self.n_head, self.n_head_kv)
-
-    def to_ggml(self) -> GGMLCompatibleTensor:
-        return self.base.to_ggml().permute(self.n_head, self.n_head_kv)
-
-    def permute(self, n_head: int, n_head_kv: int) -> Tensor:
-        raise Exception("shouldn't permute twice")
-
-
 @dataclass
 class LazyTensor:
     _load: Callable[[], Tensor]
@@ -445,7 +508,9 @@ class LazyTensor:
 
     def load(self) -> Tensor:
         ret = self._load()
-        assert ret.data_type == self.data_type, (self.data_type, ret.data_type, self.description)
+        # Should be okay if it maps to the same numpy type?
+        assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \
+                (self.data_type, ret.data_type, self.description)
         return ret
 
     def astype(self, data_type: DataType) -> 'LazyTensor':
@@ -456,8 +521,8 @@ class LazyTensor:
         return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}')
 
     def validate_conversion_to(self, data_type: DataType) -> None:
-        if data_type == self.data_type:
-            return
+        if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions:
+            raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.')
 
 
 LazyModel = Dict[str, LazyTensor]
@@ -467,7 +532,7 @@ LazyModel = Dict[str, LazyTensor]
 class ModelPlus:
     model: LazyModel
     paths: List[Path]  # Where this was read from.
-    format: Literal['ggml', 'torch', 'safetensors']
+    format: Literal['ggml', 'torch', 'safetensors', 'none']
     vocab: Optional[Vocab]  # For GGML models (which have vocab built in), the vocab.
 
 
@@ -533,12 +598,12 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTe
         return lazy_tensor.load().permute(n_head, n_head_kv)
     return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)
 
-def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor:
+def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor:
     def load() -> Tensor:
-        return lazy_tensor.load().permute_part(n_part, n_head)
+        return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv)
     s = lazy_tensor.shape.copy()
     s[0] = s[0] // 3
-    return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
+    return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)
 
 def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
     def load() -> Tensor:
@@ -583,9 +648,7 @@ class LazyUnpickler(pickle.Unpickler):
         info = self.zip_file.getinfo(filename)
 
         def load(offset: int, elm_count: int) -> NDArray:
-            dtype = DATA_TYPE_TO_NUMPY.get(data_type)
-            if dtype is None:
-                raise Exception("tensor stored in unsupported format")
+            dtype = data_type.dtype
             fp = self.zip_file.open(info)
             fp.seek(offset * dtype.itemsize)
             size = elm_count * dtype.itemsize
@@ -595,7 +658,7 @@ class LazyUnpickler(pickle.Unpickler):
         description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}'
         return LazyStorage(load=load, kind=pid[1], description=description)
 
-    # @staticmethod
+    @staticmethod
     def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any,
                                # pyright: ignore[reportSelfClsParameterName]
                                requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor:
@@ -607,13 +670,15 @@ class LazyUnpickler(pickle.Unpickler):
         description = f'pickled storage_offset={storage_offset} in {storage.description}'
         return LazyTensor(load, list(size), storage.kind.data_type, description)
 
-    # @staticmethod
+    @staticmethod
     def rebuild_from_type_v2(func, new_type, args, state):
         return func(*args)
 
-    CLASSES: Dict[Any, Any] = {
-        ('torch._tensor', '_rebuild_from_type_v2'): rebuild_from_type_v2,
-        ('torch._utils', '_rebuild_tensor_v2'): lazy_rebuild_tensor_v2,
+    CLASSES: Dict[Tuple[str, str], Any] = {
+        # getattr used here as a workaround for mypy not being smart enough to detrmine
+        # the staticmethods have a __func__ attribute.
+        ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'),
+        ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'),
         ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16),
         ('torch', 'HalfStorage'): LazyStorageKind(DT_F16),
         ('torch', 'FloatStorage'): LazyStorageKind(DT_F32),
@@ -649,7 +714,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus:
 
     def convert(info: Dict[str, Any]) -> LazyTensor:
         data_type = SAFETENSORS_DATA_TYPES[info['dtype']]
-        numpy_dtype = DATA_TYPE_TO_NUMPY[data_type]
+        numpy_dtype = data_type.dtype
         shape: List[int] = info['shape']
         begin, end = info['data_offsets']
         assert 0 <= begin <= end <= len(byte_buf)
@@ -689,23 +754,40 @@ def lazy_load_file(path: Path) -> ModelPlus:
 In = TypeVar('In')
 Out = TypeVar('Out')
 
-def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int) -> Iterable[Out]:
+def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, use_processpool_executor: bool = False) -> Iterable[Out]:
     '''Parallel map, but with backpressure.  If the caller doesn't call `next`
     fast enough, this will stop calling `func` at some point rather than
     letting results pile up in memory.  Specifically, there is a max of one
     output value buffered per thread.'''
-    with concurrent.futures.ThreadPoolExecutor() as executor:
+    if concurrency < 2:
+        yield from map(func, iterable)
+        # Not reached.
+    iterable = iter(iterable)
+    executor_class: Union[Type[ThreadPoolExecutor], Type[ProcessPoolExecutor]]
+    if use_processpool_executor:
+        executor_class = ProcessPoolExecutor
+    else:
+        executor_class = ThreadPoolExecutor
+    with executor_class(max_workers = max_workers) as executor:
         futures: List[concurrent.futures.Future[Out]] = []
-        items_rev = list(iterable)[::-1]
-        for i in range(min(concurrency, len(items_rev))):
-            futures.append(executor.submit(func, items_rev.pop()))
+        done = False
+        for _ in range(concurrency):
+            try:
+                futures.append(executor.submit(func, next(iterable)))
+            except StopIteration:
+                done = True
+                break
+
         while futures:
             result = futures.pop(0).result()
-            if items_rev:
-                futures.append(executor.submit(func, items_rev.pop()))
+            while not done and len(futures) < concurrency:
+                try:
+                    futures.append(executor.submit(func, next(iterable)))
+                except StopIteration:
+                    done = True
+                    break
             yield result
 
-
 def check_vocab_size(params: Params, vocab: Vocab) -> None:
     if params.n_vocab != vocab.vocab_size:
         assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
@@ -728,7 +810,15 @@ class OutputFile:
         self.gguf = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH])
 
     def add_meta_arch(self, params: Params) -> None:
-        self.gguf.add_name                ("LLaMA")
+        name = "LLaMA"
+
+        # TODO: better logic to determine model name
+        if (params.n_ctx == 4096):
+            name = "LLaMA v2"
+        elif params.path_model:
+            name = str(params.path_model.parent).split('/')[-1]
+
+        self.gguf.add_name                (name)
         self.gguf.add_context_length      (params.n_ctx)
         self.gguf.add_embedding_length    (params.n_embd)
         self.gguf.add_block_count         (params.n_layer)
@@ -738,29 +828,44 @@ class OutputFile:
         self.gguf.add_head_count_kv       (params.n_head_kv)
         self.gguf.add_layer_norm_rms_eps  (params.f_norm_eps)
 
+        if params.f_rope_freq_base:
+            self.gguf.add_rope_freq_base(params.f_rope_freq_base)
+
+        if params.f_rope_scale:
+            self.gguf.add_rope_scale_linear(params.f_rope_scale)
+
+        if params.ftype:
+            self.gguf.add_file_type(params.ftype)
+
     def add_meta_vocab(self, vocab: Vocab) -> None:
         tokens = []
         scores = []
         toktypes = []
-        # NOTE: `all_tokens` returns the the base vocabulary and added tokens
-        # TODO: add special tokens?
+        # NOTE: `all_tokens` returns the base vocabulary and added tokens
         for text, score, toktype in vocab.all_tokens():
             tokens.append(text)
             scores.append(score)
             toktypes.append(toktype)
 
-        self.gguf.add_tokenizer_model("llama")
+        if isinstance(vocab, SentencePieceVocab):
+            self.gguf.add_tokenizer_model("llama")
+        elif isinstance(vocab, BpeVocab):
+            self.gguf.add_tokenizer_model("gpt2")
+        else:
+            raise ValueError(f'Unknown vocab type: Not BpeVocab or SentencePieceVocab')
         self.gguf.add_token_list(tokens)
         self.gguf.add_token_scores(scores)
         self.gguf.add_token_types(toktypes)
 
+    def add_meta_special_vocab(self, svocab: gguf.SpecialVocab) -> None:
+        svocab.add_to_gguf(self.gguf)
+
     def add_tensor_info(self, name: str, tensor: LazyTensor) -> None:
-        n_elements = 1
-        for dim in tensor.shape:
-            n_elements *= dim
-        data_type = DATA_TYPE_TO_NUMPY[tensor.data_type]
-        data_nbytes = n_elements * data_type.itemsize
-        self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes)
+        n_elements = int(np.prod(tensor.shape))
+        raw_dtype = getattr(tensor.data_type, 'ggml_type', None)
+        data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype
+        data_nbytes = tensor.data_type.elements_to_bytes(n_elements)
+        self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype = raw_dtype)
 
     def write_meta(self) -> None:
         self.gguf.write_header_to_file()
@@ -773,7 +878,7 @@ class OutputFile:
         self.gguf.close()
 
     @staticmethod
-    def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab) -> None:
+    def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab) -> None:
         check_vocab_size(params, vocab)
 
         of = OutputFile(fname_out)
@@ -781,12 +886,27 @@ class OutputFile:
         # meta data
         of.add_meta_arch(params)
         of.add_meta_vocab(vocab)
+        of.add_meta_special_vocab(svocab)
+
         of.write_meta()
 
         of.close()
 
     @staticmethod
-    def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None:
+    def do_item(item: Tuple[str, LazyTensor]) -> Tuple[DataType, NDArray]:
+        name, lazy_tensor = item
+        tensor = lazy_tensor.load().to_ggml()
+        return (lazy_tensor.data_type, tensor.ndarray)
+
+    @staticmethod
+    def maybe_do_quantize(item: Tuple[DataType, NDArray]) -> NDArray:
+        dt, arr = item
+        if not isinstance(dt, QuantizedDataType):
+            return arr
+        return dt.quantize(arr)
+
+    @staticmethod
+    def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY) -> None:
         check_vocab_size(params, vocab)
 
         of = OutputFile(fname_out)
@@ -794,6 +914,7 @@ class OutputFile:
         # meta data
         of.add_meta_arch(params)
         of.add_meta_vocab(vocab)
+        of.add_meta_special_vocab(svocab)
 
         # tensor info
         for name, lazy_tensor in model.items():
@@ -802,16 +923,19 @@ class OutputFile:
         of.write_meta()
         of.write_tensor_info()
 
-        def do_item(item: Tuple[str, LazyTensor]) -> NDArray:
-            name, lazy_tensor = item
-            return lazy_tensor.load().to_ggml().ndarray
-
         # tensor data
-        ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8)
+        ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)
+        if ftype == GGMLFileType.MostlyQ8_0:
+            ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, use_processpool_executor = True)
+        else:
+            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
+
+        start = time.time()
         for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
+            elapsed = time.time() - start
             size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
             padi = len(str(len(model)))
-            print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}")
+            print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}")
             of.gguf.write_tensor_data(ndarray)
 
         of.close()
@@ -823,6 +947,8 @@ def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFi
         return GGMLFileType.AllF32
     if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)):
         return GGMLFileType.MostlyF16
+    if output_type_str == "q8_0":
+        return GGMLFileType.MostlyQ8_0
 
     name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()}
 
@@ -833,7 +959,8 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM
             for (name, tensor) in model.items()}
 
 def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
-    tmap = gguf.get_tensor_name_map(ARCH, params.n_layer)
+    tmap = gguf.TensorNameMap(ARCH, params.n_layer)
+    should_skip: Set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
 
     tmp = model
 
@@ -849,28 +976,22 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
             tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head)
             tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv)
             tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy        (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
+            del tmp[f"model.layers.{i}.self_attn.W_pack.weight"]
         else:
             break
 
     out: LazyModel = {}
     for name, lazy_tensor in model.items():
-        name_new = name
-
-        if name in tmap:
-            name_new = tmap[name]
-        elif name.endswith(".weight") and name[:-7] in tmap:
-            name_new = tmap[name[:-7]] + ".weight"
-        elif name.endswith(".bias") and name[:-5] in tmap:
-            name_new = tmap[name[:-5]] + ".bias"
-        else:
+        tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None)
+        if name_new is None:
             raise Exception(f"Unexpected tensor name: {name}")
 
-        if gguf.should_skip_tensor_TMP(ARCH, params.n_layer, name_new):
+        if tensor_type in should_skip:
             print(f"skipping tensor {name_new}")
             continue
-        else:
-            print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type} | {lazy_tensor.shape}")
-            out[name_new] = lazy_tensor
+
+        print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}")
+        out[name_new] = lazy_tensor
 
     return out
 
@@ -956,7 +1077,7 @@ def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, Sentence
             path = path3
         else:
             raise FileNotFoundError(
-                f"Could not find tokenizer.model in {path} or its parent; "
+                f"Could not find {vocab_file} in {path} or its parent; "
                 "if it's in another directory, pass the directory as --vocab-dir")
 
     print(f"Loading vocab file '{path}', type '{vocabtype}'")
@@ -974,6 +1095,7 @@ def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path:
     namestr = {
         GGMLFileType.AllF32:    "f32",
         GGMLFileType.MostlyF16: "f16",
+        GGMLFileType.MostlyQ8_0:"q8_0",
     }[file_type]
     ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf"
     if ret in model_paths:
@@ -997,19 +1119,28 @@ def main(args_in: Optional[List[str]] = None) -> None:
     parser.add_argument("--dump",        action="store_true",    help="don't convert, just show what's in the model")
     parser.add_argument("--dump-single", action="store_true",    help="don't convert, just show what's in a single model file")
     parser.add_argument("--vocab-only",  action="store_true",    help="extract only the vocab")
-    parser.add_argument("--outtype",     choices=["f32", "f16"], help="output format (default: based on input)")
+    parser.add_argument("--outtype",     choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
     parser.add_argument("--vocab-dir",   type=Path,              help="directory containing tokenizer.model, if separate from model file")
     parser.add_argument("--outfile",     type=Path,              help="path to write to; default: based on input")
     parser.add_argument("model",         type=Path,              help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
     parser.add_argument("--vocabtype",   choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm")
     parser.add_argument("--ctx",         type=int,               help="model training context (default: based on input)")
+    parser.add_argument("--concurrency", type=int,               help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY)
     args = parser.parse_args(args_in)
 
     if args.dump_single:
         model_plus = lazy_load_file(args.model)
         do_dump_model(model_plus)
+        return
 
-    model_plus = load_some_model(args.model)
+    if not args.vocab_only:
+        model_plus = load_some_model(args.model)
+    else:
+        model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None)
+
+    if args.dump:
+        do_dump_model(model_plus)
+        return
 
     params = Params.load(model_plus)
     if params.n_ctx == -1:
@@ -1020,34 +1151,45 @@ def main(args_in: Optional[List[str]] = None) -> None:
                             " - LLaMA v2: --ctx 4096\n")
         params.n_ctx = args.ctx
 
+    if args.outtype:
+        params.ftype = {
+            "f32": GGMLFileType.AllF32,
+            "f16": GGMLFileType.MostlyF16,
+            "q8_0": GGMLFileType.MostlyQ8_0,
+        }[args.outtype]
+
     print(f"params = {params}")
 
     vocab: Vocab
     if args.vocab_only:
-        vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)
         assert args.outfile, "need --outfile if using --vocab-only"
+        # FIXME: Try to respect vocab_dir somehow?
+        vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype)
+        special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe')
         outfile = args.outfile
-        OutputFile.write_vocab_only(outfile, params, vocab)
+        OutputFile.write_vocab_only(outfile, params, vocab, special_vocab)
         print(f"Wrote {outfile}")
+        return
+
+    if model_plus.vocab is not None and args.vocab_dir is None:
+        vocab = model_plus.vocab
     else:
-        if args.dump:
-            do_dump_model(model_plus)
-            return
+        vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
+        vocab = load_vocab(vocab_dir, args.vocabtype)
+    # FIXME: Try to respect vocab_dir somehow?
+    special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe')
 
-        if model_plus.vocab is not None and args.vocab_dir is None:
-            vocab = model_plus.vocab
-        else:
-            vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
-            vocab = load_vocab(vocab_dir, args.vocabtype)
+    model   = model_plus.model
+    model   = convert_model_names(model, params)
+    ftype   = pick_output_type(model, args.outtype)
+    model   = convert_to_output_type(model, ftype)
+    outfile = args.outfile or default_outfile(model_plus.paths, ftype)
 
-        model       = model_plus.model
-        model       = convert_model_names(model, params)
-        output_type = pick_output_type(model, args.outtype)
-        model       = convert_to_output_type(model, output_type)
-        outfile     = args.outfile or default_outfile(model_plus.paths, output_type)
+    params.ftype = ftype
+    print(f"Writing {outfile}, format {ftype}")
 
-        OutputFile.write_all(outfile, params, model, vocab)
-        print(f"Wrote {outfile}")
+    OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency)
+    print(f"Wrote {outfile}")
 
 
 if __name__ == '__main__':

examples/CMakeLists.txt

@@ -25,6 +25,7 @@ else()
     add_subdirectory(simple)
     add_subdirectory(embd-input)
     add_subdirectory(llama-bench)
+    add_subdirectory(beam-search)
     if (LLAMA_METAL)
         add_subdirectory(metal)
     endif()

examples/beam-search/CMakeLists.txt

@@ -0,0 +1,8 @@
+set(TARGET beam-search)
+add_executable(${TARGET} beam-search.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)
+if(TARGET BUILD_INFO)
+  add_dependencies(${TARGET} BUILD_INFO)
+endif()

examples/beam-search/beam-search.cpp

@@ -0,0 +1,188 @@
+#ifndef _GNU_SOURCE
+#define _GNU_SOURCE
+#endif
+
+#include "common.h"
+#include "llama.h"
+#include "build-info.h"
+
+#include <cassert>
+#include <cinttypes>
+#include <cmath>
+#include <cstdio>
+#include <cstring>
+#include <ctime>
+#include <fstream>
+#include <iostream>
+#include <string>
+#include <vector>
+
+#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
+#include <signal.h>
+#include <unistd.h>
+#elif defined (_WIN32)
+#define WIN32_LEAN_AND_MEAN
+#define NOMINMAX
+#include <windows.h>
+#include <signal.h>
+#endif
+
+// Used for debugging to print out beam tokens.
+struct ostream_beam_view {
+    llama_context * ctx;
+    llama_beam_view beam_view;
+};
+std::ostream& operator<<(std::ostream& os, const ostream_beam_view & obv) {
+    os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens(";
+    for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) {
+        os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]);
+    }
+    return os << ')';
+}
+
+// Put here anything you want back in beam_search_callback().
+struct beam_search_callback_data {
+    llama_context * ctx;
+    std::vector<llama_token> response;
+};
+
+// 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.
+bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, const size_t n_tokens) {
+    return n_tokens && tokens[n_tokens-1] == llama_token_eos(callback_data.ctx);
+}
+
+// Function matching type llama_beam_search_callback_fn_t.
+// Custom callback example is called each time the beams lengths increase:
+//  * Show progress by printing ',' following by number of convergent beam tokens if any.
+//  * When all beams converge to a common prefix, they are made available in beams_state.beams[0].
+//    This is also called when the stop condition is met.
+//    Collect tokens into std::vector<llama_token> response which is pointed to by callback_data.
+void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) {
+    auto& callback_data = *static_cast<beam_search_callback_data*>(callback_data_ptr);
+    // Mark beams as EOS as needed.
+    for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
+        llama_beam_view& beam_view = beams_state.beam_views[i];
+        if (!beam_view.eob && is_at_eob(callback_data, beam_view.tokens, beam_view.n_tokens)) {
+            beam_view.eob = true;
+        }
+    }
+    printf(",");  // Show progress
+    if (const size_t n = beams_state.common_prefix_length) {
+        callback_data.response.resize(callback_data.response.size() + n);
+        assert(0u < beams_state.n_beams);
+        const llama_token * tokens = beams_state.beam_views[0].tokens;
+        std::copy(tokens, tokens + n, callback_data.response.end() - n);
+        printf("%lu", n);
+    }
+    fflush(stdout);
+#if 1 // DEBUG: print current beams for this iteration
+    std::cout << "\n\nCurrent beams (last_call=" << beams_state.last_call << "):\n";
+    for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
+        std::cout << "beams["<<i<<"]: " << ostream_beam_view{callback_data.ctx,beams_state.beam_views[i]} << std::endl;
+    }
+#endif
+}
+
+int main(int argc, char ** argv)
+{
+    gpt_params params;
+    //params.n_gpu_layers = 200;
+
+    //---------------------------------
+    // Print help :
+    //---------------------------------
+
+    if ( argc < 2 || argv[1][0] == '-' )
+    {
+        printf( "Usage: %s MODEL_PATH [BEAM_WIDTH=2] [PROMPT]\n" , argv[0] );
+        return 1 ;
+    }
+
+    //---------------------------------
+    // Load parameters :
+    //---------------------------------
+
+    params.model = argv[1];
+
+    params.n_beams = 2 < argc ? std::stoi(argv[2]) : 2;
+
+    if ( argc > 3 )
+    {
+        params.prompt = argv[3];
+    }
+
+    if ( params.prompt.empty() )
+    {
+        params.prompt = "### Request:\nHow many countries are there?\n\n### Response:\n";
+    }
+
+    //---------------------------------
+    // Init LLM :
+    //---------------------------------
+
+    llama_backend_init(params.numa);
+
+    llama_model * model;
+    llama_context * ctx;
+
+    std::tie(model, ctx) = llama_init_from_gpt_params( params );
+
+    if ( model == NULL )
+    {
+        fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
+        return 1;
+    }
+
+    //---------------------------------
+    // Tokenize the prompt :
+    //---------------------------------
+
+    std::vector<llama_token> tokens_list = llama_tokenize(ctx, params.prompt, true);
+
+    const size_t max_context_size     = llama_n_ctx( ctx );
+    const size_t max_tokens_list_size = max_context_size - 4 ;
+
+    if (tokens_list.size() > max_tokens_list_size)
+    {
+        fprintf( stderr , "%s: error: prompt too long (%lu tokens, max %lu)\n" ,
+             __func__ , tokens_list.size() , max_tokens_list_size );
+        return 1;
+    }
+
+    fprintf( stderr, "\n\n" );
+
+    // Print the tokens from the prompt :
+
+    for( auto id : tokens_list )
+    {
+        std::cout << llama_token_to_piece(ctx, id);
+    }
+    std::cout << std::flush;
+
+    int n_past = llama_get_kv_cache_token_count(ctx);
+    if (llama_eval(ctx, tokens_list.data(), tokens_list.size(), n_past, params.n_threads))
+    {
+        fprintf(stderr, "%s : failed to eval prompt.\n" , __func__ );
+        return 1;
+    }
+    n_past += tokens_list.size();
+
+    beam_search_callback_data callback_data{ctx, {}};
+    size_t const beam_width = static_cast<size_t>(params.n_beams);
+    int const n_predict = 256;
+    llama_beam_search(ctx, beam_search_callback, &callback_data, beam_width, n_past, n_predict, params.n_threads);
+
+    std::cout << "\n\n";
+    for (llama_token const token_id : callback_data.response) {
+        std::cout << llama_token_to_piece(ctx,token_id);
+    }
+    std::cout << std::endl;
+
+    llama_free( ctx );
+    llama_free_model( model );
+
+    llama_backend_free();
+
+    return 0;
+}

examples/chat.sh

@@ -11,6 +11,6 @@ cd ..
 #
 #   "--keep 48" is based on the contents of prompts/chat-with-bob.txt
 #
-./main -m ./models/7B/ggml-model-q4_0.bin -c 512 -b 1024 -n 256 --keep 48 \
+./main -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \
     --repeat_penalty 1.0 --color -i \
     -r "User:" -f prompts/chat-with-bob.txt

examples/convert-llama2c-to-ggml/README.md

@@ -12,15 +12,15 @@ usage: ./convert-llama2c-to-ggml [options]
 
 options:
   -h, --help                       show this help message and exit
-  --copy-vocab-from-model FNAME    model path from which to copy vocab (default 'models/ggml-vocab.bin')
+  --copy-vocab-from-model FNAME    path of gguf llama model or llama2.c vocabulary from which to copy vocab (default 'models/7B/ggml-model-f16.gguf')
   --llama2c-model FNAME            [REQUIRED] model path from which to load Karpathy's llama2.c model
   --llama2c-output-model FNAME     model path to save the converted llama2.c model (default ak_llama_model.bin')
 ```
 
-An example command is as follows:
+An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows:
 
-`$ ./convert-llama2c-to-ggml --copy-vocab-from-model <ggml-vocab.bin> --llama2c-model <llama2.c model path> --llama2c-output-model <ggml output model path>`
+`$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin`
 
-Now you can use the model with command like:
+Now you can use the model with a command like:
 
-`$ ./main -m <ggml output model path> -p "One day, Lily met a Shoggoth" -n 500 -c 256 -eps 1e-5`
+`$ ./main -m stories42M.gguf.bin -p "One day, Lily met a Shoggoth" -n 500 -c 256`

examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp

@@ -10,13 +10,60 @@
 #include <ctime>
 #include <random>
 #include <stdexcept>
+#include <sstream>
 #include <algorithm>
 #include <string>
 
+// GGUF keys & tensor names.
+
+#define KV_GENERAL_ARCHITECTURE          "general.architecture"
+#define KV_GENERAL_NAME                  "general.name"
+
+#define KV_TOKENIZER_MODEL               "tokenizer.ggml.model"
+#define KV_TOKENIZER_LIST                "tokenizer.ggml.tokens"
+#define KV_TOKENIZER_TOKEN_TYPE          "tokenizer.ggml.token_type"
+#define KV_TOKENIZER_SCORES              "tokenizer.ggml.scores"
+#define KV_TOKENIZER_BOS_ID              "tokenizer.ggml.bos_token_id"
+#define KV_TOKENIZER_EOS_ID              "tokenizer.ggml.eos_token_id"
+#define KV_TOKENIZER_UNK_ID              "tokenizer.ggml.unknown_token_id"
+#define KV_TOKENIZER_SEP_ID              "tokenizer.ggml.seperator_token_id"
+#define KV_TOKENIZER_PAD_ID              "tokenizer.ggml.padding_token_id"
+#define KV_TOKENIZER_HF_JSON             "tokenizer.huggingface.json"
+
+#define KV_CONTEXT_LENGTH                "llama.context_length"
+#define KV_EMBEDDING_LENGTH              "llama.embedding_length"
+#define KV_BLOCK_COUNT                   "llama.block_count"
+#define KV_FEED_FORWARD_LENGTH           "llama.feed_forward_length"
+#define KV_ATTENTION_HEAD_COUNT          "llama.attention.head_count"
+#define KV_ATTENTION_HEAD_COUNT_KV       "llama.attention.head_count_kv"
+#define KV_ATTENTION_LAYERNORM_RMS_EPS   "llama.attention.layer_norm_rms_epsilon"
+#define KV_ROPE_DIMENSION_COUNT          "llama.rope.dimension_count"
+
+#define TN_TOKEN_EMBD  "token_embd.weight"
+#define TN_OUTPUT_NORM "output_norm.weight"
+#define TN_OUTPUT      "output.weight"
+#define TN_ATTN_NORM   "blk.%d.attn_norm.weight"
+#define TN_ATTN_Q      "blk.%d.attn_q.weight"
+#define TN_ATTN_K      "blk.%d.attn_k.weight"
+#define TN_ATTN_V      "blk.%d.attn_v.weight"
+#define TN_ATTN_OUTPUT "blk.%d.attn_output.weight"
+#define TN_FFN_NORM    "blk.%d.ffn_norm.weight"
+#define TN_FFN_GATE    "blk.%d.ffn_gate.weight"
+#define TN_FFN_DOWN    "blk.%d.ffn_down.weight"
+#define TN_FFN_UP      "blk.%d.ffn_up.weight"
+
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
+#define LLAMA_FILE_MAGIC_GGJT        0x67676a74u // 'ggjt'
+#define LLAMA_FILE_VERSION_GGJT_V3   3
+
+#define TOKENIZER_NAME "llama"
+#define UNKNOWN_TOKEN_ID 0
+#define BOS_TOKEN_ID 1
+#define EOS_TOKEN_ID 2
+
 //////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc.
 typedef struct {
     int dim; // transformer dimension
@@ -49,10 +96,10 @@ typedef struct {
     // float* freq_cis_real; // (seq_len, dim/2)
     // float* freq_cis_imag; // (seq_len, dim/2)
     // (optional) classifier weights for the logits, on the last layer
-    //float* wcls;
+    float* wcls;
 } TransformerWeights;
 
-void malloc_weights(TransformerWeights* w, Config* p) {
+void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) {
     // we calloc instead of malloc to keep valgrind happy
     w->token_embedding_table = new float[p->vocab_size * p->dim]();
     printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->token_embedding_table\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
@@ -86,9 +133,16 @@ void malloc_weights(TransformerWeights* w, Config* p) {
 
     w->rms_final_weight = new float[p->dim]();
     printf("[%s:AK] Allocating [%d] float space for w->rms_final_weight\n",__func__,p->dim);
+
+    if (shared_weights) {
+        w->wcls = NULL;
+    } else {
+        w->wcls = new float[p->vocab_size * p->dim]();
+        printf("[%s:AK] Allocating [%d] x [%d] = [%d] float space for w->wcls\n",__func__,p->vocab_size , p->dim, p->vocab_size * p->dim);
+    }
 }
 
-int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f) {
+int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shared_weights) {
     if (fread(w->token_embedding_table, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
     if (fread(w->rms_att_weight, sizeof(float), p->n_layers * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim)) return 1;
     if (fread(w->wq, sizeof(float), p->n_layers * p->dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->dim)) return 1;
@@ -100,6 +154,22 @@ int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f) {
     if (fread(w->w2, sizeof(float), p->n_layers * p->hidden_dim * p->dim, f) != static_cast<size_t>(p->n_layers * p->hidden_dim * p->dim)) return 1;
     if (fread(w->w3, sizeof(float), p->n_layers * p->dim * p->hidden_dim, f) != static_cast<size_t>(p->n_layers * p->dim * p->hidden_dim)) return 1;
     if (fread(w->rms_final_weight, sizeof(float), p->dim, f) != static_cast<size_t>(p->dim)) return 1;
+
+    // Skip freq_cis_real & freq_cis_imag
+    int head_size = p->dim / p->n_heads;
+    fseek(f, p->seq_len * head_size * sizeof(float), SEEK_CUR);
+
+    if (!shared_weights && fread(w->wcls, sizeof(float), p->vocab_size * p->dim, f) != static_cast<size_t>(p->vocab_size * p->dim)) return 1;
+
+    // Check we didn't forget to read anything
+    auto curr = ftell(f);
+    fseek(f, 0, SEEK_END);
+    auto end = ftell(f);
+    if (curr != end) {
+        printf("Error: failed to read the checkpoint file to the end (curr = %ld, end =  %ld)\n", curr, end);
+        return 1;
+    }
+
     return 0;
 }
 
@@ -115,6 +185,7 @@ void free_weights(TransformerWeights* w) {
     delete w->w2;
     delete w->w3;
     delete w->rms_final_weight;
+    if (w->wcls) delete w->wcls;
 }
 
 void print_sample_weights(TransformerWeights *w){
@@ -131,6 +202,7 @@ void print_sample_weights(TransformerWeights *w){
     printf("%f\n", w->w2[0]);
     printf("%f\n", w->w3[0]);
     printf("%f\n", w->rms_att_weight[0]);
+    if (w->wcls) printf("%f\n", w->wcls[0]);
 }
 ////////////////////////////////////////////////////////////////////////////////////////////////////////////
 
@@ -155,6 +227,7 @@ struct my_llama_hparams {
     uint32_t n_vocab = 32000;
     uint32_t n_ctx   = 512;   // this is provided as user input?
     uint32_t n_embd  = 4096;
+    uint32_t n_ff    = 11008;
     uint32_t n_mult  = 4;
     uint32_t n_head  = 32;
     uint32_t n_layer = 32;
@@ -186,6 +259,8 @@ struct my_llama_layer {
 struct my_llama_model {
     struct ggml_context * ctx = NULL;
 
+    std::string name;
+
     my_llama_hparams hparams;
 
     struct ggml_tensor * tok_embeddings;
@@ -248,18 +323,13 @@ struct train_params {
     int mem_compute1_gb;
 };
 
-uint32_t get_n_ff(const struct my_llama_hparams* hparams) {
-    const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult;
-    return n_ff;
-}
-
 void print_params(struct my_llama_hparams * params) {
     printf("%s: n_vocab: %d\n", __func__, params->n_vocab);
     printf("%s: n_ctx:   %d\n", __func__, params->n_ctx);
     printf("%s: n_embd:  %d\n", __func__, params->n_embd);
     printf("%s: n_mult:  %d\n", __func__, params->n_mult);
     printf("%s: n_head:  %d\n", __func__, params->n_head);
-    printf("%s: n_ff:    %d\n", __func__, get_n_ff(params));
+    printf("%s: n_ff:    %d\n", __func__, params->n_ff);
     printf("%s: n_layer: %d\n", __func__, params->n_layer);
     printf("%s: n_rot:   %d\n", __func__, params->n_rot);
 }
@@ -271,7 +341,7 @@ void init_model(struct my_llama_model * model) {
     const uint32_t n_layer = hparams.n_layer;
     const uint32_t n_vocab = hparams.n_vocab;
 
-    const uint32_t n_ff = get_n_ff(&hparams);
+    const uint32_t n_ff = hparams.n_ff;
     struct ggml_context * ctx = model->ctx;
 
     model->train_its = 0;
@@ -453,21 +523,6 @@ struct llama_file {
         return std::string(chars.data(), len);
     }
 
-    void write_raw(const void * ptr, size_t size) {
-        if (size == 0) {
-            return;
-        }
-        errno = 0;
-        size_t ret = std::fwrite(ptr, size, 1, fp);
-        if (ret != 1) {
-            throw std::runtime_error(format("write error: %s", strerror(errno)));
-        }
-    }
-
-    void write_u32(std::uint32_t val) {
-        write_raw(&val, sizeof(val));
-    }
-
     ~llama_file() {
         if (fp) {
             std::fclose(fp);
@@ -475,30 +530,6 @@ struct llama_file {
     }
 };
 
-void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) {
-    if (tensor == NULL) {
-        file->write_u32(0);
-        file->write_u32(0);
-        file->write_u32(GGML_TYPE_F32);
-        file->seek((0-file->tell()) & 31, SEEK_CUR);
-        return;
-    }
-    const char * name = ggml_get_name(tensor);
-    uint32_t name_len = strlen(name);
-    uint32_t nd = tensor->n_dims;
-    uint32_t ne[4] = { (uint32_t)tensor->ne[0],
-                       (uint32_t)tensor->ne[1],
-                       (uint32_t)tensor->ne[2],
-                       (uint32_t)tensor->ne[3] };
-    file->write_u32(nd);
-    file->write_u32(name_len);
-    file->write_u32(tensor->type);
-    file->write_raw(ne, sizeof(ne[0]) * nd);
-    file->write_raw(name, name_len);
-    file->seek((0-file->tell()) & 31, SEEK_CUR);
-    file->write_raw(tensor->data, ggml_nbytes(tensor));
-}
-
 bool is_ggml_file(const char *filename) {
     llama_file file(filename, "rb");
     if (file.size < 4) {
@@ -508,40 +539,96 @@ bool is_ggml_file(const char *filename) {
     return magic == GGUF_MAGIC;
 }
 
+static std::string llama_escape_whitespaces(const std::string& text) {
+    std::ostringstream out;
+    for (char c : text) {
+        if (c == ' ') out << "\xe2\x96\x81";
+        else out << c;
+    }
+    return out.str();
+}
+
 void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) {
-    // heuristic to infer whether vocab is from ggml or from llama2.c vocabulary
     if (is_ggml_file(filename)) {
+        struct ggml_context * ctx_data = NULL;
 
-        struct llama_context_params llama_params = llama_context_default_params();
-        llama_params.vocab_only = true;
+        struct gguf_init_params params = {
+            /*.no_alloc = */ false,
+            /*.ctx      = */ &ctx_data,
+        };
 
-        struct llama_model * lmodel = llama_load_model_from_file(filename, llama_params);
-        struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params);
+        struct gguf_context * ctx = gguf_init_from_file(filename, params);
+        GGML_ASSERT(ctx != NULL);
+
+        const int model_idx = gguf_find_key(ctx, KV_TOKENIZER_MODEL);
+        GGML_ASSERT(model_idx >= 0);
+        std::string tokenizer_name = gguf_get_val_str(ctx, model_idx);
+        GGML_ASSERT(tokenizer_name == TOKENIZER_NAME);
+
+        const int token_idx = gguf_find_key(ctx, KV_TOKENIZER_LIST);
+        GGML_ASSERT(token_idx >= 0);
+
+        const int score_idx = gguf_find_key(ctx, KV_TOKENIZER_SCORES);
+        GGML_ASSERT(score_idx >= 0);
+        const float * scores = (const float * ) gguf_get_arr_data(ctx, score_idx);
+
+        const int toktype_idx = gguf_find_key(ctx, KV_TOKENIZER_TOKEN_TYPE);
+        GGML_ASSERT(toktype_idx >= 0);
+        const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx);
+
+        const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx);
 
-        const int n_vocab = llama_n_vocab(lctx);
         vocab->id_to_token.resize(n_vocab);
-        for (int i=0; i<n_vocab; ++i) {
-            vocab->id_to_token[i].text  = llama_token_get_text(lctx, i);
-            vocab->id_to_token[i].score = llama_token_get_score(lctx, i);
-            vocab->id_to_token[i].type  = llama_token_get_type(lctx, i);
-            vocab->token_to_id.emplace(vocab->id_to_token[i].text, i);
+
+        for (uint32_t i = 0; i < n_vocab; i++) {
+            std::string word = gguf_get_arr_str(ctx, token_idx, i);
+
+            vocab->token_to_id[word] = i;
+
+            auto & token_data = vocab->id_to_token[i];
+            token_data.text  = std::move(word);
+            token_data.score = scores[i];
+            token_data.type  = (llama_token_type) toktypes[i];
         }
-        llama_free(lctx);
-        llama_free_model(lmodel);
-    } else { // assume llama2.c vocabulary
-        printf("Assuming llama2.c vocabulary since %s is not a ggml file\n", filename);
+        ggml_free(ctx_data);
+        gguf_free(ctx);
+    } else {
+        // assume llama2.c vocabulary
+        printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename);
         llama_file file(filename, "rb");
         const int  n_vocab = config->vocab_size;
         /* uint32_t max_token_length =  */ file.read_u32(); // unused
         vocab->id_to_token.resize(n_vocab);
-        for (int i=0; i<n_vocab; ++i) {
+        for (llama_vocab::id id=0; id<n_vocab; ++id) {
             float_t score = file.read_f32();
             uint32_t len = file.read_u32();
             std::string text = file.read_string(len);
-            vocab->id_to_token[i].text = text;
-            vocab->id_to_token[i].score = score;
-            vocab->id_to_token[i].type = LLAMA_TOKEN_TYPE_UNDEFINED;
-            vocab->token_to_id.emplace(text, i);
+
+            unsigned char byte_val;
+            llama_vocab::ttype type = LLAMA_TOKEN_TYPE_NORMAL;
+            if (id == UNKNOWN_TOKEN_ID) {
+                text = "<unk>";
+                type = LLAMA_TOKEN_TYPE_UNKNOWN;
+            } else if (id == BOS_TOKEN_ID) {
+                text = "<s>";
+                type = LLAMA_TOKEN_TYPE_CONTROL;
+            } else if (id == EOS_TOKEN_ID) {
+                text = "</s>";
+                type = LLAMA_TOKEN_TYPE_CONTROL;
+            } else if (text.empty()) {
+                type = LLAMA_TOKEN_TYPE_CONTROL;
+            } else if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) {
+                // Text of byte tokens is already in the expected format.
+                type = LLAMA_TOKEN_TYPE_BYTE;
+            } else {
+                type = LLAMA_TOKEN_TYPE_NORMAL;
+            }
+            text = llama_escape_whitespaces(text);
+
+            vocab->id_to_token[id].text = text;
+            vocab->id_to_token[id].score = score;
+            vocab->id_to_token[id].type = type;
+            vocab->token_to_id.emplace(text, id);
         }
     }
 }
@@ -583,89 +670,120 @@ void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * kar
 }
 
 void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename) {
-    struct llama_file file(filename, "wb");
-    if (file.fp == NULL) {
-        return;
+    // stuff AK weights into GG weights one by one.
+    // w->token_embedding_table -> model->tok_embeddings
+    // float*                   -> struct ggml_tensor
+    stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table);
+    stuff_karpathy_weights_into_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table);
+
+    stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight);
+    //print_row(model->norm, 0);
+
+    // for rms-att-weight
+    int row_length = model->hparams.n_embd;
+    int n_ff = model->hparams.n_ff;
+
+    for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
+        auto & layer = model->layers[i];
+        // 1d
+        stuff_karpathy_weights_into_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);
+        stuff_karpathy_weights_into_gg(layer.ffn_norm      , &w->rms_ffn_weight[i*row_length]);
+
+        // from 3d matrix layer x dim x dim to 2d matrix dim x dim
+        stuff_karpathy_weights_into_gg(layer.wq            , &w->wq[i*row_length*row_length]);
+        stuff_karpathy_weights_into_gg(layer.wk            , &w->wk[i*row_length*row_length]);
+        stuff_karpathy_weights_into_gg(layer.wv            , &w->wv[i*row_length*row_length]);
+        stuff_karpathy_weights_into_gg(layer.wo            , &w->wo[i*row_length*row_length]);
+
+        stuff_karpathy_weights_into_gg(layer.w1            , &w->w1[i*row_length*n_ff]);
+        stuff_karpathy_weights_into_gg(layer.w2            , &w->w2[i*n_ff*row_length]);
+        stuff_karpathy_weights_into_gg(layer.w3            , &w->w3[i*row_length*n_ff]);
     }
 
-#pragma message("TODO: implement file saving using gguf")
-    (void) vocab;
-    (void) model;
-    (void) w;
-//    // write_magic
-//    file.write_u32(LLAMA_FILE_MAGIC);   // magic
-//    file.write_u32(LLAMA_FILE_VERSION); // version
-//    // write_hparams
-//    file.write_u32(model->hparams.n_vocab);
-//    file.write_u32(model->hparams.n_embd);
-//    file.write_u32(model->hparams.n_mult);
-//    file.write_u32(model->hparams.n_head);
-//    file.write_u32(model->hparams.n_layer);
-//    file.write_u32(model->hparams.n_rot);
-//    file.write_u32(LLAMA_FTYPE_ALL_F32);
-//
-//    // write_vocab - for now we are just writing the existing BPE voc. assuming karpathy's vocabulary is the same. idk.
-//    uint32_t n_vocab = model->hparams.n_vocab;
-//    for (uint32_t i = 0; i < n_vocab; i++) {
-//        const auto & token_data = vocab->id_to_token.at(i);
-//        file.write_u32((uint32_t) token_data.tok.size());
-//        file.write_raw(token_data.tok.data(), token_data.tok.size());
-//        file.write_raw(&token_data.score, sizeof(token_data.score));
-//    }
-//
-//    // stuff AK weights into GG weights one by one.
-//    // w->token_embedding_table -> model->tok_embeddings
-//    // float*                   -> struct ggml_tensor
-//    stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table);
-//    stuff_karpathy_weights_into_gg(model->output, w->token_embedding_table);
-//
-//    stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight);
-//    //print_row(model->norm, 0);
-//
-//    // for rms-att-weight
-//    int row_length = model->hparams.n_embd;
-//    const auto & hparams = model->hparams;
-//    //int n_ff = model->hparams.n_embd;
-//    int n_ff = get_n_ff(&hparams);
-//
-//    for (uint32_t i = 0; i < model->hparams.n_layer; ++i){
-//        auto & layer = model->layers[i];
-//        // 1d
-//        stuff_karpathy_weights_into_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]);
-//        stuff_karpathy_weights_into_gg(layer.ffn_norm      , &w->rms_ffn_weight[i*row_length]);
-//
-//        // from 3d matrix layer x dim x dim to 2d matrix dim x dim
-//        stuff_karpathy_weights_into_gg(layer.wq            , &w->wq[i*row_length*row_length]);
-//        stuff_karpathy_weights_into_gg(layer.wk            , &w->wk[i*row_length*row_length]);
-//        stuff_karpathy_weights_into_gg(layer.wv            , &w->wv[i*row_length*row_length]);
-//        stuff_karpathy_weights_into_gg(layer.wo            , &w->wo[i*row_length*row_length]);
-//
-//        stuff_karpathy_weights_into_gg(layer.w1            , &w->w1[i*row_length*n_ff]);
-//        stuff_karpathy_weights_into_gg(layer.w2            , &w->w2[i*n_ff*row_length]);
-//        stuff_karpathy_weights_into_gg(layer.w3            , &w->w3[i*row_length*n_ff]);
-//    }
-//    // write tensors
-//    write_tensor(&file, model->tok_embeddings);
-//    write_tensor(&file, model->norm);
-//    write_tensor(&file, model->output); // ?
-//    for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
-//        auto & layer = model->layers[i];
-//
-//        write_tensor(&file, layer.attention_norm);
-//        write_tensor(&file, layer.wq);
-//        write_tensor(&file, layer.wk);
-//        write_tensor(&file, layer.wv);
-//        write_tensor(&file, layer.wo);
-//        write_tensor(&file, layer.ffn_norm);
-//        write_tensor(&file, layer.w1);
-//        write_tensor(&file, layer.w2);
-//        write_tensor(&file, layer.w3);
-//    }
+    struct gguf_context * ctx = gguf_init_empty();
+
+    std::vector<const char*> tokens;
+    std::vector<float> scores;
+    std::vector<llama_token_type> token_types;
+    for (const llama_vocab::token_data & token_data : vocab->id_to_token) {
+        tokens.push_back(token_data.text.c_str());
+        scores.push_back(token_data.score);
+        token_types.push_back(token_data.type);
+    }
+    gguf_set_arr_str(ctx, KV_TOKENIZER_LIST, tokens.data(), tokens.size());
+    gguf_set_arr_data(ctx, KV_TOKENIZER_SCORES, GGUF_TYPE_FLOAT32, scores.data(), scores.size());
+    gguf_set_arr_data(ctx, KV_TOKENIZER_TOKEN_TYPE, GGUF_TYPE_INT32, token_types.data(), token_types.size());
+
+    gguf_set_val_str(ctx, KV_TOKENIZER_MODEL, TOKENIZER_NAME);
+
+    gguf_set_val_str(ctx, KV_GENERAL_ARCHITECTURE, "llama");
+    gguf_set_val_str(ctx, KV_GENERAL_NAME, "llama");
+
+    // special tokens
+    gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1);
+    gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1);
+
+    gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx);
+    gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd);
+    gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff);
+    gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head);
+    // n_head_kv is optional, default to n_head
+    // gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, ...);
+    gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer);
+    gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot);
+    gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f);
+
+    // write tensors
+    ggml_set_name(model->tok_embeddings, TN_TOKEN_EMBD);
+    gguf_add_tensor(ctx, model->tok_embeddings);
+
+    ggml_set_name(model->norm, TN_OUTPUT_NORM);
+    gguf_add_tensor(ctx, model->norm);
+
+    ggml_set_name(model->output, TN_OUTPUT);
+    gguf_add_tensor(ctx, model->output);
+
+    for (uint32_t i = 0; i < model->hparams.n_layer; ++i) {
+        auto & layer = model->layers[i];
+
+        ggml_format_name(layer.wq, TN_ATTN_Q, i);
+        gguf_add_tensor(ctx, layer.wq);
+
+        ggml_format_name(layer.wk, TN_ATTN_K, i);
+        gguf_add_tensor(ctx, layer.wk);
+
+        ggml_format_name(layer.wv, TN_ATTN_V, i);
+        gguf_add_tensor(ctx, layer.wv);
+
+        ggml_format_name(layer.wo, TN_ATTN_OUTPUT, i);
+        gguf_add_tensor(ctx, layer.wo);
+
+        ggml_format_name(layer.attention_norm, TN_ATTN_NORM, i);
+        gguf_add_tensor(ctx, layer.attention_norm);
+
+        ggml_format_name(layer.w1, TN_FFN_GATE, i);
+        gguf_add_tensor(ctx, layer.w1);
+
+        ggml_format_name(layer.w2, TN_FFN_DOWN, i);
+        gguf_add_tensor(ctx, layer.w2);
+
+        ggml_format_name(layer.w3, TN_FFN_UP, i);
+        gguf_add_tensor(ctx, layer.w3);
+
+        ggml_format_name(layer.ffn_norm, TN_FFN_NORM, i);
+        gguf_add_tensor(ctx, layer.ffn_norm);
+    }
+
+    gguf_write_to_file(ctx, filename, false);
+    gguf_free(ctx);
 }
 
 struct train_params get_default_train_params() {
     struct train_params params;
-    params.fn_vocab_model    = "models/ggml-vocab.bin";
+    params.fn_vocab_model    = "models/7B/ggml-model-f16.gguf";
     params.fn_llama2c_output_model = "ak_llama_model.bin";
     params.fn_train_data     = "shakespeare.txt";
     params.fn_checkpoint_in  = "checkpoint.bin";
@@ -718,7 +836,7 @@ void print_usage(int /*argc*/, char ** argv, const struct train_params * params)
     fprintf(stderr, "\n");
     fprintf(stderr, "options:\n");
     fprintf(stderr, "  -h, --help                       show this help message and exit\n");
-    fprintf(stderr, "  --copy-vocab-from-model FNAME    llama2.c vocabulary or ggml model path from which to copy vocab (default '%s')\n", params->fn_vocab_model);
+    fprintf(stderr, "  --copy-vocab-from-model FNAME    path of gguf llama model or llama2.c vocabulary from which to copy vocab (default '%s')\n", params->fn_vocab_model);
     fprintf(stderr, "  --llama2c-model FNAME            [REQUIRED] model path from which to load Karpathy's llama2.c model\n");
     fprintf(stderr, "  --llama2c-output-model FNAME     model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model);
     fprintf(stderr, "\n");
@@ -779,6 +897,14 @@ bool params_parse(int argc, char ** argv, struct train_params * params) {
     return true;
 }
 
+std::string basename(const std::string &path) {
+    size_t pos = path.find_last_of("/");
+    if (pos == std::string::npos) {
+        return path;
+    }
+    return path.substr(pos + 1);
+}
+
 int main(int argc, char ** argv) {
     struct train_params params = get_default_train_params();
     if (!params_parse(argc, argv, &params)) {
@@ -791,9 +917,12 @@ int main(int argc, char ** argv) {
         if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; }
         // read in the config header
         if(fread(&config, sizeof(Config), 1, file) != 1) { return 1; }
+        auto shared_weights = config.vocab_size > 0;
+        config.vocab_size = abs(config.vocab_size);
+
         // read in the Transformer weights
-        malloc_weights(&weights, &config);
-        if(checkpoint_init_weights(&weights, &config, file)) { return 1; }
+        malloc_weights(&weights, &config, shared_weights);
+        if(checkpoint_init_weights(&weights, &config, file, shared_weights)) { return 1; }
         fclose(file);
     }
 
@@ -804,6 +933,7 @@ int main(int argc, char ** argv) {
     model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx);
     model.hparams.n_ctx   = params.n_ctx;
     model.hparams.n_embd  = config.dim; //params.n_embd;
+    model.hparams.n_ff    = config.hidden_dim;
     model.hparams.n_mult  = 32;//params.n_mult;
     model.hparams.n_head  = config.n_heads; //params.n_head;
     model.hparams.n_layer = config.n_layers; //params.n_layer;
@@ -817,6 +947,7 @@ int main(int argc, char ** argv) {
     model.ctx = ggml_init(lcparams);
 
     init_model(&model);
+    model.name = basename(params.fn_llama2c_model);
     save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model);
 
     printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model);

examples/embd-input/embd-input-lib.cpp

@@ -214,7 +214,7 @@ const char * sampling(struct MyModel * mymodel) {
     if (id == llama_token_eos(ctx)) {
         ret = "</s>";
     } else {
-        ret = llama_token_to_str(ctx, id);
+        ret = llama_token_to_piece(ctx, id);
     }
     eval_id(mymodel, id);
     return ret.c_str();

examples/embd-input/embd_input.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import ctypes
 from ctypes import cdll, c_char_p, c_void_p, POINTER, c_float, c_int
 import numpy as np

examples/embd-input/llava.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import sys
 import os
 sys.path.insert(0, os.path.dirname(__file__))

examples/embd-input/minigpt4.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import sys
 import os
 sys.path.insert(0, os.path.dirname(__file__))

examples/embd-input/panda_gpt.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import sys
 import os
 sys.path.insert(0, os.path.dirname(__file__))

examples/embedding/embedding.cpp

@@ -56,9 +56,6 @@ int main(int argc, char ** argv) {
 
     int n_past = 0;
 
-    // Add a space in front of the first character to match OG llama tokenizer behavior
-    params.prompt.insert(0, 1, ' ');
-
     // tokenize the prompt
     auto embd_inp = ::llama_tokenize(ctx, params.prompt, true);
 
@@ -67,28 +64,35 @@ int main(int argc, char ** argv) {
         fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
         fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
         for (int i = 0; i < (int) embd_inp.size(); i++) {
-            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
+            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
         }
         fprintf(stderr, "\n");
     }
 
-    if (params.embedding){
-        if (embd_inp.size() > 0) {
-            if (llama_eval(ctx, embd_inp.data(), embd_inp.size(), n_past, params.n_threads)) {
-                fprintf(stderr, "%s : failed to eval\n", __func__);
-                return 1;
-            }
-        }
-
-        const int n_embd = llama_n_embd(ctx);
-        const auto embeddings = llama_get_embeddings(ctx);
-
-        for (int i = 0; i < n_embd; i++) {
-            printf("%f ", embeddings[i]);
-        }
-        printf("\n");
+    if (embd_inp.size() > (size_t)params.n_ctx) {
+        fprintf(stderr, "%s: error: prompt is longer than the context window (%zu tokens, n_ctx = %d)\n",
+                __func__, embd_inp.size(), params.n_ctx);
+        return 1;
     }
 
+    while (!embd_inp.empty()) {
+        int n_tokens = std::min(params.n_batch, (int) embd_inp.size());
+        if (llama_eval(ctx, embd_inp.data(), n_tokens, n_past, params.n_threads)) {
+            fprintf(stderr, "%s : failed to eval\n", __func__);
+            return 1;
+        }
+        n_past += n_tokens;
+        embd_inp.erase(embd_inp.begin(), embd_inp.begin() + n_tokens);
+    }
+
+    const int n_embd = llama_n_embd(ctx);
+    const auto embeddings = llama_get_embeddings(ctx);
+
+    for (int i = 0; i < n_embd; i++) {
+        printf("%f ", embeddings[i]);
+    }
+    printf("\n");
+
     llama_print_timings(ctx);
     llama_free(ctx);
     llama_free_model(model);

examples/gguf/CMakeLists.txt

@@ -0,0 +1,5 @@
+set(TARGET gguf)
+add_executable(${TARGET} gguf.cpp)
+install(TARGETS ${TARGET} RUNTIME)
+target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
+target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/gguf/gguf.cpp

@@ -30,6 +30,9 @@ bool gguf_ex_write(const std::string & fname) {
     gguf_set_val_u32 (ctx, "some.parameter.uint32",   0x12345678);
     gguf_set_val_i32 (ctx, "some.parameter.int32",   -0x12345679);
     gguf_set_val_f32 (ctx, "some.parameter.float32",  0.123456789f);
+    gguf_set_val_u64 (ctx, "some.parameter.uint64",   0x123456789abcdef0ull);
+    gguf_set_val_i64 (ctx, "some.parameter.int64",   -0x123456789abcdef1ll);
+    gguf_set_val_f64 (ctx, "some.parameter.float64",  0.1234567890123456789);
     gguf_set_val_bool(ctx, "some.parameter.bool",     true);
     gguf_set_val_str (ctx, "some.parameter.string",   "hello world");
 

examples/jeopardy/graph.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import matplotlib.pyplot as plt
 import os
 import csv

examples/json-schema-to-grammar.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import argparse
 import json
 import re

examples/llama-bench/llama-bench.cpp

@@ -3,6 +3,9 @@
 #include <cassert>
 #include <chrono>
 #include <cinttypes>
+#include <clocale>
+#include <cmath>
+#include <cstdio>
 #include <cstring>
 #include <ctime>
 #include <iterator>
@@ -10,7 +13,6 @@
 #include <numeric>
 #include <regex>
 #include <sstream>
-#include <stdio.h>
 #include <string>
 #include <vector>
 
@@ -18,9 +20,7 @@
 #include "llama.h"
 #include "common.h"
 #include "build-info.h"
-#ifdef GGML_USE_CUBLAS
 #include "ggml-cuda.h"
-#endif
 
 // utils
 static uint64_t get_time_ns() {
@@ -148,7 +148,7 @@ struct cmd_params {
 };
 
 static const cmd_params cmd_params_defaults = {
-    /* model         */ {"models/7B/ggml-model-q4_0.bin"},
+    /* model         */ {"models/7B/ggml-model-q4_0.gguf"},
     /* n_prompt      */ {512},
     /* n_gen         */ {128},
     /* n_batch       */ {512},
@@ -179,12 +179,12 @@ static void print_usage(int /* argc */, char ** argv) {
     fprintf(stdout, "  -mg i, --main-gpu <n>             (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
     fprintf(stdout, "  -lv, --low-vram <0|1>             (default: %s)\n", join(cmd_params_defaults.low_vram, ",").c_str());
     fprintf(stdout, "  -mmq, --mul-mat-q <0|1>           (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
-    fprintf(stdout, "  -ts, --tensor_split <ts>                       \n");
+    fprintf(stdout, "  -ts, --tensor_split <ts0/ts1/..>               \n");
     fprintf(stdout, "  -r, --repetitions <n>             (default: %d)\n", cmd_params_defaults.reps);
-    fprintf(stdout, "  -o, --output <csv|json|md|sql>    (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : "md");
+    fprintf(stdout, "  -o, --output <csv|json|md|sql>    (default: %s)\n", cmd_params_defaults.output_format == CSV ? "csv" : cmd_params_defaults.output_format == JSON ? "json" : cmd_params_defaults.output_format == MARKDOWN ? "md" : "sql");
     fprintf(stdout, "  -v, --verbose                     (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
     fprintf(stdout, "\n");
-    fprintf(stdout, "Multiple values can be given for each parameter by separating them with ',' or by repeating the parameter.\n");
+    fprintf(stdout, "Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
 
 }
 
@@ -443,6 +443,8 @@ struct test {
     static const std::string gpu_info;
     std::string model_filename;
     std::string model_type;
+    uint64_t model_size;
+    uint64_t model_n_params;
     int n_batch;
     int n_threads;
     bool f32_kv;
@@ -459,8 +461,10 @@ struct test {
     test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) {
         model_filename = inst.model;
         char buf[128];
-        llama_model_type(lmodel, buf, sizeof(buf));
+        llama_model_desc(lmodel, buf, sizeof(buf));
         model_type = buf;
+        model_size = llama_model_size(lmodel);
+        model_n_params = llama_model_n_params(lmodel);
         n_batch = inst.n_batch;
         n_threads = inst.n_threads;
         f32_kv = inst.f32_kv;
@@ -504,7 +508,7 @@ struct test {
 
     static std::string get_backend() {
         if (cuda) {
-            return "CUDA";
+            return GGML_CUDA_NAME;
         }
         if (opencl) {
             return "OpenCL";
@@ -526,7 +530,7 @@ struct test {
             "build_commit", "build_number",
             "cuda", "opencl", "metal", "gpu_blas", "blas",
             "cpu_info", "gpu_info",
-            "model_filename", "model_type",
+            "model_filename", "model_type", "model_size", "model_n_params",
             "n_batch", "n_threads", "f16_kv",
             "n_gpu_layers", "main_gpu", "mul_mat_q", "low_vram", "tensor_split",
             "n_prompt", "n_gen", "test_time",
@@ -540,6 +544,7 @@ struct test {
 
     static field_type get_field_type(const std::string & field) {
         if (field == "build_number" || field == "n_batch" || field == "n_threads" ||
+            field == "model_size" || field == "model_n_params" ||
             field == "n_gpu_layers" || field == "main_gpu" ||
             field == "n_prompt" || field == "n_gen" ||
             field == "avg_ns" || field == "stddev_ns") {
@@ -575,7 +580,7 @@ struct test {
             build_commit, std::to_string(build_number),
             std::to_string(cuda), std::to_string(opencl), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas),
             cpu_info, gpu_info,
-            model_filename, model_type,
+            model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
             std::to_string(n_batch), std::to_string(n_threads), std::to_string(!f32_kv),
             std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), std::to_string(low_vram), tensor_split_str,
             std::to_string(n_prompt), std::to_string(n_gen), test_time,
@@ -711,8 +716,15 @@ struct markdown_printer : public printer {
             return -30;
         }
         if (field == "t/s") {
-            return 15;
+            return 16;
         }
+        if (field == "size" || field == "params") {
+            return 10;
+        }
+        if (field == "n_gpu_layers") {
+            return 3;
+        }
+
         int width = std::max((int)field.length(), 10);
 
         if (test::get_field_type(field) == test::STRING) {
@@ -721,14 +733,33 @@ struct markdown_printer : public printer {
         return width;
     }
 
+    static std::string get_field_display_name(const std::string & field) {
+        if (field == "n_gpu_layers") {
+            return "ngl";
+        }
+        if (field == "n_threads") {
+            return "threads";
+        }
+        if (field == "mul_mat_q") {
+            return "mmq";
+        }
+        if (field == "tensor_split") {
+            return "ts";
+        }
+        return field;
+    }
+
     void print_header(const cmd_params & params) override {
         // select fields to print
-        fields = { "model", "backend" };
+        fields.push_back("model");
+        fields.push_back("size");
+        fields.push_back("params");
+        fields.push_back("backend");
         bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS";
         if (!is_cpu_backend) {
             fields.push_back("n_gpu_layers");
         }
-        if (params.n_batch.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {
+        if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) {
             fields.push_back("n_threads");
         }
         if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
@@ -754,7 +785,7 @@ struct markdown_printer : public printer {
 
         fprintf(fout, "|");
         for (const auto & field : fields) {
-            fprintf(fout, " %*s |", get_field_width(field), field.c_str());
+            fprintf(fout, " %*s |", get_field_width(field), get_field_display_name(field).c_str());
         }
         fprintf(fout, "\n");
         fprintf(fout, "|");
@@ -771,12 +802,26 @@ struct markdown_printer : public printer {
         fprintf(fout, "|");
         for (const auto & field : fields) {
             std::string value;
+            char buf[128];
             if (field == "model") {
                 value = t.model_type;
+            } else if (field == "size") {
+                if (t.model_size < 1024*1024*1024) {
+                    snprintf(buf, sizeof(buf), "%.2f MiB", t.model_size / 1024.0 / 1024.0);
+                } else {
+                    snprintf(buf, sizeof(buf), "%.2f GiB", t.model_size / 1024.0 / 1024.0 / 1024.0);
+                }
+                value = buf;
+            } else if (field == "params") {
+                if (t.model_n_params < 1000*1000*1000) {
+                    snprintf(buf, sizeof(buf), "%.2f M", t.model_n_params / 1e6);
+                } else {
+                    snprintf(buf, sizeof(buf), "%.2f B", t.model_n_params / 1e9);
+                }
+                value = buf;
             } else if (field == "backend") {
                 value = test::get_backend();
             } else if (field == "test") {
-                char buf[128];
                 if (t.n_prompt > 0 && t.n_gen == 0) {
                     snprintf(buf, sizeof(buf), "pp %d", t.n_prompt);
                 } else if (t.n_gen > 0 && t.n_prompt == 0) {
@@ -787,7 +832,6 @@ struct markdown_printer : public printer {
                 }
                 value = buf;
             } else if (field == "t/s") {
-                char buf[128];
                 snprintf(buf, sizeof(buf), "%.2f ± %.2f", t.avg_ts(), t.stdev_ts());
                 value = buf;
             } else if (vmap.find(field) != vmap.end()) {
@@ -874,6 +918,9 @@ static void llama_null_log_callback(enum llama_log_level level, const char * tex
 }
 
 int main(int argc, char ** argv) {
+    // try to set locale for unicode characters in markdown
+    setlocale(LC_CTYPE, ".UTF-8");
+
 #if !defined(NDEBUG)
     fprintf(stderr, "warning: asserts enabled, performance may be affected\n");
 #endif

examples/llm.vim

@@ -8,7 +8,7 @@ function! Llm()
   let buffer_content = join(getline(1, '$'), "\n")
 
   " Create the JSON payload
-  let json_payload = {"temp":0.72,"top_k":100,"top_p":0.73,"repeat_penalty":1.100000023841858,"n_predict":10,"stream": v:false}
+  let json_payload = {"temp":0.72,"top_k":100,"top_p":0.73,"repeat_penalty":1.100000023841858,"n_predict":256,"stop": ["\n\n\n"],"stream": v:false}
   let json_payload.prompt = buffer_content
 
   " Define the curl command
@@ -25,3 +25,4 @@ function! Llm()
 endfunction
 
 command! Llm call Llm()
+noremap <F2> :Llm<CR>

examples/main/README.md

@@ -288,6 +288,10 @@ These options help improve the performance and memory usage of the LLaMA models.
 
 -   `--prompt-cache FNAME`: Specify a file to cache the model state after the initial prompt. This can significantly speed up the startup time when you're using longer prompts. The file is created during the first run and is reused and updated in subsequent runs. **Note**: Restoring a cached prompt does not imply restoring the exact state of the session at the point it was saved. So even when specifying a specific seed, you are not guaranteed to get the same sequence of tokens as the original generation.
 
+### Grammars
+
+-   `--grammar GRAMMAR`, `--grammar-file FILE`: Specify a grammar (defined inline or in a file) to constrain model output to a specific format. For example, you could force the model to output JSON or to speak only in emojis. See the [GBNF guide](../../grammars/README.md) for details on the syntax.
+
 ### Quantization
 
 For information about 4-bit quantization, which can significantly improve performance and reduce memory usage, please refer to llama.cpp's primary [README](../../README.md#prepare-data--run).

examples/main/main.cpp

@@ -4,6 +4,7 @@
 #endif
 
 #include "common.h"
+
 #include "console.h"
 #include "llama.h"
 #include "build-info.h"
@@ -17,6 +18,7 @@
 #include <ctime>
 #include <fstream>
 #include <iostream>
+#include <sstream>
 #include <string>
 #include <vector>
 
@@ -36,18 +38,67 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
-static llama_context ** g_ctx;
+static llama_context           ** g_ctx;
+static llama_model             ** g_model;
+static gpt_params               * g_params;
+static std::vector<llama_token> * g_input_tokens;
+static std::ostringstream       * g_output_ss;
+static std::vector<llama_token> * g_output_tokens;
 static bool is_interacting = false;
 
+void write_logfile(
+    const llama_context * ctx, const gpt_params & params, const llama_model * model,
+    const std::vector<llama_token> input_tokens, const std::string output, const std::vector<llama_token> output_tokens) {
+
+    if (params.logdir.empty()) {
+        return;
+    }
+
+    const std::string timestamp = get_sortable_timestamp();
+
+    const bool success = create_directory_with_parents(params.logdir);
+    if (!success) {
+        fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
+                __func__, params.logdir.c_str());
+        return;
+    }
+
+    const std::string logfile_path = params.logdir + timestamp + ".yml";
+    FILE * logfile = fopen(logfile_path.c_str(), "w");
+
+    if (logfile == NULL) {
+        fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
+        return;
+    }
+
+    fprintf(logfile, "binary: main\n");
+    char model_desc[128];
+    llama_model_desc(model, model_desc, sizeof(model_desc));
+    dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc);
+
+    fprintf(logfile, "\n");
+    fprintf(logfile, "######################\n");
+    fprintf(logfile, "# Generation Results #\n");
+    fprintf(logfile, "######################\n");
+    fprintf(logfile, "\n");
+
+    dump_string_yaml_multiline(logfile, "output", output.c_str());
+    dump_vector_int_yaml(logfile, "output_tokens", output_tokens);
+
+    llama_dump_timing_info_yaml(logfile, ctx);
+    fclose(logfile);
+}
+
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
 void sigint_handler(int signo) {
     if (signo == SIGINT) {
         if (!is_interacting) {
-            is_interacting=true;
+            is_interacting = true;
         } else {
             console::cleanup();
             printf("\n");
             llama_print_timings(*g_ctx);
+            write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens);
             _exit(130);
         }
     }
@@ -56,11 +107,21 @@ void sigint_handler(int signo) {
 
 int main(int argc, char ** argv) {
     gpt_params params;
+    g_params = &params;
 
     if (gpt_params_parse(argc, argv, params) == false) {
         return 1;
     }
 
+#ifndef LOG_DISABLE_LOGS
+    log_set_target(log_filename_generator("main", "log"));
+    LOG_TEE("Log start\n");
+    log_dump_cmdline(argc,argv);
+#endif // LOG_DISABLE_LOGS
+
+    // TODO: Dump params ?
+    //LOG("Params perplexity: %s\n", LOG_TOSTR(params.perplexity));
+
     // save choice to use color for later
     // (note for later: this is a slightly awkward choice)
     console::init(params.simple_io, params.use_color);
@@ -83,42 +144,45 @@ int main(int argc, char ** argv) {
     }
 
     if (params.rope_freq_base != 10000.0) {
-        fprintf(stderr, "%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base);
+        LOG_TEE("%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base);
     }
 
     if (params.rope_freq_scale != 1.0) {
-        fprintf(stderr, "%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale);
+        LOG_TEE("%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale);
     }
 
     if (params.n_ctx > 2048) {
         // TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048
-        fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx);
+        LOG_TEE("%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx);
     } else if (params.n_ctx < 8) {
-        fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__);
+        LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
         params.n_ctx = 8;
     }
 
-    fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
+    LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
 
     if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
     }
 
-    fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);
+    LOG_TEE("%s: seed  = %u\n", __func__, params.seed);
 
     std::mt19937 rng(params.seed);
     if (params.random_prompt) {
         params.prompt = gpt_random_prompt(rng);
     }
 
+    LOG("%s: llama backend init\n", __func__);
     llama_backend_init(params.numa);
 
     llama_model * model;
     llama_context * ctx;
     llama_context * ctx_guidance = NULL;
+    g_model = &model;
     g_ctx = &ctx;
 
     // load the model and apply lora adapter, if any
+    LOG("%s: load the model and apply lora adapter, if any\n", __func__);
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
     if (params.cfg_scale > 1.f) {
         struct llama_context_params lparams = llama_context_params_from_gpt_params(params);
@@ -126,14 +190,14 @@ int main(int argc, char ** argv) {
     }
 
     if (model == NULL) {
-        fprintf(stderr, "%s: error: unable to load model\n", __func__);
+        LOG_TEE("%s: error: unable to load model\n", __func__);
         return 1;
     }
 
     // print system information
     {
-        fprintf(stderr, "\n");
-        fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
+        LOG_TEE("\n");
+        LOG_TEE("system_info: n_threads = %d / %d | %s\n",
                 params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
     }
 
@@ -141,7 +205,7 @@ int main(int argc, char ** argv) {
     // uncomment the "used_mem" line in llama.cpp to see the results
     if (params.mem_test) {
         {
-            fprintf(stderr, "%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx);
+            LOG_TEE("%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx);
 
             const std::vector<llama_token> tmp(params.n_batch, llama_token_bos(ctx));
             llama_eval(ctx, tmp.data(), tmp.size(), params.n_ctx, params.n_threads);
@@ -167,7 +231,7 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> session_tokens;
 
     if (!path_session.empty()) {
-        fprintf(stderr, "%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
+        LOG_TEE("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str());
 
         // fopen to check for existing session
         FILE * fp = std::fopen(path_session.c_str(), "rb");
@@ -177,49 +241,70 @@ int main(int argc, char ** argv) {
             session_tokens.resize(params.n_ctx);
             size_t n_token_count_out = 0;
             if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) {
-                fprintf(stderr, "%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
+                LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str());
                 return 1;
             }
             session_tokens.resize(n_token_count_out);
             llama_set_rng_seed(ctx, params.seed);
 
-            fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
+            LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size());
         } else {
-            fprintf(stderr, "%s: session file does not exist, will create\n", __func__);
+            LOG_TEE("%s: session file does not exist, will create\n", __func__);
         }
     }
 
-    // tokenize the prompt
+    const bool add_bos = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+    LOG("add_bos: %d\n", add_bos);
+
     std::vector<llama_token> embd_inp;
+
     if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
-        embd_inp = ::llama_tokenize(ctx, params.prompt, true);
+        LOG("tokenize the prompt\n");
+        embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
     } else {
+        LOG("use session tokens\n");
         embd_inp = session_tokens;
     }
 
+    LOG("prompt: \"%s\"\n", log_tostr(params.prompt));
+    LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp));
+
+    // Should not run without any tokens
+    if (embd_inp.empty()) {
+        embd_inp.push_back(llama_token_bos(ctx));
+        LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp));
+    }
+
     // Tokenize negative prompt
     std::vector<llama_token> guidance_inp;
     int guidance_offset = 0;
     int original_prompt_len = 0;
     if (ctx_guidance) {
-        params.cfg_negative_prompt.insert(0, 1, ' ');
-        guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, true);
+        LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(params.cfg_negative_prompt));
+
+        guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos);
+        LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp));
+
+        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, add_bos);
+        LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp));
 
-        std::vector<llama_token> original_inp = ::llama_tokenize(ctx, params.prompt, true);
         original_prompt_len = original_inp.size();
         guidance_offset = (int)guidance_inp.size() - original_prompt_len;
+        LOG("original_prompt_len: %s", log_tostr(original_prompt_len));
+        LOG("guidance_offset:     %s", log_tostr(guidance_offset));
     }
 
     const int n_ctx = llama_n_ctx(ctx);
+    LOG("n_ctx: %d\n", n_ctx);
 
     if ((int) embd_inp.size() > n_ctx - 4) {
-        fprintf(stderr, "%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
+        LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4);
         return 1;
     }
 
     // debug message about similarity of saved session, if applicable
     size_t n_matching_session_tokens = 0;
-    if (session_tokens.size()) {
+    if (session_tokens.size() > 0) {
         for (llama_token id : session_tokens) {
             if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) {
                 break;
@@ -227,22 +312,27 @@ int main(int argc, char ** argv) {
             n_matching_session_tokens++;
         }
         if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {
-            fprintf(stderr, "%s: using full prompt from session file\n", __func__);
+            LOG_TEE("%s: using full prompt from session file\n", __func__);
         } else if (n_matching_session_tokens >= embd_inp.size()) {
-            fprintf(stderr, "%s: session file has exact match for prompt!\n", __func__);
+            LOG_TEE("%s: session file has exact match for prompt!\n", __func__);
         } else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
-            fprintf(stderr, "%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
+            LOG_TEE("%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n",
                 __func__, n_matching_session_tokens, embd_inp.size());
         } else {
-            fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
+            LOG_TEE("%s: session file matches %zu / %zu tokens of prompt\n",
                 __func__, n_matching_session_tokens, embd_inp.size());
         }
     }
 
+    LOGLN(
+            "recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu, embd_inp.size() %zu",
+            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
-    if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() &&
-            session_tokens.size() > embd_inp.size()) {
+    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);
+
         session_tokens.resize(embd_inp.size() - 1);
     }
 
@@ -252,8 +342,11 @@ int main(int argc, char ** argv) {
     }
 
     // prefix & suffix for instruct mode
-    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true);
-    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false);
+    const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos);
+    const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n",    false);
+
+    LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx));
+    LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx));
 
     // in instruct mode, we inject a prefix and a suffix to each input by the user
     if (params.instruct) {
@@ -267,30 +360,30 @@ int main(int argc, char ** argv) {
     }
 
     if (params.verbose_prompt) {
-        fprintf(stderr, "\n");
-        fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
-        fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
+        LOG_TEE("\n");
+        LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str());
+        LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
         for (int i = 0; i < (int) embd_inp.size(); i++) {
-            fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str());
+            LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str());
         }
 
         if (ctx_guidance) {
-            fprintf(stderr, "\n");
-            fprintf(stderr, "%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
-            fprintf(stderr, "%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
+            LOG_TEE("\n");
+            LOG_TEE("%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str());
+            LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size());
             for (int i = 0; i < (int) guidance_inp.size(); i++) {
-                fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_str(ctx, guidance_inp[i]).c_str());
+                LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str());
             }
         }
 
         if (params.n_keep > 0) {
-        fprintf(stderr, "%s: static prompt based on n_keep: '", __func__);
+        LOG_TEE("%s: static prompt based on n_keep: '", __func__);
             for (int i = 0; i < params.n_keep; i++) {
-                fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i]).c_str());
+                LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str());
             }
-            fprintf(stderr, "'\n");
+            LOG_TEE("'\n");
         }
-        fprintf(stderr, "\n");
+        LOG_TEE("\n");
     }
 
     if (params.interactive) {
@@ -307,30 +400,30 @@ int main(int argc, char ** argv) {
         SetConsoleCtrlHandler(reinterpret_cast<PHANDLER_ROUTINE>(console_ctrl_handler), true);
 #endif
 
-        fprintf(stderr, "%s: interactive mode on.\n", __func__);
+        LOG_TEE("%s: interactive mode on.\n", __func__);
 
         if (params.antiprompt.size()) {
-            for (auto antiprompt : params.antiprompt) {
-                fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str());
+            for (const auto & antiprompt : params.antiprompt) {
+                LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str());
             }
         }
 
         if (params.input_prefix_bos) {
-            fprintf(stderr, "Input prefix with BOS\n");
+            LOG_TEE("Input prefix with BOS\n");
         }
 
         if (!params.input_prefix.empty()) {
-            fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str());
+            LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str());
         }
 
         if (!params.input_suffix.empty()) {
-            fprintf(stderr, "Input suffix: '%s'\n", params.input_suffix.c_str());
+            LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str());
         }
     }
-    fprintf(stderr, "sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n",
+    LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n",
             params.repeat_last_n, params.repeat_penalty, params.presence_penalty, params.frequency_penalty, params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp, params.mirostat, params.mirostat_eta, params.mirostat_tau);
-    fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
-    fprintf(stderr, "\n\n");
+    LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
+    LOG_TEE("\n\n");
 
     grammar_parser::parse_state parsed_grammar;
     llama_grammar *             grammar = NULL;
@@ -340,14 +433,14 @@ int main(int argc, char ** argv) {
         if (parsed_grammar.rules.empty()) {
             return 1;
         }
-        fprintf(stderr, "%s: grammar:\n", __func__);
+        LOG_TEE("%s: grammar:\n", __func__);
         grammar_parser::print_grammar(stderr, parsed_grammar);
-        fprintf(stderr, "\n");
+        LOG_TEE("\n");
 
         {
             auto it = params.logit_bias.find(llama_token_eos(ctx));
             if (it != params.logit_bias.end() && it->second == -INFINITY) {
-                fprintf(stderr, "%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__);
+                LOG_TEE("%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__);
             }
         }
 
@@ -370,11 +463,11 @@ int main(int argc, char ** argv) {
                               " - To return control without starting a new line, end your input with '/'.\n"
                               " - If you want to submit another line, end your input with '\\'.\n";
         }
-        fprintf(stderr, "== Running in interactive mode. ==\n"
+        LOG_TEE("== Running in interactive mode. ==\n");
 #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
-               " - Press Ctrl+C to interject at any time.\n"
+        LOG_TEE(       " - Press Ctrl+C to interject at any time.\n");
 #endif
-               "%s\n", control_message);
+        LOG_TEE(       "%s\n", control_message);
 
         is_interacting = params.interactive_first;
     }
@@ -389,14 +482,19 @@ int main(int argc, char ** argv) {
     int n_session_consumed = 0;
     int n_past_guidance    = 0;
 
+    std::vector<int>   input_tokens;  g_input_tokens  = &input_tokens;
+    std::vector<int>   output_tokens; g_output_tokens = &output_tokens;
+    std::ostringstream output_ss;     g_output_ss     = &output_ss;
+
     // the first thing we will do is to output the prompt, so set color accordingly
     console::set_display(console::prompt);
 
     std::vector<llama_token> embd;
     std::vector<llama_token> embd_guidance;
 
-    // do one empty run to warm up the model
     {
+        LOG("warming up the model with an empty run\n");
+
         const std::vector<llama_token> tmp = { llama_token_bos(ctx), };
         llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
         llama_reset_timings(ctx);
@@ -407,15 +505,17 @@ int main(int argc, char ** argv) {
         if (embd.size() > 0) {
             // Note: n_ctx - 4 here is to match the logic for commandline prompt handling via
             // --prompt or --file which uses the same value.
-            auto max_embd_size = n_ctx - 4;
+            int max_embd_size = n_ctx - 4;
+
             // Ensure the input doesn't exceed the context size by truncating embd if necessary.
-            if ((int)embd.size() > max_embd_size) {
-                auto skipped_tokens = embd.size() - max_embd_size;
+            if ((int) embd.size() > max_embd_size) {
+                const int skipped_tokens = (int) embd.size() - max_embd_size;
+                embd.resize(max_embd_size);
+
                 console::set_display(console::error);
-                printf("<<input too long: skipped %zu token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
+                printf("<<input too long: skipped %d token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
                 console::set_display(console::reset);
                 fflush(stdout);
-                embd.resize(max_embd_size);
             }
 
             // infinite text generation via context swapping
@@ -424,28 +524,26 @@ int main(int argc, char ** argv) {
             // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches
             if (n_past + (int) embd.size() + std::max<int>(0, guidance_offset) > n_ctx) {
                 if (params.n_predict == -2) {
-                    fprintf(stderr, "\n\n%s: context full, stopping generation\n", __func__);
+                    LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict);
                     break;
                 }
 
                 const int n_left = n_past - params.n_keep;
+                LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d\n", n_past, n_left, n_ctx, params.n_keep);
+
                 // always keep the first token - BOS
-                n_past = std::max(1, params.n_keep);
+                n_past          = std::max(1, params.n_keep);
                 n_past_guidance = std::max(1, params.n_keep + guidance_offset);
 
+                LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance);
+
                 // insert n_left/2 tokens at the start of embd from last_n_tokens
                 embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size());
 
-                // stop saving session if we run out of context
-                path_session.clear();
+                LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
 
-                //printf("\n---\n");
-                //printf("resetting: '");
-                //for (int i = 0; i < (int) embd.size(); i++) {
-                //    printf("%s", llama_token_to_str(ctx, embd[i]));
-                //}
-                //printf("'\n");
-                //printf("\n---\n");
+                LOG("clear session path\n");
+                path_session.clear();
             }
 
             // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past)
@@ -475,7 +573,7 @@ int main(int argc, char ** argv) {
 
             if (ctx_guidance) {
                 int input_size = 0;
-                llama_token* input_buf = NULL;
+                llama_token * input_buf = NULL;
 
                 if (n_past_guidance < (int) guidance_inp.size()) {
                     // Guidance context should have the same data with these modifications:
@@ -491,22 +589,19 @@ int main(int argc, char ** argv) {
                         );
                     }
 
-                    input_buf = embd_guidance.data();
+                    input_buf  = embd_guidance.data();
                     input_size = embd_guidance.size();
-                    //fprintf(stderr, "\n---------------------\n");
-                    //for (int i = 0; i < (int) embd_guidance.size(); i++) {
-                        //fprintf(stderr, "%s", llama_token_to_str(ctx, embd_guidance[i]));
-                    //}
-                    //fprintf(stderr, "\n---------------------\n");
+
+                    LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance));
                 } else {
-                    input_buf = embd.data();
+                    input_buf  = embd.data();
                     input_size = embd.size();
                 }
 
                 for (int i = 0; i < input_size; i += params.n_batch) {
                     int n_eval = std::min(input_size - i, params.n_batch);
                     if (llama_eval(ctx_guidance, input_buf + i, n_eval, n_past_guidance, params.n_threads)) {
-                        fprintf(stderr, "%s : failed to eval\n", __func__);
+                        LOG_TEE("%s : failed to eval\n", __func__);
                         return 1;
                     }
 
@@ -519,11 +614,17 @@ int main(int argc, char ** argv) {
                 if (n_eval > params.n_batch) {
                     n_eval = params.n_batch;
                 }
+
+                LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd));
+
                 if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
-                    fprintf(stderr, "%s : failed to eval\n", __func__);
+                    LOG_TEE("%s : failed to eval\n", __func__);
                     return 1;
                 }
+
                 n_past += n_eval;
+
+                LOG("n_past = %d\n", n_past);
             }
 
             if (embd.size() > 0 && !path_session.empty()) {
@@ -536,7 +637,6 @@ int main(int argc, char ** argv) {
         embd_guidance.clear();
 
         if ((int) embd_inp.size() <= n_consumed && !is_interacting) {
-            // out of user input, sample next token
             const float   temp            = params.temp;
             const int32_t top_k           = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
             const float   top_p           = params.top_p;
@@ -555,6 +655,8 @@ int main(int argc, char ** argv) {
             if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) {
                 need_to_save_session = false;
                 llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
+
+                LOG("saved session to %s\n", path_session.c_str());
             }
 
             llama_token id = 0;
@@ -574,50 +676,68 @@ int main(int argc, char ** argv) {
                     candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
                 }
 
-                llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
+                llama_token_data_array cur_p = { candidates.data(), candidates.size(), false };
 
                 if (ctx_guidance) {
-                    llama_sample_classifier_free_guidance(ctx, &candidates_p, ctx_guidance, params.cfg_scale);
+                    llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale);
                 }
 
                 // Apply penalties
                 float nl_logit = logits[llama_token_nl(ctx)];
                 auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
-                llama_sample_repetition_penalty(ctx, &candidates_p,
+                llama_sample_repetition_penalty(ctx, &cur_p,
                     last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
                     last_n_repeat, repeat_penalty);
-                llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
+                llama_sample_frequency_and_presence_penalties(ctx, &cur_p,
                     last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
                     last_n_repeat, alpha_frequency, alpha_presence);
                 if (!penalize_nl) {
-                    logits[llama_token_nl(ctx)] = nl_logit;
+                    for (size_t idx = 0; idx < cur_p.size; idx++) {
+                        if (cur_p.data[idx].id == llama_token_nl(ctx)) {
+                            cur_p.data[idx].logit = nl_logit;
+                            break;
+                        }
+                    }
                 }
 
                 if (grammar != NULL) {
-                    llama_sample_grammar(ctx, &candidates_p, grammar);
+                    llama_sample_grammar(ctx, &cur_p, grammar);
                 }
 
                 if (temp <= 0) {
                     // Greedy sampling
-                    id = llama_sample_token_greedy(ctx, &candidates_p);
+                    id = llama_sample_token_greedy(ctx, &cur_p);
                 } else {
                     if (mirostat == 1) {
                         static float mirostat_mu = 2.0f * mirostat_tau;
                         const int mirostat_m = 100;
-                        llama_sample_temperature(ctx, &candidates_p, temp);
-                        id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
+                        llama_sample_temperature(ctx, &cur_p, temp);
+                        id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
                     } else if (mirostat == 2) {
                         static float mirostat_mu = 2.0f * mirostat_tau;
-                        llama_sample_temperature(ctx, &candidates_p, temp);
-                        id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
+                        llama_sample_temperature(ctx, &cur_p, temp);
+                        id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu);
                     } else {
                         // Temperature sampling
-                        llama_sample_top_k(ctx, &candidates_p, top_k, 1);
-                        llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
-                        llama_sample_typical(ctx, &candidates_p, typical_p, 1);
-                        llama_sample_top_p(ctx, &candidates_p, top_p, 1);
-                        llama_sample_temperature(ctx, &candidates_p, temp);
-                        id = llama_sample_token(ctx, &candidates_p);
+                        llama_sample_top_k      (ctx, &cur_p, top_k, 1);
+                        llama_sample_tail_free  (ctx, &cur_p, tfs_z, 1);
+                        llama_sample_typical    (ctx, &cur_p, typical_p, 1);
+                        llama_sample_top_p      (ctx, &cur_p, top_p, 1);
+                        llama_sample_temperature(ctx, &cur_p, temp);
+
+                        {
+                            const int n_top = 10;
+                            LOG("top %d candidates:\n", n_top);
+
+                            for (int i = 0; i < n_top; i++) {
+                                const llama_token id = cur_p.data[i].id;
+                                LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p);
+                            }
+                        }
+
+                        id = llama_sample_token(ctx, &cur_p);
+
+                        LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str());
                     }
                 }
                 // printf("`%d`", candidates_p.size);
@@ -628,9 +748,10 @@ int main(int argc, char ** argv) {
 
                 last_n_tokens.erase(last_n_tokens.begin());
                 last_n_tokens.push_back(id);
+
+                LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_n_tokens));
             }
 
-            // add it to the context
             embd.push_back(id);
 
             // echo this to console
@@ -638,8 +759,11 @@ int main(int argc, char ** argv) {
 
             // decrement remaining sampling budget
             --n_remain;
+
+            LOG("n_remain: %d\n", n_remain);
         } else {
             // some user input remains from prompt or interaction, forward it to processing
+            LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed);
             while ((int) embd_inp.size() > n_consumed) {
                 embd.push_back(embd_inp[n_consumed]);
                 last_n_tokens.erase(last_n_tokens.begin());
@@ -654,23 +778,30 @@ int main(int argc, char ** argv) {
         // display text
         if (input_echo) {
             for (auto id : embd) {
-                printf("%s", llama_token_to_str(ctx, id).c_str());
+                const std::string token_str = llama_token_to_piece(ctx, id);
+                printf("%s", token_str.c_str());
+
+                if (embd.size() > 1) {
+                    input_tokens.push_back(id);
+                } else {
+                    output_tokens.push_back(id);
+                    output_ss << token_str;
+                }
             }
             fflush(stdout);
         }
         // reset color to default if we there is no pending user input
-        if (input_echo && (int)embd_inp.size() == n_consumed) {
+        if (input_echo && (int) embd_inp.size() == n_consumed) {
             console::set_display(console::reset);
         }
 
         // if not currently processing queued inputs;
         if ((int) embd_inp.size() <= n_consumed) {
-
             // check for reverse prompt
             if (params.antiprompt.size()) {
                 std::string last_output;
                 for (auto id : last_n_tokens) {
-                    last_output += llama_token_to_str(ctx, id);
+                    last_output += llama_token_to_piece(ctx, id);
                 }
 
                 is_antiprompt = false;
@@ -683,7 +814,7 @@ int main(int argc, char ** argv) {
                         ? last_output.length() - static_cast<size_t>(antiprompt.length() + extra_padding)
                         : 0;
 
-                    if (last_output.find(antiprompt.c_str(), search_start_pos) != std::string::npos) {
+                    if (last_output.find(antiprompt, search_start_pos) != std::string::npos) {
                         if (params.interactive) {
                             is_interacting = true;
                             console::set_display(console::user_input);
@@ -693,10 +824,16 @@ int main(int argc, char ** argv) {
                         break;
                     }
                 }
+
+                if (is_antiprompt) {
+                    LOG("found antiprompt: %s\n", last_output.c_str());
+                }
             }
 
             // deal with end of text token in interactive mode
             if (last_n_tokens.back() == llama_token_eos(ctx)) {
+                LOG("found EOS token\n");
+
                 if (params.interactive) {
                     if (params.antiprompt.size() != 0) {
                         // tokenize and inject first reverse prompt
@@ -715,16 +852,20 @@ int main(int argc, char ** argv) {
             }
 
             if (n_past > 0 && is_interacting) {
+                LOG("waiting for user input\n");
+
                 if (params.instruct) {
                     printf("\n> ");
                 }
 
                 if (params.input_prefix_bos) {
+                    LOG("adding input prefix BOS token\n");
                     embd_inp.push_back(llama_token_bos(ctx));
                 }
 
                 std::string buffer;
                 if (!params.input_prefix.empty()) {
+                    LOG("appending input prefix: '%s'\n", params.input_prefix.c_str());
                     buffer += params.input_prefix;
                     printf("%s", buffer.c_str());
                 }
@@ -744,25 +885,43 @@ int main(int argc, char ** argv) {
                 if (buffer.length() > 1) {
                     // append input suffix if any
                     if (!params.input_suffix.empty()) {
+                        LOG("appending input suffix: '%s'\n", params.input_suffix.c_str());
                         buffer += params.input_suffix;
                         printf("%s", params.input_suffix.c_str());
                     }
 
+                    LOG("buffer: '%s'\n", buffer.c_str());
+
+                    const size_t original_size = embd_inp.size();
+
                     // instruct mode: insert instruction prefix
                     if (params.instruct && !is_antiprompt) {
+                        LOG("inserting instruction prefix\n");
                         n_consumed = embd_inp.size();
                         embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
                     }
 
-                    auto line_inp = ::llama_tokenize(ctx, buffer, false);
+                    const auto line_inp = ::llama_tokenize(ctx, buffer, false);
+                    LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp));
+
                     embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
 
                     // instruct mode: insert response suffix
                     if (params.instruct) {
+                        LOG("inserting instruction suffix\n");
                         embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
                     }
 
+                    for (size_t i = original_size; i < embd_inp.size(); ++i) {
+                        const llama_token token = embd_inp[i];
+                        output_tokens.push_back(token);
+                        output_ss << llama_token_to_piece(ctx, token);
+                    }
+
                     n_remain -= line_inp.size();
+                    LOG("n_remain: %d\n", n_remain);
+                } else {
+                    LOG("empty line, passing control back\n");
                 }
 
                 input_echo = false; // do not echo this again
@@ -786,23 +945,26 @@ int main(int argc, char ** argv) {
 
         // end of text token
         if (!embd.empty() && embd.back() == llama_token_eos(ctx) && !(params.instruct || params.interactive)) {
-            fprintf(stderr, " [end of text]\n");
+            LOG_TEE(" [end of text]\n");
             break;
         }
 
         // In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
-        if (params.interactive && n_remain <= 0 && params.n_predict != -1) {
+        // We skip this logic when n_predict == -1 (infinite) or -2 (stop at context size).
+        if (params.interactive && n_remain <= 0 && params.n_predict >= 0) {
             n_remain = params.n_predict;
             is_interacting = true;
         }
     }
 
     if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) {
-        fprintf(stderr, "\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
+        LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str());
         llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size());
     }
 
     llama_print_timings(ctx);
+    write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens);
+
     if (ctx_guidance) { llama_free(ctx_guidance); }
     llama_free(ctx);
     llama_free_model(model);
@@ -812,5 +974,9 @@ int main(int argc, char ** argv) {
     }
     llama_backend_free();
 
+#ifndef LOG_DISABLE_LOGS
+    LOG_TEE("Log end\n")
+#endif // LOG_DISABLE_LOGS
+
     return 0;
 }

examples/make-ggml.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 """
 This script converts Hugging Face llama models to GGML and quantizes them.
 

examples/perplexity/perplexity.cpp

@@ -3,14 +3,79 @@
 #include "build-info.h"
 
 #include <cmath>
+#include <cstdio>
+#include <cstring>
 #include <ctime>
 #include <sstream>
-#include <cstring>
+#include <thread>
+#include <mutex>
+#include <vector>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
+struct results_perplexity {
+    std::vector<llama_token> tokens;
+    double                   ppl_value;
+    std::vector<float>       logits;
+    std::vector<float>       probs;
+};
+
+struct results_log_softmax {
+    double log_softmax;
+    float  logit;
+    float  prob;
+};
+
+void write_logfile(const llama_context * ctx, const gpt_params & params,
+                   const llama_model * model, const struct results_perplexity & results) {
+
+    if (params.logdir.empty()) {
+        return;
+    }
+
+    if (params.hellaswag) {
+        fprintf(stderr, "%s: warning: logging results is not implemented for HellaSwag. No files will be written.\n", __func__);
+        return;
+    }
+
+    const std::string timestamp = get_sortable_timestamp();
+
+    const bool success = create_directory_with_parents(params.logdir);
+    if (!success) {
+        fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n",
+                __func__, params.logdir.c_str());
+        return;
+    }
+
+    const std::string logfile_path = params.logdir + timestamp + ".yml";
+    FILE * logfile = fopen(logfile_path.c_str(), "w");
+
+    if (logfile == NULL) {
+        fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str());
+        return;
+    }
+
+    fprintf(logfile, "binary: main\n");
+    char model_desc[128];
+    llama_model_desc(model, model_desc, sizeof(model_desc));
+    dump_non_result_info_yaml(logfile, params, ctx, timestamp, results.tokens, model_desc);
+
+    fprintf(logfile, "\n");
+    fprintf(logfile, "######################\n");
+    fprintf(logfile, "# Perplexity Results #\n");
+    fprintf(logfile, "######################\n");
+    fprintf(logfile, "\n");
+
+    dump_vector_float_yaml(logfile, "logits", results.logits);
+    fprintf(logfile, "ppl_value: %f\n", results.ppl_value);
+    dump_vector_float_yaml(logfile, "probs", results.probs);
+
+    llama_dump_timing_info_yaml(logfile, ctx);
+    fclose(logfile);
+}
+
 std::vector<float> softmax(const std::vector<float>& logits) {
     std::vector<float> probs(logits.size());
     float max_logit = logits[0];
@@ -27,14 +92,86 @@ std::vector<float> softmax(const std::vector<float>& logits) {
     return probs;
 }
 
-void perplexity(llama_context * ctx, const gpt_params & params) {
+results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
+    float max_logit = logits[0];
+    for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]);
+    double sum_exp = 0.0;
+    for (int i = 0; i < n_vocab; ++i) sum_exp += expf(logits[i] - max_logit);
+    return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
+}
+
+void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
+        double & nll, double & nll2, float * logit_history, float * prob_history) {
+
+    std::mutex mutex;
+    int counter = 0;
+    auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
+        double local_nll = 0, local_nll2 = 0;
+        while (true) {
+            std::unique_lock<std::mutex> lock(mutex);
+            int i = counter++;
+            if (i >= n_token) {
+                nll += local_nll; nll2 += local_nll2;
+                break;
+            }
+            lock.unlock();
+            const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
+            const double v = -results.log_softmax;
+            local_nll += v;
+            local_nll2 += v*v;
+
+            logit_history[i] = results.logit;
+            prob_history[i]  = results.prob;
+        }
+    };
+    for (auto & w : workers) w = std::thread(compute);
+    compute();
+    for (auto & w : workers) w.join();
+
+}
+
+results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) {
     // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
     // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
     // Output: `perplexity: 13.5106 [114/114]`
     // BOS tokens will be added for each chunk before eval
-    auto tokens = ::llama_tokenize(ctx, params.prompt, true);
 
-    const int n_chunk_max = tokens.size() / params.n_ctx;
+    const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+    const bool add_bos = is_spm;
+
+    fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
+
+    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+
+    if (int(tokens.size()) < 2*params.n_ctx) {
+        fprintf(stderr, "%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*params.n_ctx,
+                params.n_ctx);
+        fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
+        return {std::move(tokens), 0., {}, {}};
+    }
+
+    std::vector<float>       logit_history;
+    std::vector<float>       prob_history;
+
+    logit_history.resize(tokens.size());
+    prob_history.resize(tokens.size());
+
+    if (params.ppl_stride <= 0) {
+        fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride);
+        return {tokens, -1, logit_history, prob_history};
+    }
+
+    const int calc_chunk = params.n_ctx;
+
+    fprintf(stderr, "%s: have %zu tokens. Calculation chunk = %d\n", __func__, tokens.size(), calc_chunk);
+
+    if (int(tokens.size()) <= calc_chunk) {
+        fprintf(stderr, "%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__,
+                tokens.size(), params.n_ctx, params.ppl_stride);
+        return {tokens, -1, logit_history, prob_history};
+    }
+
+    const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1)  / params.ppl_stride;
 
     const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
     const int n_vocab = llama_n_vocab(ctx);
@@ -45,6 +182,133 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
 
     fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
 
+    for (int i = 0; i < n_chunk; ++i) {
+        const int start =     i * params.ppl_stride;
+        const int end   = start + calc_chunk;
+
+        const int num_batches = (calc_chunk + n_batch - 1) / n_batch;
+        //fprintf(stderr, "%s: evaluating %d...%d using %d batches\n", __func__, start, end, num_batches);
+
+        std::vector<float> logits;
+
+        const auto t_start = std::chrono::high_resolution_clock::now();
+
+        for (int j = 0; j < num_batches; ++j) {
+            const int batch_start = start + j * n_batch;
+            const int batch_size  = std::min(end - batch_start, n_batch);
+
+            //fprintf(stderr, "    Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch);
+            if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
+                //fprintf(stderr, "%s : failed to eval\n", __func__);
+                return {tokens, -1, logit_history, prob_history};
+            }
+
+            // save original token and restore it after eval
+            const auto token_org = tokens[batch_start];
+
+            // add BOS token for the first batch of each chunk
+            if (add_bos && j == 0) {
+                tokens[batch_start] = llama_token_bos(ctx);
+            }
+
+            const auto batch_logits = llama_get_logits(ctx);
+            logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
+
+            if (j == 0) {
+                tokens[batch_start] = token_org;
+            }
+        }
+
+        const auto t_end = std::chrono::high_resolution_clock::now();
+
+        if (i == 0) {
+            const float t_total = std::chrono::duration<float>(t_end - t_start).count();
+            fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
+            int total_seconds = (int)(t_total * n_chunk);
+            if (total_seconds >= 60*60) {
+                fprintf(stderr, "%d hours ", total_seconds / (60*60));
+                total_seconds = total_seconds % (60*60);
+            }
+            fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
+        }
+
+        //fprintf(stderr, "%s: using tokens %d...%d\n",__func__,params.n_ctx - params.ppl_stride + start, params.n_ctx + start);
+        for (int j = params.n_ctx - params.ppl_stride - 1; j < params.n_ctx - 1; ++j) {
+
+            // Calculate probability of next token, given the previous ones.
+            const std::vector<float> tok_logits(
+                logits.begin() + (j + 0) * n_vocab,
+                logits.begin() + (j + 1) * n_vocab);
+
+            const float prob = softmax(tok_logits)[tokens[start + j + 1]];
+            logit_history[start + j + 1] = tok_logits[tokens[start + j + 1]];
+            prob_history[start + j + 1]  = prob;
+
+            nll += -std::log(prob);
+            ++count;
+        }
+        // perplexity is e^(average negative log-likelihood)
+        if (params.ppl_output_type == 0) {
+            printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+        } else {
+            printf("%8d  %.4lf\n", i*params.ppl_stride, std::exp(nll / count));
+        }
+        fflush(stdout);
+    }
+    printf("\n");
+
+    return {tokens, std::exp(nll / count), logit_history, prob_history};
+}
+
+results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {
+
+    if (params.ppl_stride > 0) {
+        return perplexity_v2(ctx, params);
+    }
+
+    // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
+    // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
+    // Output: `perplexity: 13.5106 [114/114]`
+    // BOS tokens will be added for each chunk before eval
+
+    const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+    const bool add_bos = is_spm;
+
+    auto tim1 = std::chrono::high_resolution_clock::now();
+    fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
+
+    std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
+
+    auto tim2 = std::chrono::high_resolution_clock::now();
+    fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
+
+    if (int(tokens.size()) < 2*params.n_ctx) {
+        fprintf(stderr, "%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*params.n_ctx,
+                params.n_ctx);
+        fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
+        return {std::move(tokens), 0., {}, {}};
+    }
+
+    std::vector<float> logit_history;
+    logit_history.resize(tokens.size());
+
+    std::vector<float> prob_history;
+    prob_history.resize(tokens.size());
+
+    const int n_chunk_max = tokens.size() / params.n_ctx;
+
+    const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
+    const int n_vocab = llama_n_vocab(ctx);
+    const int n_batch = params.n_batch;
+
+    int count = 0;
+    double nll = 0.0;
+    double nll2 = 0.0;
+
+    fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
+
+    std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
+
     for (int i = 0; i < n_chunk; ++i) {
         const int start =     i * params.n_ctx;
         const int end   = start + params.n_ctx;
@@ -63,13 +327,13 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
             const auto token_org = tokens[batch_start];
 
             // add BOS token for the first batch of each chunk
-            if (j == 0) {
+            if (add_bos && j == 0) {
                 tokens[batch_start] = llama_token_bos(ctx);
             }
 
             if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
-                return;
+                return {tokens, -1, logit_history, prob_history};
             }
 
             // restore the original token in case it was set to BOS
@@ -104,22 +368,36 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
         // Example, we have a context window of 512, we will compute perplexity for each of the
         // last 256 tokens.  Then, we split the input up into context window size chunks to
         // process the entire prompt.
-        for (int j = std::min(512, params.n_ctx / 2); j < params.n_ctx - 1; ++j) {
-            // Calculate probability of next token, given the previous ones.
-            const std::vector<float> tok_logits(
-                logits.begin() + (j + 0) * n_vocab,
-                logits.begin() + (j + 1) * n_vocab);
+        const int first = std::min(512, params.n_ctx/2);
+        process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first,
+                       workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
+        count += params.n_ctx - first - 1;
 
-            const float prob = softmax(tok_logits)[tokens[start + j + 1]];
-
-            nll += -std::log(prob);
-            ++count;
-        }
         // perplexity is e^(average negative log-likelihood)
-        printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+        if (params.ppl_output_type == 0) {
+            printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+        } else {
+            double av = nll/count;
+            double av2 = nll2/count - av*av;
+            if (av2 > 0) av2 = sqrt(av2/(count-1));
+            printf("%8d  %.4lf  %4lf  %4lf\n", i*params.n_ctx, std::exp(nll / count), av, av2);
+        }
         fflush(stdout);
     }
     printf("\n");
+
+    nll2 /= count;
+    nll /= count;
+    const double ppl = exp(nll);
+    nll2 -= nll * nll;
+    if (nll2 > 0) {
+        nll2 = sqrt(nll2/(count-1));
+        printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
+    } else {
+        printf("Unexpected negative standard deviation of log(prob)\n");
+    }
+
+    return {tokens, ppl, logit_history, prob_history};
 }
 
 std::vector<float> hellaswag_evaluate_tokens(llama_context * ctx, const std::vector<int>& tokens, int n_past, int n_batch,
@@ -177,8 +455,11 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     size_t hs_task_count = prompt_lines.size()/6;
     fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count);
 
+    const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM;
+    fprintf(stderr, "================================= is_spm = %d\n", is_spm);
+
     // This is needed as usual for LLaMA models
-    bool prepend_bos = true;
+    const bool add_bos = is_spm;
 
     // Number of tasks to use when computing the score
     if ( params.hellaswag_tasks < hs_task_count  ) {
@@ -216,7 +497,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         hs_data[i].context = prompt_lines[idx*6];
         hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] );
         for (size_t j=0; j < 4; j++) {
-            hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j];
+            hs_data[i].ending[j] = prompt_lines[idx*6+2+j];
         }
 
         // Delete the selected random example from the prompt
@@ -231,19 +512,30 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     double acc = 0.0f;
     const int n_vocab = llama_n_vocab(ctx);
 
+    std::vector<std::vector<int>> ending_tokens(4);
+
     std::vector<float> tok_logits(n_vocab);
 
     for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) {
-
         // Tokenize the context to count tokens
-        std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, prepend_bos);
+        std::vector<int> context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, add_bos);
         size_t context_size = context_embd.size();
 
+        for (int i = 0; i < 4; ++i) {
+            ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + " " + hs_data[task_idx].ending[i], add_bos);
+            for (int k = 0; k < int(context_size); ++k) {
+                if (ending_tokens[i][k] != context_embd[k]) {
+                    fprintf(stderr, "Oops: ending %d of task %d differs from context at position %d\n",i,int(task_idx),k);
+                    break;
+                }
+            }
+        }
+
         // Do the 1st ending
         // In this case we include the context when evaluating
-        auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], prepend_bos);
+        //auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos);
+        auto query_embd = ending_tokens[0];
         auto query_size = query_embd.size();
-        //printf("First query: %d\n",(int)query_size);
 
         // Stop if query wont fit the ctx window
         if (query_size > (size_t)params.n_ctx) {
@@ -288,7 +580,8 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) {
         for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) {
 
             // Tokenize the query
-            query_embd = ::llama_tokenize(ctx, hs_data[task_idx].ending[ending_idx], false);
+            query_embd.resize(ending_tokens[ending_idx].size() - context_size);
+            std::memcpy(query_embd.data(), ending_tokens[ending_idx].data() + context_size, query_embd.size()*sizeof(int));
             query_size = query_embd.size();
 
             // Stop if query wont fit the ctx window
@@ -369,6 +662,12 @@ int main(int argc, char ** argv) {
     params.perplexity = true;
     params.n_batch = std::min(params.n_batch, params.n_ctx);
 
+    if (params.ppl_stride > 0) {
+        fprintf(stderr, "Will perform strided perplexity calculation -> adjusting context size from %d to %d\n",
+                params.n_ctx, params.n_ctx + params.ppl_stride/2);
+        params.n_ctx += params.ppl_stride/2;
+    }
+
     if (params.n_ctx > 2048) {
         fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
                 "expect poor results\n", __func__, params.n_ctx);
@@ -406,13 +705,16 @@ int main(int argc, char ** argv) {
                 params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
     }
 
+    struct results_perplexity results;
     if (params.hellaswag) {
         hellaswag_score(ctx, params);
     } else {
-        perplexity(ctx, params);
+        results = perplexity(ctx, params);
     }
 
     llama_print_timings(ctx);
+    write_logfile(ctx, params, model, results);
+
     llama_free(ctx);
     llama_free_model(model);
 

examples/quantize/quantize.cpp

@@ -14,25 +14,25 @@ struct quant_option {
 };
 
 static const std::vector<struct quant_option> QUANT_OPTIONS = {
-    { "Q4_0",   LLAMA_FTYPE_MOSTLY_Q4_0,   " 3.50G, +0.2499 ppl @ 7B", },
-    { "Q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1,   " 3.90G, +0.1846 ppl @ 7B", },
-    { "Q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0,   " 4.30G, +0.0796 ppl @ 7B", },
-    { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 4.70G, +0.0415 ppl @ 7B", },
+    { "Q4_0",   LLAMA_FTYPE_MOSTLY_Q4_0,   " 3.56G, +0.2166 ppl @ LLaMA-v1-7B", },
+    { "Q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1,   " 3.90G, +0.1585 ppl @ LLaMA-v1-7B", },
+    { "Q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0,   " 4.33G, +0.0683 ppl @ LLaMA-v1-7B", },
+    { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 4.70G, +0.0349 ppl @ LLaMA-v1-7B", },
 #ifdef GGML_USE_K_QUANTS
-    { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.67G, +0.8698 ppl @ 7B", },
+    { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", },
     { "Q3_K",   LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
-    { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5505 ppl @ 7B", },
-    { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.06G, +0.2437 ppl @ 7B", },
-    { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1803 ppl @ 7B", },
+    { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", },
+    { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.07G, +0.2496 ppl @ LLaMA-v1-7B", },
+    { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1764 ppl @ LLaMA-v1-7B", },
     { "Q4_K",   LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
-    { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.56G, +0.1149 ppl @ 7B", },
-    { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0535 ppl @ 7B", },
+    { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.59G, +0.0992 ppl @ LLaMA-v1-7B", },
+    { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0532 ppl @ LLaMA-v1-7B", },
     { "Q5_K",   LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", },
-    { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0353 ppl @ 7B", },
-    { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0142 ppl @ 7B", },
-    { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 5.15G, +0.0044 ppl @ 7B", },
+    { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0400 ppl @ LLaMA-v1-7B", },
+    { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", },
+    { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 5.15G, -0.0008 ppl @ LLaMA-v1-7B", },
 #endif
-    { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 6.70G, +0.0004 ppl @ 7B", },
+    { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", },
     { "F16",    LLAMA_FTYPE_MOSTLY_F16,    "13.00G              @ 7B", },
     { "F32",    LLAMA_FTYPE_ALL_F32,       "26.00G              @ 7B", },
 };
@@ -100,7 +100,7 @@ int main(int argc, char ** argv) {
         }
     }
 
-    if (argc - arg_idx < 3) {
+    if (argc - arg_idx < 2) {
         usage(argv[0]);
     }
 
@@ -114,7 +114,7 @@ int main(int argc, char ** argv) {
     std::string ftype_str;
     if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
         std::string fpath;
-        const size_t pos = fname_inp.find_last_of('/');
+        const size_t pos = fname_inp.find_last_of("/\\");
         if (pos != std::string::npos) {
             fpath = fname_inp.substr(0, pos + 1);
         }

examples/reason-act.sh

@@ -1,4 +1,3 @@
-
 #!/bin/bash
 
 cd `dirname $0`

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

@@ -87,7 +87,7 @@ int main(int argc, char ** argv) {
         }
         llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
         auto next_token = llama_sample_token(ctx, &candidates_p);
-        auto next_token_str = llama_token_to_str(ctx, next_token);
+        auto next_token_str = llama_token_to_piece(ctx, next_token);
         last_n_tokens_data.push_back(next_token);
 
         printf("%s", next_token_str.c_str());
@@ -147,7 +147,7 @@ int main(int argc, char ** argv) {
         }
         llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
         auto next_token = llama_sample_token(ctx2, &candidates_p);
-        auto next_token_str = llama_token_to_str(ctx2, next_token);
+        auto next_token_str = llama_token_to_piece(ctx2, next_token);
         last_n_tokens_data.push_back(next_token);
 
         printf("%s", next_token_str.c_str());

examples/server/README.md

@@ -77,34 +77,31 @@ You need to have [Node.js](https://nodejs.org/en) installed.
 ```bash
 mkdir llama-client
 cd llama-client
-npm init
-npm install axios
 ```
 
 Create a index.js file and put inside this:
 
 ```javascript
-const axios = require("axios");
-
 const prompt = `Building a website can be done in 10 simple steps:`;
 
 async function Test() {
-    let result = await axios.post("http://127.0.0.1:8080/completion", {
-        prompt,
-        n_predict: 512,
-    });
-
-    // the response is received until completion finish
-    console.log(result.data.content);
+    let response = await fetch("http://127.0.0.1:8080/completion", {
+        method: 'POST',
+        body: JSON.stringify({
+            prompt,
+            n_predict: 512,
+        })
+    })
+    console.log((await response.json()).content)
 }
 
-Test();
+Test()
 ```
 
 And run it:
 
 ```bash
-node .
+node index.js
 ```
 
 ## API Endpoints
@@ -126,7 +123,7 @@ node .
 
     `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
 
-    `prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate. A space is inserted in the front like main.cpp does.
+    `prompt`: Provide a prompt as a string, or as an array of strings and numbers representing tokens. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate. If the prompt is a string, or an array with the first element given as a string, a space is inserted in the front like main.cpp does.
 
     `stop`: Specify a JSON array of stopping strings.
     These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []).
@@ -167,6 +164,12 @@ node .
 
     Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.
 
+-   **POST** `/detokenize`: Convert tokens to text.
+
+    *Options:*
+
+    `tokens`: Set the tokens to detokenize.
+
 -   **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.
 
     *Options:*

examples/server/api_like_OAI.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 import argparse
 from flask import Flask, jsonify, request, Response
 import urllib.parse

examples/server/public/index.html

@@ -102,6 +102,17 @@
       padding: 0.5em;
     }
 
+    .prob-set {
+      padding: 0.3em;
+      border-bottom: 1px solid #ccc;
+    }
+
+    .popover-content {
+      position: absolute;
+      background-color: white;
+      padding: 0.2em;
+      box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
+    }
 
     textarea {
       padding: 5px;
@@ -133,11 +144,17 @@
       font-size: 80%;
       color: #888;
     }
+
+    @media (prefers-color-scheme: dark) {
+      .popover-content {
+        background-color: black;
+      }
+    }
   </style>
 
   <script type="module">
     import {
-      html, h, signal, effect, computed, render, useSignal, useEffect, useRef
+      html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component
     } from '/index.js';
 
     import { llama } from '/completion.js';
@@ -168,6 +185,7 @@
       mirostat_tau: 5, // target entropy
       mirostat_eta: 0.1, // learning rate
       grammar: '',
+      n_probs: 0, // no completion_probabilities
     })
 
     /* START: Support for storing prompt templates and parameters in borwser LocalStorage */
@@ -334,10 +352,21 @@
 
       const prompt = template(session.value.template, {
         message: msg,
-        history: session.value.transcript.flatMap(([name, message]) => template(session.value.historyTemplate, {name, message})).join("\n"),
+        history: session.value.transcript.flatMap(
+          ([name, data]) =>
+            template(
+              session.value.historyTemplate,
+              {
+                name,
+                message: Array.isArray(data) ?
+                  data.map(msg => msg.content).join('').replace(/^\s/, '') :
+                  data,
+              }
+            )
+        ).join("\n"),
       });
 
-      let currentMessage = '';
+      const currentMessages = [];
       const history = session.value.transcript
 
       const llamaParams = {
@@ -347,15 +376,19 @@
 
       for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
         const data = chunk.data;
-        currentMessage += data.content;
-
-        // remove leading whitespace
-        currentMessage = currentMessage.replace(/^\s+/, "")
-
-        transcriptUpdate([...history, ["{{char}}", currentMessage]])
 
         if (data.stop) {
-          console.log("Completion finished: '", currentMessage, "', summary: ", data);
+          while (
+            currentMessages.length > 0 &&
+            currentMessages[currentMessages.length - 1].content.match(/\n$/) != null
+          ) {
+            currentMessages.pop();
+          }
+          transcriptUpdate([...history, ["{{char}}", currentMessages]])
+          console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data);
+        } else {
+          currentMessages.push(data);
+          transcriptUpdate([...history, ["{{char}}", currentMessages]])
         }
 
         if (data.timings) {
@@ -420,8 +453,18 @@
         }
       }, [messages])
 
-      const chatLine = ([user, msg]) => {
-        return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdownish} text=${template(msg)} /></p>`
+      const chatLine = ([user, data], index) => {
+        let message
+        const isArrayMessage = Array.isArray(data)
+        if (params.value.n_probs > 0 && isArrayMessage) {
+          message = html`<${Probabilities} data=${data} />`
+        } else {
+          const text = isArrayMessage ?
+            data.map(msg => msg.content).join('').replace(/^\s+/, '') :
+            data;
+          message = html`<${Markdownish} text=${template(text)} />`
+        }
+        return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>`
       };
 
       return html`
@@ -568,10 +611,71 @@
               ${FloatField({label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau})}
               ${FloatField({label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta})}
             </fieldset>
+            <fieldset>
+              ${IntField({label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs})}
+            </fieldset>
           </details>
         </form>
       `
     }
+
+    const probColor = (p) => {
+      const r = Math.floor(192 * (1 - p));
+      const g = Math.floor(192 * p);
+      return `rgba(${r},${g},0,0.3)`;
+    }
+
+    const Probabilities = (params) => {
+      return params.data.map(msg => {
+        const { completion_probabilities } = msg;
+        if (
+          !completion_probabilities ||
+          completion_probabilities.length === 0
+        ) return msg.content
+
+        if (completion_probabilities.length > 1) {
+          // Not for byte pair
+          if (completion_probabilities[0].content.startsWith('byte: \\')) return msg.content
+
+          const splitData = completion_probabilities.map(prob => ({
+            content: prob.content,
+            completion_probabilities: [prob]
+          }))
+          return html`<${Probabilities} data=${splitData} />`
+        }
+
+        const { probs, content } = completion_probabilities[0]
+        const found = probs.find(p => p.tok_str === msg.content)
+        const pColor = found ? probColor(found.prob) : 'transparent'
+
+        const popoverChildren = html`
+          <div class="prob-set">
+            ${probs.map((p, index) => {
+              return html`
+                <div
+                  key=${index}
+                  title=${`prob: ${p.prob}`}
+                  style=${{
+                    padding: '0.3em',
+                    backgroundColor: p.tok_str === content ? probColor(p.prob) : 'transparent'
+                  }}
+                >
+                  <span>${p.tok_str}: </span>
+                  <span>${Math.floor(p.prob * 100)}%</span>
+                </div>
+              `
+            })}
+          </div>
+        `
+
+        return html`
+          <${Popover} style=${{ backgroundColor: pColor }} popoverChildren=${popoverChildren}>
+            ${msg.content.match(/\n/gim) ? html`<br />` : msg.content}
+          </>
+        `
+      });
+    }
+
     // poor mans markdown replacement
     const Markdownish = (params) => {
       const md = params.text
@@ -600,10 +704,121 @@
       `
     }
 
+    // simple popover impl
+    const Popover = (props) => {
+      const isOpen = useSignal(false);
+      const position = useSignal({ top: '0px', left: '0px' });
+      const buttonRef = useRef(null);
+      const popoverRef = useRef(null);
+
+      const togglePopover = () => {
+        if (buttonRef.current) {
+          const rect = buttonRef.current.getBoundingClientRect();
+          position.value = {
+            top: `${rect.bottom + window.scrollY}px`,
+            left: `${rect.left + window.scrollX}px`,
+          };
+        }
+        isOpen.value = !isOpen.value;
+      };
+
+      const handleClickOutside = (event) => {
+        if (popoverRef.current && !popoverRef.current.contains(event.target) && !buttonRef.current.contains(event.target)) {
+          isOpen.value = false;
+        }
+      };
+
+      useEffect(() => {
+        document.addEventListener('mousedown', handleClickOutside);
+        return () => {
+          document.removeEventListener('mousedown', handleClickOutside);
+        };
+      }, []);
+
+      return html`
+        <span style=${props.style} ref=${buttonRef} onClick=${togglePopover}>${props.children}</span>
+        ${isOpen.value && html`
+          <${Portal} into="#portal">
+            <div
+              ref=${popoverRef}
+              class="popover-content"
+              style=${{
+                top: position.value.top,
+                left: position.value.left,
+              }}
+            >
+              ${props.popoverChildren}
+            </div>
+          </${Portal}>
+        `}
+      `;
+    };
+
+    // Source: preact-portal (https://github.com/developit/preact-portal/blob/master/src/preact-portal.js)
+    /** Redirect rendering of descendants into the given CSS selector */
+    class Portal extends Component {
+      componentDidUpdate(props) {
+        for (let i in props) {
+          if (props[i] !== this.props[i]) {
+            return setTimeout(this.renderLayer);
+          }
+        }
+      }
+
+      componentDidMount() {
+        this.isMounted = true;
+        this.renderLayer = this.renderLayer.bind(this);
+        this.renderLayer();
+      }
+
+      componentWillUnmount() {
+        this.renderLayer(false);
+        this.isMounted = false;
+        if (this.remote && this.remote.parentNode) this.remote.parentNode.removeChild(this.remote);
+      }
+
+      findNode(node) {
+        return typeof node === 'string' ? document.querySelector(node) : node;
+      }
+
+      renderLayer(show = true) {
+        if (!this.isMounted) return;
+
+        // clean up old node if moving bases:
+        if (this.props.into !== this.intoPointer) {
+          this.intoPointer = this.props.into;
+          if (this.into && this.remote) {
+            this.remote = render(html`<${PortalProxy} />`, this.into, this.remote);
+          }
+          this.into = this.findNode(this.props.into);
+        }
+
+        this.remote = render(html`
+          <${PortalProxy} context=${this.context}>
+            ${show && this.props.children || null}
+          </${PortalProxy}>
+        `, this.into, this.remote);
+      }
+
+      render() {
+        return null;
+      }
+    }
+    // high-order component that renders its first child if it exists.
+    // used as a conditional rendering proxy.
+    class PortalProxy extends Component {
+      getChildContext() {
+        return this.props.context;
+      }
+      render({ children }) {
+        return children || null;
+      }
+    }
+
     function App(props) {
 
       return html`
-        <div id="container">
+        <div>
           <header>
             <h1>llama.cpp</h1>
           </header>
@@ -624,11 +839,13 @@
       `;
     }
 
-    render(h(App), document.body);
+    render(h(App), document.querySelector('#container'));
   </script>
 </head>
 
 <body>
+  <div id="container"></div>
+  <div id="portal"></div>
 </body>
 
 </html>

examples/server/server.cpp

@@ -94,7 +94,7 @@ static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end)
     std::string ret;
     for (; begin != end; ++begin)
     {
-        ret += llama_token_to_str(ctx, *begin);
+        ret += llama_token_to_piece(ctx, *begin);
     }
     return ret;
 }
@@ -123,9 +123,10 @@ static void server_log(const char *level, const char *function, int line,
 // format incomplete utf-8 multibyte character for output
 static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token)
 {
-    std::string out = token == -1 ? "" : llama_token_to_str(ctx, token);
-    // if first bit is 1, meaning it's a partial character
-    if (out.size() > 0 && (out[0] & 0x80) == 0x80)
+    std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
+    // if the size is 1 and first bit is 1, meaning it's a partial character
+    //   (size > 1 meaning it's already a known token)
+    if (out.size() == 1 && (out[0] & 0x80) == 0x80)
     {
         std::stringstream ss;
         ss << std::hex << (out[0] & 0xff);
@@ -190,6 +191,7 @@ struct llama_server_context
     size_t n_past = 0;
     size_t n_remain = 0;
 
+    json prompt;
     std::vector<llama_token> embd;
     std::vector<llama_token> last_n_tokens;
 
@@ -267,6 +269,51 @@ struct llama_server_context
         return true;
     }
 
+    std::vector<llama_token> tokenize(json json_prompt, bool add_bos)
+    {
+        // If `add_bos` is true, we only add BOS, when json_prompt is a string,
+        // or the first element of the json_prompt array is a string.
+        std::vector<llama_token> prompt_tokens;
+
+        if (json_prompt.is_array())
+        {
+            bool first = true;
+            for (const auto& p : json_prompt)
+            {
+                if (p.is_string())
+                {
+                    auto s = p.template get<std::string>();
+                    std::vector<llama_token> p;
+                    if (first)
+                    {
+                        p = ::llama_tokenize(ctx, s, add_bos);
+                        first = false;
+                    }
+                    else
+                    {
+                        p = ::llama_tokenize(ctx, s, false);
+                    }
+                    prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
+                }
+                else
+                {
+                    if (first)
+                    {
+                        first = false;
+                    }
+                    prompt_tokens.push_back(p.template get<llama_token>());
+                }
+            }
+        }
+        else
+        {
+            auto s = json_prompt.template get<std::string>();
+            prompt_tokens = ::llama_tokenize(ctx, s, add_bos);
+        }
+
+        return prompt_tokens;
+    }
+
     bool loadGrammar()
     {
         if (!params.grammar.empty()) {
@@ -294,8 +341,8 @@ struct llama_server_context
 
     void loadPrompt()
     {
-        params.prompt.insert(0, 1, ' '); // always add a first space
-        std::vector<llama_token> prompt_tokens = ::llama_tokenize(ctx, params.prompt, true);
+        auto prompt_tokens = tokenize(prompt, true);  // always add BOS
+
         num_prompt_tokens = prompt_tokens.size();
 
         if (params.n_keep < 0)
@@ -517,7 +564,7 @@ struct llama_server_context
 
         if (!embd.empty() && embd.back() == llama_token_eos(ctx))
         {
-            // stopping_word = llama_token_to_str(ctx, embd.back());
+            // stopping_word = llama_token_to_piece(ctx, embd.back());
             has_next_token = false;
             stopped_eos = true;
             LOG_VERBOSE("eos token found", {});
@@ -564,7 +611,7 @@ struct llama_server_context
     {
         const completion_token_output token_with_probs = nextToken();
 
-        const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok);
+        const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok);
         generated_text += token_text;
 
         if (params.n_probs > 0)
@@ -671,12 +718,11 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     fprintf(stdout, "                        number of layers to store in VRAM\n");
     fprintf(stdout, "  -ts SPLIT --tensor-split SPLIT\n");
     fprintf(stdout, "                        how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
-    fprintf(stdout, "                        how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
     fprintf(stdout, "  -mg i, --main-gpu i   the GPU to use for scratch and small tensors\n");
-    fprintf(stdout, "  -lv, --low-vram don't allocate VRAM scratch buffer\n");
-    fprintf(stdout, "  -mmq, --mul-mat-q     use experimental mul_mat_q CUDA kernels instead of cuBLAS. TEMP!!!\n" );
-    fprintf(stdout, "                        Reduces VRAM usage by 700/970/1430 MiB for 7b/13b/33b but prompt processing speed\n" );
-    fprintf(stdout, "                        is still suboptimal, especially q2_K, q3_K, q5_K, and q6_K.\n" );
+    fprintf(stdout, "  -lv, --low-vram       don't allocate VRAM scratch buffer\n");
+    fprintf(stdout, "  -nommq, --no-mul-mat-q\n");
+    fprintf(stdout, "                        use cuBLAS instead of custom mul_mat_q CUDA kernels.\n");
+    fprintf(stdout, "                        Not recommended since this is both slower and uses more VRAM.\n");
 #endif
     fprintf(stdout, "  -m FNAME, --model FNAME\n");
     fprintf(stdout, "                        model path (default: %s)\n", params.model.c_str());
@@ -867,12 +913,12 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n", {});
 #endif // GGML_USE_CUBLAS
         }
-        else if (arg == "--mul-mat-q" || arg == "-mmq")
+        else if (arg == "--no-mul-mat-q" || arg == "-nommq")
         {
 #ifdef GGML_USE_CUBLAS
-            params.mul_mat_q = true;
+            params.mul_mat_q = false;
 #else
-            LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. It is not possible to use mul_mat_q kernels.\n", {});
+            LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {});
 #endif // GGML_USE_CUBLAS
         }
         else if (arg == "--main-gpu" || arg == "-mg")
@@ -1017,7 +1063,7 @@ static json format_final_response(llama_server_context &llama, const std::string
         {"tokens_predicted", llama.num_tokens_predicted},
         {"tokens_evaluated", llama.num_prompt_tokens},
         {"generation_settings", format_generation_settings(llama)},
-        {"prompt", llama.params.prompt},
+        {"prompt", llama.prompt},
         {"truncated", llama.truncated},
         {"stopped_eos", llama.stopped_eos},
         {"stopped_word", llama.stopped_word},
@@ -1056,33 +1102,56 @@ static json format_tokenizer_response(const std::vector<llama_token> &tokens)
         {"tokens", tokens}};
 }
 
+static json format_detokenized_response(std::string content)
+{
+    return json{
+        {"content", content}};
+}
+
+template <typename T>
+static T json_value(const json &body, const std::string &key, const T &default_value)
+{
+    // Fallback null to default value
+    return body.contains(key) && !body.at(key).is_null()
+        ? body.value(key, default_value)
+        : default_value;
+}
+
 static void parse_options_completion(const json &body, llama_server_context &llama)
 {
     gpt_params default_params;
 
-    llama.stream = body.value("stream", false);
-    llama.params.n_predict = body.value("n_predict", default_params.n_predict);
-    llama.params.top_k = body.value("top_k", default_params.top_k);
-    llama.params.top_p = body.value("top_p", default_params.top_p);
-    llama.params.tfs_z = body.value("tfs_z", default_params.tfs_z);
-    llama.params.typical_p = body.value("typical_p", default_params.typical_p);
-    llama.params.repeat_last_n = body.value("repeat_last_n", default_params.repeat_last_n);
-    llama.params.temp = body.value("temperature", default_params.temp);
-    llama.params.repeat_penalty = body.value("repeat_penalty", default_params.repeat_penalty);
-    llama.params.presence_penalty = body.value("presence_penalty", default_params.presence_penalty);
-    llama.params.frequency_penalty = body.value("frequency_penalty", default_params.frequency_penalty);
-    llama.params.mirostat = body.value("mirostat", default_params.mirostat);
-    llama.params.mirostat_tau = body.value("mirostat_tau", default_params.mirostat_tau);
-    llama.params.mirostat_eta = body.value("mirostat_eta", default_params.mirostat_eta);
-    llama.params.penalize_nl = body.value("penalize_nl", default_params.penalize_nl);
-    llama.params.n_keep = body.value("n_keep", default_params.n_keep);
-    llama.params.seed = body.value("seed", default_params.seed);
-    llama.params.prompt = body.value("prompt", default_params.prompt);
-    llama.params.grammar = body.value("grammar", default_params.grammar);
-    llama.params.n_probs = body.value("n_probs", default_params.n_probs);
+    llama.stream = json_value(body, "stream", false);
+    llama.params.n_predict = json_value(body, "n_predict", default_params.n_predict);
+    llama.params.top_k = json_value(body, "top_k", default_params.top_k);
+    llama.params.top_p = json_value(body, "top_p", default_params.top_p);
+    llama.params.tfs_z = json_value(body, "tfs_z", default_params.tfs_z);
+    llama.params.typical_p = json_value(body, "typical_p", default_params.typical_p);
+    llama.params.repeat_last_n = json_value(body, "repeat_last_n", default_params.repeat_last_n);
+    llama.params.temp = json_value(body, "temperature", default_params.temp);
+    llama.params.repeat_penalty = json_value(body, "repeat_penalty", default_params.repeat_penalty);
+    llama.params.presence_penalty = json_value(body, "presence_penalty", default_params.presence_penalty);
+    llama.params.frequency_penalty = json_value(body, "frequency_penalty", default_params.frequency_penalty);
+    llama.params.mirostat = json_value(body, "mirostat", default_params.mirostat);
+    llama.params.mirostat_tau = json_value(body, "mirostat_tau", default_params.mirostat_tau);
+    llama.params.mirostat_eta = json_value(body, "mirostat_eta", default_params.mirostat_eta);
+    llama.params.penalize_nl = json_value(body, "penalize_nl", default_params.penalize_nl);
+    llama.params.n_keep = json_value(body, "n_keep", default_params.n_keep);
+    llama.params.seed = json_value(body, "seed", default_params.seed);
+    llama.params.grammar = json_value(body, "grammar", default_params.grammar);
+    llama.params.n_probs = json_value(body, "n_probs", default_params.n_probs);
+
+    if (body.count("prompt") != 0)
+    {
+        llama.prompt = body["prompt"];
+    }
+    else
+    {
+        llama.prompt = "";
+    }
 
     llama.params.logit_bias.clear();
-    if (body.value("ignore_eos", false))
+    if (json_value(body, "ignore_eos", false))
     {
         llama.params.logit_bias[llama_token_eos(llama.ctx)] = -INFINITY;
     }
@@ -1144,6 +1213,62 @@ static void log_server_request(const Request &req, const Response &res)
                            });
 }
 
+bool is_at_eob(llama_server_context & server_context, const llama_token * tokens, const size_t n_tokens) {
+    return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context.ctx);
+}
+
+// Function matching type llama_beam_search_callback_fn_t.
+// Custom callback example is called each time the beams lengths increase:
+//  * Show progress by printing ',' following by number of convergent beam tokens if any.
+//  * When all beams converge to a common prefix, they are made available in beams_state.beams[0].
+//    This is also called when the stop condition is met.
+//    Collect tokens into std::vector<llama_token> response which is pointed to by callback_data.
+void beam_search_callback(void * callback_data, llama_beams_state beams_state) {
+    auto & llama = *static_cast<llama_server_context*>(callback_data);
+    // Mark beams as EOS as needed.
+    for (size_t i = 0 ; i < beams_state.n_beams ; ++i) {
+        llama_beam_view& beam_view = beams_state.beam_views[i];
+        if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) {
+            beam_view.eob = true;
+        }
+    }
+    printf(",");  // Show progress
+    if (const size_t n = beams_state.common_prefix_length) {
+        llama.generated_token_probs.resize(llama.generated_token_probs.size() + n);
+        assert(0u < beams_state.n_beams);
+        const llama_token * tokens = beams_state.beam_views[0].tokens;
+        const auto map = [](llama_token tok) { return completion_token_output{{},tok}; };
+        std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map);
+        printf("%lu", n);
+    }
+    fflush(stdout);
+#if 0 // DEBUG: print current beams for this iteration
+    std::cout << "\n\nCurrent beams:\n";
+    for (size_t i=0 ; i < beams_state.n_beams ; ++i) {
+        std::cout << "beams["<<i<<"]: " << ostream_beam_view{state.ctx,beams_state.beam_views[i]} << std::endl;
+    }
+#endif
+}
+
+struct token_translator {
+    llama_context * ctx;
+    std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); }
+    std::string operator()(completion_token_output cto) const { return (*this)(cto.tok); }
+};
+
+void append_to_generated_text_from_generated_token_probs(llama_server_context & llama) {
+    auto & gtps = llama.generated_token_probs;
+    auto translator = token_translator{llama.ctx};
+    auto add_strlen = [=](size_t sum, const completion_token_output & cto) { return sum + translator(cto).size(); };
+    const size_t len = std::accumulate(gtps.begin(), gtps.end(), size_t(0), add_strlen);
+    if (llama.generated_text.capacity() < llama.generated_text.size() + len) {
+        llama.generated_text.reserve(llama.generated_text.size() + len);
+    }
+    for (const completion_token_output & cto : gtps) {
+        llama.generated_text += translator(cto);
+    }
+}
+
 int main(int argc, char **argv)
 {
     // own arguments required by this example
@@ -1226,22 +1351,30 @@ int main(int argc, char **argv)
         llama.beginCompletion();
 
         if (!llama.stream) {
-            size_t stop_pos = std::string::npos;
+            if (llama.params.n_beams) {
+                // Fill llama.generated_token_probs vector with final beam.
+                llama_beam_search(llama.ctx, beam_search_callback, &llama, llama.params.n_beams,
+                                  llama.n_past, llama.n_remain, llama.params.n_threads);
+                // Translate llama.generated_token_probs to llama.generated_text.
+                append_to_generated_text_from_generated_token_probs(llama);
+            } else {
+                size_t stop_pos = std::string::npos;
 
-            while (llama.has_next_token) {
-                const completion_token_output token_with_probs = llama.doCompletion();
-                const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
+                while (llama.has_next_token) {
+                    const completion_token_output token_with_probs = llama.doCompletion();
+                    const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok);
 
-                stop_pos = llama.findStoppingStrings(llama.generated_text,
-                    token_text.size(), STOP_FULL);
-            }
+                    stop_pos = llama.findStoppingStrings(llama.generated_text,
+                        token_text.size(), STOP_FULL);
+                }
 
-            if (stop_pos == std::string::npos) {
-                stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL);
-            }
-            if (stop_pos != std::string::npos) {
-                llama.generated_text.erase(llama.generated_text.begin() + stop_pos,
-                    llama.generated_text.end());
+                if (stop_pos == std::string::npos) {
+                    stop_pos = llama.findStoppingStrings(llama.generated_text, 0, STOP_PARTIAL);
+                }
+                if (stop_pos != std::string::npos) {
+                    llama.generated_text.erase(llama.generated_text.begin() + stop_pos,
+                        llama.generated_text.end());
+                }
             }
 
             const json data = format_final_response(llama, llama.generated_text, llama.generated_token_probs);
@@ -1257,59 +1390,86 @@ int main(int argc, char **argv)
 
                 while (llama.has_next_token) {
                     const completion_token_output token_with_probs = llama.doCompletion();
-                    const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok);
-                    if (llama.multibyte_pending > 0) {
+                    if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) {
                         continue;
                     }
+                    const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok);
 
                     size_t pos = std::min(sent_count, llama.generated_text.size());
 
                     const std::string str_test = llama.generated_text.substr(pos);
+                    bool is_stop_full = false;
                     size_t stop_pos =
                         llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL);
                     if (stop_pos != std::string::npos) {
+                        is_stop_full = true;
                         llama.generated_text.erase(
                             llama.generated_text.begin() + pos + stop_pos,
                             llama.generated_text.end());
                         pos = std::min(sent_count, llama.generated_text.size());
                     } else {
+                        is_stop_full = false;
                         stop_pos = llama.findStoppingStrings(str_test, token_text.size(),
                             STOP_PARTIAL);
                     }
 
-                    const std::string to_send = llama.generated_text.substr(pos, stop_pos);
-                    sent_count += to_send.size();
+                    if (
+                        stop_pos == std::string::npos ||
+                        // Send rest of the text if we are at the end of the generation
+                        (!llama.has_next_token && !is_stop_full && stop_pos > 0)
+                    ) {
+                        const std::string to_send = llama.generated_text.substr(pos, std::string::npos);
 
-                    std::vector<completion_token_output> probs_output = {};
+                        sent_count += to_send.size();
 
-                    if (llama.params.n_probs > 0) {
-                        const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, to_send, false);
-                        size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size());
-                        size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size());
-                        if (probs_pos < probs_stop_pos) {
-                            probs_output = std::vector<completion_token_output>(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos);
+                        std::vector<completion_token_output> probs_output = {};
+
+                        if (llama.params.n_probs > 0) {
+                            const std::vector<llama_token> to_send_toks = llama_tokenize(llama.ctx, to_send, false);
+                            size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size());
+                            size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size());
+                            if (probs_pos < probs_stop_pos) {
+                                probs_output = std::vector<completion_token_output>(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos);
+                            }
+                            sent_token_probs_index = probs_stop_pos;
+                        }
+
+                        const json data = format_partial_response(llama, to_send, probs_output);
+
+                        const std::string str =
+                            "data: " +
+                            data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
+
+                        LOG_VERBOSE("data stream", {
+                            { "to_send", str }
+                        });
+
+                        if (!sink.write(str.data(), str.size())) {
+                            LOG_VERBOSE("stream closed", {});
+                            llama_print_timings(llama.ctx);
+                            return false;
                         }
-                        sent_token_probs_index = probs_stop_pos;
                     }
 
-                    const json data = llama.has_next_token
-                                          ? format_partial_response(llama, to_send, probs_output)
-                                          // Generation is done, send extra information.
-                                          : format_final_response(llama, to_send, llama.generated_token_probs);
+                    if (!llama.has_next_token) {
+                        // Generation is done, send extra information.
+                        const json data = format_final_response(llama, "", llama.generated_token_probs);
 
-                    const std::string str =
-                        "data: " +
-                        data.dump(-1, ' ', false, json::error_handler_t::replace) +
-                        "\n\n";
+                        const std::string str =
+                            "data: " +
+                            data.dump(-1, ' ', false, json::error_handler_t::replace) +
+                            "\n\n";
 
-                    LOG_VERBOSE("data stream", {
-                        { "to_send", str }
-                    });
+                        LOG_VERBOSE("data stream", {
+                            { "to_send", str }
+                        });
 
-                    if (!sink.write(str.data(), str.size())) {
-                        LOG_VERBOSE("stream closed", {});
-                        llama_print_timings(llama.ctx);
-                        return false;
+                        if (!sink.write(str.data(), str.size())) {
+                            LOG_VERBOSE("stream closed", {});
+                            llama_print_timings(llama.ctx);
+                            return false;
+                        }
                     }
                 }
 
@@ -1337,11 +1497,29 @@ int main(int argc, char **argv)
         auto lock = llama.lock();
 
         const json body = json::parse(req.body);
-        const std::string content = body.value("content", "");
-        const std::vector<llama_token> tokens = llama_tokenize(llama.ctx, content, false);
+        std::vector<llama_token> tokens;
+        if (body.count("content") != 0)
+        {
+            tokens = llama.tokenize(body["content"], false);
+        }
         const json data = format_tokenizer_response(tokens);
         return res.set_content(data.dump(), "application/json"); });
 
+    svr.Post("/detokenize", [&llama](const Request &req, Response &res)
+             {
+        auto lock = llama.lock();
+
+        const json body = json::parse(req.body);
+        std::string content;
+        if (body.count("tokens") != 0)
+        {
+            const std::vector<llama_token> tokens = body["tokens"];
+            content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend());
+        }
+
+        const json data = format_detokenized_response(content);
+        return res.set_content(data.dump(), "application/json"); });
+
     svr.Post("/embedding", [&llama](const Request &req, Response &res)
              {
         auto lock = llama.lock();
@@ -1350,7 +1528,14 @@ int main(int argc, char **argv)
 
         llama.rewind();
         llama_reset_timings(llama.ctx);
-        llama.params.prompt = body.value("content", "");
+        if (body.count("content") != 0)
+        {
+            llama.prompt = body["content"];
+        }
+        else
+        {
+            llama.prompt = "";
+        }
         llama.params.n_predict = 0;
         llama.loadPrompt();
         llama.beginCompletion();
@@ -1379,7 +1564,7 @@ int main(int argc, char **argv)
                           {
         if (res.status == 400) {
             res.set_content("Invalid request", "text/plain");
-        } else {
+        } else if (res.status != 500) {
             res.set_content("File Not Found", "text/plain");
             res.status = 404;
         } });

examples/simple/simple.cpp

@@ -63,7 +63,7 @@ int main(int argc, char ** argv) {
     fprintf(stderr, "\n\n");
 
     for (auto id : tokens_list) {
-        fprintf(stderr, "%s", llama_token_to_str(ctx, id).c_str());
+        fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
     }
 
     fflush(stderr);
@@ -112,7 +112,7 @@ int main(int argc, char ** argv) {
         }
 
         // print the new token :
-        printf("%s", llama_token_to_str(ctx, new_token_id).c_str());
+        printf("%s", llama_token_to_piece(ctx, new_token_id).c_str());
         fflush(stdout);
 
         // push this new token for next evaluation

examples/train-text-from-scratch/README.md

@@ -8,15 +8,15 @@ wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/s
 
 # train
 ./bin/train-text-from-scratch \
-        --vocab-model ../models/ggml-vocab.bin \
+        --vocab-model ../models/ggml-vocab-llama.gguf \
         --ctx 64 --embd 256 --head 8 --layer 16 \
-        --checkpoint-in  chk-shakespeare-256x16.bin \
-        --checkpoint-out chk-shakespeare-256x16.bin \
-        --model-out ggml-shakespeare-256x16-f32.bin \
+        --checkpoint-in  chk-shakespeare-256x16.gguf \
+        --checkpoint-out chk-shakespeare-256x16.gguf \
+        --model-out ggml-shakespeare-256x16-f32.gguf \
         --train-data "shakespeare.txt" \
-        -t 6 -b 16 -n 32 --seed 1 --adam-iter 16 \
-        --print-details-interval 0 --predict 16 --use-flash
+        -t 6 -b 16 --seed 1 --adam-iter 256 \
+        --no-checkpointing
 
 # predict
-./bin/main -m ggml-shakespeare-256x16-f32.bin
+./bin/main -m ggml-shakespeare-256x16-f32.gguf
 ```

examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py

@@ -0,0 +1,492 @@
+#!/usr/bin/env python3
+# train-text-from-scratch checkpoint --> gguf conversion
+
+import argparse
+import gguf
+import os
+import struct
+import sys
+import numpy as np
+from pathlib import Path
+
+# gguf constants
+LLM_KV_OPTIMIZER_TYPE = "optimizer.type"
+LLM_KV_OPTIMIZER_TYPE_ADAM  = "adam"
+LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"
+LLM_KV_OPTIMIZER_FILE_VERSION               = "optimizer.file_version"
+LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT     = "optimizer.convergence_past_count"
+LLM_KV_OPTIMIZER_PARAMETER_COUNT            = "optimizer.parameter_count"
+LLM_KV_OPTIMIZER_ITERATION_COUNT            = "optimizer.iteration_count"
+LLM_KV_OPTIMIZER_JUST_INITIALIZED           = "optimizer.just_initialized"
+LLM_KV_OPTIMIZER_ADAM_BEST_LOSS             = "optimizer.adam.best_loss"
+LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS         = "optimizer.adam.previous_loss"
+LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT  = "optimizer.adam.no_improvement_count"
+LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"
+LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS            = "optimizer.lbfgs.best_loss"
+LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP     = "optimizer.lbfgs.line_search_step"
+LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J        = "optimizer.lbfgs.line_search_j"
+LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K        = "optimizer.lbfgs.line_search_k"
+LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END      = "optimizer.lbfgs.line_search_end"
+LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"
+
+LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS    = "optimizer.adam.first_moments"
+LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS   = "optimizer.adam.second_moments"
+LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"
+
+LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS  = "optimizer.lbfgs.current_parameters"
+LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"
+LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS   = "optimizer.lbfgs.current_gradients"
+LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS  = "optimizer.lbfgs.previous_gradients"
+LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION    = "optimizer.lbfgs.search_direction"
+LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES    = "optimizer.lbfgs.past_loss_values"
+LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA        = "optimizer.lbfgs.memory_alpha"
+LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS           = "optimizer.lbfgs.memory_ys"
+LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S            = "optimizer.lbfgs.memory_s"
+LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y            = "optimizer.lbfgs.memory_y"
+
+LLM_KV_TRAINING_FILE_VERSION    = "training.file_version"
+LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count"
+LLM_KV_TRAINING_SAMPLE_COUNT    = "training.sample_count"
+LLM_KV_TRAINING_TOKEN_COUNT     = "training.token_count"
+
+class Tensor:
+    def __init__(self, dtype='f', ne=None):
+        if ne is None:
+            ne = []
+        self.dtype = dtype
+        self.ne = ne
+        self.nbytes = 0
+        if self.dtype == 'f':
+            if len(self.ne) == 0:
+                self.nbytes = 0
+            else:
+                self.nbytes = int(np.product(self.ne)) * 4
+        else:
+            raise ValueError(f"Unhandled data type '{self.dtype}'")
+
+    def load(self, data, offset):
+        nd = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+        namelen = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+        dtype = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+
+        assert(nd == len(self.ne))
+        ne = []
+        for d in range(nd):
+            n = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+            ne.append(n)
+
+        assert(tuple(ne) == tuple(self.ne))
+
+        if self.dtype == 'f':
+            assert(dtype == 0)
+        else:
+            raise ValueError(f"Unhandled data type '{self.dtype}'")
+
+        self.name = bytes(data[offset:offset+namelen]); offset += namelen
+        # 32-byte alignment
+        offset += (0 - offset) & 31
+        self.data = data[offset:offset+self.nbytes]
+        offset += self.nbytes
+        return offset
+
+    def max_storage_size(self):
+        result = 0
+        result += 4 # nd
+        result += 4 # namelen
+        result += 4 # dtype
+        result += len(self.ne)*8 # ne
+        result += 48 # name (maximum as of commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9)
+        result += 31 # 32-byte alignment
+        result += self.nbytes
+        return result
+
+    def save_gguf(self, gguf_writer, name):
+        gguf_writer.add_tensor(
+            name=name,
+            tensor=self.data,
+            raw_shape=np.array(list(reversed(self.ne))),
+            raw_dtype=gguf.GGMLQuantizationType.F32)
+
+class OptimizationParamsV0:
+    def __init__(self):
+        pass
+
+    def load(self, data, offset):
+        self.type                 = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_threads            = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.past                 = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.delta                = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.print_forward_graph  = struct.unpack('<?', bytes(data[offset:offset + 1]))[0];  offset += 4 # 32bit-aligned
+        self.print_backward_graph = struct.unpack('<?', bytes(data[offset:offset + 1]))[0];  offset += 4 # 32bit-aligned
+        self.adam_n_iter          = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_sched           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_decay           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_alpha           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_beta1           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_beta2           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_eps             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_eps_f           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.adam_eps_g           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_m              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_n_iter         = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_max_linesearch = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_eps            = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_ftol           = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_wolfe          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_min_step       = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_max_step       = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.lbfgs_linesearch     = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        return offset
+
+class OptimizationContext:
+    def __init__(self):
+        pass
+
+    def load(self, data, offset):
+        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]
+        offset += 4
+
+        if self.version == 0:
+            params = OptimizationParamsV0()
+            offset = params.load(data, offset)
+            self.past = params.past
+            self.lbfgs_m = params.lbfgs_m
+            self.nx = struct.unpack('N', bytes(data[offset:offset + 8]))[0];  offset += 8
+            self.iter = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+            self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]);  offset += 4
+            self.type = params.type
+
+            self.adam_m  = Tensor('f', [self.nx])
+            self.adam_v  = Tensor('f', [self.nx])
+            self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
+
+            self.lbfgs_x    = Tensor('f', [self.nx])
+            self.lbfgs_xp   = Tensor('f', [self.nx])
+            self.lbfgs_g    = Tensor('f', [self.nx])
+            self.lbfgs_gp   = Tensor('f', [self.nx])
+            self.lbfgs_d    = Tensor('f', [self.nx])
+            self.lbfgs_pf   = Tensor('f', [self.past] if self.past > 0 else [])
+            self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
+            self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
+            self.lbfgs_lms  = Tensor('f', [self.nx, self.lbfgs_m])
+            self.lbfgs_lmy  = Tensor('f', [self.nx, self.lbfgs_m])
+
+            if self.type == 0:
+                # these tensors are stored, but we don't need their data
+                x  = Tensor('f', [self.nx])
+                g  = Tensor('f', [self.nx])
+                g2 = Tensor('f', [self.nx])
+                mh = Tensor('f', [self.nx])
+                vh = Tensor('f', [self.nx])
+
+                offset = x.load(data, offset)
+                offset = g.load(data, offset)
+                offset = g2.load(data, offset)
+                offset = self.adam_m.load(data, offset)
+                offset = self.adam_v.load(data, offset)
+                offset = mh.load(data, offset)
+                offset = vh.load(data, offset)
+                offset = self.adam_pf.load(data, offset)
+
+                self.adam_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.adam_fx_prev          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+
+            elif self.type == 1:
+                offset = self.lbfgs_x.load(data, offset)
+                offset = self.lbfgs_xp.load(data, offset)
+                offset = self.lbfgs_g.load(data, offset)
+                offset = self.lbfgs_gp.load(data, offset)
+                offset = self.lbfgs_d.load(data, offset)
+                offset = self.lbfgs_pf.load(data, offset)
+                offset = self.lbfgs_lmal.load(data, offset)
+                offset = self.lbfgs_lmys.load(data, offset)
+                offset = self.lbfgs_lms.load(data, offset)
+                offset = self.lbfgs_lmy.load(data, offset)
+
+                self.lbfgs_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_step             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_j                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_k                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_end              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+
+            else:
+                raise ValueError('Unknown optimizer type')
+
+
+        elif self.version == 1:
+            self.past    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+            self.lbfgs_m = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+            self.nx      = struct.unpack('N',  bytes(data[offset:offset + 8]))[0];  offset += 8
+            self.iter    = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+            self.just_initialized = bool(struct.unpack('<i', bytes(data[offset:offset + 4]))[0]);  offset += 4
+
+            self.adam_m  = Tensor('f', [self.nx])
+            self.adam_v  = Tensor('f', [self.nx])
+            self.adam_pf = Tensor('f', [self.past] if self.past > 0 else [])
+
+            self.lbfgs_x    = Tensor('f', [self.nx])
+            self.lbfgs_xp   = Tensor('f', [self.nx])
+            self.lbfgs_g    = Tensor('f', [self.nx])
+            self.lbfgs_gp   = Tensor('f', [self.nx])
+            self.lbfgs_d    = Tensor('f', [self.nx])
+            self.lbfgs_pf   = Tensor('f', [self.past] if self.past > 0 else [])
+            self.lbfgs_lmal = Tensor('f', [self.lbfgs_m])
+            self.lbfgs_lmys = Tensor('f', [self.lbfgs_m])
+            self.lbfgs_lms  = Tensor('f', [self.nx, self.lbfgs_m])
+            self.lbfgs_lmy  = Tensor('f', [self.nx, self.lbfgs_m])
+
+            # forgot to save type in version 1:
+            # guess self.type from number of remaining bytes
+            size_type_0 = 12 + sum([t.max_storage_size() for t in
+                                    [self.adam_m, self.adam_v]
+                                    +([self.adam_pf] if (self.past > 0) else [])])
+            size_type_1 = 24 + sum([t.max_storage_size() for t in
+                                    [self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g,
+                                     self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf,
+                                     self.lbfgs_lmal, self.lbfgs_lmys,
+                                     self.lbfgs_lms, self.lbfgs_lmy]
+                                     +([self.lbfgs_pf] if (self.past > 0) else [])])
+            # due to alignment padding the size might not by exact
+            # but the difference in size for both types is significant,
+            # so we can just use whichever is closest
+            remaining = len(data) - offset
+            if abs(remaining - size_type_0) < abs(remaining - size_type_1):
+                self.type = 0
+            else:
+                self.type = 1
+
+            if self.type == 0:
+                offset = self.adam_m.load(data, offset)
+                offset = self.adam_v.load(data, offset)
+                offset = self.adam_pf.load(data,offset)
+
+                self.adam_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.adam_fx_prev          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.adam_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+
+            elif self.type == 1:
+                offset = self.lbfgs_x.load(data, offset)
+                offset = self.lbfgs_xp.load(data, offset)
+                offset = self.lbfgs_g.load(data, offset)
+                offset = self.lbfgs_gp.load(data, offset)
+                offset = self.lbfgs_d.load(data, offset)
+                offset = self.lbfgs_pf.load(data, offset)
+                offset = self.lbfgs_lmal.load(data, offset)
+                offset = self.lbfgs_lmys.load(data, offset)
+                offset = self.lbfgs_lms.load(data, offset)
+                offset = self.lbfgs_lmy.load(data, offset)
+
+                self.lbfgs_fx_best          = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_step             = struct.unpack('<f', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_j                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_k                = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_end              = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+                self.lbfgs_n_no_improvement = struct.unpack('<i', bytes(data[offset:offset + 4]))[0];  offset += 4
+
+        else:
+            raise ValueError('Invalid version of checkpoint file')
+
+        return offset
+
+    def save_gguf(self, gguf_writer):
+        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_FILE_VERSION, 0)
+        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, self.past)
+        gguf_writer.add_uint64(LLM_KV_OPTIMIZER_PARAMETER_COUNT, self.nx)
+        gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ITERATION_COUNT, self.iter)
+        gguf_writer.add_bool(LLM_KV_OPTIMIZER_JUST_INITIALIZED, self.just_initialized)
+
+        if self.type == 0:
+            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM)
+            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, self.adam_fx_best)
+            gguf_writer.add_float32(LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, self.adam_fx_prev)
+            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, self.adam_n_no_improvement)
+
+            self.adam_m.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS)
+            self.adam_v.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS)
+            if self.past > 0:
+                self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES)
+
+        elif self.type == 1:
+            gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS)
+            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m)
+            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best)
+            gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step)
+            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j)
+            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k)
+            gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end)
+            gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement)
+
+            self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS)
+            self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS)
+            self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS)
+            self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS)
+            self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION)
+            if self.past > 0:
+                self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES)
+            self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA)
+            self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS)
+            self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S)
+            self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y)
+        else:
+            raise ValueError('Unknown optimizer type')
+
+class ModelParams:
+    def __init__(self):
+        pass
+
+    def load(self, data, offset):
+        self.n_vocab = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_embd  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_mult  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_head  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_layer = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        self.n_rot   = struct.unpack('<I', bytes(data[offset:offset + 4]))[0];  offset += 4
+        return offset
+
+    def get_n_ff(self):
+        # struct my_llama_model::get_n_ff in train-text-from-scratch.cpp commit 3b5515bbe0e2224425986ba24f1f5d84aa38dce9
+        return ((2*(4*self.n_embd)//3 + self.n_mult - 1)//self.n_mult)*self.n_mult
+
+    def save_gguf(self, gguf_writer):
+        # self.n_vocab not saved
+        gguf_writer.add_embedding_length(self.n_embd)
+        gguf_writer.add_head_count(self.n_head)
+        gguf_writer.add_block_count(self.n_layer)
+        gguf_writer.add_rope_dimension_count(self.n_rot)
+        gguf_writer.add_feed_forward_length(self.get_n_ff())
+
+def tensor_name(key, bid=None):
+    return gguf.MODEL_TENSOR_NAMES[gguf.MODEL_ARCH.LLAMA][key].format(bid=bid) + ".weight"
+
+class Layer:
+    def __init__(self, params, bid):
+        self.bid = bid
+        self.att_norm = Tensor('f', [params.n_embd])
+        self.wq       = Tensor('f', [params.n_embd, params.n_embd])
+        self.wk       = Tensor('f', [params.n_embd, params.n_embd])
+        self.wv       = Tensor('f', [params.n_embd, params.n_embd])
+        self.wo       = Tensor('f', [params.n_embd, params.n_embd])
+        self.ffn_norm = Tensor('f', [params.n_embd])
+        self.w1       = Tensor('f', [params.n_embd, params.get_n_ff()])
+        self.w2       = Tensor('f', [params.get_n_ff(), params.n_embd])
+        self.w3       = Tensor('f', [params.n_embd, params.get_n_ff()])
+
+    def load(self, data, offset):
+        offset = self.att_norm.load(data, offset)
+        offset = self.wq.load(data, offset)
+        offset = self.wk.load(data, offset)
+        offset = self.wv.load(data, offset)
+        offset = self.wo.load(data, offset)
+        offset = self.ffn_norm.load(data, offset)
+        offset = self.w1.load(data, offset)
+        offset = self.w2.load(data, offset)
+        offset = self.w3.load(data, offset)
+        return offset
+
+    def save_gguf(self, gguf_writer):
+        self.att_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_NORM, self.bid))
+        self.wq.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_Q,    self.bid))
+        self.wk.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_K,    self.bid))
+        self.wv.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_V,    self.bid))
+        self.wo.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.ATTN_OUT,  self.bid))
+        self.ffn_norm.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_NORM,  self.bid))
+        self.w1.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_GATE,  self.bid))
+        self.w2.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_DOWN,  self.bid))
+        self.w3.save_gguf      (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.FFN_UP,    self.bid))
+
+class Model:
+    def __init__(self):
+        self.params = ModelParams()
+        self.layers = []
+
+    def load(self, data, offset):
+        offset = self.params.load(data, offset)
+
+        self.tok_embd = Tensor('f', [self.params.n_embd, self.params.n_vocab])
+        self.norm     = Tensor('f', [self.params.n_embd])
+        self.output   = Tensor('f', [self.params.n_embd, self.params.n_vocab])
+
+        offset = self.tok_embd.load(data, offset)
+        offset = self.norm.load(data, offset)
+        offset = self.output.load(data, offset)
+
+        self.layers.clear()
+        for bid in range(self.params.n_layer):
+            layer = Layer(self.params, bid)
+            offset = layer.load(data, offset)
+            self.layers.append(layer)
+
+        return offset
+
+    def save_gguf(self, gguf_writer):
+        self.params.save_gguf(gguf_writer)
+
+        self.tok_embd.save_gguf(gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD))
+        self.norm.save_gguf    (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT_NORM))
+        self.output.save_gguf  (gguf_writer, name=tensor_name(gguf.MODEL_TENSOR.OUTPUT))
+
+        for layer in self.layers:
+            layer.save_gguf(gguf_writer)
+
+class Checkpoint:
+    def __init__(self):
+        self.model = Model()
+        self.opt_ctx = OptimizationContext()
+
+    def load(self, data, offset):
+        magic   = bytes(reversed(data[offset:offset + 4])); offset += 4
+        if magic != b'ggcp':
+            raise ValueError(f"File header magic indicates, that this is no checkpoint file. Expected 'ggcp', Got '{str(magic)}'")
+
+        self.version = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+        if self.version != 0:
+            raise ValueError('Invalid version of checkpoint file')
+
+        self.train_its     = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+        self.train_samples = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+        self.train_tokens  = struct.unpack('<I', bytes(data[offset:offset + 4]))[0]; offset += 4
+
+        offset = self.model.load(data, offset)
+        offset = self.opt_ctx.load(data, offset)
+
+        return offset
+
+    def save_gguf(self, gguf_writer):
+        gguf_writer.add_file_type(gguf.GGMLQuantizationType.F32)
+        gguf_writer.add_layer_norm_rms_eps(1e-5)
+        gguf_writer.add_uint32(LLM_KV_TRAINING_FILE_VERSION,    0)
+        gguf_writer.add_uint32(LLM_KV_TRAINING_ITERATION_COUNT, self.train_its)
+        gguf_writer.add_uint32(LLM_KV_TRAINING_SAMPLE_COUNT,    self.train_samples)
+        gguf_writer.add_uint32(LLM_KV_TRAINING_TOKEN_COUNT,     self.train_tokens)
+        self.model.save_gguf(gguf_writer)
+        self.opt_ctx.save_gguf(gguf_writer)
+
+def handle_args():
+    parser = argparse.ArgumentParser(description = 'Convert train-text-from-scratch checkpoints to GGUF')
+    parser.add_argument('--input',  '-i', type = Path, help = 'Input train checkpoint filename', required=True)
+    parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename', required=True)
+    return parser.parse_args()
+
+def main():
+    cfg = handle_args()
+    data = np.memmap(cfg.input, mode = 'r')
+    chk = Checkpoint()
+    offset = 0
+    offset = chk.load(data, offset)
+    # we should have read all available data
+    assert(offset == len(data))
+
+    gguf_writer = gguf.GGUFWriter(cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False)
+    chk.save_gguf(gguf_writer)
+    print("    gguf: write header")
+    gguf_writer.write_header_to_file()
+    print("    gguf: write metadata")
+    gguf_writer.write_kv_data_to_file()
+    print("    gguf: write tensors")
+    gguf_writer.write_tensors_to_file()
+    gguf_writer.close()
+
+if __name__ == '__main__':
+    main()

flake.lock

@@ -5,11 +5,11 @@
         "systems": "systems"
       },
       "locked": {
-        "lastModified": 1685518550,
-        "narHash": "sha256-o2d0KcvaXzTrPRIo0kOLV0/QXHhDQ5DTi+OxcjO8xqY=",
+        "lastModified": 1692799911,
+        "narHash": "sha256-3eihraek4qL744EvQXsK1Ha6C3CR7nnT8X2qWap4RNk=",
         "owner": "numtide",
         "repo": "flake-utils",
-        "rev": "a1720a10a6cfe8234c0e93907ffe81be440f4cef",
+        "rev": "f9e7cf818399d17d347f847525c5a5a8032e4e44",
         "type": "github"
       },
       "original": {
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1685931219,
-        "narHash": "sha256-8EWeOZ6LKQfgAjB/USffUSELPRjw88A+xTcXnOUvO5M=",
+        "lastModified": 1692913444,
+        "narHash": "sha256-1SvMQm2DwofNxXVtNWWtIcTh7GctEVrS/Xel/mdc6iY=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "7409480d5c8584a1a83c422530419efe4afb0d19",
+        "rev": "18324978d632ffc55ef1d928e81630c620f4f447",
         "type": "github"
       },
       "original": {

flake.nix

@@ -6,6 +6,9 @@
   outputs = { self, nixpkgs, flake-utils }:
     flake-utils.lib.eachDefaultSystem (system:
       let
+        name = "llama.cpp";
+        src = ./.;
+        meta.mainProgram = "llama";
         inherit (pkgs.stdenv) isAarch32 isAarch64 isDarwin;
         buildInputs = with pkgs; [ openmpi ];
         osSpecific = with pkgs; buildInputs ++
@@ -21,11 +24,17 @@
               CoreGraphics
               CoreVideo
             ]
+          else if isDarwin then
+            with pkgs.darwin.apple_sdk.frameworks; [
+              Accelerate
+              CoreGraphics
+              CoreVideo
+            ]
           else
             with pkgs; [ openblas ]
         );
         pkgs = import nixpkgs { inherit system; };
-        nativeBuildInputs = with pkgs; [ cmake pkgconfig ];
+        nativeBuildInputs = with pkgs; [ cmake ninja pkgconfig ];
         llama-python =
           pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece ]);
         postPatch = ''
@@ -38,35 +47,35 @@
           mv $out/bin/server $out/bin/llama-server
         '';
         cmakeFlags = [ "-DLLAMA_BUILD_SERVER=ON" "-DLLAMA_MPI=ON" "-DBUILD_SHARED_LIBS=ON" "-DCMAKE_SKIP_BUILD_RPATH=ON" ];
-      in {
+      in
+      {
         packages.default = pkgs.stdenv.mkDerivation {
-          name = "llama.cpp";
-          src = ./.;
-          postPatch = postPatch;
-          nativeBuildInputs = nativeBuildInputs;
-          buildInputs = osSpecific;
+          inherit name src meta postPatch nativeBuildInputs buildInputs postInstall;
           cmakeFlags = cmakeFlags
             ++ (if isAarch64 && isDarwin then [
-              "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1"
-              "-DLLAMA_METAL=ON"
-            ] else [
-              "-DLLAMA_BLAS=ON"
-              "-DLLAMA_BLAS_VENDOR=OpenBLAS"
+            "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1"
+            "-DLLAMA_METAL=ON"
+          ] else [
+            "-DLLAMA_BLAS=ON"
+            "-DLLAMA_BLAS_VENDOR=OpenBLAS"
           ]);
-          postInstall = postInstall;
-          meta.mainProgram = "llama";
         };
         packages.opencl = pkgs.stdenv.mkDerivation {
-          name = "llama.cpp";
-          src = ./.;
-          postPatch = postPatch;
-          nativeBuildInputs = nativeBuildInputs;
+          inherit name src meta postPatch nativeBuildInputs postInstall;
           buildInputs = with pkgs; buildInputs ++ [ clblast ];
           cmakeFlags = cmakeFlags ++ [
             "-DLLAMA_CLBLAST=ON"
           ];
-          postInstall = postInstall;
-          meta.mainProgram = "llama";
+        };
+        packages.rocm = pkgs.stdenv.mkDerivation {
+          inherit name src meta postPatch nativeBuildInputs postInstall;
+          buildInputs = with pkgs; buildInputs ++ [ hip hipblas rocblas ];
+          cmakeFlags = cmakeFlags ++ [
+            "-DLLAMA_HIPBLAS=1"
+            "-DCMAKE_C_COMPILER=hipcc"
+            "-DCMAKE_CXX_COMPILER=hipcc"
+            "-DCMAKE_POSITION_INDEPENDENT_CODE=ON"
+          ];
         };
         apps.llama-server = {
           type = "app";
@@ -80,8 +89,13 @@
           type = "app";
           program = "${self.packages.${system}.default}/bin/llama";
         };
+        apps.quantize = {
+          type = "app";
+          program = "${self.packages.${system}.default}/bin/quantize";
+        };
         apps.default = self.apps.${system}.llama;
         devShells.default = pkgs.mkShell {
+          buildInputs = [ llama-python ];
           packages = nativeBuildInputs ++ osSpecific;
         };
       });

ggml-alloc.c

@@ -8,6 +8,7 @@
 
 #define UNUSED(x) (void)(x)
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
+#define GGML_MAX_CONCUR (2*GGML_MAX_NODES)
 
 //#define GGML_ALLOCATOR_DEBUG
 
@@ -67,8 +68,8 @@ struct ggml_allocr {
     struct hash_node hash_table[GGML_GRAPH_HASHTABLE_SIZE];
     size_t max_size;
     bool measure;
-    int parse_seq[GGML_MAX_NODES];
-    bool has_parse_seq;
+    int parse_seq[GGML_MAX_CONCUR];
+    int parse_seq_len;
 
 #ifdef GGML_ALLOCATOR_DEBUG
     struct ggml_tensor * allocated_tensors[1024];
@@ -76,7 +77,7 @@ struct ggml_allocr {
 };
 
 #ifdef GGML_ALLOCATOR_DEBUG
-static void add_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
+static void add_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
     for (int i = 0; i < 1024; i++) {
         if (alloc->allocated_tensors[i] == NULL) {
             alloc->allocated_tensors[i] = tensor;
@@ -85,7 +86,7 @@ static void add_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tens
     }
     GGML_ASSERT(!"out of allocated_tensors");
 }
-static void remove_allocated_tensor(struct ggml_allocator * alloc, struct ggml_tensor * tensor) {
+static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
     for (int i = 0; i < 1024; i++) {
         if (alloc->allocated_tensors[i] == tensor ||
             (alloc->allocated_tensors[i] != NULL && alloc->allocated_tensors[i]->data == tensor->data)) {
@@ -106,6 +107,10 @@ static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct g
 }
 
 void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) {
+#ifdef GGML_ALLOCATOR_DEBUG
+    GGML_ASSERT(ggml_is_view(tensor) == false); // views generally get data pointer from one of their sources
+    GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated
+#endif
     size_t size = ggml_allocator_get_alloc_size(alloc, tensor);
     size = aligned_offset(NULL, size, alloc->alignment);
 
@@ -238,15 +243,11 @@ static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_t
     alloc->n_free_blocks++;
 }
 
-void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, int * list, int n) {
-    int pos = 0;
+void ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n) {
     for (int i = 0; i < n; i++) {
-        if (list[i] != -1) {
-            alloc->parse_seq[pos] = list[i];
-            pos++;
-        }
+        alloc->parse_seq[i] = list[i];
     }
-    alloc->has_parse_seq = true;
+    alloc->parse_seq_len = n;
 }
 
 void ggml_allocr_reset(struct ggml_allocr * alloc) {
@@ -269,9 +270,9 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment)
         /*.max_size      = */ 0,
         /*.measure       = */ false,
         /*.parse_seq     = */ {0},
-        /*.has_parse_seq = */ false,
+        /*.parse_seq_len = */ 0,
 #ifdef GGML_ALLOCATOR_DEBUG
-        /*.allocated_tensors = */ = {0},
+        /*.allocated_tensors = */ {0},
 #endif
     };
 
@@ -298,9 +299,9 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) {
         /*.max_size      = */ 0,
         /*.measure       = */ true,
         /*.parse_seq     = */ {0},
-        /*.has_parse_seq = */ false,
+        /*.parse_seq_len = */ 0,
 #ifdef GGML_ALLOCATOR_DEBUG
-        /*.allocated_tensors = */ = {0},
+        /*.allocated_tensors = */ {0},
 #endif
     };
 
@@ -320,8 +321,7 @@ bool ggml_allocr_is_measure(struct ggml_allocr * alloc) {
 //////////// compute graph allocator
 
 static bool ggml_is_view(struct ggml_tensor * t) {
-    return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE ||
-           t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY;
+    return t->view_src != NULL;
 }
 
 static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
@@ -339,28 +339,6 @@ static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml
     return true;
 }
 
-static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) {
-    switch (t->op) {
-        case GGML_OP_PERMUTE:
-        case GGML_OP_RESHAPE:
-        case GGML_OP_TRANSPOSE:
-        case GGML_OP_VIEW:
-            return t->src[0];
-        case GGML_OP_CPY:
-            return t->src[1];
-        default:
-            return NULL;
-    }
-}
-
-static struct ggml_tensor * get_view_source(struct ggml_tensor * t) {
-    struct ggml_tensor * parent = t;
-    do {
-        parent = get_view_parent(parent);
-    } while (ggml_is_view(parent));
-    return parent;
-}
-
 static bool ggml_op_can_inplace(enum ggml_op op) {
     switch (op) {
         case GGML_OP_SCALE:
@@ -368,7 +346,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
         case GGML_OP_DIAG_MASK_INF:
         case GGML_OP_ADD:
         case GGML_OP_ADD1:
-        case GGML_OP_ACC:
         case GGML_OP_SUB:
         case GGML_OP_MUL:
         case GGML_OP_DIV:
@@ -378,7 +355,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
         case GGML_OP_UNARY:
         case GGML_OP_ROPE:
         case GGML_OP_RMS_NORM:
-        case GGML_OP_SET:
         case GGML_OP_SOFT_MAX:
         case GGML_OP_CONT:
             return true;
@@ -392,24 +368,8 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
     struct hash_node * ht = alloc->hash_table;
     if (node->data == NULL) {
         if (ggml_is_view(node)) {
-            size_t offset;
-            switch(node->op) {
-                case GGML_OP_VIEW:
-                    memcpy(&offset, node->op_params, sizeof(size_t));
-                    node->data = (char *) node->src[0]->data + offset;
-                    break;
-                case GGML_OP_PERMUTE:
-                case GGML_OP_RESHAPE:
-                case GGML_OP_TRANSPOSE:
-                    node->data = node->src[0]->data;
-                    break;
-                case GGML_OP_CPY:
-                    node->data = node->src[1]->data;
-                    break;
-                default:
-                    GGML_ASSERT(!"unknown view op");
-                    break;
-            }
+            assert(node->view_src->data != NULL);
+            node->data = (char *)node->view_src->data + node->view_offs;
         } else {
             // see if we can reuse a parent's buffer (inplace)
             if (ggml_op_can_inplace(node->op)) {
@@ -429,7 +389,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
                     struct hash_node * p_hn = hash_get(ht, parent);
                     if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) {
                         if (ggml_is_view(parent)) {
-                            struct ggml_tensor * view_src = get_view_source(parent);
+                            struct ggml_tensor * view_src = parent->view_src;
                             struct hash_node * view_src_hn = hash_get(ht, view_src);
                             if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
                                 // TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite
@@ -445,8 +405,8 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node)
                         else {
                             AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
                             node->data = parent->data;
+                            return;
                         }
-                        return;
                     }
                 }
             }
@@ -471,7 +431,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
             struct ggml_tensor * node = gf->nodes[i];
 
             if (ggml_is_view(node)) {
-                struct ggml_tensor * view_src = get_view_source(node);
+                struct ggml_tensor * view_src = node->view_src;
                 hash_get(ht, view_src)->n_views += 1;
             }
 
@@ -497,69 +457,86 @@ static size_t ggml_allocator_alloc_graph_tensors_n(
                 allocate_node(alloc, input);
             }
         }
-        for (int ind = 0; ind < gf->n_nodes; ind++) {
-            int i;
-            if (alloc->has_parse_seq) {
-                i = alloc->parse_seq[ind];
-            } else {
-                i = ind;
-            }
-            struct ggml_tensor * node = gf->nodes[i];
+        // if we have parse_seq then we allocate nodes following the list, and we only free nodes at barriers
+        int last_barrier_pos = 0;
+        int n_nodes = alloc->parse_seq_len ? alloc->parse_seq_len : gf->n_nodes;
 
-            // allocate parents (leafs)
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
+        for (int ind = 0; ind < n_nodes; ind++) {
+            // allocate a node if there is no parse_seq or this is not a barrier
+            if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] != -1) {
+                int i = alloc->parse_seq_len ? alloc->parse_seq[ind] : ind;
+                struct ggml_tensor * node = gf->nodes[i];
+
+                // allocate parents (leafs)
+                for (int j = 0; j < GGML_MAX_SRC; j++) {
+                    struct ggml_tensor * parent = node->src[j];
+                    if (parent == NULL) {
+                        break;
+                    }
+                    allocate_node(alloc, parent);
                 }
-                allocate_node(alloc, parent);
+
+                // allocate node
+                allocate_node(alloc, node);
+
+                AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
+                for (int j = 0; j < GGML_MAX_SRC; j++) {
+                    struct ggml_tensor * parent = node->src[j];
+                    if (parent == NULL) {
+                        break;
+                    }
+                    AT_PRINTF("%s", parent->name);
+                    if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
+                        AT_PRINTF(", ");
+                    }
+                }
+                AT_PRINTF("\n");
             }
 
-            // allocate node
-            allocate_node(alloc, node);
-
-            AT_PRINTF("exec: %s (%s) <= ", ggml_op_name(node->op), node->name);
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
-                }
-                AT_PRINTF("%s", parent->name);
-                if (j < GGML_MAX_SRC - 1 && node->src[j + 1] != NULL) {
-                    AT_PRINTF(", ");
-                }
-            }
-            AT_PRINTF("\n");
 
             // update parents
-            for (int j = 0; j < GGML_MAX_SRC; j++) {
-                struct ggml_tensor * parent = node->src[j];
-                if (parent == NULL) {
-                    break;
+            // update immediately if there is no parse_seq
+            // update only at barriers if there is parse_seq
+            if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] == -1) {
+                int update_start = alloc->parse_seq_len ? last_barrier_pos : ind;
+                int update_end   = alloc->parse_seq_len ? ind              : ind + 1;
+                for (int i = update_start; i < update_end; i++) {
+                    int node_i = alloc->parse_seq_len ? alloc->parse_seq[i] : i;
+                    struct ggml_tensor * node = gf->nodes[node_i];
+
+                    for (int j = 0; j < GGML_MAX_SRC; j++) {
+                        struct ggml_tensor * parent = node->src[j];
+                        if (parent == NULL) {
+                            break;
+                        }
+                        struct hash_node * p_hn = hash_get(ht, parent);
+                        p_hn->n_children -= 1;
+
+                        //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
+
+                        if (p_hn->n_children == 0 && p_hn->n_views == 0) {
+                            if (ggml_is_view(parent)) {
+                                struct ggml_tensor * view_src = parent->view_src;
+                                struct hash_node * view_src_hn = hash_get(ht, view_src);
+                                view_src_hn->n_views -= 1;
+                                AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views);
+                                if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
+                                    ggml_allocator_free_tensor(alloc, view_src);
+                                }
+                            }
+                            else {
+                                if (parent->data != node->data) {
+                                    ggml_allocator_free_tensor(alloc, parent);
+                                }
+                            }
+                        }
+                    }
                 }
-                struct hash_node * p_hn = hash_get(ht, parent);
-                p_hn->n_children -= 1;
-
-                //AT_PRINTF("parent %s: %d children, %d views\n", parent->name, parent->n_children, parent->n_views);
-
-                if (p_hn->n_children == 0 && p_hn->n_views == 0) {
-                    if (ggml_is_view(parent)) {
-                        struct ggml_tensor * view_src = get_view_source(parent);
-                        struct hash_node * view_src_hn = hash_get(ht, view_src);
-                        view_src_hn->n_views -= 1;
-                        AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src->n_children, view_src->n_views);
-                        if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) {
-                            ggml_allocator_free_tensor(alloc, view_src);
-                        }
-                    }
-                    else {
-                        if (parent->data != node->data) {
-                            ggml_allocator_free_tensor(alloc, parent);
-                        }
-                    }
+                AT_PRINTF("\n");
+                if (alloc->parse_seq_len) {
+                    last_barrier_pos = ind + 1;
                 }
             }
-            AT_PRINTF("\n");
         }
         // free graph outputs here that wouldn't be freed otherwise because they have no children
         if (outputs != NULL && outputs[g] != NULL) {

ggml-alloc.h

@@ -12,7 +12,7 @@ GGML_API struct ggml_allocr * ggml_allocr_new_measure(size_t alignment);
 
 // tell the allocator to parse nodes following the order described in the list
 // you should call this if your graph are optimized to execute out-of-order
-GGML_API void   ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, int * list, int n);
+GGML_API void   ggml_allocr_set_parse_seq(struct ggml_allocr * alloc, const int * list, int n);
 
 GGML_API void   ggml_allocr_free(struct ggml_allocr * alloc);
 GGML_API bool   ggml_allocr_is_measure(struct ggml_allocr * alloc);

ggml-cuda.cu

@@ -6,15 +6,116 @@
 #include <atomic>
 #include <assert.h>
 
+#if defined(GGML_USE_HIPBLAS)
+#include <hip/hip_runtime.h>
+#include <hipblas/hipblas.h>
+#include <hip/hip_fp16.h>
+#ifdef __HIP_PLATFORM_AMD__
+// for rocblas_initialize()
+#include "rocblas/rocblas.h"
+#endif
+#define CUBLAS_COMPUTE_32F HIPBLAS_R_32F
+#define CUBLAS_COMPUTE_32F_FAST_16F HIPBLAS_R_32F
+#define CUBLAS_GEMM_DEFAULT HIPBLAS_GEMM_DEFAULT
+#define CUBLAS_OP_N HIPBLAS_OP_N
+#define CUBLAS_OP_T HIPBLAS_OP_T
+#define CUBLAS_STATUS_SUCCESS HIPBLAS_STATUS_SUCCESS
+#define CUBLAS_TF32_TENSOR_OP_MATH 0
+#define CUDA_R_16F  HIPBLAS_R_16F
+#define CUDA_R_32F  HIPBLAS_R_32F
+#define __shfl_xor_sync(mask, var, laneMask, width) __shfl_xor(var, laneMask, width)
+#define cublasCreate hipblasCreate
+#define cublasGemmEx hipblasGemmEx
+#define cublasHandle_t hipblasHandle_t
+#define cublasSetMathMode(handle, mode) CUBLAS_STATUS_SUCCESS
+#define cublasSetStream hipblasSetStream
+#define cublasSgemm hipblasSgemm
+#define cublasStatus_t hipblasStatus_t
+#define cudaDeviceProp hipDeviceProp_t
+#define cudaDeviceSynchronize hipDeviceSynchronize
+#define cudaError_t hipError_t
+#define cudaEventCreateWithFlags hipEventCreateWithFlags
+#define cudaEventDisableTiming hipEventDisableTiming
+#define cudaEventRecord hipEventRecord
+#define cudaEvent_t hipEvent_t
+#define cudaEventDestroy hipEventDestroy
+#define cudaFree hipFree
+#define cudaFreeHost hipHostFree
+#define cudaGetDevice hipGetDevice
+#define cudaGetDeviceCount hipGetDeviceCount
+#define cudaGetDeviceProperties hipGetDeviceProperties
+#define cudaGetErrorString hipGetErrorString
+#define cudaGetLastError hipGetLastError
+#define cudaMalloc hipMalloc
+#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault)
+#define cudaMemcpy hipMemcpy
+#define cudaMemcpy2DAsync hipMemcpy2DAsync
+#define cudaMemcpyAsync hipMemcpyAsync
+#define cudaMemcpyDeviceToDevice hipMemcpyDeviceToDevice
+#define cudaMemcpyDeviceToHost hipMemcpyDeviceToHost
+#define cudaMemcpyHostToDevice hipMemcpyHostToDevice
+#define cudaMemcpyKind hipMemcpyKind
+#define cudaMemset hipMemset
+#define cudaOccupancyMaxPotentialBlockSize hipOccupancyMaxPotentialBlockSize
+#define cudaSetDevice hipSetDevice
+#define cudaStreamCreateWithFlags hipStreamCreateWithFlags
+#define cudaStreamNonBlocking hipStreamNonBlocking
+#define cudaStreamSynchronize hipStreamSynchronize
+#define cudaStreamWaitEvent(stream, event) hipStreamWaitEvent(stream, event, 0)
+#define cudaStream_t hipStream_t
+#define cudaSuccess hipSuccess
+#else
 #include <cuda_runtime.h>
 #include <cublas_v2.h>
 #include <cuda_fp16.h>
+#endif
 
 #include "ggml-cuda.h"
 #include "ggml.h"
 
 #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products
+#ifndef CC_TURING
 #define CC_TURING   700
+#endif
+
+#if defined(GGML_USE_HIPBLAS)
+#define __CUDA_ARCH__ 1300
+
+typedef int8_t int8x4_t __attribute__((ext_vector_type(4)));
+static __device__ __forceinline__ int __vsubss4(const int a, const int b) {
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+    const int8x4_t c = __builtin_elementwise_sub_sat(va, vb);
+    return reinterpret_cast<const int&>(c);
+}
+
+static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) {
+#if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__)
+    c = __builtin_amdgcn_sdot4(a, b, c, false);
+#elif defined(__gfx1100__)
+    c = __builtin_amdgcn_sudot4( true, a, true, b, c, false);
+#elif defined(__gfx1010__) || defined(__gfx900__)
+    int tmp1;
+    int tmp2;
+    asm("\n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_0 src1_sel:BYTE_0 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_1 src1_sel:BYTE_1 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        v_mul_i32_i24 %1, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_2 src1_sel:BYTE_2 \n \
+        v_mul_i32_i24 %2, sext(%3), sext(%4) dst_sel:DWORD dst_unused:UNUSED_PAD src0_sel:BYTE_3 src1_sel:BYTE_3 \n \
+        v_add3_u32 %0, %1, %2, %0 \n \
+        "
+        : "+v"(c), "=&v"(tmp1), "=&v"(tmp2)
+        : "v"(a), "v"(b)
+    );
+#else
+    const int8x4_t va = reinterpret_cast<const int8x4_t&>(a);
+    const int8x4_t vb = reinterpret_cast<const int8x4_t&>(b);
+    c += va[0] * vb[0] + va[1] * vb[1] + va[2] * vb[2] + va[3] * vb[3];
+#endif
+    return c;
+}
+#endif
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -62,21 +163,31 @@ typedef float2 dfloat2;
 #endif //GGML_CUDA_F16
 
 static __device__ __forceinline__ int get_int_from_int8(const int8_t * x8, const int & i32) {
-    const uint16_t * x16 = (uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+    //const uint16_t * x16 = (uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+    x8 += sizeof(int) * i32;
 
     int x32 = 0;
-    x32 |= x16[0] <<  0;
-    x32 |= x16[1] << 16;
+    //x32 |= x16[0] <<  0;
+    //x32 |= x16[1] << 16;
+    x32 |= ((uint32_t)(x8[0])) <<  0;
+    x32 |= ((uint32_t)(x8[1])) <<  8;
+    x32 |= ((uint32_t)(x8[2])) << 16;
+    x32 |= ((uint32_t)(x8[3])) << 24;
 
     return x32;
 }
 
 static __device__ __forceinline__ int get_int_from_uint8(const uint8_t * x8, const int & i32) {
-    const uint16_t * x16 = (uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+    //const uint16_t * x16 = (uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment
+    x8 += sizeof(int) * i32;
 
     int x32 = 0;
-    x32 |= x16[0] <<  0;
-    x32 |= x16[1] << 16;
+    //x32 |= x16[0] <<  0;
+    //x32 |= x16[1] << 16;
+    x32 |= ((uint32_t)(x8[0])) <<  0;
+    x32 |= ((uint32_t)(x8[1])) <<  8;
+    x32 |= ((uint32_t)(x8[2])) << 16;
+    x32 |= ((uint32_t)(x8[3])) << 24;
 
     return x32;
 }
@@ -103,23 +214,31 @@ typedef void (*ggml_cuda_op_t)(
 // QR = QK / number of values before dequantization
 // QI = number of 32 bit integers before dequantization
 
+#define Q4_0DM   (1.0f/8.0f)
+#define Q4_0D(x) (((x)*Q4_0DM) / 127.0f)
+
 #define QK4_0 32
 #define QR4_0 2
 #define QI4_0 (QK4_0 / (4 * QR4_0))
 typedef struct {
-    half    d;              // delta
+    int8_t  d;              // delta
     uint8_t qs[QK4_0 / 2];  // nibbles / quants
 } block_q4_0;
-static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
+static_assert(sizeof(block_q4_0) == sizeof(int8_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
+
+#define Q4_1DM   (2.0f/15.0f)
+#define Q4_1MM   (2.0f      )
+#define Q4_1D(x) (        (((x) &  0xFF)*Q4_1DM) / 255.0f)
+#define Q4_1M(x) (-1.0f + (((x) >>    8)*Q4_1MM) / 255.0f)
 
 #define QK4_1 32
 #define QR4_1 2
 #define QI4_1 (QK4_1 / (4 * QR4_1))
 typedef struct {
-    half2   dm;             // dm.x = delta, dm.y = min
-    uint8_t qs[QK4_1 / 2];  // nibbles / quants
+    uint16_t dm;             // 8-bit delta + 8-bit min (can be adjusted easily)
+    uint8_t  qs[QK4_1 / 2];  // nibbles / quants
 } block_q4_1;
-static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding");
+static_assert(sizeof(block_q4_1) == sizeof(uint16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
 
 #define QK5_0 32
 #define QR5_0 2
@@ -131,15 +250,20 @@ typedef struct {
 } block_q5_0;
 static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
 
+#define Q5_1DM   (2.0f/31.0f)
+#define Q5_1MM   (2.0f      )
+#define Q5_1D(x) (        (((x) &  0x0F)*Q5_1DM) / 15.0f)
+#define Q5_1M(x) (-1.0f + (((x) >>    4)*Q5_1MM) / 15.0f)
+
 #define QK5_1 32
 #define QR5_1 2
 #define QI5_1 (QK5_1 / (4 * QR5_1))
 typedef struct {
-    half2 dm;               // dm.x = delta, dm.y = min
+    uint8_t dm;             // 4-bit delta + 4-bit min
     uint8_t qh[4];          // 5-th bit of quants
     uint8_t qs[QK5_1 / 2];  // nibbles / quants
 } block_q5_1;
-static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
+static_assert(sizeof(block_q5_1) == sizeof(uint8_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
 
 #define QK8_0 32
 #define QR8_0 1
@@ -205,11 +329,11 @@ typedef struct {
 #define QI4_K (QK_K / (4*QR4_K))
 #ifdef GGML_QKK_64
 typedef struct {
-    half    d[2];              // super-block scales/mins
+    half    dm[2];             // super-block scales/mins
     uint8_t scales[2];         // 4-bit block scales/mins
     uint8_t qs[QK_K/2];        // 4--bit quants
 } block_q4_K;
-static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
+static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding");
 #else
 typedef struct {
     half2 dm;                  // super-block scale for quantized scales/mins
@@ -259,6 +383,7 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_
 #define CUDA_CPY_BLOCK_SIZE 32
 #define CUDA_SCALE_BLOCK_SIZE 256
 #define CUDA_ROPE_BLOCK_SIZE 256
+#define CUDA_ALIBI_BLOCK_SIZE 32
 #define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32
 #define CUDA_QUANTIZE_BLOCK_SIZE 256
 #define CUDA_DEQUANTIZE_BLOCK_SIZE 256
@@ -286,7 +411,7 @@ static int g_device_count = -1;
 static int g_main_device = 0;
 static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES];
 static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0};
-static bool g_mul_mat_q = false;
+static bool g_mul_mat_q = true;
 
 static void * g_scratch_buffer = nullptr;
 static size_t g_scratch_size = 1024*1024*1024; // 1 GB by default
@@ -404,7 +529,7 @@ static __global__ void rms_norm_f32(const float * x, float * dst, const int ncol
 static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){
     const block_q4_0 * x = (const block_q4_0 *) vx;
 
-    const dfloat d = x[ib].d;
+    const dfloat d = Q4_0D(x[ib].d);
 
     const int vui = x[ib].qs[iqs];
 
@@ -423,8 +548,8 @@ static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const in
 static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
     const block_q4_1 * x = (const block_q4_1 *) vx;
 
-    const dfloat d = x[ib].dm.x;
-    const dfloat m = x[ib].dm.y;
+    const dfloat d = Q4_1D(x[ib].dm);
+    const dfloat m = Q4_1M(x[ib].dm);
 
     const int vui = x[ib].qs[iqs];
 
@@ -466,8 +591,8 @@ static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const in
 static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){
     const block_q5_1 * x = (const block_q5_1 *) vx;
 
-    const dfloat d = x[ib].dm.x;
-    const dfloat m = x[ib].dm.y;
+    const dfloat d = Q5_1D(x[ib].dm);
+    const dfloat m = Q5_1M(x[ib].dm);
 
     uint32_t qh;
     memcpy(&qh, x[ib].qh, sizeof(qh));
@@ -519,8 +644,8 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, float
     const uint8_t q = x[i].qs[32*n + l];
     float * y = yy + i*QK_K + 128*n;
 
-    float dall = x[i].dm.x;
-    float dmin = x[i].dm.y;
+    float dall = __low2half(x[i].dm);
+    float dmin = __high2half(x[i].dm);
     y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
     y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4);
     y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4);
@@ -530,8 +655,8 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, float
     const int il = tid%16;  // 0...15
     const uint8_t q = x[i].qs[il] >> (2*is);
     float * y = yy + i*QK_K + 16*is + il;
-    float dall = x[i].dm.x;
-    float dmin = x[i].dm.y;
+    float dall = __low2half(x[i].dm);
+    float dmin = __high2half(x[i].dm);
     y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4);
     y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4);
 #endif
@@ -617,8 +742,8 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, float
 
     float * y = yy + i*QK_K + 64*il + n*ir;
 
-    const float dall = x[i].dm.x;
-    const float dmin = x[i].dm.y;
+    const float dall = __low2half(x[i].dm);
+    const float dmin = __high2half(x[i].dm);
 
     const uint8_t * q = x[i].qs + 32*il + n*ir;
 
@@ -635,8 +760,8 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, float
     const int tid = threadIdx.x;
     const uint8_t * q = x[i].qs;
     float * y = yy + i*QK_K;
-    const float d = (float)x[i].d[0];
-    const float m = (float)x[i].d[1];
+    const float d = (float)x[i].dm[0];
+    const float m = (float)x[i].dm[1];
     y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4);
     y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >>  4) - m * (x[i].scales[1] >> 4);
 #endif
@@ -656,8 +781,8 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, float
 
     float * y = yy + i*QK_K + 64*il + 2*ir;
 
-    const float dall = x[i].dm.x;
-    const float dmin = x[i].dm.y;
+    const float dall = __low2half(x[i].dm);
+    const float dmin = __high2half(x[i].dm);
 
     const uint8_t * ql = x[i].qs + 32*il + 2*ir;
     const uint8_t * qh = x[i].qh + 2*ir;
@@ -769,8 +894,8 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx,
         const float   * y = yy + i * QK_K + y_offset;
         const uint8_t * q = x[i].qs + q_offset;
 
-        const float dall = x[i].dm.x;
-        const float dmin = x[i].dm.y;
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
 
         const uint32_t * a = (const uint32_t *)(x[i].scales + s_offset);
         aux[0] = a[0] & 0x0f0f0f0f;
@@ -990,8 +1115,8 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
         const float   * y1 = yy + i*QK_K + y_offset;
         const float   * y2 = y1 + 128;
 
-        const float dall = x[i].dm.x;
-        const float dmin = x[i].dm.y;
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
 
         const uint16_t * a = (const uint16_t *)x[i].scales;
         aux[0] = a[im+0] & kmask1;
@@ -1053,8 +1178,8 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx,
         const uint16_t * a = (const uint16_t *)x[i].scales;
         aux16[0] = a[0] & 0x0f0f;
         aux16[1] = (a[0] >> 4) & 0x0f0f;
-        const float d = (float)x[i].d[0];
-        const float m = (float)x[i].d[1];
+        const float d = (float)x[i].dm[0];
+        const float m = (float)x[i].dm[1];
         float sum = 0.f;
         for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) {
             sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2])
@@ -1123,8 +1248,8 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx,
         const float   * y1  = yy + i*QK_K + y_offset;
         const float   * y2  = y1 + 128;
 
-        const float dall = x[i].dm.x;
-        const float dmin = x[i].dm.y;
+        const float dall = __low2half(x[i].dm);
+        const float dmin = __high2half(x[i].dm);
 
         const uint16_t * a = (const uint16_t *)x[i].scales;
         aux[0] = a[im+0] & kmask1;
@@ -1347,8 +1472,8 @@ static __global__ void quantize_q8_1(const float * __restrict__ x, void * __rest
         return;
     }
 
-    y[ib].ds.x = d;
-    y[ib].ds.y = sum;
+    reinterpret_cast<half&>(y[ib].ds.x) = d;
+    reinterpret_cast<half&>(y[ib].ds.y) = sum;
 }
 
 template <int qk, int qr, dequantize_kernel_t dequantize_kernel>
@@ -1939,7 +2064,7 @@ static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
         u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0);
     }
 
-    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
+    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, Q4_0D(bq4_0->d), bq8_1->ds);
 }
 
 template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
@@ -1978,7 +2103,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
         const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx;
 
         x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
-        // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d;
+        //x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = Q4_0D(bxi->d);
     }
 
     const int blocks_per_tile_x_row = WARP_SIZE / QI4_0;
@@ -1994,7 +2119,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd;
 
-        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d;
+        x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = Q4_0D(bxi->d);
     }
 }
 
@@ -2028,12 +2153,17 @@ static __device__ __forceinline__ float vec_dot_q4_1_q8_1(
 
 #pragma unroll
     for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) {
-        v[i]    = get_int_from_uint8_aligned(bq4_1->qs, iqs + i);
+        v[i]    = get_int_from_uint8(bq4_1->qs, iqs + i);
         u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
         u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1);
     }
 
-    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, bq4_1->dm, bq8_1->ds);
+    const float d = Q4_1D(bq4_1->dm);
+    const float m = Q4_1M(bq4_1->dm);
+
+    const half2 dm = {d, m};
+
+    return vec_dot_q4_1_q8_1_impl<VDR_Q4_1_Q8_1_MMVQ>(v, u, dm, bq8_1->ds);
 }
 
 template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q4_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
@@ -2069,7 +2199,7 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx;
 
-        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx);
+        x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx);
     }
 
     const int blocks_per_tile_x_row = WARP_SIZE / QI4_1;
@@ -2085,7 +2215,8 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd;
 
-        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm;
+        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd].x = Q4_1D(bxi->dm);
+        x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd].y = Q4_1M(bxi->dm);
     }
 }
 
@@ -2233,13 +2364,18 @@ static __device__ __forceinline__ float vec_dot_q5_1_q8_1(
 
 #pragma unroll
     for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) {
-        vl[i]   = get_int_from_uint8_aligned(bq5_1->qs, iqs + i);
-        vh[i]   = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i));
+        vl[i]   = get_int_from_uint8(bq5_1->qs, iqs + i);
+        vh[i]   = get_int_from_uint8(bq5_1->qh, 0) >> (4 * (iqs + i));
         u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
         u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1);
     }
 
-    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, bq5_1->dm, bq8_1->ds);
+    const half d = Q5_1D(bq5_1->dm);
+    const half m = Q5_1M(bq5_1->dm);
+
+    const half2 dm = {d, m};
+
+    return vec_dot_q5_1_q8_1_impl<VDR_Q5_1_Q8_1_MMVQ>(vl, vh, u, dm, bq8_1->ds);
 }
 
 template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q5_1(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
@@ -2275,8 +2411,8 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx;
 
-        const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx);
-        const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1));
+        const int ql = get_int_from_uint8(bxi->qs, kqsx);
+        const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_1));
 
         int qs0 = (ql >>  0) & 0x0F0F0F0F;
         qs0    |= (qh <<  4) & 0x00000010; // 0 ->  4
@@ -2308,7 +2444,8 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd;
 
-        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm;
+        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd].x = Q5_1D(bxi->dm);
+        x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd].y = Q5_1M(bxi->dm);
     }
 }
 
@@ -2345,7 +2482,7 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1(
         u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i);
     }
 
-    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, bq8_1->ds.x);
+    return vec_dot_q8_0_q8_1_impl<VDR_Q8_0_Q8_1_MMVQ>(v, u, bq8_0->d, __low2half(bq8_1->ds));
 }
 
 template <int mmq_y> static __device__ __forceinline__ void allocate_tiles_q8_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) {
@@ -2431,7 +2568,7 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1(
 #pragma unroll
     for (int i = 0; i < QR2_K; ++ i) {
         u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = bq8_1[bq8_offset + i].ds.x;
+        d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
     }
 
     return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8);
@@ -2550,7 +2687,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1(
 #pragma unroll
     for (int i = 0; i < QR3_K; ++i) {
         u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1);
-        d8[i] = bq8_1[bq8_offset + i].ds.x;
+        d8[i] = __low2half(bq8_1[bq8_offset + i].ds);
     }
 
     return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8);
@@ -2719,7 +2856,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
 
     for (int i = 0; i < QR4_K; ++i) {
         const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = bq8i->ds.x;
+        d8[i] = __low2half(bq8i->ds);
 
         const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
         u[2*i+0] = q8[0];
@@ -2743,11 +2880,11 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1(
     aux16[0] = a[0] & 0x0f0f;
     aux16[1] = (a[0] >> 4) & 0x0f0f;
 
-    const float dall = bq4_K->d[0];
-    const float dmin = bq4_K->d[1];
+    const float dall = bq4_K->dm[0];
+    const float dmin = bq4_K->dm[1];
 
-    const float d8_1 = bq8_1[0].ds.x;
-    const float d8_2 = bq8_1[1].ds.x;
+    const float d8_1 = __low2float(bq8_1[0].ds);
+    const float d8_2 = __low2float(bq8_1[1].ds);
 
     const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
     const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
@@ -2827,7 +2964,11 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd;
 
+#if QK_K == 256
         x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm;
+#else
+        x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]};
+#endif
     }
 
 #pragma unroll
@@ -2900,7 +3041,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
 #pragma unroll
     for (int i = 0; i < QR5_K; ++i) {
         const block_q8_1 * bq8i = bq8_1 + bq8_offset + i;
-        d8[i] = bq8i->ds.x;
+        d8[i] = __low2float(bq8i->ds);
 
         const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4);
         u[2*i+0] = q8[0];
@@ -2918,8 +3059,8 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1(
 
     const float d = bq5_K->d;
 
-    const float d8_1 = bq8_1[0].ds.x;
-    const float d8_2 = bq8_1[1].ds.x;
+    const float d8_1 = __low2half(bq8_1[0].ds);
+    const float d8_2 = __low2half(bq8_1[1].ds);
 
     const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2));
     const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4);
@@ -3017,7 +3158,9 @@ template <int mmq_y, int nwarps, bool need_check> static __device__ __forceinlin
 
         const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd;
 
+#if QK_K == 256
         x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm;
+#endif
     }
 
 #pragma unroll
@@ -3074,7 +3217,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1(
 #pragma unroll
     for (int i = 0; i < QR6_K; ++i) {
         u[i]  = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1);
-        d8[i] = bq8_1[bq8_offset + 2*i].ds.x;
+        d8[i] = __low2half(bq8_1[bq8_offset + 2*i].ds);
     }
 
     return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8);
@@ -3242,7 +3385,7 @@ static __device__ __forceinline__ void mul_mat_q(
                     *dsi_dst = *dsi_src;
                 } else {
                     float * dfi_dst = (float *) dsi_dst;
-                    *dfi_dst = (*dsi_src).x;
+                    *dfi_dst = __low2half(*dsi_src);
                 }
             }
 
@@ -3886,13 +4029,13 @@ static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne,
 // rope == RoPE == rotary positional embedding
 static __global__ void rope_f32(const float * x, float * dst, const int ncols, const float p0,
                                 const float p_delta, const int p_delta_rows, const float theta_scale) {
-    const int col = 2*(blockDim.x*blockIdx.x + threadIdx.x);
+    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
 
     if (col >= ncols) {
         return;
     }
 
-    const int row = blockDim.y*blockIdx.y + threadIdx.y;
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
     const int i = row*ncols + col;
 
     const float theta = (p0 + p_delta * (row/p_delta_rows))*powf(theta_scale, col/2);
@@ -3906,6 +4049,28 @@ static __global__ void rope_f32(const float * x, float * dst, const int ncols, c
     dst[i + 1] = x0*sin_theta + x1*cos_theta;
 }
 
+static __global__ void rope_neox_f32(const float * x, float * dst, const int ncols, const float p0,
+                                const float p_delta, const int p_delta_rows, const float theta_scale) {
+    const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y);
+
+    if (col >= ncols) {
+        return;
+    }
+
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int i = row*ncols + col/2;
+
+    const float theta = (p0 + p_delta * (row/p_delta_rows))*powf(theta_scale, col/2);
+    const float sin_theta = sinf(theta);
+    const float cos_theta = cosf(theta);
+
+    const float x0 = x[i + 0];
+    const float x1 = x[i + ncols/2];
+
+    dst[i + 0]       = x0*cos_theta - x1*sin_theta;
+    dst[i + ncols/2] = x0*sin_theta + x1*cos_theta;
+}
+
 static __global__ void rope_glm_f32(const float * x, float * dst, const int ncols, const float p, const float block_p, const float theta_scale) {
     const int col = blockDim.x*blockIdx.x + threadIdx.x;
     const int half_n_dims = ncols/4;
@@ -3940,9 +4105,32 @@ static __global__ void rope_glm_f32(const float * x, float * dst, const int ncol
     dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta;
 }
 
-static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) {
+static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows,
+                                 const int n_heads_log2_floor, const float m0, const float m1) {
     const int col = blockDim.x*blockIdx.x + threadIdx.x;
+
+    if (col >= ncols) {
+        return;
+    }
+
     const int row = blockDim.y*blockIdx.y + threadIdx.y;
+    const int i = row*ncols + col;
+
+    const int k = row/k_rows;
+
+    float m_k;
+    if (k < n_heads_log2_floor) {
+        m_k = powf(m0, k + 1);
+    } else {
+        m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1);
+    }
+
+    dst[i] = col * m_k + x[i];
+}
+
+static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past) {
+    const int col = blockDim.y*blockIdx.y + threadIdx.y;
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
 
     if (col >= ncols) {
         return;
@@ -3955,24 +4143,29 @@ static __global__ void diag_mask_inf_f32(const float * x, float * dst, const int
 
 // the CUDA soft max implementation differs from the CPU implementation
 // instead of doubles floats are used
-// values are also not normalized to the maximum value by subtracting it in the exponential function
-// theoretically these changes could cause problems with rounding error and arithmetic overflow but for LLaMa it seems to be fine
 static __global__ void soft_max_f32(const float * x, float * dst, const int ncols) {
-    const int row = blockDim.y*blockIdx.y + threadIdx.y;
-    const int block_size = blockDim.x;
-    const int tid = threadIdx.x;
+    const int row = blockDim.x*blockIdx.x + threadIdx.x;
+    const int block_size = blockDim.y;
+    const int tid = threadIdx.y;
 
-    float tmp = 0.0;
-
-    for (int block_start = 0; block_start < ncols; block_start += block_size) {
-        const int col = block_start + tid;
-
-        if (col >= ncols) {
-            break;
-        }
+    float max_val = -INFINITY;
 
+    for (int col = tid; col < ncols; col += block_size) {
         const int i = row*ncols + col;
-        const float val = expf(x[i]);
+        max_val = max(max_val, x[i]);
+    }
+
+    // find the max value in the block
+#pragma unroll
+    for (int mask = 16; mask > 0; mask >>= 1) {
+        max_val = max(max_val, __shfl_xor_sync(0xffffffff, max_val, mask, 32));
+    }
+
+    float tmp = 0.f;
+
+    for (int col = tid; col < ncols; col += block_size) {
+        const int i = row*ncols + col;
+        const float val = expf(x[i] - max_val);
         tmp += val;
         dst[i] = val;
     }
@@ -3983,15 +4176,11 @@ static __global__ void soft_max_f32(const float * x, float * dst, const int ncol
         tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32);
     }
 
-    for (int block_start = 0; block_start < ncols; block_start += block_size) {
-        const int col = block_start + tid;
-
-        if (col >= ncols) {
-            break;
-        }
+    const float inv_tmp = 1.f / tmp;
 
+    for (int col = tid; col < ncols; col += block_size) {
         const int i = row*ncols + col;
-        dst[i] /= tmp;
+        dst[i] *= inv_tmp;
     }
 }
 
@@ -4561,6 +4750,8 @@ static void ggml_mul_mat_q3_K_q8_1_cuda(
     const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x,
     const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) {
 
+#if QK_K == 256
+
     int id;
     CUDA_CHECK(cudaGetDevice(&id));
     const int compute_capability = g_compute_capabilities[id];
@@ -4592,6 +4783,7 @@ static void ggml_mul_mat_q3_K_q8_1_cuda(
         mul_mat_q3_K<need_check><<<block_nums, block_dims, 0, stream>>>
             (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst);
     }
+#endif
 }
 
 static void ggml_mul_mat_q4_K_q8_1_cuda(
@@ -4751,13 +4943,22 @@ static void scale_f32_cuda(const float * x, float * dst, const float scale, cons
 
 static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
                           const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
-    GGML_ASSERT(nrows % 2 == 0);
-    const dim3 block_dims(2*CUDA_ROPE_BLOCK_SIZE, 1, 1);
+    GGML_ASSERT(ncols % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
     const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
-    const dim3 block_nums(num_blocks_x, nrows, 1);
+    const dim3 block_nums(nrows, num_blocks_x, 1);
     rope_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
 }
 
+static void rope_neox_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0,
+                          const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) {
+    GGML_ASSERT(ncols % 2 == 0);
+    const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1);
+    const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE);
+    const dim3 block_nums(nrows, num_blocks_x, 1);
+    rope_neox_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale);
+}
+
 static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p, const float block_p, const float theta_scale, cudaStream_t stream) {
     GGML_ASSERT(nrows % 4 == 0);
     const dim3 block_dims(4*CUDA_ROPE_BLOCK_SIZE, 1, 1);
@@ -4766,16 +4967,25 @@ static void rope_glm_f32_cuda(const float * x, float * dst, const int ncols, con
     rope_glm_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, p, block_p, theta_scale);
 }
 
+static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows,
+                           const int k_rows, const int n_heads_log2_floor, const float m0,
+                           const float m1, cudaStream_t stream) {
+    const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1);
+    const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE);
+    const dim3 block_nums(num_blocks_x, nrows, 1);
+    alibi_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1);
+}
+
 static void diag_mask_inf_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, const int rows_per_channel, const int n_past, cudaStream_t stream) {
-    const dim3 block_dims(CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1, 1);
+    const dim3 block_dims(1, CUDA_DIAG_MASK_INF_BLOCK_SIZE, 1);
     const int block_num_x = (ncols_x + CUDA_DIAG_MASK_INF_BLOCK_SIZE - 1) / CUDA_DIAG_MASK_INF_BLOCK_SIZE;
-    const dim3 block_nums(block_num_x, nrows_x, 1);
+    const dim3 block_nums(nrows_x, block_num_x, 1);
     diag_mask_inf_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x, rows_per_channel, n_past);
 }
 
 static void soft_max_f32_cuda(const float * x, float * dst, const int ncols_x, const int nrows_x, cudaStream_t stream) {
-    const dim3 block_dims(WARP_SIZE, 1, 1);
-    const dim3 block_nums(1, nrows_x, 1);
+    const dim3 block_dims(1, WARP_SIZE, 1);
+    const dim3 block_nums(nrows_x, 1, 1);
     soft_max_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols_x);
 }
 
@@ -4880,10 +5090,18 @@ void ggml_init_cublas() {
     static bool initialized = false;
 
     if (!initialized) {
+
+#ifdef __HIP_PLATFORM_AMD__
+        // Workaround for a rocBLAS bug when using multiple graphics cards:
+        // https://github.com/ROCmSoftwarePlatform/rocBLAS/issues/1346
+        rocblas_initialize();
+        CUDA_CHECK(cudaDeviceSynchronize());
+#endif
+
         CUDA_CHECK(cudaGetDeviceCount(&g_device_count));
         GGML_ASSERT(g_device_count <= GGML_CUDA_MAX_DEVICES);
         int64_t total_vram = 0;
-        fprintf(stderr, "%s: found %d CUDA devices:\n", __func__, g_device_count);
+        fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, g_device_count);
         for (int id = 0; id < g_device_count; ++id) {
             cudaDeviceProp prop;
             CUDA_CHECK(cudaGetDeviceProperties(&prop, id));
@@ -5481,7 +5699,8 @@ inline void ggml_cuda_op_rope(
 
     const float theta_scale = powf(freq_base, -2.0f/n_dims);
 
-    const bool is_glm = mode & 4;
+    const bool is_neox = mode & 2;
+    const bool is_glm  = mode & 4;
 
     // compute
     if (is_glm) {
@@ -5489,6 +5708,10 @@ inline void ggml_cuda_op_rope(
         const float id_p = min(p, n_ctx - 2.f);
         const float block_p = max(p - (n_ctx - 2.f), 0.f);
         rope_glm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, id_p, block_p, theta_scale, cudaStream_main);
+    } else if (is_neox) {
+        GGML_ASSERT(ne00 == n_dims && "ne00 != n_dims is not implemented for CUDA yet");
+        const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
+        rope_neox_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p0, freq_scale, ne01, theta_scale, cudaStream_main);
     } else {
         const float p0 = (((mode & 1) == 0 ? n_past : 0)) * freq_scale;
         rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p0, freq_scale, ne01, theta_scale, cudaStream_main);
@@ -5501,6 +5724,41 @@ inline void ggml_cuda_op_rope(
     (void) i1;
 }
 
+inline void ggml_cuda_op_alibi(
+    const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
+    float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
+    cudaStream_t & cudaStream_main){
+
+    GGML_ASSERT(src0_ddf_i != nullptr);
+    GGML_ASSERT(dst_ddf_i != nullptr);
+
+    const int64_t ne00 = src0->ne[0];
+    const int64_t ne01 = src0->ne[1];
+    const int64_t ne02 = src0->ne[2];
+    const int64_t i01_diff = i01_high - i01_low;
+
+    const int n_past = ((int32_t *) dst->op_params)[0];
+    const int n_head = ((int32_t *) dst->op_params)[1];
+    float max_bias;
+    memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
+
+    GGML_ASSERT(ne01 + n_past == ne00);
+    GGML_ASSERT(n_head == ne02);
+
+    const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
+
+    const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
+    const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor);
+
+    // compute
+    alibi_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, ne01, n_heads_log2_floor, m0, m1, cudaStream_main);
+
+    (void) src1;
+    (void) src0_ddq_i;
+    (void) src1_ddf_i;
+    (void) i1;
+}
+
 inline void ggml_cuda_op_diag_mask_inf(
     const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i,
     float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1,
@@ -6115,12 +6373,19 @@ void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml
 
 void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented
 
     const int mode = ((int32_t *) dst->op_params)[2];
     const bool is_glm = mode & 4;
+
     ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, !is_glm); // flatten support not implemented for glm
 }
 
+void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
+    GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32);
+    ggml_cuda_op(src0, src1, dst, ggml_cuda_op_alibi, true, true);
+}
+
 void ggml_cuda_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
     (void) src0;
     (void) src1;
@@ -6240,7 +6505,7 @@ static struct ggml_tensor_extra_gpu * ggml_cuda_alloc_temp_tensor_extra() {
     return extra;
 }
 
-void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace) {
+void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace, bool no_alloc) {
     if (scratch && g_scratch_size == 0) {
         return;
     }
@@ -6249,14 +6514,19 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bo
     if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) {
         const ggml_op src0_op = tensor->src[0]->op;
         if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) {
-            ggml_cuda_assign_buffers_impl(tensor->src[0], scratch, force_inplace);
+            ggml_cuda_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc);
         }
     }
     if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) {
-        ggml_cuda_assign_buffers_impl(tensor->src[1], scratch, force_inplace);
+        ggml_cuda_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc);
     }
 
     tensor->backend = GGML_BACKEND_GPU;
+
+    if (scratch && no_alloc) {
+        return;
+    }
+
     struct ggml_tensor_extra_gpu * extra;
 
     const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
@@ -6308,16 +6578,48 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bo
     tensor->extra = extra;
 }
 
+void ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset) {
+    if (g_scratch_size == 0) {
+        return;
+    }
+    if (g_scratch_buffer == nullptr) {
+        CUDA_CHECK(cudaMalloc(&g_scratch_buffer, g_scratch_size));
+    }
+
+    struct ggml_tensor_extra_gpu * extra = ggml_cuda_alloc_temp_tensor_extra();
+
+    const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) ||
+        tensor->op == GGML_OP_VIEW;
+
+    if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) {
+        struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra;
+        char * src0_ddc = (char *) src0_extra->data_device[g_main_device];
+        size_t view_offset = 0;
+        if (tensor->op == GGML_OP_VIEW) {
+            memcpy(&view_offset, tensor->op_params, sizeof(size_t));
+        }
+        extra->data_device[g_main_device] = src0_ddc + view_offset;
+    } else {
+        extra->data_device[g_main_device] = (char *) g_scratch_buffer + offset;
+    }
+
+    tensor->extra = extra;
+}
+
 void ggml_cuda_assign_buffers(struct ggml_tensor * tensor) {
-    ggml_cuda_assign_buffers_impl(tensor, true, false);
+    ggml_cuda_assign_buffers_impl(tensor, true, false, false);
+}
+
+void ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor) {
+    ggml_cuda_assign_buffers_impl(tensor, true, false, true);
 }
 
 void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) {
-    ggml_cuda_assign_buffers_impl(tensor, false, false);
+    ggml_cuda_assign_buffers_impl(tensor, false, false, false);
 }
 
 void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) {
-    ggml_cuda_assign_buffers_impl(tensor, false, true);
+    ggml_cuda_assign_buffers_impl(tensor, false, true, false);
 }
 
 void ggml_cuda_set_main_device(int main_device) {
@@ -6456,6 +6758,12 @@ bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_
             }
             func = ggml_cuda_rope;
             break;
+        case GGML_OP_ALIBI:
+            if (!any_on_device) {
+                return false;
+            }
+            func = ggml_cuda_alibi;
+            break;
         default:
             return false;
     }

ggml-cuda.h

@@ -2,6 +2,14 @@
 
 #include "ggml.h"
 
+#ifdef GGML_USE_HIPBLAS
+#define GGML_CUDA_NAME "ROCm"
+#define GGML_CUBLAS_NAME "hipBLAS"
+#else
+#define GGML_CUDA_NAME "CUDA"
+#define GGML_CUBLAS_NAME "cuBLAS"
+#endif
+
 #ifdef  __cplusplus
 extern "C" {
 #endif
@@ -16,9 +24,14 @@ GGML_API bool   ggml_cuda_can_mul_mat(const struct ggml_tensor * src0, const str
 GGML_API void   ggml_cuda_set_tensor_split(const float * tensor_split);
 GGML_API void   ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
 GGML_API void   ggml_cuda_free_data(struct ggml_tensor * tensor);
+
 GGML_API void   ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
 GGML_API void   ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
 GGML_API void   ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
+
+GGML_API void   ggml_cuda_assign_buffers_no_alloc(struct ggml_tensor * tensor);
+GGML_API void   ggml_cuda_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset);
+
 GGML_API void   ggml_cuda_set_main_device(int main_device);
 GGML_API void   ggml_cuda_set_mul_mat_q(bool mul_mat_q);
 GGML_API void   ggml_cuda_set_scratch_size(size_t scratch_size);

ggml-metal.h

@@ -24,6 +24,7 @@
 
 // max memory buffers that can be mapped to the device
 #define GGML_METAL_MAX_BUFFERS 16
+#define GGML_METAL_MAX_COMMAND_BUFFERS 32
 
 struct ggml_tensor;
 struct ggml_cgraph;

ggml-metal.m

@@ -11,6 +11,7 @@
 #define MIN(a, b) ((a) < (b) ? (a) : (b))
 #define MAX(a, b) ((a) > (b) ? (a) : (b))
 
+// TODO: temporary - reuse llama.cpp logging
 #ifdef GGML_METAL_NDEBUG
 #define metal_printf(...)
 #else
@@ -33,12 +34,15 @@ struct ggml_metal_buffer {
 struct ggml_metal_context {
     int n_cb;
 
-    float * logits;
-
     id<MTLDevice>       device;
     id<MTLCommandQueue> queue;
     id<MTLLibrary>      library;
 
+    id<MTLCommandBuffer>         command_buffers [GGML_METAL_MAX_COMMAND_BUFFERS];
+    id<MTLComputeCommandEncoder> command_encoders[GGML_METAL_MAX_COMMAND_BUFFERS];
+
+    dispatch_queue_t d_queue;
+
     int n_buffers;
     struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS];
 
@@ -63,6 +67,7 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(get_rows_f16);
     GGML_METAL_DECL_KERNEL(get_rows_q4_0);
     GGML_METAL_DECL_KERNEL(get_rows_q4_1);
+    GGML_METAL_DECL_KERNEL(get_rows_q8_0);
     GGML_METAL_DECL_KERNEL(get_rows_q2_K);
     GGML_METAL_DECL_KERNEL(get_rows_q3_K);
     GGML_METAL_DECL_KERNEL(get_rows_q4_K);
@@ -73,6 +78,7 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32);
+    GGML_METAL_DECL_KERNEL(mul_mat_q8_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q2_K_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q3_K_f32);
     GGML_METAL_DECL_KERNEL(mul_mat_q4_K_f32);
@@ -81,6 +87,7 @@ struct ggml_metal_context {
     GGML_METAL_DECL_KERNEL(mul_mm_f16_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q4_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q4_1_f32);
+    GGML_METAL_DECL_KERNEL(mul_mm_q8_0_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q2_K_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q3_K_f32);
     GGML_METAL_DECL_KERNEL(mul_mm_q4_K_f32);
@@ -107,16 +114,17 @@ static NSString * const msl_library_source = @"see metal.metal";
 @end
 
 struct ggml_metal_context * ggml_metal_init(int n_cb) {
-    fprintf(stderr, "%s: allocating\n", __func__);
+    metal_printf("%s: allocating\n", __func__);
 
     struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context));
 
-    ctx->n_cb   = n_cb;
+    ctx->n_cb   = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
     ctx->device = MTLCreateSystemDefaultDevice();
     ctx->queue  = [ctx->device newCommandQueue];
     ctx->n_buffers = 0;
     ctx->concur_list_len = 0;
 
+    ctx->d_queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
 
 #if 0
     // compile from source string and show compile log
@@ -125,7 +133,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
 
         ctx->library = [ctx->device newLibraryWithSource:msl_library_source options:nil error:&error];
         if (error) {
-            fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
+            metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]);
             return NULL;
         }
     }
@@ -139,11 +147,11 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         //NSString * path = [[NSBundle mainBundle] pathForResource:@"../../examples/metal/metal" ofType:@"metal"];
         NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
         NSString * path = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
-        fprintf(stderr, "%s: loading '%s'\n", __func__, [path UTF8String]);
+        metal_printf("%s: loading '%s'\n", __func__, [path UTF8String]);
 
         NSString * src  = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:&error];
         if (error) {
-            fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
+            metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]);
             return NULL;
         }
 
@@ -155,7 +163,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         ctx->library = [ctx->device newLibraryWithSource:src options:nil error:&error];
 #endif
         if (error) {
-            fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
+            metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]);
             return NULL;
         }
     }
@@ -167,9 +175,11 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
 #define GGML_METAL_ADD_KERNEL(name) \
         ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \
         ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \
-        fprintf(stderr, "%s: loaded %-32s %16p\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name); \
+        metal_printf("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \
+                (int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \
+                (int) ctx->pipeline_##name.threadExecutionWidth); \
         if (error) { \
-            fprintf(stderr, "%s: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
+            metal_printf("%s: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \
             return NULL; \
         }
 
@@ -186,6 +196,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(get_rows_f16);
         GGML_METAL_ADD_KERNEL(get_rows_q4_0);
         GGML_METAL_ADD_KERNEL(get_rows_q4_1);
+        GGML_METAL_ADD_KERNEL(get_rows_q8_0);
         GGML_METAL_ADD_KERNEL(get_rows_q2_K);
         GGML_METAL_ADD_KERNEL(get_rows_q3_K);
         GGML_METAL_ADD_KERNEL(get_rows_q4_K);
@@ -196,6 +207,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32);
+        GGML_METAL_ADD_KERNEL(mul_mat_q8_0_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q2_K_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q3_K_f32);
         GGML_METAL_ADD_KERNEL(mul_mat_q4_K_f32);
@@ -203,6 +215,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(mul_mat_q6_K_f32);
         GGML_METAL_ADD_KERNEL(mul_mm_f16_f32);
         GGML_METAL_ADD_KERNEL(mul_mm_q4_0_f32);
+        GGML_METAL_ADD_KERNEL(mul_mm_q8_0_f32);
         GGML_METAL_ADD_KERNEL(mul_mm_q4_1_f32);
         GGML_METAL_ADD_KERNEL(mul_mm_q2_K_f32);
         GGML_METAL_ADD_KERNEL(mul_mm_q3_K_f32);
@@ -218,22 +231,80 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) {
 #undef GGML_METAL_ADD_KERNEL
     }
 
-    fprintf(stderr, "%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
-    fprintf(stderr, "%s: hasUnifiedMemory             = %s\n",       __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
+    metal_printf("%s: recommendedMaxWorkingSetSize  = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
+    metal_printf("%s: hasUnifiedMemory              = %s\n",       __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
     if (ctx->device.maxTransferRate != 0) {
-        fprintf(stderr, "%s: maxTransferRate              = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
+        metal_printf("%s: maxTransferRate               = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
     } else {
-        fprintf(stderr, "%s: maxTransferRate              = built-in GPU\n", __func__);
+        metal_printf("%s: maxTransferRate               = built-in GPU\n", __func__);
     }
 
     return ctx;
 }
 
 void ggml_metal_free(struct ggml_metal_context * ctx) {
-    fprintf(stderr, "%s: deallocating\n", __func__);
+    metal_printf("%s: deallocating\n", __func__);
+#define GGML_METAL_DEL_KERNEL(name) \
+    [ctx->function_##name release]; \
+    [ctx->pipeline_##name release];
+
+    GGML_METAL_DEL_KERNEL(add);
+    GGML_METAL_DEL_KERNEL(add_row);
+    GGML_METAL_DEL_KERNEL(mul);
+    GGML_METAL_DEL_KERNEL(mul_row);
+    GGML_METAL_DEL_KERNEL(scale);
+    GGML_METAL_DEL_KERNEL(silu);
+    GGML_METAL_DEL_KERNEL(relu);
+    GGML_METAL_DEL_KERNEL(gelu);
+    GGML_METAL_DEL_KERNEL(soft_max);
+    GGML_METAL_DEL_KERNEL(diag_mask_inf);
+    GGML_METAL_DEL_KERNEL(get_rows_f16);
+    GGML_METAL_DEL_KERNEL(get_rows_q4_0);
+    GGML_METAL_DEL_KERNEL(get_rows_q4_1);
+    GGML_METAL_DEL_KERNEL(get_rows_q8_0);
+    GGML_METAL_DEL_KERNEL(get_rows_q2_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q3_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q4_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q5_K);
+    GGML_METAL_DEL_KERNEL(get_rows_q6_K);
+    GGML_METAL_DEL_KERNEL(rms_norm);
+    GGML_METAL_DEL_KERNEL(norm);
+    GGML_METAL_DEL_KERNEL(mul_mat_f16_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q4_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q4_1_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q8_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q2_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q3_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q4_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q5_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mat_q6_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_f16_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q4_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q4_1_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q2_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q3_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32);
+    GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32);
+    GGML_METAL_DEL_KERNEL(rope);
+    GGML_METAL_DEL_KERNEL(alibi_f32);
+    GGML_METAL_DEL_KERNEL(cpy_f32_f16);
+    GGML_METAL_DEL_KERNEL(cpy_f32_f32);
+    GGML_METAL_DEL_KERNEL(cpy_f16_f16);
+
+#undef GGML_METAL_DEL_KERNEL
+
     for (int i = 0; i < ctx->n_buffers; ++i) {
         [ctx->buffers[i].metal release];
     }
+
+    [ctx->library release];
+    [ctx->queue release];
+    [ctx->device release];
+
+    dispatch_release(ctx->d_queue);
+
     free(ctx);
 }
 
@@ -241,7 +312,7 @@ void * ggml_metal_host_malloc(size_t n) {
     void * data = NULL;
     const int result = posix_memalign((void **) &data, getpagesize(), n);
     if (result != 0) {
-        fprintf(stderr, "%s: error: posix_memalign failed\n", __func__);
+        metal_printf("%s: error: posix_memalign failed\n", __func__);
         return NULL;
     }
 
@@ -253,7 +324,7 @@ void ggml_metal_host_free(void * data) {
 }
 
 void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) {
-    ctx->n_cb = n_cb;
+    ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS);
 }
 
 int ggml_metal_if_optimized(struct ggml_metal_context * ctx) {
@@ -269,7 +340,7 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) {
 // Metal buffer based on the host memory pointer
 //
 static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) {
-    //fprintf(stderr, "%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
+    //metal_printf("%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
 
     const int64_t tsize = ggml_nbytes(t);
 
@@ -280,13 +351,13 @@ static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru
         if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) {
             *offs = (size_t) ioffs;
 
-            //fprintf(stderr, "%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs);
+            //metal_printf("%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs);
 
             return ctx->buffers[i].metal;
         }
     }
 
-    fprintf(stderr, "%s: error: buffer is nil\n", __func__);
+    metal_printf("%s: error: buffer is nil\n", __func__);
 
     return nil;
 }
@@ -298,7 +369,7 @@ bool ggml_metal_add_buffer(
                          size_t   size,
                          size_t   max_size) {
     if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) {
-        fprintf(stderr, "%s: too many buffers\n", __func__);
+        metal_printf("%s: too many buffers\n", __func__);
         return false;
     }
 
@@ -308,7 +379,7 @@ bool ggml_metal_add_buffer(
             const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data;
 
             if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
-                fprintf(stderr, "%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name);
+                metal_printf("%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name);
                 return false;
             }
         }
@@ -329,11 +400,11 @@ bool ggml_metal_add_buffer(
             ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
 
             if (ctx->buffers[ctx->n_buffers].metal == nil) {
-                fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
+                metal_printf("%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
                 return false;
             }
 
-            fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0);
+            metal_printf("%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0);
 
             ++ctx->n_buffers;
         } else {
@@ -353,27 +424,27 @@ bool ggml_metal_add_buffer(
                 ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
 
                 if (ctx->buffers[ctx->n_buffers].metal == nil) {
-                    fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
+                    metal_printf("%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
                     return false;
                 }
 
-                fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
+                metal_printf("%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
                 if (i + size_step < size) {
-                    fprintf(stderr, "\n");
+                    metal_printf("\n");
                 }
 
                 ++ctx->n_buffers;
             }
         }
 
-        fprintf(stderr, ", (%8.2f / %8.2f)",
+        metal_printf(", (%8.2f / %8.2f)",
                 ctx->device.currentAllocatedSize / 1024.0 / 1024.0,
                 ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
 
         if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) {
-            fprintf(stderr, ", warning: current allocated size is greater than the recommended max working set size\n");
+            metal_printf(", warning: current allocated size is greater than the recommended max working set size\n");
         } else {
-            fprintf(stderr, "\n");
+            metal_printf("\n");
         }
     }
 
@@ -383,8 +454,6 @@ bool ggml_metal_add_buffer(
 void ggml_metal_set_tensor(
         struct ggml_metal_context * ctx,
         struct ggml_tensor * t) {
-    metal_printf("%s: set input for tensor '%s'\n", __func__, t->name);
-
     size_t offs;
     id<MTLBuffer> id_dst = ggml_metal_get_buffer(ctx, t, &offs);
 
@@ -394,8 +463,6 @@ void ggml_metal_set_tensor(
 void ggml_metal_get_tensor(
         struct ggml_metal_context * ctx,
         struct ggml_tensor * t) {
-    metal_printf("%s: extract results for tensor '%s'\n", __func__, t->name);
-
     size_t offs;
     id<MTLBuffer> id_src = ggml_metal_get_buffer(ctx, t, &offs);
 
@@ -490,14 +557,14 @@ void ggml_metal_graph_find_concurrency(
     }
 
     if (ctx->concur_list_len > GGML_MAX_CONCUR) {
-        fprintf(stderr, "%s: too many elements for metal ctx->concur_list!\n", __func__);
+        metal_printf("%s: too many elements for metal ctx->concur_list!\n", __func__);
     }
 }
 
 void ggml_metal_graph_compute(
         struct ggml_metal_context * ctx,
                struct ggml_cgraph * gf) {
-    metal_printf("%s: evaluating graph\n", __func__);
+    @autoreleasepool {
 
     // if there is ctx->concur_list, dispatch concurrently
     // else fallback to serial dispatch
@@ -513,32 +580,28 @@ void ggml_metal_graph_compute(
 
     const int n_cb = ctx->n_cb;
 
-    NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb];
-
     for (int i = 0; i < n_cb; ++i) {
-        command_buffers[i] = [ctx->queue commandBuffer];
+        ctx->command_buffers[i] = [ctx->queue commandBuffer];
 
         // enqueue the command buffers in order to specify their execution order
-        [command_buffers[i] enqueue];
-    }
+        [ctx->command_buffers[i] enqueue];
 
-    // TODO: is this the best way to start threads?
-    dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
+        ctx->command_encoders[i] = [ctx->command_buffers[i] computeCommandEncoderWithDescriptor: edesc];
+    }
 
     for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
         const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb;
 
-        dispatch_async(queue, ^{
+        dispatch_async(ctx->d_queue, ^{
             size_t offs_src0 = 0;
             size_t offs_src1 = 0;
             size_t offs_dst  = 0;
 
-            id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx];
+            id<MTLCommandBuffer> command_buffer  = ctx->command_buffers[cb_idx];
+            id<MTLComputeCommandEncoder> encoder = ctx->command_encoders[cb_idx];
 
-            id<MTLComputeCommandEncoder> encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc];
-
-            const int node_start =                                  (cb_idx + 0) * n_nodes_per_cb;
-            const int node_end   = (cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb;
+            const int node_start =                                      (cb_idx + 0) * n_nodes_per_cb;
+            const int node_end   = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes);
 
             for (int ind = node_start; ind < node_end; ++ind) {
                 const int i = has_concur ? ctx->concur_list[ind] : ind;
@@ -548,7 +611,7 @@ void ggml_metal_graph_compute(
                     continue;
                 }
 
-                metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
+                //metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
 
                 struct ggml_tensor * src0 = gf->nodes[i]->src[0];
                 struct ggml_tensor * src1 = gf->nodes[i]->src[1];
@@ -634,6 +697,9 @@ void ggml_metal_graph_compute(
                         } break;
                     case GGML_OP_MUL:
                         {
+                            GGML_ASSERT(ne00 % 4 == 0);
+                            const int64_t nb = ne00/4;
+
                             if (ggml_nelements(src1) == ne10) {
                                 // src1 is a row
                                 [encoder setComputePipelineState:ctx->pipeline_mul_row];
@@ -643,9 +709,9 @@ void ggml_metal_graph_compute(
                             [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 setBytes:&ne00 length:sizeof(ne00) atIndex:3];
+                            [encoder setBytes:&nb     length:sizeof(nb) atIndex:3];
 
-                            const int64_t n = ggml_nelements(dst);
+                            const int64_t n = ggml_nelements(dst)/4;
 
                             [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                         } break;
@@ -696,7 +762,7 @@ void ggml_metal_graph_compute(
                                 } break;
                             default:
                                 {
-                                    fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
+                                    metal_printf("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
                                     GGML_ASSERT(false);
                                 }
                         } break;
@@ -744,32 +810,32 @@ void ggml_metal_graph_compute(
                                 [ctx->device supportsFamily:MTLGPUFamilyApple7] &&
                                 ne00%32 == 0 &&
                                 ne11 > 1) {
-                                    switch (src0->type) {
-                                        case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32]; break;
-                                        case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break;
-                                        case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break;
-                                        case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q2_K_f32]; break;
-                                        case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q3_K_f32]; break;
-                                        case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_K_f32]; break;
-                                        case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break;
-                                        case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break;
-                                        default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
-                                    }
-                                    [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 setBytes:&ne00 length:sizeof(ne00) atIndex:3];
-                                    [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4];
-                                    [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:5];
-                                    [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:6];
-                                    [encoder setBytes:&ne12 length:sizeof(ne12) atIndex:7];
-                                    [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:8];
-                                    [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:9];
-                                    [encoder setBytes:&gqa length:sizeof(gqa) atIndex:10];
-                                    [encoder setThreadgroupMemoryLength:8192 atIndex:0];
-                                    [encoder dispatchThreadgroups:MTLSizeMake( (ne11+31)/32, (ne01+63) / 64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
+                                switch (src0->type) {
+                                    case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_mul_mm_f16_f32];  break;
+                                    case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_0_f32]; break;
+                                    case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_1_f32]; break;
+                                    case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q8_0_f32]; break;
+                                    case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q2_K_f32]; break;
+                                    case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q3_K_f32]; break;
+                                    case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q4_K_f32]; break;
+                                    case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q5_K_f32]; break;
+                                    case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_mul_mm_q6_K_f32]; break;
+                                    default: GGML_ASSERT(false && "MUL MAT-MAT not implemented");
                                 }
-                            else {
+                                [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 setBytes:&ne00    length:sizeof(ne00) atIndex:3];
+                                [encoder setBytes:&ne02    length:sizeof(ne02) atIndex:4];
+                                [encoder setBytes:&nb01    length:sizeof(nb01) atIndex:5];
+                                [encoder setBytes:&nb02    length:sizeof(nb02) atIndex:6];
+                                [encoder setBytes:&ne12    length:sizeof(ne12) atIndex:7];
+                                [encoder setBytes:&ne0     length:sizeof(ne0)  atIndex:8];
+                                [encoder setBytes:&ne1     length:sizeof(ne1)  atIndex:9];
+                                [encoder setBytes:&gqa     length:sizeof(gqa)  atIndex:10];
+                                [encoder setThreadgroupMemoryLength:8192 atIndex:0];
+                                [encoder dispatchThreadgroups:MTLSizeMake( (ne11+31)/32, (ne01+63) / 64, ne12) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
+                            } else {
                                 int nth0 = 32;
                                 int nth1 = 1;
 
@@ -799,6 +865,15 @@ void ggml_metal_graph_compute(
                                             nth1 = 8;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_1_f32];
                                         } break;
+                                    case GGML_TYPE_Q8_0:
+                                        {
+                                            GGML_ASSERT(ne02 == 1);
+                                            GGML_ASSERT(ne12 == 1);
+
+                                            nth0 = 8;
+                                            nth1 = 8;
+                                            [encoder setComputePipelineState:ctx->pipeline_mul_mat_q8_0_f32];
+                                        } break;
                                     case GGML_TYPE_Q2_K:
                                         {
                                             GGML_ASSERT(ne02 == 1);
@@ -846,7 +921,7 @@ void ggml_metal_graph_compute(
                                         } break;
                                     default:
                                         {
-                                            fprintf(stderr, "Asserting on type %d\n",(int)src0t);
+                                            metal_printf("Asserting on type %d\n",(int)src0t);
                                             GGML_ASSERT(false && "not implemented");
                                         }
                                 };
@@ -868,24 +943,24 @@ void ggml_metal_graph_compute(
                                 [encoder setBytes:&nb12 length:sizeof(nb12) atIndex:14];
                                 [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:15];
                                 [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:16];
-                                [encoder setBytes:&gqa length:sizeof(gqa) atIndex:17];
+                                [encoder setBytes:&gqa  length:sizeof(gqa)  atIndex:17];
 
-                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
+                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q8_0 ||
                                     src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7) / 8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 }
                                 else if (src0t == GGML_TYPE_Q3_K) {
 #ifdef GGML_QKK_64
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
 #else
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
 #endif
                                 }
                                 else if (src0t == GGML_TYPE_Q5_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3) / 4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 }
                                 else if (src0t == GGML_TYPE_Q6_K) {
-                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 } else {
                                     [encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
                                     [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@@ -895,9 +970,10 @@ void ggml_metal_graph_compute(
                     case GGML_OP_GET_ROWS:
                         {
                             switch (src0->type) {
-                                case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break;
+                                case GGML_TYPE_F16:  [encoder setComputePipelineState:ctx->pipeline_get_rows_f16];  break;
                                 case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
                                 case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break;
+                                case GGML_TYPE_Q8_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q8_0]; break;
                                 case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_K]; break;
                                 case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_K]; break;
                                 case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break;
@@ -938,16 +1014,17 @@ void ggml_metal_graph_compute(
                         } break;
                     case GGML_OP_NORM:
                         {
-                            const float eps = 1e-5f;
+                            float eps;
+                            memcpy(&eps, dst->op_params, sizeof(float));
 
                             const int nth = 256;
 
                             [encoder setComputePipelineState:ctx->pipeline_norm];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
-                            [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
-                            [encoder setBytes:&eps  length:sizeof(   float) atIndex:4];
+                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
+                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:3];
+                            [encoder setBytes:&eps     length:sizeof(   float) atIndex:4];
                             [encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];
 
                             const int64_t nrows = ggml_nrows(src0);
@@ -990,7 +1067,9 @@ void ggml_metal_graph_compute(
                             [encoder setBytes:&nb2  length:sizeof(uint64_t) atIndex:16];
                             [encoder setBytes:&nb3  length:sizeof(uint64_t) atIndex:17];
                             [encoder setBytes:&m0  length:sizeof(    float) atIndex:18];
+
                             const int nth = 32;
+
                             [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
                     case GGML_OP_ROPE:
@@ -1005,8 +1084,8 @@ void ggml_metal_graph_compute(
                             memcpy(&freq_scale, (int32_t *) dst->op_params + 5, sizeof(float));
 
                             [encoder setComputePipelineState:ctx->pipeline_rope];
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
+                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
                             [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
                             [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:3];
                             [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:4];
@@ -1057,30 +1136,30 @@ void ggml_metal_graph_compute(
                                 default: GGML_ASSERT(false && "not implemented");
                             }
 
-                            [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                            [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
-                            [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
-                            [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
-                            [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
-                            [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
-                            [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
-                            [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
-                            [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
-                            [encoder setBytes:&ne0  length:sizeof( int64_t) atIndex:10];
-                            [encoder setBytes:&ne1  length:sizeof( int64_t) atIndex:11];
-                            [encoder setBytes:&ne2  length:sizeof( int64_t) atIndex:12];
-                            [encoder setBytes:&ne3  length:sizeof( int64_t) atIndex:13];
-                            [encoder setBytes:&nb0  length:sizeof(uint64_t) atIndex:14];
-                            [encoder setBytes:&nb1  length:sizeof(uint64_t) atIndex:15];
-                            [encoder setBytes:&nb2  length:sizeof(uint64_t) atIndex:16];
-                            [encoder setBytes:&nb3  length:sizeof(uint64_t) atIndex:17];
+                            [encoder setBuffer:id_src0 offset:offs_src0        atIndex:0];
+                            [encoder setBuffer:id_dst  offset:offs_dst         atIndex:1];
+                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                            [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:3];
+                            [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:4];
+                            [encoder setBytes:&ne03    length:sizeof( int64_t) atIndex:5];
+                            [encoder setBytes:&nb00    length:sizeof(uint64_t) atIndex:6];
+                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:7];
+                            [encoder setBytes:&nb02    length:sizeof(uint64_t) atIndex:8];
+                            [encoder setBytes:&nb03    length:sizeof(uint64_t) atIndex:9];
+                            [encoder setBytes:&ne0     length:sizeof( int64_t) atIndex:10];
+                            [encoder setBytes:&ne1     length:sizeof( int64_t) atIndex:11];
+                            [encoder setBytes:&ne2     length:sizeof( int64_t) atIndex:12];
+                            [encoder setBytes:&ne3     length:sizeof( int64_t) atIndex:13];
+                            [encoder setBytes:&nb0     length:sizeof(uint64_t) atIndex:14];
+                            [encoder setBytes:&nb1     length:sizeof(uint64_t) atIndex:15];
+                            [encoder setBytes:&nb2     length:sizeof(uint64_t) atIndex:16];
+                            [encoder setBytes:&nb3     length:sizeof(uint64_t) atIndex:17];
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                         } break;
                     default:
                         {
-                            fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
+                            metal_printf("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op));
                             GGML_ASSERT(false);
                         }
                 }
@@ -1096,17 +1175,19 @@ void ggml_metal_graph_compute(
     }
 
     // wait for all threads to finish
-    dispatch_barrier_sync(queue, ^{});
-
-    [command_buffers[n_cb - 1] waitUntilCompleted];
+    dispatch_barrier_sync(ctx->d_queue, ^{});
 
     // check status of command buffers
     // needed to detect if the device ran out-of-memory for example (#1881)
     for (int i = 0; i < n_cb; i++) {
-        MTLCommandBufferStatus status = (MTLCommandBufferStatus) [command_buffers[i] status];
+        [ctx->command_buffers[i] waitUntilCompleted];
+
+        MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status];
         if (status != MTLCommandBufferStatusCompleted) {
-            fprintf(stderr, "%s: command buffer %d failed with status %lu\n", __func__, i, status);
+            metal_printf("%s: command buffer %d failed with status %lu\n", __func__, i, status);
             GGML_ASSERT(false);
         }
     }
+
+    }
 }

ggml-metal.metal

@@ -4,20 +4,31 @@ using namespace metal;
 
 #define MAX(x, y) ((x) > (y) ? (x) : (y))
 
+#define Q4_0DM   (1.0f/8.0f)
+#define Q4_0D(x) (((x)*Q4_0DM) / 127.0f)
 #define QK4_0 32
 #define QR4_0 2
 typedef struct {
-    half    d;             // delta
+    int8_t  d;             // delta
     uint8_t qs[QK4_0 / 2]; // nibbles / quants
 } block_q4_0;
 
+#define Q4_1DM   (2.0f/15.0f)
+#define Q4_1MM   (2.0f      )
+#define Q4_1D(x) (        (((x) &  0xFF)*Q4_1DM) / 255.0f)
+#define Q4_1M(x) (-1.0f + (((x) >>    8)*Q4_1MM) / 255.0f)
 #define QK4_1 32
 typedef struct {
-    half d;          // delta
-    half m;          // min
+    uint16_t dm;
     uint8_t qs[QK4_1 / 2];  // nibbles / quants
 } block_q4_1;
 
+#define QK8_0 32
+typedef struct {
+    half    d;         // delta
+    int8_t  qs[QK8_0]; // quants
+} block_q8_0;
+
 kernel void kernel_add(
         device const float * src0,
         device const float * src1,
@@ -38,9 +49,9 @@ kernel void kernel_add_row(
 }
 
 kernel void kernel_mul(
-        device const float * src0,
-        device const float * src1,
-        device       float * dst,
+        device const float4 * src0,
+        device const float4 * src1,
+        device       float4 * dst,
         uint tpig[[thread_position_in_grid]]) {
     dst[tpig] = src0[tpig] * src1[tpig];
 }
@@ -48,12 +59,12 @@ kernel void kernel_mul(
 // assumption: src1 is a row
 // broadcast src1 into src0
 kernel void kernel_mul_row(
-        device const float * src0,
-        device const float * src1,
-        device       float * dst,
-        constant   int64_t & ne00,
+        device const float4 * src0,
+        device const float4 * src1,
+        device       float4 * dst,
+        constant    int64_t & nb,
         uint tpig[[thread_position_in_grid]]) {
-    dst[tpig] = src0[tpig] * src1[tpig % ne00];
+    dst[tpig] = src0[tpig] * src1[tpig % nb];
 }
 
 kernel void kernel_scale(
@@ -87,7 +98,12 @@ kernel void kernel_gelu(
     device       float * dst,
     uint tpig[[thread_position_in_grid]]) {
     float x = src0[tpig];
-    dst[tpig] = 0.5f*x*(1.0f + tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
+
+    // BEWARE !!!
+    // Simply using "tanh" instead of "precise::tanh" will sometimes results in NaNs!
+    // This was observed with Falcon 7B and 40B models
+    //
+    dst[tpig] = 0.5f*x*(1.0f + precise::tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x)));
 }
 
 kernel void kernel_soft_max(
@@ -303,14 +319,18 @@ kernel void kernel_rms_norm(
 // we assume that the yl's have been multiplied with the appropriate scale factor
 // that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096)
 inline float block_q_n_dot_y(device const block_q4_0 * qb_curr, float sumy, thread float * yl, int il) {
-    float d = qb_curr->d;
+    float d = Q4_0D(qb_curr->d);
     float2 acc = 0.f;
-    device const uint16_t * qs = ((device const uint16_t *)qb_curr + 1 + il/2);
+    device const uint8_t * qs = ((device const uint8_t *)qb_curr->qs + il);
+    uint16_t qs16;
     for (int i = 0; i < 8; i+=2) {
-        acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F)
-                + yl[i + 1] * (qs[i / 2] & 0x0F00);
-        acc[1] += yl[i + 8] * (qs[i / 2] & 0x00F0)
-                + yl[i + 9] * (qs[i / 2] & 0xF000);
+        qs16 = qs[i+1];
+        qs16 <<= 8;
+        qs16 |= qs[i];
+        acc[0] += yl[i + 0] * (qs16 & 0x000F)
+                + yl[i + 1] * (qs16 & 0x0F00);
+        acc[1] += yl[i + 8] * (qs16 & 0x00F0)
+                + yl[i + 9] * (qs16 & 0xF000);
     }
     return d * (sumy * -8.f + acc[0] + acc[1]);
 }
@@ -320,9 +340,9 @@ inline float block_q_n_dot_y(device const block_q4_0 * qb_curr, float sumy, thre
 // we assume that the yl's have been multiplied with the appropriate scale factor
 // that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096)
 inline float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thread float * yl, int il) {
-    float d = qb_curr->d;
-    float m = qb_curr->m;
-    device const uint16_t * qs = ((device const uint16_t *)qb_curr + 2 + il/2);
+    float d = Q4_1D(qb_curr->dm);
+    float m = Q4_1M(qb_curr->dm);
+    device const uint16_t * qs = ((device const uint16_t *)qb_curr + 1 + il/2);
     float2 acc = 0.f;
     for (int i = 0; i < 8; i+=2) {
         acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F)
@@ -352,7 +372,7 @@ void mul_vec_q_n_f32(device const void * src0, device const float * src1, device
     const int first_row = (r0 * nsg + sgitg) * nr;
     const uint offset0 = first_row * nb + im/gqa*(nb*ne0);
     device const block_q_type * x = (device const block_q_type *) src0 + offset0;
-    device const float      * y = (device const float      *) src1 + r1*ne10 + im*ne00*ne1;
+    device const float        * y = (device const float        *) src1 + r1*ne10 + im*ne00*ne1;
     float yl[16];       // src1 vector cache
     float sumf[nr]={0.f};
 
@@ -424,6 +444,68 @@ kernel void kernel_mul_mat_q4_1_f32(
      mul_vec_q_n_f32<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg);
 }
 
+kernel void kernel_mul_mat_q8_0_f32(
+        device const  void * src0,
+        device const float * src1,
+        device       float * dst,
+        constant   int64_t & ne00,
+        constant   int64_t & ne01[[buffer(4)]],
+        constant   int64_t & ne02[[buffer(5)]],
+        constant   int64_t & ne10[[buffer(9)]],
+        constant   int64_t & ne12[[buffer(11)]],
+        constant   int64_t & ne0[[buffer(15)]],
+        constant   int64_t & ne1[[buffer(16)]],
+        constant   uint    & gqa[[buffer(17)]],
+        uint3 tgpig[[threadgroup_position_in_grid]],
+        uint tiisg[[thread_index_in_simdgroup]],
+        uint sgitg[[simdgroup_index_in_threadgroup]]) {
+    const int nr  = N_DST;
+    const int nsg = N_SIMDGROUP;
+    const int nw  = N_SIMDWIDTH;
+
+    const int nb = ne00/QK8_0;
+    const int r0 = tgpig.x;
+    const int r1 = tgpig.y;
+    const int im = tgpig.z;
+    const int first_row = (r0 * nsg + sgitg) * nr;
+    const uint offset0 = first_row * nb + im/gqa*(nb*ne0);
+    device const block_q8_0 * x = (device const block_q8_0 *) src0 + offset0;
+    device const float      * y = (device const float      *) src1 + r1*ne10 + im*ne00*ne1;
+
+    float yl[16];
+    float sumf[nr]={0.f};
+
+    const int ix = tiisg/2;
+    const int il = tiisg%2;
+
+    device const float * yb = y + ix * QK8_0 + 16*il;
+
+    // each thread in a SIMD group deals with half a block.
+    for (int ib = ix; ib < nb; ib += nw/2) {
+        for (int i = 0; i < 16; ++i) {
+            yl[i] = yb[i];
+        }
+
+        for (int row = 0; row < nr; row++) {
+            device const int8_t * qs = x[ib+row*nb].qs + 16*il;
+            float sumq = 0.f;
+            for (int iq = 0; iq < 16; ++iq) {
+                sumq += qs[iq] * yl[iq];
+            }
+            sumf[row] += sumq*x[ib+row*nb].d;
+        }
+
+        yb += QK8_0 * 16;
+    }
+
+    for (int row = 0; row < nr; ++row) {
+        const float tot = simd_sum(sumf[row]);
+        if (tiisg == 0 && first_row + row < ne01) {
+            dst[r1*ne0 + im*ne0*ne1 + first_row + row] = tot;
+        }
+    }
+}
+
 kernel void kernel_mul_mat_f16_f32(
         device const  char * src0,
         device const  char * src1,
@@ -475,7 +557,6 @@ kernel void kernel_mul_mat_f16_f32(
     }
 }
 
-
 kernel void kernel_alibi_f32(
         device const float * src0,
         device       float * dst,
@@ -571,7 +652,25 @@ kernel void kernel_rope(
             dst_data[1] = x0*sin_theta + x1*cos_theta;
         }
     } else {
-        // TODO: implement
+        for (int64_t ib = 0; ib < ne0/n_dims; ++ib) {
+            for (int64_t ic = 0; ic < n_dims; ic += 2) {
+                const float cos_theta = cos(theta);
+                const float sin_theta = sin(theta);
+
+                theta *= theta_scale;
+
+                const int64_t i0 = ib*n_dims + ic/2;
+
+                device const float * const src = (device float *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00);
+                device       float * dst_data  = (device float *)((device char *)  dst + i3*nb3  + i2*nb2  + i1*nb1  + i0*nb0);
+
+                const float x0 = src[0];
+                const float x1 = src[n_dims/2];
+
+                dst_data[0]        = x0*cos_theta - x1*sin_theta;
+                dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta;
+            }
+        }
     }
 }
 
@@ -1596,32 +1695,46 @@ void dequantize_f16(device const half4x4 * src, short il, thread type4x4 & reg)
 
 template <typename type4x4>
 void dequantize_q4_0(device const block_q4_0 *xb, short il, thread type4x4 & reg) {
-    device const uint16_t * qs = ((device const uint16_t *)xb + 1);
-    const half d = il ? (xb->d / 16.h) : xb->d;
-    const half m = il ? (-8.h * 16.h) : -8.h;
+    device const uint8_t * qs = ((device const uint8_t *)xb->qs);
+    const half d = il ? (Q4_0D(xb->d) / 16.h) : Q4_0D(xb->d);
+    const half m = il ? ( -8.h * 16.h) : -8.h;
     const ushort mask0 = il ? 0x00F0 : 0x000F;
     const ushort mask1 = il ? 0xF000 : 0x0F00;
 
+    uint16_t qs16;
     for (int i=0;i<8;i++) {
-        reg[i/2][2*(i%2)] = (((qs[i] & mask0)) + m) * d;
-        reg[i/2][2*(i%2)+1] = (((qs[i] & mask1) >> 8) + m) * d;
+        qs16 = qs[2*i+1];
+        qs16 <<= 8;
+        qs16 |= qs[2*i];
+        reg[i/2][2*(i%2)]   = (((qs16 & mask0)     ) + m) * d;
+        reg[i/2][2*(i%2)+1] = (((qs16 & mask1) >> 8) + m) * d;
     }
 }
 
 template <typename type4x4>
 void dequantize_q4_1(device const block_q4_1 *xb, short il, thread type4x4 & reg) {
-    device const uint16_t * qs = ((device const uint16_t *)xb + 2);
-    const half d = il ? (xb->d / 16.h) : xb->d;
-    const half m = xb->m;
+    device const uint16_t * qs = ((device const uint16_t *)xb + 1);
+    const half d = il ? (Q4_1D(xb->dm) / 16.h) : Q4_1D(xb->dm);
+    const half m = Q4_1M(xb->dm);
     const ushort mask0 = il ? 0x00F0 : 0x000F;
     const ushort mask1 = il ? 0xF000 : 0x0F00;
 
     for (int i=0;i<8;i++) {
-        reg[i/2][2*(i%2)] = (((qs[i] & mask0)) * d) + m;
+        reg[i/2][2*(i%2)]   = (((qs[i] & mask0)     ) * d) + m;
         reg[i/2][2*(i%2)+1] = (((qs[i] & mask1) >> 8) * d) + m;
     }
 }
 
+template <typename type4x4>
+void dequantize_q8_0(device const block_q8_0 *xb, short il, thread type4x4 & reg) {
+    device const int8_t * qs = ((device const int8_t *)xb->qs);
+    const half d = xb->d;
+
+    for (int i=0;i<16;i++) {
+        reg[i/4][i%4] = (qs[i + 16*il] * d);
+    }
+}
+
 template <typename type4x4>
 void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg) {
     const half d = xb->d;
@@ -1850,6 +1963,7 @@ kernel void kernel_mul_mm(device const  uchar * src0,
         //load data and store to threadgroup memory
         half4x4 temp_a;
         dequantize_func(x, il, temp_a);
+        threadgroup_barrier(mem_flags::mem_threadgroup);
         #pragma unroll(16)
         for (int i = 0; i < 16; i++) {
             *(sa + SG_MAT_SIZE * ((tiitg / THREAD_PER_ROW / 8) \
@@ -1895,14 +2009,14 @@ kernel void kernel_mul_mm(device const  uchar * src0,
         }
     } else {
         // block is smaller than 64x32, we should avoid writing data outside of the matrix
+        threadgroup_barrier(mem_flags::mem_threadgroup);
         threadgroup float *temp_str = ((threadgroup float *)shared_memory) \
                                       + 32 * (sgitg&1) + (16 * (sgitg>>1)) * BLOCK_SIZE_M;
         for (int i = 0; i < 8; i++) {
-            threadgroup_barrier(mem_flags::mem_device);
             simdgroup_store(c_res[i], temp_str + 8 * (i%4) + 8 * BLOCK_SIZE_M * (i/4), BLOCK_SIZE_M);
         }
 
-        threadgroup_barrier(mem_flags::mem_device);
+        threadgroup_barrier(mem_flags::mem_threadgroup);
         device float *C = dst + BLOCK_SIZE_M * r0 + (BLOCK_SIZE_N * r1) * ne0 + im*ne1*ne0;
         if (sgitg==0) {
             for (int i = 0; i < n_rows; i++) {
@@ -1923,9 +2037,10 @@ kernel void kernel_mul_mm(device const  uchar * src0,
 typedef void (get_rows_t)(device const void *, device const int *, device float *, constant int64_t &, \
                           constant uint64_t &, constant uint64_t &, uint, uint, uint);
 
-template [[host_name("kernel_get_rows_f16")]] kernel get_rows_t kernel_get_rows<half4x4, 1, dequantize_f16>;
+template [[host_name("kernel_get_rows_f16")]]  kernel get_rows_t kernel_get_rows<half4x4,    1, dequantize_f16>;
 template [[host_name("kernel_get_rows_q4_0")]] kernel get_rows_t kernel_get_rows<block_q4_0, 2, dequantize_q4_0>;
 template [[host_name("kernel_get_rows_q4_1")]] kernel get_rows_t kernel_get_rows<block_q4_1, 2, dequantize_q4_1>;
+template [[host_name("kernel_get_rows_q8_0")]] kernel get_rows_t kernel_get_rows<block_q8_0, 2, dequantize_q8_0>;
 template [[host_name("kernel_get_rows_q2_K")]] kernel get_rows_t kernel_get_rows<block_q2_K, QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_get_rows_q3_K")]] kernel get_rows_t kernel_get_rows<block_q3_K, QK_NL, dequantize_q3_K>;
 template [[host_name("kernel_get_rows_q4_K")]] kernel get_rows_t kernel_get_rows<block_q4_K, QK_NL, dequantize_q4_K>;
@@ -1936,9 +2051,10 @@ typedef void (mat_mm_t)(device const uchar *, device const float *, device float
                              constant int64_t &, constant int64_t &, constant int64_t &, constant int64_t &, \
                              constant int64_t &, constant int64_t &, constant uint &, threadgroup uchar *, uint3, uint, uint);
 
-template [[host_name("kernel_mul_mm_f16_f32")]] kernel mat_mm_t kernel_mul_mm<half4x4, 1, dequantize_f16>;
+template [[host_name("kernel_mul_mm_f16_f32")]]  kernel mat_mm_t kernel_mul_mm<half4x4,    1, dequantize_f16>;
 template [[host_name("kernel_mul_mm_q4_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_0, 2, dequantize_q4_0>;
 template [[host_name("kernel_mul_mm_q4_1_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_1, 2, dequantize_q4_1>;
+template [[host_name("kernel_mul_mm_q8_0_f32")]] kernel mat_mm_t kernel_mul_mm<block_q8_0, 2, dequantize_q8_0>;
 template [[host_name("kernel_mul_mm_q2_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q2_K, QK_NL, dequantize_q2_K>;
 template [[host_name("kernel_mul_mm_q3_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q3_K, QK_NL, dequantize_q3_K>;
 template [[host_name("kernel_mul_mm_q4_K_f32")]] kernel mat_mm_t kernel_mul_mm<block_q4_K, QK_NL, dequantize_q4_K>;

ggml.h

@@ -130,13 +130,16 @@
 // The data of the tensor is accessed via the "data" pointer. For example:
 //
 //   {
-//       struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2, 3);
+//       const int nx = 2;
+//       const int ny = 3;
 //
-//       // a[2, 1] = 1.0f;
-//       *(float *) ((char *) a->data + 2*a->nb[1] + 1*a->nb[0]) = 1.0f;
+//       struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, ny);
 //
-//       // a[0, 2] = 2.0f;
-//       *(float *) ((char *) a->data + 0*a->nb[1] + 2*a->nb[0]) = 2.0f;
+//       for (int y = 0; y < ny; y++) {
+//           for (int x = 0; x < nx; x++) {
+//               *(float *) ((char *) a->data + y*a->nb[1] + x*a->nb[0]) = x + y;
+//           }
+//       }
 //
 //       ...
 //   }
@@ -211,11 +214,17 @@
 #define GGML_MAX_OP_PARAMS     32
 #define GGML_DEFAULT_N_THREADS 4
 
+#if UINTPTR_MAX == 0xFFFFFFFF
+    #define GGML_MEM_ALIGN 4
+#else
+    #define GGML_MEM_ALIGN 16
+#endif
+
 #define GGML_EXIT_SUCCESS 0
 #define GGML_EXIT_ABORTED 1
 
 #define GGUF_MAGIC   0x46554747 // "GGUF"
-#define GGUF_VERSION 1
+#define GGUF_VERSION 2
 
 #define GGUF_DEFAULT_ALIGNMENT 32
 
@@ -259,8 +268,9 @@
 extern "C" {
 #endif
 
-#ifdef __ARM_NEON
-    // we use the built-in 16-bit float type
+#if defined(__ARM_NEON) && defined(__CUDACC__)
+    typedef half ggml_fp16_t;
+#elif defined(__ARM_NEON)
     typedef __fp16 ggml_fp16_t;
 #else
     typedef uint16_t ggml_fp16_t;
@@ -344,10 +354,12 @@ extern "C" {
         GGML_OP_ARGMAX,
         GGML_OP_REPEAT,
         GGML_OP_REPEAT_BACK,
+        GGML_OP_CONCAT,
         GGML_OP_SILU_BACK,
         GGML_OP_NORM, // normalize
         GGML_OP_RMS_NORM,
         GGML_OP_RMS_NORM_BACK,
+        GGML_OP_GROUP_NORM,
 
         GGML_OP_MUL_MAT,
         GGML_OP_OUT_PROD,
@@ -373,14 +385,19 @@ extern "C" {
         GGML_OP_CLAMP,
         GGML_OP_CONV_1D,
         GGML_OP_CONV_2D,
+        GGML_OP_CONV_TRANSPOSE_2D,
         GGML_OP_POOL_1D,
         GGML_OP_POOL_2D,
 
+        GGML_OP_UPSCALE, // nearest interpolate
+
         GGML_OP_FLASH_ATTN,
         GGML_OP_FLASH_FF,
         GGML_OP_FLASH_ATTN_BACK,
         GGML_OP_WIN_PART,
         GGML_OP_WIN_UNPART,
+        GGML_OP_GET_REL_POS,
+        GGML_OP_ADD_REL_POS,
 
         GGML_OP_UNARY,
 
@@ -462,6 +479,9 @@ extern "C" {
         int64_t perf_cycles;
         int64_t perf_time_us;
 
+        struct ggml_tensor * view_src;
+        size_t               view_offs;
+
         void * data;
 
         char name[GGML_MAX_NAME];
@@ -644,7 +664,7 @@ extern "C" {
     GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
 
     GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
-    GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src);
+    GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src);
 
     GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name);
 
@@ -804,6 +824,13 @@ extern "C" {
             struct ggml_tensor  * a,
             struct ggml_tensor  * b);
 
+    // concat a and b on dim 2
+    // used in stable-diffusion
+    GGML_API struct ggml_tensor * ggml_concat(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
     GGML_API struct ggml_tensor * ggml_abs(
             struct ggml_context * ctx,
             struct ggml_tensor  * a);
@@ -893,14 +920,15 @@ extern "C" {
             struct ggml_tensor  * b);
 
     // normalize along rows
-    // TODO: eps is hardcoded to 1e-5 for now
     GGML_API struct ggml_tensor * ggml_norm(
             struct ggml_context * ctx,
-            struct ggml_tensor  * a);
+            struct ggml_tensor  * a,
+            float                 eps);
 
     GGML_API struct ggml_tensor * ggml_norm_inplace(
             struct ggml_context * ctx,
-            struct ggml_tensor  * a);
+            struct ggml_tensor  * a,
+            float                 eps);
 
     GGML_API struct ggml_tensor * ggml_rms_norm(
             struct ggml_context * ctx,
@@ -912,13 +940,26 @@ extern "C" {
             struct ggml_tensor  * a,
             float                 eps);
 
+    // group normalize along ne0*ne1*n_groups
+    // used in stable-diffusion
+    // TODO: eps is hardcoded to 1e-6 for now
+    GGML_API struct ggml_tensor * ggml_group_norm(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_groups);
+
+    GGML_API struct ggml_tensor * ggml_group_norm_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_groups);
+
     // a - x
     // b - dy
-    // TODO: update with configurable eps
     GGML_API struct ggml_tensor * ggml_rms_norm_back(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
-            struct ggml_tensor  * b);
+            struct ggml_tensor  * b,
+            float                 eps);
 
     // A: n columns, m rows
     // B: n columns, p rows  (i.e. we transpose it internally)
@@ -1212,6 +1253,15 @@ extern "C" {
             float                 freq_base,
             float                 freq_scale);
 
+    // xPos RoPE, in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_rope_xpos_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past,
+            int                   n_dims,
+            float                 base,
+            bool                  down);
+
     // rotary position embedding backward, i.e compute dx from dy
     // a - dy
     GGML_API struct ggml_tensor * ggml_rope_back(
@@ -1220,7 +1270,11 @@ extern "C" {
             int                   n_past,
             int                   n_dims,
             int                   mode,
-            int                   n_ctx);
+            int                   n_ctx,
+            float                 freq_base,
+            float                 freq_scale,
+            float                 xpos_base,
+            bool                  xpos_down);
 
     // alibi position embedding
     // in-place, returns view(a)
@@ -1247,6 +1301,15 @@ extern "C" {
             int                   p0,  // padding
             int                   d0); // dilation
 
+    // conv_1d with padding = half
+    // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
+    GGML_API struct ggml_tensor* ggml_conv_1d_ph(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b,
+            int                   s,
+            int                   d);
+
     GGML_API struct ggml_tensor * ggml_conv_2d(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
@@ -1258,14 +1321,38 @@ extern "C" {
             int                   d0,
             int                   d1);
 
-    // conv_1d with padding = half
-    // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
-    GGML_API struct ggml_tensor * ggml_conv_1d_ph(
+
+    // kernel size is a->ne[0] x a->ne[1]
+    // stride is equal to kernel size
+    // padding is zero
+    // example:
+    // a:     16   16    3  768
+    // b:   1024 1024    3    1
+    // res:   64   64  768    1
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    // kernel size is a->ne[0] x a->ne[1]
+    // stride is 1
+    // padding is half
+    // example:
+    // a:      3    3    256  256
+    // b:     64   64    256    1
+    // res:   64   64    256    1
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_conv_2d_s1_ph(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * b);
+
+    GGML_API struct ggml_tensor * ggml_conv_transpose_2d_p0(
             struct ggml_context * ctx,
             struct ggml_tensor  * a,
             struct ggml_tensor  * b,
-            int                   s,
-            int                   d);
+            int                   stride);
 
     enum ggml_op_pool {
         GGML_OP_POOL_MAX,
@@ -1292,6 +1379,13 @@ extern "C" {
             int                   p0,
             int                   p1);
 
+    // nearest interpolate
+    // used in stable-diffusion
+    GGML_API struct ggml_tensor * ggml_upscale(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   scale_factor);
+
     GGML_API struct ggml_tensor * ggml_flash_attn(
             struct ggml_context * ctx,
             struct ggml_tensor  * q,
@@ -1345,6 +1439,27 @@ extern "C" {
         struct ggml_tensor  * a,
         enum ggml_unary_op op);
 
+    // used in sam
+    GGML_API struct ggml_tensor * ggml_get_rel_pos(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   qh,
+            int                   kh);
+
+    // used in sam
+
+    GGML_API struct ggml_tensor * ggml_add_rel_pos(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * pw,
+            struct ggml_tensor  * ph);
+
+    GGML_API struct ggml_tensor * ggml_add_rel_pos_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            struct ggml_tensor  * pw,
+            struct ggml_tensor  * ph);
+
     // custom operators
 
     typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
@@ -1499,7 +1614,9 @@ extern "C" {
             struct ggml_context * ctx,
             struct ggml_tensor  * tensor);
 
-    GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
+
+    GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
+    GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep);
 
     GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
     GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
@@ -1564,6 +1681,8 @@ extern "C" {
         GGML_LINESEARCH_INVALID_PARAMETERS,
     };
 
+    typedef void (*ggml_opt_callback)(void * data, float * sched);
+
     // optimization parameters
     //
     //   see ggml.c (ggml_opt_default_params) for default values
@@ -1599,12 +1718,14 @@ extern "C" {
 
             float sched; // schedule multiplier (fixed, decay or warmup)
             float decay; // weight decay for AdamW, use 0.0f to disable
+            int   decay_min_ndim; // minimum number of tensor dimension to apply weight decay
             float alpha; // learning rate
             float beta1;
             float beta2;
             float eps;   // epsilon for numerical stability
             float eps_f; // epsilon for convergence test
             float eps_g; // epsilon for convergence test
+            float gclip; // gradient clipping
         } adam;
 
         // LBFGS parameters
@@ -1632,14 +1753,12 @@ extern "C" {
 
         bool just_initialized;
 
+        float loss_before;
+        float loss_after;
+
         struct {
-            struct ggml_tensor * x;  // view of the parameters
-            struct ggml_tensor * g1; // gradient
-            struct ggml_tensor * g2; // gradient squared
             struct ggml_tensor * m;  // first moment
             struct ggml_tensor * v;  // second moment
-            struct ggml_tensor * mh; // first moment hat
-            struct ggml_tensor * vh; // second moment hat
             struct ggml_tensor * pf; // past function values
             float fx_best;
             float fx_prev;
@@ -1676,10 +1795,10 @@ extern "C" {
 
     // initialize optimizer context
     GGML_API void ggml_opt_init(
-            struct ggml_context * ctx,
+            struct ggml_context     * ctx,
             struct ggml_opt_context * opt,
-            struct ggml_opt_params params,
-            int64_t nx);
+            struct ggml_opt_params    params,
+            int64_t                   nx);
 
     // continue optimizing the function defined by the tensor f
     GGML_API enum ggml_opt_result ggml_opt_resume(
@@ -1693,7 +1812,9 @@ extern "C" {
             struct ggml_opt_context * opt,
             struct ggml_tensor * f,
             struct ggml_cgraph * gf,
-            struct ggml_cgraph * gb);
+            struct ggml_cgraph * gb,
+            ggml_opt_callback callback,
+            void * callback_data);
 
     //
     // quantization
@@ -1722,6 +1843,9 @@ extern "C" {
         GGUF_TYPE_BOOL    = 7,
         GGUF_TYPE_STRING  = 8,
         GGUF_TYPE_ARRAY   = 9,
+        GGUF_TYPE_UINT64  = 10,
+        GGUF_TYPE_INT64   = 11,
+        GGUF_TYPE_FLOAT64 = 12,
         GGUF_TYPE_COUNT,       // marks the end of the enum
     };
 
@@ -1762,6 +1886,9 @@ extern "C" {
     GGML_API uint32_t     gguf_get_val_u32 (struct gguf_context * ctx, int i);
     GGML_API int32_t      gguf_get_val_i32 (struct gguf_context * ctx, int i);
     GGML_API float        gguf_get_val_f32 (struct gguf_context * ctx, int i);
+    GGML_API uint64_t     gguf_get_val_u64 (struct gguf_context * ctx, int i);
+    GGML_API int64_t      gguf_get_val_i64 (struct gguf_context * ctx, int i);
+    GGML_API double       gguf_get_val_f64 (struct gguf_context * ctx, int i);
     GGML_API bool         gguf_get_val_bool(struct gguf_context * ctx, int i);
     GGML_API const char * gguf_get_val_str (struct gguf_context * ctx, int i);
     GGML_API int          gguf_get_arr_n   (struct gguf_context * ctx, int i);
@@ -1781,6 +1908,9 @@ extern "C" {
     GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val);
     GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t  val);
     GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float    val);
+    GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val);
+    GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t  val);
+    GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double   val);
     GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool     val);
     GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val);
     GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n);
@@ -1839,6 +1969,7 @@ extern "C" {
     GGML_API int ggml_cpu_has_clblast    (void);
     GGML_API int ggml_cpu_has_gpublas    (void);
     GGML_API int ggml_cpu_has_sse3       (void);
+    GGML_API int ggml_cpu_has_ssse3      (void);
     GGML_API int ggml_cpu_has_vsx        (void);
 
     //

gguf-py/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2023 Georgi Gerganov
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

gguf-py/README.md

@@ -0,0 +1,72 @@
+## gguf
+
+This is a Python package for writing binary files in the [GGUF](https://github.com/ggerganov/ggml/pull/302)
+(GGML Universal File) format.
+
+See [convert-llama-hf-to-gguf.py](https://github.com/ggerganov/llama.cpp/blob/master/convert-llama-hf-to-gguf.py)
+as an example for its usage.
+
+## Installation
+```sh
+pip install gguf
+```
+
+## Development
+Maintainers who participate in development of this package are advised to install it in editable mode:
+
+```sh
+cd /path/to/llama.cpp/gguf-py
+
+pip install --editable .
+```
+
+**Note**: This may require to upgrade your Pip installation, with a message saying that editable installation currently requires `setup.py`.
+In this case, upgrade Pip to the latest:
+
+```sh
+pip install --upgrade pip
+```
+
+## Automatic publishing with CI
+
+There's a GitHub workflow to make a release automatically upon creation of tags in a specified format.
+
+1. Bump the version in `pyproject.toml`.
+2. Create a tag named `gguf-vx.x.x` where `x.x.x` is the semantic version number.
+
+```sh
+git tag -a gguf-v1.0.0 -m "Version 1.0 release"
+```
+
+3. Push the tags.
+
+```sh
+git push origin --tags
+```
+
+## Manual publishing
+If you want to publish the package manually for any reason, you need to have `twine` and `build` installed:
+
+```sh
+pip install build twine
+```
+
+Then, folow these steps to release a new version:
+
+1. Bump the version in `pyproject.toml`.
+2. Build the package:
+
+```sh
+python -m build
+```
+
+3. Upload the generated distribution archives:
+
+```sh
+python -m twine upload dist/*
+```
+
+## TODO
+- [ ] Add tests
+- [ ] Include conversion scripts as command line entry points in this package.
+- Add CI workflow for releasing the package.

gguf-py/gguf/__init__.py

@@ -0,0 +1 @@
+from .gguf import *

gguf-py/gguf/gguf.py

@@ -0,0 +1,858 @@
+#!/usr/bin/env python3
+import shutil
+import sys
+import struct
+import tempfile
+import numpy as np
+import json
+import os
+from pathlib import Path
+
+from enum import IntEnum, auto
+from io import BufferedWriter
+from typing import Any, BinaryIO, Callable, IO, Dict, List, Optional, Sequence, Tuple, Union
+
+#
+# constants
+#
+
+GGUF_MAGIC             = 0x46554747
+GGUF_VERSION           = 2
+GGUF_DEFAULT_ALIGNMENT = 32
+
+# general
+KEY_GENERAL_ARCHITECTURE         = "general.architecture"
+KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version"
+KEY_GENERAL_ALIGNMENT            = "general.alignment"
+KEY_GENERAL_NAME                 = "general.name"
+KEY_GENERAL_AUTHOR               = "general.author"
+KEY_GENERAL_URL                  = "general.url"
+KEY_GENERAL_DESCRIPTION          = "general.description"
+KEY_GENERAL_LICENSE              = "general.license"
+KEY_GENERAL_SOURCE_URL           = "general.source.url"
+KEY_GENERAL_SOURCE_HF_REPO       = "general.source.hugginface.repository"
+KEY_GENERAL_FILE_TYPE            = "general.file_type"
+
+# LLM
+KEY_CONTEXT_LENGTH        = "{arch}.context_length"
+KEY_EMBEDDING_LENGTH      = "{arch}.embedding_length"
+KEY_BLOCK_COUNT           = "{arch}.block_count"
+KEY_FEED_FORWARD_LENGTH   = "{arch}.feed_forward_length"
+KEY_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
+KEY_TENSOR_DATA_LAYOUT    = "{arch}.tensor_data_layout"
+
+# attention
+KEY_ATTENTION_HEAD_COUNT        = "{arch}.attention.head_count"
+KEY_ATTENTION_HEAD_COUNT_KV     = "{arch}.attention.head_count_kv"
+KEY_ATTENTION_MAX_ALIBI_BIAS    = "{arch}.attention.max_alibi_bias"
+KEY_ATTENTION_CLAMP_KQV         = "{arch}.attention.clamp_kqv"
+KEY_ATTENTION_LAYERNORM_EPS     = "{arch}.attention.layer_norm_epsilon"
+KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
+
+# RoPE
+KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
+KEY_ROPE_FREQ_BASE       = "{arch}.rope.freq_base"
+KEY_ROPE_SCALE_LINEAR    = "{arch}.rope.scale_linear"
+
+# tokenization
+KEY_TOKENIZER_MODEL      = "tokenizer.ggml.model"
+KEY_TOKENIZER_LIST       = "tokenizer.ggml.tokens"
+KEY_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type"
+KEY_TOKENIZER_SCORES     = "tokenizer.ggml.scores"
+KEY_TOKENIZER_MERGES     = "tokenizer.ggml.merges"
+KEY_TOKENIZER_BOS_ID     = "tokenizer.ggml.bos_token_id"
+KEY_TOKENIZER_EOS_ID     = "tokenizer.ggml.eos_token_id"
+KEY_TOKENIZER_UNK_ID     = "tokenizer.ggml.unknown_token_id"
+KEY_TOKENIZER_SEP_ID     = "tokenizer.ggml.seperator_token_id"
+KEY_TOKENIZER_PAD_ID     = "tokenizer.ggml.padding_token_id"
+KEY_TOKENIZER_HF_JSON    = "tokenizer.huggingface.json"
+KEY_TOKENIZER_RWKV       = "tokenizer.rwkv.world"
+
+
+#
+# recommended mapping of model tensor names for storage in gguf
+#
+
+
+class MODEL_ARCH(IntEnum):
+    LLAMA  : int = auto()
+    FALCON : int = auto()
+    GPT2   : int = auto()
+    GPTJ   : int = auto()
+    GPTNEOX: int = auto()
+    MPT    : int = auto()
+
+
+class MODEL_TENSOR(IntEnum):
+    TOKEN_EMBD   : int = auto()
+    POS_EMBD     : int = auto()
+    OUTPUT       : int = auto()
+    OUTPUT_NORM  : int = auto()
+    ROPE_FREQS   : int = auto()
+    ATTN_Q       : int = auto()
+    ATTN_K       : int = auto()
+    ATTN_V       : int = auto()
+    ATTN_QKV     : int = auto()
+    ATTN_OUT     : int = auto()
+    ATTN_NORM    : int = auto()
+    ATTN_NORM_2  : int = auto()
+    ATTN_ROT_EMBD: int = auto()
+    FFN_GATE     : int = auto()
+    FFN_DOWN     : int = auto()
+    FFN_UP       : int = auto()
+    FFN_NORM     : int = auto()
+
+
+MODEL_ARCH_NAMES: Dict[MODEL_ARCH, str] = {
+    MODEL_ARCH.LLAMA:   "llama",
+    MODEL_ARCH.FALCON:  "falcon",
+    MODEL_ARCH.GPT2:    "gpt2",
+    MODEL_ARCH.GPTJ:    "gptj",
+    MODEL_ARCH.GPTNEOX: "gptneox",
+    MODEL_ARCH.MPT:     "mpt",
+}
+
+MODEL_TENSOR_NAMES: Dict[MODEL_ARCH, Dict[MODEL_TENSOR, str]] = {
+    MODEL_ARCH.LLAMA: {
+        MODEL_TENSOR.TOKEN_EMBD:    "token_embd",
+        MODEL_TENSOR.OUTPUT_NORM:   "output_norm",
+        MODEL_TENSOR.OUTPUT:        "output",
+        MODEL_TENSOR.ROPE_FREQS:    "rope_freqs",
+        MODEL_TENSOR.ATTN_NORM:     "blk.{bid}.attn_norm",
+        MODEL_TENSOR.ATTN_Q:        "blk.{bid}.attn_q",
+        MODEL_TENSOR.ATTN_K:        "blk.{bid}.attn_k",
+        MODEL_TENSOR.ATTN_V:        "blk.{bid}.attn_v",
+        MODEL_TENSOR.ATTN_OUT:      "blk.{bid}.attn_output",
+        MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
+        MODEL_TENSOR.FFN_NORM:      "blk.{bid}.ffn_norm",
+        MODEL_TENSOR.FFN_GATE:      "blk.{bid}.ffn_gate",
+        MODEL_TENSOR.FFN_DOWN:      "blk.{bid}.ffn_down",
+        MODEL_TENSOR.FFN_UP:        "blk.{bid}.ffn_up",
+    },
+    MODEL_ARCH.GPTNEOX: {
+        MODEL_TENSOR.TOKEN_EMBD:    "token_embd",
+        MODEL_TENSOR.OUTPUT_NORM:   "output_norm",
+        MODEL_TENSOR.OUTPUT:        "output",
+        MODEL_TENSOR.ATTN_NORM:     "blk.{bid}.attn_norm",
+        MODEL_TENSOR.ATTN_QKV:      "blk.{bid}.attn_qkv",
+        MODEL_TENSOR.ATTN_OUT:      "blk.{bid}.attn_output",
+        MODEL_TENSOR.FFN_NORM:      "blk.{bid}.ffn_norm",
+        MODEL_TENSOR.FFN_DOWN:      "blk.{bid}.ffn_down",
+        MODEL_TENSOR.FFN_UP:        "blk.{bid}.ffn_up",
+    },
+    MODEL_ARCH.FALCON: {
+        MODEL_TENSOR.TOKEN_EMBD:  "token_embd",
+        MODEL_TENSOR.OUTPUT_NORM: "output_norm",
+        MODEL_TENSOR.OUTPUT:      "output",
+        MODEL_TENSOR.ATTN_NORM:   "blk.{bid}.attn_norm",
+        MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2",
+        MODEL_TENSOR.ATTN_QKV:    "blk.{bid}.attn_qkv",
+        MODEL_TENSOR.ATTN_OUT:    "blk.{bid}.attn_output",
+        MODEL_TENSOR.FFN_DOWN:    "blk.{bid}.ffn_down",
+        MODEL_TENSOR.FFN_UP:      "blk.{bid}.ffn_up",
+    },
+    MODEL_ARCH.GPT2: {
+        # TODO
+    },
+    # TODO
+}
+
+# tensors that will not be serialized
+MODEL_TENSOR_SKIP: Dict[MODEL_ARCH, List[MODEL_TENSOR]] = {
+    MODEL_ARCH.LLAMA: [
+        MODEL_TENSOR.ROPE_FREQS,
+        MODEL_TENSOR.ATTN_ROT_EMBD,
+    ],
+}
+
+
+class TensorNameMap:
+    mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = {
+        # Token embeddings
+        MODEL_TENSOR.TOKEN_EMBD: (
+            "gpt_neox.embed_in",           # gptneox
+            "transformer.wte",             # gpt2 mpt
+            "transformer.word_embeddings", # falcon
+            "model.embed_tokens",          # llama-hf
+            "tok_embeddings",              # llama-pth
+        ),
+
+        # Position embeddings
+        MODEL_TENSOR.POS_EMBD: (
+            "transformer.wpe", # gpt2
+        ),
+
+        # Output
+        MODEL_TENSOR.OUTPUT: (
+            "embed_out", # gptneox
+            "lm_head",   # gpt2 mpt falcon llama-hf
+            "output",    # llama-pth
+        ),
+
+        # Output norm
+        MODEL_TENSOR.OUTPUT_NORM: (
+            "gpt_neox.final_layer_norm", # gptneox
+            "transformer.ln_f",          # gpt2 falcon
+            "model.norm",                # llama-hf
+            "norm",                      # llama-pth
+        ),
+
+        # Rope frequencies
+        MODEL_TENSOR.ROPE_FREQS: (
+            "rope.freqs", # llama-pth
+        ),
+    }
+
+    block_mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = {
+        # Attention norm
+        MODEL_TENSOR.ATTN_NORM: (
+            "gpt_neox.layers.{bid}.input_layernorm", # gptneox
+            "transformer.h.{bid}.ln_1",              # gpt2
+            "transformer.blocks.{bid}.norm_1",       # mpt
+            "transformer.h.{bid}.input_layernorm",   # falcon7b
+            "transformer.h.{bid}.ln_mlp",            # falcon40b
+            "model.layers.{bid}.input_layernorm",    # llama-hf
+            "layers.{bid}.attention_norm",           # llama-pth
+        ),
+
+        # Attention norm 2
+        MODEL_TENSOR.ATTN_NORM_2: (
+            "transformer.h.{bid}.ln_attn", # falcon40b
+        ),
+
+        # Attention query-key-value
+        MODEL_TENSOR.ATTN_QKV: (
+            "gpt_neox.layers.{bid}.attention.query_key_value",    # gptneox
+            "transformer.h.{bid}.attn.c_attn",                    # gpt2
+            "transformer.blocks.{bid}.attn.Wqkv",                 # mpt
+            "transformer.h.{bid}.self_attention.query_key_value", # falcon
+        ),
+
+        # Attention query
+        MODEL_TENSOR.ATTN_Q: (
+            "model.layers.{bid}.self_attn.q_proj", # llama-hf
+            "layers.{bid}.attention.wq",           # llama-pth
+        ),
+
+        # Attention key
+        MODEL_TENSOR.ATTN_K: (
+            "model.layers.{bid}.self_attn.k_proj", # llama-hf
+            "layers.{bid}.attention.wk",           # llama-pth
+        ),
+
+        # Attention value
+        MODEL_TENSOR.ATTN_V: (
+            "model.layers.{bid}.self_attn.v_proj", # llama-hf
+            "layers.{bid}.attention.wv",           # llama-pth
+        ),
+
+        # Attention output
+        MODEL_TENSOR.ATTN_OUT: (
+            "gpt_neox.layers.{bid}.attention.dense",    # gptneox
+            "transformer.h.{bid}.attn.c_proj",          # gpt2
+            "transformer.blocks.{bid}.attn.out_proj",   # mpt
+            "transformer.h.{bid}.self_attention.dense", # falcon
+            "model.layers.{bid}.self_attn.o_proj",      # llama-hf
+            "layers.{bid}.attention.wo",                # llama-pth
+        ),
+
+        # Rotary embeddings
+        MODEL_TENSOR.ATTN_ROT_EMBD: (
+            "model.layers.{bid}.self_attn.rotary_emb.inv_freq",  # llama-hf
+            "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth
+        ),
+
+        # Feed-forward norm
+        MODEL_TENSOR.FFN_NORM: (
+            "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
+            "transformer.h.{bid}.ln_2",                       # gpt2
+            "transformer.blocks.{bid}.norm_2",                # mpt
+            "model.layers.{bid}.post_attention_layernorm",    # llama-hf
+            "layers.{bid}.ffn_norm",                          # llama-pth
+        ),
+
+        # Feed-forward up
+        MODEL_TENSOR.FFN_UP: (
+            "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox
+            "transformer.h.{bid}.mlp.c_fc",            # gpt2
+            "transformer.blocks.{bid}.ffn.up_proj",    # mpt
+            "transformer.h.{bid}.mlp.dense_h_to_4h",   # falcon
+            "model.layers.{bid}.mlp.up_proj",          # llama-hf
+            "layers.{bid}.feed_forward.w3",            # llama-pth
+        ),
+
+        # Feed-forward gate
+        MODEL_TENSOR.FFN_GATE: (
+            "model.layers.{bid}.mlp.gate_proj", # llama-hf
+            "layers.{bid}.feed_forward.w1",     # llama-pth
+        ),
+
+        # Feed-forward down
+        MODEL_TENSOR.FFN_DOWN: (
+            "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
+            "transformer.h.{bid}.mlp.c_proj",          # gpt2
+            "transformer.blocks.{bid}.ffn.down_proj",  # mpt
+            "transformer.h.{bid}.mlp.dense_4h_to_h",   # falcon
+            "model.layers.{bid}.mlp.down_proj",        # llama-hf
+            "layers.{bid}.feed_forward.w2",            # llama-pth
+        ),
+    }
+
+    mapping: Dict[str, Tuple[MODEL_TENSOR, str]]
+
+    tensor_names: Dict[MODEL_TENSOR, str]
+
+    def __init__(self, arch: MODEL_ARCH, n_blocks: int):
+        mapping = self.mapping = {}
+        tensor_names = self.tensor_names = MODEL_TENSOR_NAMES[arch]
+        for tensor, keys in self.mappings_cfg.items():
+            tensor_name = tensor_names.get(tensor)
+            if tensor_name is None:
+                continue
+            for key in keys:
+                mapping[key] = (tensor, tensor_name)
+        for bid in range(n_blocks):
+            for tensor, keys in self.block_mappings_cfg.items():
+                tensor_name = tensor_names.get(tensor)
+                if tensor_name is None:
+                    continue
+                tensor_name = tensor_name.format(bid = bid)
+                for key in keys:
+                    key = key.format(bid = bid)
+                    mapping[key] = (tensor, tensor_name)
+
+    def get_type_and_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[Tuple[MODEL_TENSOR, str]]:
+        result = self.mapping.get(key)
+        if result is not None:
+            return result
+        for suffix in try_suffixes:
+            if key.endswith(suffix):
+                result = self.mapping.get(key[:-len(suffix)])
+                if result is not None:
+                    return (result[0], result[1] + suffix)
+        return None
+
+    def get_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[str]:
+        result = self.get_type_and_name(key, try_suffixes = try_suffixes)
+        if result is None:
+            return None
+        return result[1]
+
+    def get_type(self, key: str, try_suffixes: Sequence[str]) -> Optional[MODEL_TENSOR]:
+        result = self.get_type_and_name(key, try_suffixes = try_suffixes)
+        if result is None:
+            return None
+        return result[0]
+
+    def __getitem__(self, key: str) -> str:
+        try:
+            return self.mapping[key][1]
+        except KeyError:
+            raise KeyError(key)
+
+    def __contains__(self, key: str) -> bool:
+        return key in self.mapping
+
+    def __repr__(self) -> str:
+        return repr(self.mapping)
+
+def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap:
+    return TensorNameMap(arch, n_blocks)
+
+class TokenType(IntEnum):
+    NORMAL       = 1
+    UNKNOWN      = 2
+    CONTROL      = 3
+    USER_DEFINED = 4
+    UNUSED       = 5
+    BYTE         = 6
+
+#
+# implementation
+#
+
+
+class GGMLQuantizationType(IntEnum):
+    F32  = 0
+    F16  = 1
+    Q4_0 = 2
+    Q4_1 = 3
+    Q5_0 = 6
+    Q5_1 = 7
+    Q8_0 = 8
+    Q8_1 = 9
+    Q2_K = 10
+    Q3_K = 11
+    Q4_K = 12
+    Q5_K = 13
+    Q6_K = 14
+    Q8_K = 15
+
+
+class GGUFValueType(IntEnum):
+    UINT8   = 0
+    INT8    = 1
+    UINT16  = 2
+    INT16   = 3
+    UINT32  = 4
+    INT32   = 5
+    FLOAT32 = 6
+    BOOL    = 7
+    STRING  = 8
+    ARRAY   = 9
+    UINT64  = 10
+    INT64   = 11
+    FLOAT64 = 12
+
+    @staticmethod
+    def get_type(val):
+        if isinstance(val, str) or isinstance(val, bytes) or isinstance(val, bytearray):
+            return GGUFValueType.STRING
+        elif isinstance(val, list):
+            return GGUFValueType.ARRAY
+        elif isinstance(val, float):
+            return GGUFValueType.FLOAT32
+        elif isinstance(val, bool):
+            return GGUFValueType.BOOL
+        elif isinstance(val, int):
+            return GGUFValueType.INT32
+        # TODO: need help with 64-bit types in Python
+        else:
+            print("Unknown type: "+str(type(val)))
+            sys.exit()
+
+
+class GGUFWriter:
+    fout: BufferedWriter
+    arch: str
+    offset_tensor = 0
+    data_alignment = GGUF_DEFAULT_ALIGNMENT
+    kv_data = b""
+    kv_data_count = 0
+    ti_data = b""
+    ti_data_count = 0
+    use_temp_file: bool
+    temp_file: Optional[tempfile.SpooledTemporaryFile[bytes]] = None
+    tensors: List[Tuple[np.ndarray[Any, Any], int]]
+
+    def __init__(self, path: Union[os.PathLike[str], str], arch: str, use_temp_file = True):
+        self.fout = open(path, "wb")
+        self.arch = arch
+        self.add_architecture()
+        self.use_temp_file = use_temp_file
+        self.tensors = []
+
+    def write_header_to_file(self):
+        self.fout.write(struct.pack("<I", GGUF_MAGIC))
+        self.fout.write(struct.pack("<I", GGUF_VERSION))
+        self.fout.write(struct.pack("<Q", self.ti_data_count))
+        self.fout.write(struct.pack("<Q", self.kv_data_count))
+        self.flush()
+#        print("tensors " + str(self.ti_data_count) + " kv " + str(self.kv_data_count))
+
+    def write_kv_data_to_file(self):
+        self.fout.write(self.kv_data)
+        self.flush()
+
+    def write_ti_data_to_file(self):
+        self.fout.write(self.ti_data)
+        self.flush()
+
+    def add_key(self, key: str):
+        self.add_val(key, GGUFValueType.STRING, add_vtype=False)
+
+    def add_uint8(self, key: str, val: int):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.UINT8)
+
+    def add_int8(self, key: str, val: int):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.INT8)
+
+    def add_uint16(self, key: str, val: int):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.UINT16)
+
+    def add_int16(self, key: str, val: int):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.INT16)
+
+    def add_uint32(self, key: str, val: int):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.UINT32)
+
+    def add_int32(self, key: str, val: int):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.INT32)
+
+    def add_float32(self, key: str, val: float):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.FLOAT32)
+
+    def add_uint64(self, key: str, val: int):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.UINT64)
+
+    def add_int64(self, key: str, val: int):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.INT64)
+
+    def add_float64(self, key: str, val: float):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.FLOAT64)
+
+    def add_bool(self, key: str, val: bool):
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.BOOL)
+
+    def add_string(self, key: str, val: str):
+        if len(val) == 0:
+            return
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.STRING)
+
+    def add_array(self, key: str, val: Sequence[Any]):
+        if not isinstance(val, Sequence):
+            raise ValueError("Value must be a sequence for array type")
+
+        self.add_key(key)
+        self.add_val(val, GGUFValueType.ARRAY)
+
+    _simple_value_packing = {
+        GGUFValueType.UINT8:   "<B",
+        GGUFValueType.INT8:    "<b",
+        GGUFValueType.UINT16:  "<H",
+        GGUFValueType.INT16:   "<h",
+        GGUFValueType.UINT32:  "<I",
+        GGUFValueType.INT32:   "<i",
+        GGUFValueType.FLOAT32: "<f",
+        GGUFValueType.UINT64:  "<Q",
+        GGUFValueType.INT64:   "<q",
+        GGUFValueType.FLOAT64: "<d",
+        GGUFValueType.BOOL:    "?" ,
+    }
+    def add_val(self, val: Any, vtype: Optional[GGUFValueType] = None, add_vtype: bool = True):
+        if vtype is None:
+            vtype = GGUFValueType.get_type(val)
+
+        if add_vtype:
+            self.kv_data += struct.pack("<I", vtype)
+            self.kv_data_count += 1
+
+        pack_fmt = self._simple_value_packing.get(vtype)
+        if pack_fmt is not None:
+            self.kv_data += struct.pack(pack_fmt, val)
+        elif vtype == GGUFValueType.STRING:
+            encoded_val = val.encode("utf8") if isinstance(val, str) else val
+            self.kv_data += struct.pack("<Q", len(encoded_val))
+            self.kv_data += encoded_val
+        elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and len(val) > 0:
+            ltype = GGUFValueType.get_type(val[0])
+            if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]):
+                raise ValueError("All items in a GGUF array should be of the same type")
+            self.kv_data += struct.pack("<I", ltype)
+            self.kv_data += struct.pack("<Q", len(val))
+            for item in val:
+                self.add_val(item, add_vtype=False)
+        else:
+            raise ValueError("Invalid GGUF metadata value type or value")
+
+    @staticmethod
+    def ggml_pad(x: int, n: int) -> int:
+        return ((x + n - 1) // n) * n
+
+    def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: Union[np.dtype[np.float16], np.dtype[np.float32]], tensor_nbytes: int, raw_dtype: Optional[GGMLQuantizationType] = None):
+        assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
+
+        encoded_name = name.encode("utf8")
+        self.ti_data += struct.pack("<Q", len(encoded_name))
+        self.ti_data += encoded_name
+        n_dims = len(tensor_shape)
+        self.ti_data += struct.pack("<I", n_dims)
+        for i in range(n_dims):
+            self.ti_data += struct.pack("<Q", tensor_shape[n_dims - 1 - i])
+        if raw_dtype is None:
+            dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
+        else:
+            dtype = raw_dtype
+        self.ti_data += struct.pack("<I", dtype)
+        self.ti_data += struct.pack("<Q", self.offset_tensor)
+        self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
+        self.ti_data_count += 1
+
+    def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Optional[Sequence[int]] = None, raw_dtype: Optional[GGMLQuantizationType] = None):
+        if self.use_temp_file and self.temp_file is None:
+            fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
+            fp.seek(0)
+            self.temp_file = fp
+
+        shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape
+        self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype)
+
+        pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
+
+        if  self.temp_file is None:
+            self.tensors.append((tensor, pad))
+            return
+
+        tensor.tofile(self.temp_file)
+
+        if pad != 0:
+            self.temp_file.write(bytes([0] * pad))
+
+    def write_padding(self, fp: BinaryIO, n: int, align: Optional[int] = None):
+        pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n
+        if pad != 0:
+            fp.write(bytes([0] * pad))
+
+    def write_tensor_data(self, tensor: np.ndarray[Any, Any]):
+        self.write_padding(self.fout, self.fout.tell())
+        tensor.tofile(self.fout)
+        self.write_padding(self.fout, tensor.nbytes)
+
+    def write_tensors_to_file(self):
+        self.write_ti_data_to_file()
+
+        self.write_padding(self.fout, self.fout.tell())
+
+        if self.temp_file is None:
+            for (currtensor, currpad) in self.tensors:
+                currtensor.tofile(self.fout)
+                if currpad != 0:
+                    self.fout.write(bytes([0] * currpad))
+            return
+
+        self.temp_file.seek(0)
+
+        shutil.copyfileobj(self.temp_file, self.fout)
+        self.flush()
+        self.temp_file.close()
+
+    def flush(self):
+        self.fout.flush()
+
+    def close(self):
+        self.fout.close()
+
+    def add_architecture(self):
+        self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch)
+
+    def add_author(self, author: str):
+        self.add_string(KEY_GENERAL_AUTHOR, author)
+
+    def add_tensor_data_layout(self, layout: str):
+        self.add_string(KEY_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
+
+    def add_url(self, url: str):
+        self.add_string(KEY_GENERAL_URL, url)
+
+    def add_description(self, description: str):
+        self.add_string(KEY_GENERAL_DESCRIPTION, description)
+
+    def add_source_url(self, url: str):
+        self.add_string(KEY_GENERAL_SOURCE_URL, url)
+
+    def add_source_hf_repo(self, repo: str):
+        self.add_string(KEY_GENERAL_SOURCE_HF_REPO, repo)
+
+    def add_file_type(self, ftype: int):
+        self.add_uint32(KEY_GENERAL_FILE_TYPE, ftype)
+
+    def add_name(self, name: str):
+        self.add_string(KEY_GENERAL_NAME, name)
+
+    def add_quantization_version(self, quantization_version: GGMLQuantizationType):
+        self.add_uint32(
+            KEY_GENERAL_QUANTIZATION_VERSION, quantization_version)
+
+    def add_custom_alignment(self, alignment: int):
+        self.data_alignment = alignment
+        self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment)
+
+    def add_context_length(self, length: int):
+        self.add_uint32(
+            KEY_CONTEXT_LENGTH.format(arch=self.arch), length)
+
+    def add_embedding_length(self, length: int):
+        self.add_uint32(
+            KEY_EMBEDDING_LENGTH.format(arch=self.arch), length)
+
+    def add_block_count(self, length: int):
+        self.add_uint32(
+            KEY_BLOCK_COUNT.format(arch=self.arch), length)
+
+    def add_feed_forward_length(self, length: int):
+        self.add_uint32(
+            KEY_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
+
+    def add_parallel_residual(self, use: bool):
+        self.add_bool(
+            KEY_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
+
+    def add_head_count(self, count: int):
+        self.add_uint32(
+            KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count)
+
+    def add_head_count_kv(self, count: int):
+        self.add_uint32(
+            KEY_ATTENTION_HEAD_COUNT_KV.format(arch=self.arch), count)
+
+    def add_max_alibi_bias(self, bias: float):
+        self.add_float32(
+            KEY_ATTENTION_MAX_ALIBI_BIAS.format(arch=self.arch), bias)
+
+    def add_clamp_kqv(self, value: float):
+        self.add_float32(
+            KEY_ATTENTION_CLAMP_KQV.format(arch=self.arch), value)
+
+    def add_layer_norm_eps(self, value: float):
+        self.add_float32(
+            KEY_ATTENTION_LAYERNORM_EPS.format(arch=self.arch), value)
+
+    def add_layer_norm_rms_eps(self, value: float):
+        self.add_float32(
+            KEY_ATTENTION_LAYERNORM_RMS_EPS.format(arch=self.arch), value)
+
+    def add_rope_dimension_count(self, count: int):
+        self.add_uint32(
+            KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count)
+
+    def add_rope_freq_base(self, value: float):
+        self.add_float32(KEY_ROPE_FREQ_BASE.format(arch=self.arch), value)
+
+    def add_rope_scale_linear(self, value: float):
+        self.add_float32(KEY_ROPE_SCALE_LINEAR.format(arch=self.arch), value)
+
+    def add_tokenizer_model(self, model: str):
+        self.add_string(KEY_TOKENIZER_MODEL, model)
+
+    def add_token_list(self, tokens: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]):
+        self.add_array(KEY_TOKENIZER_LIST, tokens)
+
+    def add_token_merges(self, merges: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]):
+        self.add_array(KEY_TOKENIZER_MERGES, merges)
+
+    def add_token_types(self, types: Union[Sequence[TokenType], Sequence[int]]):
+        self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types)
+
+    def add_token_scores(self, scores: Sequence[float]):
+        self.add_array(KEY_TOKENIZER_SCORES, scores)
+
+    def add_bos_token_id(self, id: int):
+        self.add_uint32(KEY_TOKENIZER_BOS_ID, id)
+
+    def add_eos_token_id(self, id: int):
+        self.add_uint32(KEY_TOKENIZER_EOS_ID, id)
+
+    def add_unk_token_id(self, id: int):
+        self.add_uint32(KEY_TOKENIZER_UNK_ID, id)
+
+    def add_sep_token_id(self, id: int):
+        self.add_uint32(KEY_TOKENIZER_SEP_ID, id)
+
+    def add_pad_token_id(self, id: int):
+        self.add_uint32(KEY_TOKENIZER_PAD_ID, id)
+
+
+class SpecialVocab:
+    load_merges: bool = False
+    merges: List[str] = []
+    special_token_types: Tuple[str, ...] = tuple(('bos', 'eos', 'unk', 'sep', 'pad'))
+    special_token_ids: Dict[str, int] = {}
+
+    def __init__(self, path: Path, load_merges: bool = False, special_token_types: Optional[Tuple[str, ...]] = None):
+        self.special_token_ids = {}
+        self.load_merges = load_merges
+        if special_token_types is not None:
+            self.special_token_types = special_token_types
+        self.load(path)
+
+    def load(self, path: Path):
+        if not self.try_load_from_tokenizer_json(path):
+            self.try_load_from_config_json(path)
+
+    def try_load_from_tokenizer_json(self, path: Path) -> bool:
+        tokenizer_file = path / 'tokenizer.json'
+        if not tokenizer_file.is_file():
+            return False
+        with open(tokenizer_file, 'r', encoding = 'utf-8') as f:
+            tokenizer = json.load(f)
+        if self.load_merges:
+            merges = tokenizer.get('model', {}).get('merges')
+            if isinstance(merges, list) and len(merges) > 0 and isinstance(merges[0], str):
+                self.merges = merges
+        tokenizer_config_file = path / 'tokenizer_config.json'
+        added_tokens = tokenizer.get('added_tokens')
+        if added_tokens is None or not tokenizer_config_file.is_file():
+            return True
+        with open(tokenizer_config_file, 'r', encoding = 'utf-8') as f:
+            tokenizer_config = json.load(f)
+        for typ in self.special_token_types:
+            entry = tokenizer_config.get(f'{typ}_token')
+            if isinstance(entry, str):
+                tc_content = entry
+            elif isinstance(entry, dict):
+                entry_content = entry.get('content')
+                if not isinstance(entry_content, str):
+                    continue
+                tc_content = entry_content
+            else:
+                continue
+            for maybe_token_id in (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content):
+                if isinstance(maybe_token_id, int):
+                    self.special_token_ids[typ] = maybe_token_id
+                break
+        return True
+
+    def try_load_from_config_json(self, path: Path) -> bool:
+        config_file = path / 'config.json'
+        if not config_file.is_file():
+            return False
+        with open(config_file, 'r', encoding = 'utf-8') as f:
+            config = json.load(f)
+        for typ in self.special_token_types:
+            maybe_token_id = config.get(f'{typ}_token_id')
+            if isinstance(maybe_token_id, int):
+                self.special_token_ids[typ] = maybe_token_id
+        return True
+
+    def add_to_gguf(self, gw: GGUFWriter):
+        if len(self.merges) > 0:
+            print(f'gguf: Adding {len(self.merges)} merge(s).')
+            gw.add_token_merges(self.merges)
+        for typ, tokid in self.special_token_ids.items():
+            handler: Optional[Callable[[int], None]] = getattr(gw, f'add_{typ}_token_id', None)
+            if handler is None:
+                print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping')
+                continue
+            print(f'gguf: Setting special token type {typ} to {tokid}')
+            handler(tokid)
+
+    def __repr__(self):
+        return f'<SpecialVocab with {len(self.merges)} merges and special tokens {self.special_token_ids if self.special_token_ids else "unset"}>'
+
+
+# Example usage:
+if __name__ == "__main__":
+    # Example usage with a file
+    gguf_writer = GGUFWriter("example.gguf", "llama")
+
+    gguf_writer.add_architecture()
+    gguf_writer.add_block_count(12)
+    gguf_writer.add_uint32("answer", 42)  # Write a 32-bit integer
+    gguf_writer.add_float32("answer_in_float", 42.0)  # Write a 32-bit float
+    gguf_writer.add_custom_alignment(64)
+
+    tensor1 = np.ones((32,), dtype=np.float32) * 100.0
+    tensor2 = np.ones((64,), dtype=np.float32) * 101.0
+    tensor3 = np.ones((96,), dtype=np.float32) * 102.0
+
+    gguf_writer.add_tensor("tensor1", tensor1)
+    gguf_writer.add_tensor("tensor2", tensor2)
+    gguf_writer.add_tensor("tensor3", tensor3)
+
+    gguf_writer.write_header_to_file()
+    gguf_writer.write_kv_data_to_file()
+    gguf_writer.write_tensors_to_file()
+
+    gguf_writer.close()

gguf-py/pyproject.toml

@@ -0,0 +1,29 @@
+[tool.poetry]
+name = "gguf"
+version = "0.2.1"
+description = "Write ML models in GGUF for GGML"
+authors = ["GGML <ggml@ggml.ai>"]
+packages = [
+    {include = "gguf"},
+    {include = "gguf/py.typed"},
+]
+readme = "README.md"
+homepage = "https://ggml.ai"
+repository = "https://github.com/ggerganov/llama.cpp"
+keywords = ["ggml", "gguf", "llama.cpp"]
+classifiers = [
+    "Programming Language :: Python :: 3",
+    "License :: OSI Approved :: MIT License",
+    "Operating System :: OS Independent",
+]
+
+[tool.poetry.dependencies]
+python = ">=3.8"
+numpy = ">=1.17"
+
+[tool.poetry.dev-dependencies]
+pytest = "^5.2"
+
+[build-system]
+requires = ["poetry-core>=1.0.0"]
+build-backend = "poetry.core.masonry.api"

gguf-py/tests/test_gguf.py

@@ -0,0 +1,7 @@
+import gguf
+
+# TODO: add tests
+
+
+def test_write_gguf():
+    pass

gguf.py

@@ -1,718 +0,0 @@
-import shutil
-import sys
-import struct
-import tempfile
-import numpy as np
-
-from enum import IntEnum, auto
-from typing import Any, IO, List, Optional
-
-#
-# constants
-#
-
-GGUF_MAGIC             = 0x46554747
-GGUF_VERSION           = 1
-GGUF_DEFAULT_ALIGNMENT = 32
-
-# general
-KEY_GENERAL_ARCHITECTURE         = "general.architecture"
-KEY_GENERAL_QUANTIZATION_VERSION = "general.quantization_version"
-KEY_GENERAL_ALIGNMENT            = "general.alignment"
-KEY_GENERAL_NAME                 = "general.name"
-KEY_GENERAL_AUTHOR               = "general.author"
-KEY_GENERAL_URL                  = "general.url"
-KEY_GENERAL_DESCRIPTION          = "general.description"
-KEY_GENERAL_LICENSE              = "general.license"
-KEY_GENERAL_SOURCE_URL           = "general.source.url"
-KEY_GENERAL_SOURCE_HF_REPO       = "general.source.hugginface.repository"
-
-# LLM
-KEY_LLM_CONTEXT_LENGTH        = "{arch}.context_length"
-KEY_LLM_EMBEDDING_LENGTH      = "{arch}.embedding_length"
-KEY_LLM_BLOCK_COUNT           = "{arch}.block_count"
-KEY_LLM_FEED_FORWARD_LENGTH   = "{arch}.feed_forward_length"
-KEY_LLM_USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
-KEY_LLM_TENSOR_DATA_LAYOUT    = "{arch}.tensor_data_layout"
-
-# attention
-KEY_ATTENTION_HEAD_COUNT        = "{arch}.attention.head_count"
-KEY_ATTENTION_HEAD_COUNT_KV     = "{arch}.attention.head_count_kv"
-KEY_ATTENTION_MAX_ALIBI_BIAS    = "{arch}.attention.max_alibi_bias"
-KEY_ATTENTION_CLAMP_KQV         = "{arch}.attention.clamp_kqv"
-KEY_ATTENTION_LAYERNORM_EPS     = "{arch}.attention.layer_norm_epsilon"
-KEY_ATTENTION_LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
-
-# RoPE
-KEY_ROPE_DIMENSION_COUNT = "{arch}.rope.dimension_count"
-KEY_ROPE_SCALE_LINEAR    = "{arch}.rope.scale_linear"
-
-# tokenization
-KEY_TOKENIZER_MODEL      = "tokenizer.ggml.model"
-KEY_TOKENIZER_LIST       = "tokenizer.ggml.tokens"
-KEY_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type"
-KEY_TOKENIZER_SCORES     = "tokenizer.ggml.scores"
-KEY_TOKENIZER_MERGES     = "tokenizer.ggml.merges"
-KEY_TOKENIZER_BOS_ID     = "tokenizer.ggml.bos_token_id"
-KEY_TOKENIZER_EOS_ID     = "tokenizer.ggml.eos_token_id"
-KEY_TOKENIZER_UNK_ID     = "tokenizer.ggml.unknown_token_id"
-KEY_TOKENIZER_SEP_ID     = "tokenizer.ggml.seperator_token_id"
-KEY_TOKENIZER_PAD_ID     = "tokenizer.ggml.padding_token_id"
-KEY_TOKENIZER_HF_JSON    = "tokenizer.huggingface.json"
-KEY_TOKENIZER_RWKV       = "tokenizer.rwkv.world"
-
-
-#
-# recommended mapping of model tensor names for storage in gguf
-#
-
-
-class MODEL_ARCH(IntEnum):
-    LLAMA   = auto()
-    FALCON  = auto()
-    GPT2    = auto()
-    GPTJ    = auto()
-    GPTNEOX = auto()
-    MPT     = auto()
-
-
-class MODEL_TENSOR(IntEnum):
-    TOKEN_EMBD    = auto()
-    POS_EMBD      = auto()
-    OUTPUT        = auto()
-    OUTPUT_NORM   = auto()
-    ROPE_FREQS    = auto()
-    ATTN_Q        = auto()
-    ATTN_K        = auto()
-    ATTN_V        = auto()
-    ATTN_QKV      = auto()
-    ATTN_OUT      = auto()
-    ATTN_NORM     = auto()
-    ATTN_NORM_2   = auto()
-    ATTN_ROT_EMBD = auto()
-    FFN_GATE      = auto()
-    FFN_DOWN      = auto()
-    FFN_UP        = auto()
-    FFN_NORM      = auto()
-
-
-MODEL_ARCH_NAMES = {
-    MODEL_ARCH.LLAMA:   "llama",
-    MODEL_ARCH.FALCON:  "falcon",
-    MODEL_ARCH.GPT2:    "gpt2",
-    MODEL_ARCH.GPTJ:    "gptj",
-    MODEL_ARCH.GPTNEOX: "gptneox",
-    MODEL_ARCH.MPT:     "mpt",
-}
-
-MODEL_TENSOR_NAMES = {
-    MODEL_ARCH.LLAMA: {
-        MODEL_TENSOR.TOKEN_EMBD:    "token_embd",
-        MODEL_TENSOR.OUTPUT_NORM:   "output_norm",
-        MODEL_TENSOR.OUTPUT:        "output",
-        MODEL_TENSOR.ROPE_FREQS:    "rope_freqs",
-        MODEL_TENSOR.ATTN_NORM:     "blk.{bid}.attn_norm",
-        MODEL_TENSOR.ATTN_Q:        "blk.{bid}.attn_q",
-        MODEL_TENSOR.ATTN_K:        "blk.{bid}.attn_k",
-        MODEL_TENSOR.ATTN_V:        "blk.{bid}.attn_v",
-        MODEL_TENSOR.ATTN_OUT:      "blk.{bid}.attn_output",
-        MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd",
-        MODEL_TENSOR.FFN_NORM:      "blk.{bid}.ffn_norm",
-        MODEL_TENSOR.FFN_GATE:      "blk.{bid}.ffn_gate",
-        MODEL_TENSOR.FFN_DOWN:      "blk.{bid}.ffn_down",
-        MODEL_TENSOR.FFN_UP:        "blk.{bid}.ffn_up",
-    },
-    MODEL_ARCH.GPTNEOX: {
-        MODEL_TENSOR.TOKEN_EMBD:    "token_embd",
-        MODEL_TENSOR.OUTPUT_NORM:   "output_norm",
-        MODEL_TENSOR.OUTPUT:        "output",
-        MODEL_TENSOR.ATTN_NORM:     "blk.{bid}.attn_norm",
-        MODEL_TENSOR.ATTN_QKV:      "blk.{bid}.attn_qkv",
-        MODEL_TENSOR.ATTN_OUT:      "blk.{bid}.attn_output",
-        MODEL_TENSOR.FFN_NORM:      "blk.{bid}.ffn_norm",
-        MODEL_TENSOR.FFN_DOWN:      "blk.{bid}.ffn_down",
-        MODEL_TENSOR.FFN_UP:        "blk.{bid}.ffn_up",
-    },
-    MODEL_ARCH.FALCON: {
-        MODEL_TENSOR.TOKEN_EMBD:  "token_embd",
-        MODEL_TENSOR.OUTPUT_NORM: "output_norm",
-        MODEL_TENSOR.OUTPUT:      "output",
-        MODEL_TENSOR.ATTN_NORM:   "blk.{bid}.attn_norm",
-        MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2",
-        MODEL_TENSOR.ATTN_QKV:    "blk.{bid}.attn_qkv",
-        MODEL_TENSOR.ATTN_OUT:    "blk.{bid}.attn_output",
-        MODEL_TENSOR.FFN_DOWN:    "blk.{bid}.ffn_down",
-        MODEL_TENSOR.FFN_UP:      "blk.{bid}.ffn_up",
-    },
-    MODEL_ARCH.GPT2: {
-        # TODO
-    },
-    # TODO
-}
-
-# tensors that will not be serialized
-MODEL_TENSOR_SKIP = {
-    MODEL_ARCH.LLAMA: [
-        MODEL_TENSOR.ROPE_FREQS,
-        MODEL_TENSOR.ATTN_ROT_EMBD,
-    ],
-}
-
-
-# TODO: the following helper functions should be removed
-#       instead, get_tensor_name_map should return tuples of (name, MODEL_TENSOR)
-#       however, my Python is very bad, and I couldn't figure out how to do this, hence these functions
-# REMOVE
-def should_skip_tensor_TMP(arch: MODEL_ARCH, n_blocks: int, name: str) -> bool:
-    for skip in MODEL_TENSOR_SKIP.get(arch, []):
-        for i in range(n_blocks):
-            if name == MODEL_TENSOR_NAMES[arch][skip].format(bid=i):
-                return True
-
-    return False
-
-
-def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict:
-    tensor_map = {}
-
-    # Token embeddings
-    mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.TOKEN_EMBD, None)
-
-    tensor_map["gpt_neox.embed_in"]           = mapped_to  # gptneox
-    tensor_map["transformer.wte"]             = mapped_to  # gpt2 mpt
-    tensor_map["transformer.word_embeddings"] = mapped_to  # falcon
-    tensor_map["model.embed_tokens"]          = mapped_to  # llama-hf
-    tensor_map["tok_embeddings"]              = mapped_to  # llama-pth
-
-    # Position embeddings
-    mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.POS_EMBD, None)
-
-    tensor_map["transformer.wpe"] = mapped_to  # gpt2
-
-    # Output
-    mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT, None)
-
-    tensor_map["embed_out"] = mapped_to  # gptneox
-    tensor_map["lm_head"]   = mapped_to  # gpt2 mpt falcon llama-hf
-    tensor_map["output"]    = mapped_to  # llama-pth
-
-    # Output norm
-    mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT_NORM, None)
-
-    tensor_map["gpt_neox.final_layer_norm"] = mapped_to  # gptneox
-    tensor_map["transformer.ln_f"]          = mapped_to  # gpt2 falcon
-    tensor_map["transformer.norm_f"]        = mapped_to  # mpt
-    tensor_map["model.norm"]                = mapped_to  # llama-hf
-    tensor_map["norm"]                      = mapped_to  # llama-pth
-
-    # Rope frequencies
-    mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ROPE_FREQS, None)
-
-    tensor_map["rope.freqs"] = mapped_to  # llama-pth
-
-    # Attention and feed-forward blocks
-    for i in range(0, n_blocks):
-        # Attention norm
-        # TODO: is there are simpler way to write these 2 lines in Python?
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to else None
-
-        tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to  # gptneox
-        tensor_map["transformer.h."+str(i)+".ln_1"]              = mapped_to  # gpt2
-        tensor_map["transformer.blocks."+str(i)+".norm_1"]       = mapped_to  # mpt
-        tensor_map["transformer.h."+str(i)+".input_layernorm"]   = mapped_to  # falcon7b
-        tensor_map["transformer.h."+str(i)+".ln_mlp"]            = mapped_to  # falcon40b
-        tensor_map["model.layers."+str(i)+".input_layernorm"]    = mapped_to  # llama-hf
-        tensor_map["layers."+str(i)+".attention_norm"]           = mapped_to  # llama-pth
-
-        # Attention norm 2
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM_2, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
-
-        tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to  # falcon40b
-
-        # Attention query-key-value
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_QKV, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
-
-        tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"]    = mapped_to  # gptneox
-        tensor_map["transformer.h."+str(i)+".attn.c_attn"]                    = mapped_to  # gpt2
-        tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"]                 = mapped_to  # mpt
-        tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to  # falcon
-
-        # Attention query
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_Q, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
-
-        tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to  # llama-hf
-        tensor_map["layers."+str(i)+".attention.wq"]           = mapped_to  # llama-pth
-
-        # Attention key
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_K, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
-
-        tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to  # llama-hf
-        tensor_map["layers."+str(i)+".attention.wk"]           = mapped_to  # llama-pth
-
-        # Attention value
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_V, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
-
-        tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to  # llama-hf
-        tensor_map["layers."+str(i)+".attention.wv"]           = mapped_to  # llama-pth
-
-        # Attention output
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_OUT, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
-
-        tensor_map["gpt_neox.layers."+str(i)+".attention.dense"]    = mapped_to  # gptneox
-        tensor_map["transformer.h."+str(i)+".attn.c_proj"]          = mapped_to  # gpt2
-        tensor_map["transformer.blocks."+str(i)+".attn.out_proj"]   = mapped_to  # mpt
-        tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to  # falcon
-        tensor_map["model.layers."+str(i)+".self_attn.o_proj"]      = mapped_to  # llama-hf
-        tensor_map["layers."+str(i)+".attention.wo"]                = mapped_to  # llama-pth
-
-        # Rotary embeddings
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_ROT_EMBD, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
-
-        tensor_map["model.layers."+str(i)+".self_attn.rotary_emb.inv_freq"]  = mapped_to  # llama-hf
-        tensor_map["layers."+str(i)+".attention.inner_attention.rope.freqs"] = mapped_to  # llama-pth
-
-        # Feed-forward norm
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_NORM, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
-
-        tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to  # gptneox
-        tensor_map["transformer.h."+str(i)+".ln_2"]                       = mapped_to  # gpt2
-        tensor_map["transformer.blocks."+str(i)+".norm_2"]                = mapped_to  # mpt
-        tensor_map["model.layers."+str(i)+".post_attention_layernorm"]    = mapped_to  # llama-hf
-        tensor_map["layers."+str(i)+".ffn_norm"]                          = mapped_to  # llama-pth
-
-        # Feed-forward up
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_UP, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
-
-        tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to  # gptneox
-        tensor_map["transformer.h."+str(i)+".mlp.c_fc"]            = mapped_to  # gpt2
-        tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"]    = mapped_to  # mpt
-        tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"]   = mapped_to  # falcon
-        tensor_map["model.layers."+str(i)+".mlp.up_proj"]          = mapped_to  # llama-hf
-        tensor_map["layers."+str(i)+".feed_forward.w3"]            = mapped_to  # llama-pth
-
-        # Feed-forward gate
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_GATE, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
-
-        tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to  # llama-hf
-        tensor_map["layers."+str(i)+".feed_forward.w1"]     = mapped_to  # llama-pth
-
-        # Feed-forward down
-        mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_DOWN, None)
-        mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None
-
-        tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to  # gptneox
-        tensor_map["transformer.h."+str(i)+".mlp.c_proj"]          = mapped_to  # gpt2
-        tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"]  = mapped_to  # mpt
-        tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"]   = mapped_to  # falcon
-        tensor_map["model.layers."+str(i)+".mlp.down_proj"]        = mapped_to  # llama-hf
-        tensor_map["layers."+str(i)+".feed_forward.w2"]            = mapped_to  # llama-pth
-
-    return tensor_map
-
-
-class TokenType(IntEnum):
-    NORMAL       = 1
-    UNKNOWN      = 2
-    CONTROL      = 3
-    USER_DEFINED = 4
-    UNUSED       = 5
-    BYTE         = 6
-
-#
-# implementation
-#
-
-
-class GGMLQuantizationType(IntEnum):
-    F32  = 0
-    F16  = 1
-    Q4_0 = 2
-    Q4_1 = 3
-    Q5_0 = 6
-    Q5_1 = 7
-    Q8_0 = 8
-    Q8_1 = 9
-    Q2_K = 10
-    Q3_K = 11
-    Q4_K = 12
-    Q5_K = 13
-    Q6_K = 14
-    Q8_K = 15
-
-
-class GGUFValueType(IntEnum):
-    UINT8   = 0
-    INT8    = 1
-    UINT16  = 2
-    INT16   = 3
-    UINT32  = 4
-    INT32   = 5
-    FLOAT32 = 6
-    BOOL    = 7
-    STRING  = 8
-    ARRAY   = 9
-
-    @staticmethod
-    def get_type(val):
-        if isinstance(val, str) or isinstance(val, bytes) or isinstance(val, bytearray):
-            return GGUFValueType.STRING
-        elif isinstance(val, list):
-            return GGUFValueType.ARRAY
-        elif isinstance(val, float):
-            return GGUFValueType.FLOAT32
-        elif isinstance(val, bool):
-            return GGUFValueType.BOOL
-        elif isinstance(val, int):
-            return GGUFValueType.INT32
-        else:
-            print("Unknown type: "+str(type(val)))
-            sys.exit()
-
-
-class GGUFWriter:
-    def __init__(self, path: str, arch: str, use_temp_file = True):
-        self.fout = open(path, "wb")
-        self.arch = arch
-        self.offset_tensor = 0
-        self.data_alignment = GGUF_DEFAULT_ALIGNMENT
-        self.kv_data = b""
-        self.kv_data_count = 0
-        self.ti_data = b""
-        self.ti_data_count = 0
-        self.add_architecture()
-        self.use_temp_file = use_temp_file
-        self.tensors = []
-
-    def write_header_to_file(self):
-        self.fout.write(struct.pack("<I", GGUF_MAGIC))
-        self.fout.write(struct.pack("<I", GGUF_VERSION))
-        self.fout.write(struct.pack("<I", self.ti_data_count))
-        self.fout.write(struct.pack("<I", self.kv_data_count))
-        self.flush()
-#        print("tensors " + str(self.ti_data_count) + " kv " + str(self.kv_data_count))
-
-    def write_kv_data_to_file(self):
-        self.fout.write(self.kv_data)
-        self.flush()
-
-    def write_ti_data_to_file(self):
-        self.fout.write(self.ti_data)
-        self.flush()
-
-    def add_key(self, key: str):
-        self.add_val(key, GGUFValueType.STRING, add_vtype=False)
-
-    def add_uint8(self, key: str, val: int):
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.UINT8)
-
-    def add_int8(self, key: str, val: int):
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.INT8)
-
-    def add_uint16(self, key: str, val: int):
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.UINT16)
-
-    def add_int16(self, key: str, val: int):
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.INT16)
-
-    def add_uint32(self, key: str, val: int):
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.UINT32)
-
-    def add_int32(self, key: str, val: int):
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.INT32)
-
-    def add_float32(self, key: str, val: float):
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.FLOAT32)
-
-    def add_bool(self, key: str, val: bool):
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.BOOL)
-
-    def add_string(self, key: str, val: str):
-        if len(val) == 0:
-            return
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.STRING)
-
-    def add_array(self, key: str, val: list):
-        if not isinstance(val, list):
-            raise ValueError("Value must be a list for array type")
-
-        self.add_key(key)
-        self.add_val(val, GGUFValueType.ARRAY)
-
-    def add_val(self: str, val: Any, vtype: GGUFValueType = None, add_vtype: bool = True):
-        if vtype is None:
-            vtype = GGUFValueType.get_type(val)
-
-        if add_vtype:
-            self.kv_data += struct.pack("<I", vtype)
-            self.kv_data_count += 1
-
-        if vtype == GGUFValueType.UINT8:
-            self.kv_data += struct.pack("<B", val)
-        elif vtype == GGUFValueType.INT8:
-            self.kv_data += struct.pack("<b", val)
-        elif vtype == GGUFValueType.UINT16:
-            self.kv_data += struct.pack("<H", val)
-        elif vtype == GGUFValueType.INT16:
-            self.kv_data += struct.pack("<h", val)
-        elif vtype == GGUFValueType.UINT32:
-            self.kv_data += struct.pack("<I", val)
-        elif vtype == GGUFValueType.INT32:
-            self.kv_data += struct.pack("<i", val)
-        elif vtype == GGUFValueType.FLOAT32:
-            self.kv_data += struct.pack("<f", val)
-        elif vtype == GGUFValueType.BOOL:
-            self.kv_data += struct.pack("?", val)
-        elif vtype == GGUFValueType.STRING:
-            encoded_val = val.encode("utf8") if isinstance(val, str) else val
-            self.kv_data += struct.pack("<I", len(encoded_val))
-            self.kv_data += encoded_val
-        elif vtype == GGUFValueType.ARRAY:
-            ltype = set([GGUFValueType.get_type(item) for item in val])
-            assert len(ltype) == 1, "All items in a GGUF array should be of the same type"
-            self.kv_data += struct.pack("<I", list(ltype)[0])
-            self.kv_data += struct.pack("<I", len(val))
-            for item in val:
-                self.add_val(item, add_vtype=False)
-        else:
-            raise ValueError("Invalid GGUF metadata value type")
-
-    @staticmethod
-    def ggml_pad(x: int, n: int) -> int:
-        return ((x + n - 1) // n) * n
-
-    def add_tensor_info(self, name: str, tensor_shape: np.ndarray, tensor_dtype: np.dtype, tensor_nbytes: int, raw_dtype: Optional[GGMLQuantizationType] = None):
-        assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now"
-
-        encoded_name = name.encode("utf8")
-        self.ti_data += struct.pack("<I", len(encoded_name))
-        self.ti_data += encoded_name
-        n_dims = len(tensor_shape)
-        self.ti_data += struct.pack("<I", n_dims)
-        for i in range(n_dims):
-            self.ti_data += struct.pack("<I", tensor_shape[n_dims - 1 - i])
-        if raw_dtype is None:
-            dtype = GGMLQuantizationType.F32 if tensor_dtype == np.float32 else GGMLQuantizationType.F16
-        else:
-            dtype = raw_dtype
-        self.ti_data += struct.pack("<I", dtype)
-        self.ti_data += struct.pack("<Q", self.offset_tensor)
-        self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment)
-        self.ti_data_count += 1
-
-    def add_tensor(self, name: str, tensor: np.ndarray, raw_shape: Optional[np.ndarray] = None, raw_dtype: Optional[GGMLQuantizationType] = None):
-        if self.use_temp_file and not hasattr(self, "temp_file"):
-            self.temp_file = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024)
-            self.temp_file.seek(0)
-
-        self.add_tensor_info(name, raw_shape if raw_shape is not None else tensor.shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype)
-
-        pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
-
-        if not self.use_temp_file:
-            self.tensors.append((tensor, pad))
-            return
-
-        tensor.tofile(self.temp_file)
-
-        if pad != 0:
-            self.temp_file.write(bytes([0] * pad))
-
-    def write_tensor_data(self, tensor: np.ndarray):
-        pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
-        if pad != 0:
-            self.fout.write(bytes([0] * pad))
-
-        tensor.tofile(self.fout)
-
-        pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes
-        if pad != 0:
-            self.fout.write(bytes([0] * pad))
-
-    def write_tensors_to_file(self):
-        self.write_ti_data_to_file()
-
-        pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell()
-        if pad != 0:
-            self.fout.write(bytes([0] * pad))
-
-        if not self.use_temp_file:
-            for (currtensor, currpad) in self.tensors:
-                currtensor.tofile(self.fout)
-                if currpad != 0:
-                    self.fout.write(bytes([0] * currpad))
-            return
-
-        self.temp_file.seek(0)
-
-        shutil.copyfileobj(self.temp_file, self.fout)
-        self.flush()
-        self.temp_file.close()
-
-    def flush(self):
-        self.fout.flush()
-
-    def close(self):
-        self.fout.close()
-
-    def add_architecture(self):
-        self.add_string(KEY_GENERAL_ARCHITECTURE, self.arch)
-
-    def add_author(self, author: str):
-        self.add_string(KEY_GENERAL_AUTHOR, author)
-
-    def add_tensor_data_layout(self, layout: str):
-        self.add_string(KEY_LLM_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
-
-    def add_url(self, url: str):
-        self.add_string(KEY_GENERAL_URL, url)
-
-    def add_description(self, description: str):
-        self.add_string(KEY_GENERAL_DESCRIPTION, description)
-
-    def add_source_url(self, url: str):
-        self.add_string(KEY_GENERAL_SOURCE_URL, url)
-
-    def add_source_hf_repo(self, repo: str):
-        self.add_string(KEY_GENERAL_SOURCE_HF_REPO, repo)
-
-    def add_name(self, name: str):
-        self.add_string(KEY_GENERAL_NAME, name)
-
-    def add_quantization_version(self, quantization_version: GGMLQuantizationType):
-        self.add_uint32(
-            KEY_GENERAL_QUANTIZATION_VERSION, quantization_version)
-
-    def add_custom_alignment(self, alignment: int):
-        self.data_alignment = alignment
-        self.add_uint32(KEY_GENERAL_ALIGNMENT, alignment)
-
-    def add_context_length(self, length: int):
-        self.add_uint32(
-            KEY_LLM_CONTEXT_LENGTH.format(arch=self.arch), length)
-
-    def add_embedding_length(self, length: int):
-        self.add_uint32(
-            KEY_LLM_EMBEDDING_LENGTH.format(arch=self.arch), length)
-
-    def add_block_count(self, length: int):
-        self.add_uint32(
-            KEY_LLM_BLOCK_COUNT.format(arch=self.arch), length)
-
-    def add_feed_forward_length(self, length: int):
-        self.add_uint32(
-            KEY_LLM_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
-
-    def add_parallel_residual(self, use: bool):
-        self.add_bool(
-            KEY_LLM_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use)
-
-    def add_tensor_data_layout(self, layout: str):
-        self.add_string(
-            KEY_LLM_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout)
-
-    def add_head_count(self, count: int):
-        self.add_uint32(
-            KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count)
-
-    def add_head_count_kv(self, count: int):
-        self.add_uint32(
-            KEY_ATTENTION_HEAD_COUNT_KV.format(arch=self.arch), count)
-
-    def add_max_alibi_bias(self, bias: float):
-        self.add_float32(
-            KEY_ATTENTION_MAX_ALIBI_BIAS.format(arch=self.arch), bias)
-
-    def add_clamp_kqv(self, value: float):
-        self.add_float32(
-            KEY_ATTENTION_CLAMP_KQV.format(arch=self.arch), value)
-
-    def add_layer_norm_eps(self, value: float):
-        self.add_float32(
-            KEY_ATTENTION_LAYERNORM_EPS.format(arch=self.arch), value)
-
-    def add_layer_norm_rms_eps(self, value: float):
-        self.add_float32(
-            KEY_ATTENTION_LAYERNORM_RMS_EPS.format(arch=self.arch), value)
-
-    def add_rope_dimension_count(self, count: int):
-        self.add_uint32(
-            KEY_ROPE_DIMENSION_COUNT.format(arch=self.arch), count)
-
-    def add_rope_scale_linear(self, value:  float):
-        self.add_float32(KEY_ROPE_SCALE_LINEAR.format(arch=self.arch), value)
-
-    def add_tokenizer_model(self, model: str):
-        self.add_string(KEY_TOKENIZER_MODEL, model)
-
-    def add_token_list(self, tokens: List):
-        self.add_array(KEY_TOKENIZER_LIST, tokens)
-
-    def add_token_merges(self, merges: List):
-        self.add_array(KEY_TOKENIZER_MERGES, merges)
-
-    def add_token_types(self, types: List[int]):
-        self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types)
-
-    def add_token_scores(self, scores: List[float]):
-        self.add_array(KEY_TOKENIZER_SCORES, scores)
-
-    def add_bos_token_id(self, id: int):
-        self.add_uint32(KEY_TOKENIZER_BOS_ID, id)
-
-    def add_eos_token_id(self, id: int):
-        self.add_uint32(KEY_TOKENIZER_EOS_ID, id)
-
-    def add_unk_token_id(self, id: int):
-        self.add_uint32(KEY_TOKENIZER_UNK_ID, id)
-
-    def add_sep_token_id(self, id: int):
-        self.add_uint32(KEY_TOKENIZER_SEP_ID, id)
-
-    def add_pad_token_id(self, id: int):
-        self.add_uint32(KEY_TOKENIZER_PAD_ID, id)
-
-
-# Example usage:
-if __name__ == "__main__":
-    # Example usage with a file
-    gguf_writer = GGUFWriter("example.gguf", "llama")
-
-    gguf_writer.add_architecture()
-    gguf_writer.add_block_count(12)
-    gguf_writer.add_uint32("answer", 42)  # Write a 32-bit integer
-    gguf_writer.add_float32("answer_in_float", 42.0)  # Write a 32-bit float
-    gguf_writer.add_custom_alignment(64)
-
-    tensor1 = np.ones((32,), dtype=np.float32) * 100.0
-    tensor2 = np.ones((64,), dtype=np.float32) * 101.0
-    tensor3 = np.ones((96,), dtype=np.float32) * 102.0
-
-    gguf_writer.add_tensor("tensor1", tensor1)
-    gguf_writer.add_tensor("tensor2", tensor2)
-    gguf_writer.add_tensor("tensor3", tensor3)
-
-    gguf_writer.write_header_to_file()
-    gguf_writer.write_kv_data_to_file()
-    gguf_writer.write_tensors_to_file()
-
-    gguf_writer.close()

grammars/README.md

@@ -0,0 +1,91 @@
+# GBNF Guide
+
+GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `examples/main` and `examples/server`.
+
+## Background
+
+[Bakus-Naur Form (BNF)](https://en.wikipedia.org/wiki/Backus%E2%80%93Naur_form) is a notation for describing the syntax of formal languages like programming languages, file formats, and protocols. GBNF is an extension of BNF that primarily adds a few modern regex-like features.
+
+## Basics
+
+In GBNF, we define *production rules* that specify how a *non-terminal* (rule name) can be replaced with sequences of *terminals* (characters, specifically Unicode [code points](https://en.wikipedia.org/wiki/Code_point)) and other non-terminals. The basic format of a production rule is `nonterminal ::= sequence...`.
+
+## Example
+
+Before going deeper, let's look at some of the features demonstrated in `grammars/chess.gbnf`, a small chess notation grammar:
+```
+# `root` specifies the pattern for the overall output
+root ::= (
+    # it must start with the characters "1. " followed by a sequence
+    # of characters that match the `move` rule, followed by a space, followed
+    # by another move, and then a newline
+    "1. " move " " move "\n"
+
+    # it's followed by one or more subsequent moves, numbered with one or two digits
+    ([1-9] [0-9]? ". " move " " move "\n")+
+)
+
+# `move` is an abstract representation, which can be a pawn, nonpawn, or castle.
+# The `[+#]?` denotes the possibility of checking or mate signs after moves
+move ::= (pawn | nonpawn | castle) [+#]?
+
+pawn ::= ...
+nonpawn ::= ...
+castle ::= ...
+```
+
+## Non-Terminals and Terminals
+
+Non-terminal symbols (rule names) stand for a pattern of terminals and other non-terminals. They are required to be a dashed lowercase word, like `move`, `castle`, or `check-mate`.
+
+Terminals are actual characters ([code points](https://en.wikipedia.org/wiki/Code_point)). They can be specified as a sequence like `"1"` or `"O-O"` or as ranges like `[1-9]` or `[NBKQR]`.
+
+## Characters and character ranges
+
+Terminals support the full range of Unicode. Unicode characters can be specified directly in the grammar, for example `hiragana ::= [ぁ-ゟ]`, or with escapes: 8-bit (`\xXX`), 16-bit (`\uXXXX`) or 32-bit (`\UXXXXXXXX`).
+
+Character ranges can be negated with `^`:
+```
+single-line ::= [^\n]+ "\n"`
+```
+
+## Sequences and Alternatives
+
+The order of symbols in a sequence matter. For example, in `"1. " move " " move "\n"`, the `"1. "` must come before the first `move`, etc.
+
+Alternatives, denoted by `|`, give different sequences that are acceptable. For example, in `move ::= pawn | nonpawn | castle`, `move` can be a `pawn` move, a `nonpawn` move, or a `castle`.
+
+Parentheses `()` can be used to group sequences, which allows for embedding alternatives in a larger rule or applying repetition and optptional symbols (below) to a sequence.
+
+## Repetition and Optional Symbols
+
+- `*` after a symbol or sequence means that it can be repeated zero or more times.
+- `+` denotes that the symbol or sequence should appear one or more times.
+- `?` makes the preceding symbol or sequence optional.
+
+## Comments and newlines
+
+Comments can be specified with `#`:
+```
+# defines optional whitspace
+ws ::= [ \t\n]+
+```
+
+Newlines are allowed between rules and between symbols or sequences nested inside parentheses. Additionally, a newline after an alternate marker `|` will continue the current rule, even outside of parentheses.
+
+## The root rule
+
+In a full grammar, the `root` rule always defines the starting point of the grammar. In other words, it specifies what the entire output must match.
+
+```
+# a grammar for lists
+root ::= ("- " item)+
+item ::= [^\n]+ "\n"
+```
+
+## Next steps
+
+This guide provides a brief overview. Check out the GBNF files in this directory (`grammars/`) for examples of full grammars. You can try them out with:
+```
+./main -m <model> --grammar-file grammars/some-grammar.gbnf -p 'Some prompt'
+```

k_quants.c

@@ -77,6 +77,11 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t *
         }
         return 1/iscale;
     }
+    bool return_early = false;
+    if (rmse_type < 0) {
+        rmse_type = -rmse_type;
+        return_early = true;
+    }
     int weight_type = rmse_type%2;
     float sumlx = 0;
     float suml2 = 0;
@@ -89,56 +94,9 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t *
         suml2 += w*l*l;
     }
     float scale = sumlx/suml2;
+    if (return_early) return suml2 > 0 ? 0.5f*(scale + 1/iscale) : 1/iscale;
     float best = scale * sumlx;
-    for (int itry = 0; itry < 3; ++itry) {
-        iscale = 1/scale;
-        float slx = 0;
-        float sl2 = 0;
-        bool changed = false;
-        for (int i = 0; i < n; ++i) {
-            int l = nearest_int(iscale * x[i]);
-            l = MAX(-nmax, MIN(nmax-1, l));
-            if (l + nmax != L[i]) { changed = true; }
-            float w = weight_type == 1 ? x[i] * x[i] : 1.f;
-            slx += w*x[i]*l;
-            sl2 += w*l*l;
-        }
-        if (!changed || sl2 == 0 || slx*slx <= best*sl2) { break; }
-        for (int i = 0; i < n; ++i) {
-            int l = nearest_int(iscale * x[i]);
-            L[i] = nmax + MAX(-nmax, MIN(nmax-1, l));
-        }
-        sumlx = slx; suml2 = sl2;
-        scale = sumlx/suml2;
-        best = scale * sumlx;
-    }
-    for (int itry = 0; itry < 5; ++itry) {
-        int n_changed = 0;
-        for (int i = 0; i < n; ++i) {
-            float w = weight_type == 1 ? x[i]*x[i] : 1;
-            int l = L[i] - nmax;
-            float slx = sumlx - w*x[i]*l;
-            if (slx > 0) {
-                float sl2 = suml2 - w*l*l;
-                int new_l = nearest_int(x[i] * sl2 / slx);
-                new_l = MAX(-nmax, MIN(nmax-1, new_l));
-                if (new_l != l) {
-                    slx += w*x[i]*new_l;
-                    sl2 += w*new_l*new_l;
-                    if (sl2 > 0 && slx*slx*suml2 > sumlx*sumlx*sl2) {
-                        L[i] = nmax + new_l; sumlx = slx; suml2 = sl2;
-                        scale = sumlx / suml2; best = scale * sumlx;
-                        ++n_changed;
-                    }
-                }
-            }
-        }
-        if (!n_changed) { break; }
-    }
-    if (rmse_type < 3) {
-        return scale;
-    }
-    for (int is = -4; is <= 4; ++is) {
+    for (int is = -9; is <= 9; ++is) {
         if (is == 0) {
             continue;
         }
@@ -221,12 +179,17 @@ static float make_q3_quants(int n, int nmax, const float * restrict x, int8_t *
     return 1/iscale;
 }
 
-static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, float * restrict the_min, int ntry) {
+static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t * restrict L, float * restrict the_min,
+        int ntry, float alpha) {
     float min = x[0];
     float max = x[0];
+    float sum_x = 0;
+    float sum_x2 = 0;
     for (int i = 1; i < n; ++i) {
         if (x[i] < min) min = x[i];
         if (x[i] > max) max = x[i];
+        sum_x += x[i];
+        sum_x2 += x[i]*x[i];
     }
     if (max == min) {
         for (int i = 0; i < n; ++i) L[i] = 0;
@@ -254,7 +217,7 @@ static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t
         for (int i = 0; i < n; ++i) {
             sum += x[i] - scale*L[i];
         }
-        min = sum/n;
+        min = alpha*min + (1 - alpha)*sum/n;
         if (min > 0) min = 0;
         iscale = 1/scale;
         if (!did_change) break;
@@ -263,6 +226,82 @@ static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t
     return scale;
 }
 
+static float make_qkx2_quants(int n, int nmax, const float * restrict x, const float * restrict weights,
+        uint8_t * restrict L, float * restrict the_min, uint8_t * restrict Laux,
+        float rmin, float rdelta, int nstep, bool use_mad) {
+    float min = x[0];
+    float max = x[0];
+    float sum_w = weights[0];
+    float sum_x = sum_w * x[0];
+    for (int i = 1; i < n; ++i) {
+        if (x[i] < min) min = x[i];
+        if (x[i] > max) max = x[i];
+        float w = weights[i];
+        sum_w += w;
+        sum_x += w * x[i];
+    }
+    if (min > 0) min = 0;
+    if (max == min) {
+        for (int i = 0; i < n; ++i) L[i] = 0;
+        *the_min = -min;
+        return 0.f;
+    }
+    float iscale = nmax/(max - min);
+    float scale = 1/iscale;
+    float best_mad = 0;
+    for (int i = 0; i < n; ++i) {
+        int l = nearest_int(iscale*(x[i] - min));
+        L[i] = MAX(0, MIN(nmax, l));
+        float diff = scale * L[i] + min - x[i];
+        diff = use_mad ? fabsf(diff) : diff * diff;
+        float w = weights[i];
+        best_mad += w * diff;
+    }
+    if (nstep < 1) {
+        *the_min = -min;
+        return scale;
+    }
+    for (int is = 0; is <= nstep; ++is) {
+        iscale = (rmin + rdelta*is + nmax)/(max - min);
+        float sum_l = 0, sum_l2 = 0, sum_xl = 0;
+        for (int i = 0; i < n; ++i) {
+            int l = nearest_int(iscale*(x[i] - min));
+            l = MAX(0, MIN(nmax, l));
+            Laux[i] = l;
+            float w = weights[i];
+            sum_l += w*l;
+            sum_l2 += w*l*l;
+            sum_xl += w*l*x[i];
+        }
+        float D = sum_w * sum_l2 - sum_l * sum_l;
+        if (D > 0) {
+            float this_scale = (sum_w * sum_xl - sum_x * sum_l)/D;
+            float this_min   = (sum_l2 * sum_x - sum_l * sum_xl)/D;
+            if (this_min > 0) {
+                this_min = 0;
+                this_scale = sum_xl / sum_l2;
+            }
+            float mad = 0;
+            for (int i = 0; i < n; ++i) {
+                float diff = this_scale * Laux[i] + this_min - x[i];
+                diff = use_mad ? fabsf(diff) : diff * diff;
+                float w = weights[i];
+                mad += w * diff;
+            }
+            if (mad < best_mad) {
+                for (int i = 0; i < n; ++i) {
+                    L[i] = Laux[i];
+                }
+                best_mad = mad;
+                scale = this_scale;
+                min = this_min;
+            }
+        }
+    }
+    *the_min = -min;
+    return scale;
+}
+
 #if QK_K == 256
 static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) {
     if (j < 4) {
@@ -281,6 +320,8 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict
     const int nb = k / QK_K;
 
     uint8_t L[QK_K];
+    uint8_t Laux[16];
+    float   weights[16];
     float mins[QK_K/16];
     float scales[QK_K/16];
 
@@ -291,7 +332,8 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict
         float max_scale = 0; // as we are deducting the min, scales are always positive
         float max_min = 0;
         for (int j = 0; j < QK_K/16; ++j) {
-            scales[j] = make_qkx1_quants(16, 3, x + 16*j, L + 16*j, &mins[j], 5);
+            for (int l = 0; l < 16; ++l) weights[l] = fabsf(x[16*j + l]);
+            scales[j] = make_qkx2_quants(16, 3, x + 16*j, weights, L + 16*j, &mins[j], Laux, -0.5f, 0.1f, 15, true);
             float scale = scales[j];
             if (scale > max_scale) {
                 max_scale = scale;
@@ -637,6 +679,8 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
     const int nb = k / QK_K;
 
     uint8_t L[QK_K];
+    uint8_t Laux[32];
+    float   weights[32];
     float mins[QK_K/32];
     float scales[QK_K/32];
 
@@ -645,7 +689,12 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict
         float max_scale = 0; // as we are deducting the min, scales are always positive
         float max_min = 0;
         for (int j = 0; j < QK_K/32; ++j) {
-            scales[j] = make_qkx1_quants(32, 15, x + 32*j, L + 32*j, &mins[j], 5);
+            //scales[j] = make_qkx1_quants(32, 15, x + 32*j, L + 32*j, &mins[j], 9, 0.5f);
+            float sum_x2 = 0;
+            for (int l = 0; l < 32; ++l) sum_x2 += x[32*j + l] * x[32*j + l];
+            float av_x = sqrtf(sum_x2/32);
+            for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
+            scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false);
             float scale = scales[j];
             if (scale > max_scale) {
                 max_scale = scale;
@@ -798,6 +847,8 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
     uint8_t L[QK_K];
     float mins[QK_K/32];
     float scales[QK_K/32];
+    float weights[32];
+    uint8_t Laux[32];
 #else
     int8_t L[QK_K];
     float scales[QK_K/16];
@@ -810,7 +861,12 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict
         float max_scale = 0; // as we are deducting the min, scales are always positive
         float max_min = 0;
         for (int j = 0; j < QK_K/32; ++j) {
-            scales[j] = make_qkx1_quants(32, 31, x + 32*j, L + 32*j, &mins[j], 5);
+            //scales[j] = make_qkx1_quants(32, 31, x + 32*j, L + 32*j, &mins[j], 9, 0.5f);
+            float sum_x2 = 0;
+            for (int l = 0; l < 32; ++l) sum_x2 += x[32*j + l] * x[32*j + l];
+            float av_x = sqrtf(sum_x2/32);
+            for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]);
+            scales[j] = make_qkx2_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.5f, 0.1f, 15, false);
             float scale = scales[j];
             if (scale > max_scale) {
                 max_scale = scale;
@@ -2638,13 +2694,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri
             const __m256i q8l = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
             __m256i p16l = _mm256_maddubs_epi16(q4l, q8l);
             p16l = _mm256_madd_epi16(scale_l, p16l);
-            sumi = _mm256_add_epi32(sumi, p16l);
 
             const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32;
             __m256i p16h = _mm256_maddubs_epi16(q4h, q8h);
             p16h = _mm256_madd_epi16(scale_h, p16h);
-            sumi = _mm256_add_epi32(sumi, p16h);
+            const __m256i sumj = _mm256_add_epi32(p16l, p16h);
 
+            sumi = _mm256_add_epi32(sumi, sumj);
         }
 
         __m256 vd = _mm256_set1_ps(d);

llama.h

@@ -10,6 +10,7 @@
 #endif // GGML_USE_CUBLAS
 #include <stddef.h>
 #include <stdint.h>
+#include <stdio.h>
 #include <stdbool.h>
 
 #ifdef LLAMA_SHARED
@@ -103,6 +104,8 @@ extern "C" {
         LLAMA_FTYPE_MOSTLY_Q5_K_S        = 16,// except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q5_K_M        = 17,// except 1d tensors
         LLAMA_FTYPE_MOSTLY_Q6_K          = 18,// except 1d tensors
+
+        LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file
     };
 
     typedef struct llama_token_data {
@@ -245,12 +248,18 @@ extern "C" {
     LLAMA_API int llama_n_ctx  (const struct llama_context * ctx);
     LLAMA_API int llama_n_embd (const struct llama_context * ctx);
 
+    LLAMA_API enum llama_vocab_type llama_vocab_type(const struct llama_context * ctx);
+
     LLAMA_API int llama_model_n_vocab(const struct llama_model * model);
     LLAMA_API int llama_model_n_ctx  (const struct llama_model * model);
     LLAMA_API int llama_model_n_embd (const struct llama_model * model);
 
     // Get a string describing the model type
-    LLAMA_API int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size);
+    LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size);
+    // Returns the total size of all the tensors in the model in bytes
+    LLAMA_API uint64_t llama_model_size(const struct llama_model * model);
+    // Returns the total number of parameters in the model
+    LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model);
 
     // Returns 0 on success
     LLAMA_API int llama_model_quantize(
@@ -344,7 +353,7 @@ extern "C" {
 
     LLAMA_API float llama_token_get_score(const struct llama_context * ctx, llama_token token);
 
-    LLAMA_API llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
+    LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_context * ctx, llama_token token);
 
     // Special tokens
     LLAMA_API llama_token llama_token_bos(const struct llama_context * ctx);  // beginning-of-sentence
@@ -366,13 +375,6 @@ extern "C" {
                              int   n_max_tokens,
                             bool   add_bos);
 
-    LLAMA_API int llama_tokenize_bpe(
-            struct llama_context * ctx,
-                      const char * text,
-                     llama_token * tokens,
-                             int   n_max_tokens,
-                            bool   add_bos);
-
     LLAMA_API int llama_tokenize_with_model(
         const struct llama_model * model,
                       const char * text,
@@ -380,21 +382,17 @@ extern "C" {
                              int   n_max_tokens,
                             bool   add_bos);
 
-    // Token Id -> String. Uses the vocabulary in the provided context
-    // Does not write null terminator to the buffer
-    LLAMA_API int llama_token_to_str(
+    // Token Id -> Piece.
+    // Uses the vocabulary in the provided context.
+    // Does not write null terminator to the buffer.
+    // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens.
+    LLAMA_API int llama_token_to_piece(
             const struct llama_context * ctx,
                            llama_token   token,
                                   char * buf,
                                   int    length);
 
-    LLAMA_API int llama_token_to_str_bpe(
-            const struct llama_context * ctx,
-                           llama_token   token,
-                                  char * buf,
-                                  int    length);
-
-    LLAMA_API int llama_token_to_str_with_model(
+    LLAMA_API int llama_token_to_piece_with_model(
               const struct llama_model * model,
                            llama_token   token,
                                   char * buf,
@@ -474,6 +472,43 @@ extern "C" {
     /// @details Accepts the sampled token into the grammar
     LLAMA_API void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token);
 
+    //
+    // Beam search
+    //
+
+    struct llama_beam_view {
+        const llama_token * tokens;
+        size_t n_tokens;
+        float p;   // Cumulative beam probability (renormalized relative to all beams)
+        bool eob;  // Callback should set this to true when a beam is at end-of-beam.
+    };
+
+    // Passed to beam_search_callback function.
+    // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams
+    // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks.
+    // These pointers are valid only during the synchronous callback, so should not be saved.
+    struct llama_beams_state {
+        struct llama_beam_view * beam_views;
+        size_t n_beams;               // Number of elements in beam_views[].
+        size_t common_prefix_length;  // Current max length of prefix tokens shared by all beams.
+        bool last_call;               // True iff this is the last callback invocation.
+    };
+
+    // Type of pointer to the beam_search_callback function.
+    // void* callback_data is any custom data passed to llama_beam_search, that is subsequently
+    // passed back to beam_search_callback. This avoids having to use global variables in the callback.
+    typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state);
+
+    /// @details Deterministically returns entire sentence constructed by a beam search.
+    /// @param ctx Pointer to the llama_context.
+    /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state.
+    /// @param callback_data A pointer that is simply passed back to callback.
+    /// @param n_beams Number of beams to use.
+    /// @param n_past Number of tokens already evaluated.
+    /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier.
+    /// @param n_threads Number of threads as passed to llama_eval().
+    LLAMA_API void llama_beam_search(struct llama_context * ctx, llama_beam_search_callback_fn_t callback, void * callback_data, size_t n_beams, int n_past, int n_predict, int n_threads);
+
     // Performance information
     LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
     LLAMA_API void llama_print_timings(struct llama_context * ctx);
@@ -486,6 +521,8 @@ extern "C" {
     // If this is not called, or NULL is supplied, everything is output on stderr.
     LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data);
 
+    LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx);
+
 #ifdef __cplusplus
 }
 #endif

requirements.txt

@@ -1,2 +1,3 @@
 numpy==1.24
 sentencepiece==0.1.98
+gguf>=0.1.0

run_with_preset.py

@@ -0,0 +1,140 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import subprocess
+import sys
+
+import yaml
+
+CLI_ARGS_MAIN_PERPLEXITY = [
+    "batch-size", "cfg-negative-prompt", "cfg-scale", "chunks", "color", "ctx-size", "escape",
+    "export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag",
+    "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", "instruct",
+    "interactive", "interactive-first", "keep", "logdir", "logit-bias", "lora", "lora-base",
+    "low-vram", "main-gpu", "memory-f32", "mirostat", "mirostat-ent", "mirostat-lr", "mlock",
+    "model", "mtest", "multiline-input", "n-gpu-layers", "n-predict", "no-mmap", "no-mul-mat-q",
+    "np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt",
+    "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "random-prompt", "repeat-last-n",
+    "repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed",
+    "simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical",
+    "verbose-prompt"
+]
+
+CLI_ARGS_LLAMA_BENCH = [
+    "batch-size", "memory-f32", "low-vram", "model", "mul-mat-q", "n-gen", "n-gpu-layers",
+    "n-prompt", "output", "repetitions", "tensor-split", "threads", "verbose"
+]
+
+CLI_ARGS_SERVER = [
+    "alias", "batch-size", "ctx-size", "embedding", "host", "memory-f32", "lora", "lora-base",
+    "low-vram", "main-gpu", "mlock", "model", "n-gpu-layers", "n-probs", "no-mmap", "no-mul-mat-q",
+    "numa", "path", "port", "rope-freq-base", "timeout", "rope-freq-scale", "tensor-split",
+    "threads", "verbose"
+]
+
+description = """Run llama.cpp binaries with presets from YAML file(s).
+To specify which binary should be run, specify the "binary" property (main, perplexity, llama-bench, and server are supported).
+To get a preset file template, run a llama.cpp binary with the "--logdir" CLI argument.
+
+Formatting considerations:
+- The YAML property names are the same as the CLI argument names of the corresponding binary.
+- Properties must use the long name of their corresponding llama.cpp CLI arguments.
+- Like the llama.cpp binaries the property names do not differentiate between hyphens and underscores.
+- Flags must be defined as "<PROPERTY_NAME>: true" to be effective.
+- To define the logit_bias property, the expected format is "<TOKEN_ID>: <BIAS>" in the "logit_bias" namespace.
+- To define multiple "reverse_prompt" properties simultaneously the expected format is a list of strings.
+- To define a tensor split, pass a list of floats.
+"""
+usage = "run_with_preset.py [-h] [yaml_files ...] [--<ARG_NAME> <ARG_VALUE> ...]"
+epilog = ("  --<ARG_NAME> specify additional CLI ars to be passed to the binary (override all preset files). "
+          "Unknown args will be ignored.")
+
+parser = argparse.ArgumentParser(
+    description=description, usage=usage, epilog=epilog, formatter_class=argparse.RawTextHelpFormatter)
+parser.add_argument("-bin", "--binary", help="The binary to run.")
+parser.add_argument("yaml_files", nargs="*",
+                    help="Arbitrary number of YAML files from which to read preset values. "
+                    "If two files specify the same values the later one will be used.")
+
+known_args, unknown_args = parser.parse_known_args()
+
+if not known_args.yaml_files and not unknown_args:
+    parser.print_help()
+    sys.exit(0)
+
+props = dict()
+
+for yaml_file in known_args.yaml_files:
+    with open(yaml_file, "r") as f:
+        props.update(yaml.load(f, yaml.SafeLoader))
+
+props = {prop.replace("_", "-"): val for prop, val in props.items()}
+
+binary = props.pop("binary", "main")
+if known_args.binary:
+    binary = known_args.binary
+
+if os.path.exists(f"./{binary}"):
+    binary = f"./{binary}"
+
+if binary.lower().endswith("main") or binary.lower().endswith("perplexity"):
+    cli_args = CLI_ARGS_MAIN_PERPLEXITY
+elif binary.lower().endswith("llama-bench"):
+    cli_args = CLI_ARGS_LLAMA_BENCH
+elif binary.lower().endswith("server"):
+    cli_args = CLI_ARGS_SERVER
+else:
+    print(f"Unknown binary: {binary}")
+    sys.exit(1)
+
+command_list = [binary]
+
+for cli_arg in cli_args:
+    value = props.pop(cli_arg, None)
+
+    if not value or value == -1:
+        continue
+
+    if cli_arg == "logit-bias":
+        for token, bias in value.items():
+            command_list.append("--logit-bias")
+            command_list.append(f"{token}{bias:+}")
+        continue
+
+    if cli_arg == "reverse-prompt" and not isinstance(value, str):
+        for rp in value:
+            command_list.append("--reverse-prompt")
+            command_list.append(str(rp))
+        continue
+
+    command_list.append(f"--{cli_arg}")
+
+    if cli_arg == "tensor-split":
+        command_list.append(",".join([str(v) for v in value]))
+        continue
+
+    value = str(value)
+
+    if value != "True":
+        command_list.append(str(value))
+
+num_unused = len(props)
+if num_unused > 10:
+    print(f"The preset file contained a total of {num_unused} unused properties.")
+elif num_unused > 0:
+    print("The preset file contained the following unused properties:")
+    for prop, value in props.items():
+        print(f"  {prop}: {value}")
+
+command_list += unknown_args
+
+sp = subprocess.Popen(command_list)
+
+while sp.returncode is None:
+    try:
+        sp.wait()
+    except KeyboardInterrupt:
+        pass
+
+sys.exit(sp.returncode)

scripts/convert-gg.sh

@@ -0,0 +1,26 @@
+#!/bin/bash
+
+set -e
+
+# LLaMA v1
+python3 convert.py ../llama1/7B  --outfile models/llama-7b/ggml-model-f16.gguf  --outtype f16
+python3 convert.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16
+python3 convert.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16
+python3 convert.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16
+
+# LLaMA v2
+python3 convert.py ../llama2/llama-2-7b  --outfile models/llama-7b-v2/ggml-model-f16.gguf  --outtype f16
+python3 convert.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16
+python3 convert.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16
+
+# Code Llama
+python3 convert.py ../codellama/CodeLlama-7b/  --outfile models/codellama-7b/ggml-model-f16.gguf  --outtype f16
+python3 convert.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16
+python3 convert.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16
+
+# Falcon
+python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-7b  1
+mv -v ../falcon/falcon-7b/ggml-model-f16.gguf models/falcon-7b/ggml-model-f16.gguf
+
+python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-40b 1
+mv -v ../falcon/falcon-40b/ggml-model-f16.gguf models/falcon-40b/ggml-model-f16.gguf

scripts/perf-run-all.sh

@@ -1,93 +0,0 @@
-#!/bin/bash
-#
-# Measure the performance (time per token) of the various quantization techniques
-#
-
-QUANTIZE=0
-if [ "$1" != "" ]; then
-    echo "Quantizing"
-    QUANTIZE=1
-fi
-
-if [ "$QUANTIZE" != "0" ]; then
-    #
-    # quantize
-    #
-
-    # 7B
-    time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q4_0.bin q4_0 2>&1 | tee ../qnt-7b-q4_0.txt
-    time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q4_1.bin q4_1 2>&1 | tee ../qnt-7b-q4_1.txt
-    time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q5_0.bin q5_0 2>&1 | tee ../qnt-7b-q5_0.txt
-    time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q5_1.bin q5_1 2>&1 | tee ../qnt-7b-q5_1.txt
-    time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q8_0.bin q8_0 2>&1 | tee ../qnt-7b-q8_0.txt
-
-    # 13B
-    time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q4_0.bin q4_0 2>&1 | tee ../qnt-13b-q4_0.txt
-    time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q4_1.bin q4_1 2>&1 | tee ../qnt-13b-q4_1.txt
-    time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q5_0.bin q5_0 2>&1 | tee ../qnt-13b-q5_0.txt
-    time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q5_1.bin q5_1 2>&1 | tee ../qnt-13b-q5_1.txt
-    time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q8_0.bin q8_0 2>&1 | tee ../qnt-13b-q8_0.txt
-fi
-
-#
-# perf
-# run each command twice
-#
-
-set -x
-
-# 7B - 4 threads
-     ./bin/main -m ../models/7B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-f16.txt  | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-q4_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-q4_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-q5_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-q5_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-7b-q8_0.txt | grep llama_print_timings
-
-# 7B - 8 threads
-     ./bin/main -m ../models/7B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-f16.txt  | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-q4_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-q4_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-q5_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-q5_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/7B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/7B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-7b-q8_0.txt | grep llama_print_timings
-
-# 13B - 4 threads
-     ./bin/main -m ../models/13B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-f16.txt  | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-q4_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-q4_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-q5_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-q5_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 4 2>&1 | tee ../perf-13b-q8_0.txt | grep llama_print_timings
-
-# 13B - 8 threads
-     ./bin/main -m ../models/13B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-f16.bin  -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-f16.txt  | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q4_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-q4_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q4_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-q4_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q5_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-q5_0.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q5_1.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-q5_1.txt | grep llama_print_timings
-     ./bin/main -m ../models/13B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | grep "I believe"
-time ./bin/main -m ../models/13B/ggml-model-q8_0.bin -p "I believe the meaning of life is" --no-mmap -c 2048 --ignore-eos -s 1 -n 64 -t 8 2>&1 | tee ../perf-13b-q8_0.txt | grep llama_print_timings

scripts/ppl-run-all.sh

@@ -1,39 +0,0 @@
-#!/bin/bash
-
-#
-# quantize
-#
-
-# 7B
-time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q4_0.bin q4_0 2>&1 | tee ../qnt-7b-q4_0.txt
-time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q4_1.bin q4_1 2>&1 | tee ../qnt-7b-q4_1.txt
-time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q5_0.bin q5_0 2>&1 | tee ../qnt-7b-q5_0.txt
-time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q5_1.bin q5_1 2>&1 | tee ../qnt-7b-q5_1.txt
-time ./bin/quantize ../models/7B/ggml-model-f16.bin ../models/7B/ggml-model-q8_0.bin q8_0 2>&1 | tee ../qnt-7b-q8_0.txt
-
-# 13B
-time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q4_0.bin q4_0 2>&1 | tee ../qnt-13b-q4_0.txt
-time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q4_1.bin q4_1 2>&1 | tee ../qnt-13b-q4_1.txt
-time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q5_0.bin q5_0 2>&1 | tee ../qnt-13b-q5_0.txt
-time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q5_1.bin q5_1 2>&1 | tee ../qnt-13b-q5_1.txt
-time ./bin/quantize ../models/13B/ggml-model-f16.bin ../models/13B/ggml-model-q8_0.bin q8_0 2>&1 | tee ../qnt-13b-q8_0.txt
-
-#
-# perplexity
-#
-
-# 7B
-time ./bin/perplexity -m ../models/7B/ggml-model-f16.bin  -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-f16.txt
-time ./bin/perplexity -m ../models/7B/ggml-model-q4_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q4_0.txt
-time ./bin/perplexity -m ../models/7B/ggml-model-q4_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q4_1.txt
-time ./bin/perplexity -m ../models/7B/ggml-model-q5_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q5_0.txt
-time ./bin/perplexity -m ../models/7B/ggml-model-q5_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q5_1.txt
-time ./bin/perplexity -m ../models/7B/ggml-model-q8_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-7b-q8_0.txt
-
-# 13B
-time ./bin/perplexity -m ../models/13B/ggml-model-f16.bin  -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-f16.txt
-time ./bin/perplexity -m ../models/13B/ggml-model-q4_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q4_0.txt
-time ./bin/perplexity -m ../models/13B/ggml-model-q4_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q4_1.txt
-time ./bin/perplexity -m ../models/13B/ggml-model-q5_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q5_0.txt
-time ./bin/perplexity -m ../models/13B/ggml-model-q5_1.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q5_1.txt
-time ./bin/perplexity -m ../models/13B/ggml-model-q8_0.bin -f ./wiki.test.raw --no-mmap -t 12 2>&1 | tee ../ppl-13b-q8_0.txt

scripts/qnt-all.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+
+qnt=(q8_0 q6_k q5_k q5_1 q5_0 q4_k q4_1 q4_0 q3_k q2_k)
+args=""
+
+if [ -z "$1" ]; then
+    echo "usage: $0 <model> [qnt] [args]"
+    echo "default: $0 <model> \"${qnt[@]}\" \"${args}\""
+    exit 1
+fi
+
+if [ ! -z "$2" ]; then
+    qnt=($2)
+fi
+
+if [ ! -z "$3" ]; then
+    args="$3"
+fi
+
+model="$1"
+out="../tmp/results-${model}"
+
+set -o pipefail
+set -e
+
+mkdir -p ${out}
+
+for q in ${qnt[@]}; do
+    time ./bin/quantize ../models/${model}/ggml-model-f16.gguf ../models/${model}/ggml-model-${q}.gguf ${q} 2>&1 ${args} | tee ${out}/qnt-${q}.txt
+done

scripts/run-all-perf.sh

@@ -0,0 +1,34 @@
+#!/bin/bash
+
+qnt=(f16 q8_0 q6_k q5_k q5_1 q5_0 q4_k q4_1 q4_0 q3_k q2_k)
+args="-ngl 999 -n 64 -p 512"
+
+if [ -z "$1" ]; then
+    echo "usage: $0 <model> [qnt] [args]"
+    echo "default: $0 <model> \"${qnt[@]}\" \"${args}\""
+    exit 1
+fi
+
+if [ ! -z "$2" ]; then
+    qnt=($2)
+fi
+
+if [ ! -z "$3" ]; then
+    args="$3"
+fi
+
+model="$1"
+out="../tmp/results-${model}"
+
+set -o pipefail
+set -e
+
+mkdir -p ${out}
+
+mstr=""
+
+for q in ${qnt[@]}; do
+    mstr="${mstr} -m ../models/${model}/ggml-model-${q}.gguf"
+done
+
+./bin/llama-bench ${mstr} ${args} 2> /dev/null

scripts/run-all-ppl.sh

@@ -0,0 +1,30 @@
+#!/bin/bash
+
+qnt=(f16 q8_0 q6_k q5_k q5_1 q5_0 q4_k q4_1 q4_0 q3_k q2_k)
+args="-ngl 999 -t 8"
+
+if [ -z "$1" ]; then
+    echo "usage: $0 <model> [qnt] [args]"
+    echo "default: $0 <model> \"${qnt[@]}\" \"${args}\""
+    exit 1
+fi
+
+if [ ! -z "$2" ]; then
+    qnt=($2)
+fi
+
+if [ ! -z "$3" ]; then
+    args="$3"
+fi
+
+set -o pipefail
+set -e
+
+model="$1"
+out="../tmp/results-${model}"
+
+mkdir -p ${out}
+
+for q in ${qnt[@]}; do
+    time ./bin/perplexity -m ../models/${model}/ggml-model-f16.gguf -f ./wiki.test.raw ${args} 2>&1 | tee ${out}/ppl-${q}.txt
+done

scripts/sync-ggml.sh

@@ -1,14 +1,16 @@
 #!/bin/bash
 
-cp -rpv ../ggml/src/ggml.c           ./ggml.c
-cp -rpv ../ggml/src/ggml-cuda.h      ./ggml-cuda.h
-cp -rpv ../ggml/src/ggml-cuda.cu     ./ggml-cuda.cu
-cp -rpv ../ggml/src/ggml-opencl.h    ./ggml-opencl.h
-cp -rpv ../ggml/src/ggml-opencl.cpp  ./ggml-opencl.cpp
-cp -rpv ../ggml/src/ggml-metal.h     ./ggml-metal.h
-cp -rpv ../ggml/src/ggml-metal.m     ./ggml-metal.m
-cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal
-cp -rpv ../ggml/include/ggml/ggml.h  ./ggml.h
+cp -rpv ../ggml/src/ggml.c                ./ggml.c
+cp -rpv ../ggml/src/ggml-alloc.c          ./ggml-alloc.c
+cp -rpv ../ggml/src/ggml-cuda.h           ./ggml-cuda.h
+cp -rpv ../ggml/src/ggml-cuda.cu          ./ggml-cuda.cu
+cp -rpv ../ggml/src/ggml-opencl.h         ./ggml-opencl.h
+cp -rpv ../ggml/src/ggml-opencl.cpp       ./ggml-opencl.cpp
+cp -rpv ../ggml/src/ggml-metal.h          ./ggml-metal.h
+cp -rpv ../ggml/src/ggml-metal.m          ./ggml-metal.m
+cp -rpv ../ggml/src/ggml-metal.metal      ./ggml-metal.metal
+cp -rpv ../ggml/include/ggml/ggml.h       ./ggml.h
+cp -rpv ../ggml/include/ggml/ggml-alloc.h ./ggml-alloc.h
 
 cp -rpv ../ggml/tests/test-opt.cpp    ./tests/test-opt.cpp
 cp -rpv ../ggml/tests/test-grad0.cpp  ./tests/test-grad0.cpp

tests/CMakeLists.txt

@@ -25,12 +25,20 @@ endfunction()
 llama_build_and_test_executable(test-quantize-fns.cpp)
 llama_build_and_test_executable(test-quantize-perf.cpp)
 llama_build_and_test_executable(test-sampling.cpp)
-llama_build_executable(test-tokenizer-0.cpp)
-llama_test_executable (test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+llama_build_executable(test-tokenizer-0-llama.cpp)
+llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+llama_build_executable(test-tokenizer-0-falcon.cpp)
+#llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 llama_build_executable(test-tokenizer-1.cpp)
-llama_test_executable (test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
+# test-tokenizer-1 requires a BPE vocab. re-enable when we have one.
+#llama_test_executable (test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
 #llama_test_executable(test-tokenizer-1.aquila test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
 llama_build_and_test_executable(test-grammar-parser.cpp)
 llama_build_and_test_executable(test-llama-grammar.cpp)
 llama_build_and_test_executable(test-grad0.cpp) # SLOW
 # llama_build_and_test_executable(test-opt.cpp) # SLOW
+
+# dummy executable - not installed
+get_filename_component(TEST_TARGET test-c.c NAME_WE)
+add_executable(${TEST_TARGET} test-c.c)
+target_link_libraries(${TEST_TARGET} PRIVATE llama)

tests/test-c.c

@@ -0,0 +1,3 @@
+#include "llama.h"
+
+int main(void) {}

tests/test-grad0.cpp

@@ -275,14 +275,14 @@ static bool check_gradient(
 
             ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
 
-            const float f0 = ggml_get_f32_1d(f, 0);
+            const double f0 = ggml_get_f32_1d(f, 0);
 
             ggml_set_f32_1d(x[i], k, xm);
 
             ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
 
-            const float f1 = ggml_get_f32_1d(f, 0);
-            const float g0 = (f0 - f1)/(2.0f*eps);
+            const double f1 = ggml_get_f32_1d(f, 0);
+            const double g0 = (f0 - f1)/(2.0*(double) eps);
 
             ggml_set_f32_1d(x[i], k, x0);
 
@@ -292,10 +292,10 @@ static bool check_gradient(
 
             ggml_graph_compute_with_ctx(ctx0, &gb, n_threads);
 
-            const float g1 = ggml_get_f32_1d(x[i]->grad, k);
+            const double g1 = ggml_get_f32_1d(x[i]->grad, k);
 
-            const float error_abs = fabsf(g0 - g1);
-            const float error_rel = g0 != 0 ? fabsf(g0 - g1)/fabsf(g0) : 0;
+            const double error_abs = fabs(g0 - g1);
+            const double error_rel = g0 != 0 ? fabs(g0 - g1)/fabs(g0) : 0;
 
             if (error_abs > max_error_abs || error_rel > max_error_rel) {
                 printf("%s: ndims=%d, i=%d, k=%d, x0=%f, xm=%f, xp=%f, f0=%f, f1=%f, g0=%f, g1=%f, eps=%f, error_abs=%f, error_rel=%f\n",
@@ -531,7 +531,7 @@ int main(int argc, const char ** argv) {
 
                 struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqrt(ctx0, x[0]));
 
-                check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f);
+                check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, 2e-2f, 1e-1f);
             }
         }
 
@@ -1345,9 +1345,18 @@ int main(int argc, const char ** argv) {
                 x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
                 ggml_set_param(ctx0, x[0]);
 
-                struct ggml_tensor * f = ggml_sum(ctx0, ggml_soft_max(ctx0, x[0]));
+                float eps = 1e-6f;
+                // dont use only sum as aggregation, because sum of softmax is always 1 -> finite differences should not work
+                // instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0)
+                struct ggml_tensor * f = ggml_sum(ctx0,
+                                            ggml_log(ctx0,
+                                                ggml_add1(ctx0,
+                                                    ggml_scale(ctx0,
+                                                        ggml_soft_max(ctx0, x[0]),
+                                                        ggml_new_f32(ctx0, 1.0f - eps)),
+                                                    ggml_new_f32(ctx0, eps))));
 
-                check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY);
+                check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 2e-1f, INFINITY);
             }
         }
 
@@ -1358,15 +1367,26 @@ int main(int argc, const char ** argv) {
             int64_t ne2[4];
             get_random_dims(ne2, 4);
 
-            for (int ndims = 1; ndims <= 3; ++ndims) {
-                x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f);
+            for (int ndims = 1; ndims <= 4; ++ndims) {
+                x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -0.1f, 0.1f);
                 x[1] = get_random_tensor_f32(ctx0, ndims, ne2, 0.0f, 1.0f);
+                // the second argument to cross_entropy_loss must sum up to 1 for each row
+                int nr = ggml_nrows(x[1]);
+                int nc = ggml_nelements(x[1]) / nr;
+                for (int ir = 0; ir < nr; ++ir) {
+                    float sum = 0;
+                    for (int ic = 0; ic < nc; ++ic) {
+                        sum += ((float *) x[1]->data)[ic + ir*nc];
+                    }
+                    for (int ic = 0; ic < nc; ++ic) {
+                        ((float *) x[1]->data)[ic + ir*nc] /= sum;
+                    }
+                }
                 ggml_set_param(ctx0, x[0]);
 
-                struct ggml_tensor * f = ggml_sum(ctx0, ggml_cross_entropy_loss(ctx0, x[0], x[1]));
+                struct ggml_tensor * f = ggml_cross_entropy_loss(ctx0, x[0], x[1]);
 
-                check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-1f, 1e-2f, INFINITY);
-                // finite differences regularly fails!
+                check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-4f, 1e-3f, INFINITY);
             }
         }
 
@@ -1473,7 +1493,7 @@ int main(int argc, const char ** argv) {
 
                     struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
 
-                    check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
+                    check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY);
                 }
             }
         }
@@ -1514,7 +1534,7 @@ int main(int argc, const char ** argv) {
 
                     struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
 
-                    check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
+                    check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY);
                 }
             }
         }

tests/test-tokenizer-0-falcon.cpp

@@ -0,0 +1,178 @@
+#include "llama.h"
+#include "common.h"
+
+#include <cstdio>
+#include <string>
+#include <map>
+#include <vector>
+#include <fstream>
+
+// generate using test-tokenizer-0-falcon.py
+static const std::map<std::string, std::vector<llama_token>> & k_tests() {
+    static std::map<std::string, std::vector<llama_token>> _k_tests = {
+        { ""                      , {  }, },
+        { " "                     , {     204, }, },
+        { "  "                    , {     258, }, },
+        { "   "                   , {     466, }, },
+        { "\t"                    , {     192, }, },
+        { "\n"                    , {     193, }, },
+        { "\t\n"                  , {   19125, }, },
+        { "Hello world"           , {    9856,   1079, }, },
+        { " Hello world"          , {   23090,   1079, }, },
+        { "Hello World"           , {    9856,   2889, }, },
+        { " Hello World"          , {   23090,   2889, }, },
+        { " Hello World!"         , {   23090,   2889,     12, }, },
+        { "Hello, world!"         , {    9856,     23,   1079,     12, }, },
+        { " Hello, world!"        , {   23090,     23,   1079,     12, }, },
+        { " this is 🦙.cpp"        , {     414,    304,   3346,    111,    231,     25,  29247, }, },
+        { "w048 7tuijk dsdfhu"    , {      98,  55866,    204,     34,  16682,   7149,  36190,   6869,  11481, }, },
+        { "нещо на Български"     , {     150,    133,   6207,    151,    215,    150,    134,   5052,    133,   6279,   5052,    223,    151,    216,  49679,    123,  53110,  47043,   7795, }, },
+        { "កាន់តែពិសេសអាចខលចេញ"   , {   38154,    206,  38154,    126,  38154,    225,    167,    237,    217,  38154,    221,    167,    237,    208,  38154,    228,  38154,    127,  38154,    237,    167,    237,    207,  38154,    237,  38154,    107,  38154,    126,  38154,    211,  38154,    207,  38154,    233,  38154,    211,    167,    237,    207,  38154,    215, }, },
+        { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", {    2571,    232,    206,    204,     19,  11003,     20,   8196,    126,    283,    219,  48778,    116,  13392,    204,     19,  51831,    732,  63209,   1741,   7955,    522,     20,  22438,    211,    204,     19,   7927,  53360,    325,    504,    701,    946,  10930,     20, }, },
+        { "Hello"                 , {    9856, }, },
+        { " Hello"                , {   23090, }, },
+        { "  Hello"               , {     204,  23090, }, },
+        { "   Hello"              , {     258,  23090, }, },
+        { "    Hello"             , {     466,  23090, }, },
+        { "    Hello\n    Hello"  , {     466,  23090,    742,  23090, }, },
+    };
+
+    return _k_tests;
+}
+
+int main(int argc, char **argv) {
+    if (argc < 2) {
+        fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]);
+        return 1;
+    }
+
+    const std::string fname = argv[1];
+
+    std::string fname_text;
+    if (argc > 2) {
+        fname_text = argv[2];
+    }
+
+    fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
+
+    llama_model * model;
+    llama_context * ctx;
+
+    llama_backend_init(false);
+
+    // load the vocab
+    {
+        auto lparams = llama_context_default_params();
+
+        lparams.vocab_only = true;
+
+        model = llama_load_model_from_file(fname.c_str(), lparams);
+
+        if (model == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            return 1;
+        }
+
+        ctx = llama_new_context_with_model(model, lparams);
+
+        if (ctx == NULL) {
+            fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
+            llama_free_model(model);
+            return 1;
+        }
+    }
+
+    if (llama_vocab_type(ctx) != LLAMA_VOCAB_TYPE_BPE) {
+        fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__);
+        llama_free_model(model);
+        llama_free(ctx);
+        return 2;
+    }
+
+    bool success = true;
+
+    for (const auto & test_kv : k_tests()) {
+        const std::vector<llama_token> res = llama_tokenize(ctx, test_kv.first, false);
+
+        printf("\n");
+        printf("src: '%s'\n", test_kv.first.c_str());
+        printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str());
+        printf("tok: ");
+        for (const auto & tok : res) {
+            printf("%d ", tok);
+        }
+        printf("\n");
+
+        bool correct = res.size() == test_kv.second.size();
+
+        for (int i = 0; i < (int) res.size() && correct; ++i) {
+            if (test_kv.second[i] != res[i]) {
+                correct = false;
+            }
+        }
+
+        if (!correct) {
+            fprintf(stderr, "%s : failed test:    '%s'\n", __func__, test_kv.first.c_str());
+            fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__,
+                llama_detokenize_bpe(ctx, res).c_str(),
+                llama_detokenize_bpe(ctx, test_kv.second).c_str());
+            fprintf(stderr, "%s : expected tokens: ", __func__);
+            for (const auto & t : test_kv.second) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+            fprintf(stderr, "%s : got tokens:      ", __func__);
+            for (const auto & t : res) {
+                fprintf(stderr, "%6d, ", t);
+            }
+            fprintf(stderr, "\n");
+
+            success = false;
+        }
+    }
+
+    if (!fname_text.empty()) {
+        fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str());
+
+        std::string text;
+        {
+            std::ifstream ifs(fname_text);
+            if (!ifs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str());
+                return 1;
+            }
+            text = std::string(std::istreambuf_iterator<char>(ifs), std::istreambuf_iterator<char>());
+        }
+
+        fprintf(stderr, "%s : text size: %zu\n", __func__, text.size());
+
+        const std::vector<llama_token> res = llama_tokenize(ctx, text, true);
+
+        fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size());
+
+        {
+            const std::string fname_out = fname_text + ".tokcpp";
+
+            std::ofstream ofs(fname_out);
+            if (!ofs) {
+                fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str());
+                return 1;
+            }
+
+            for (const auto & tok : res) {
+                ofs << tok << " ";
+            }
+
+            ofs << "\n";
+        }
+
+        fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str());
+    }
+
+    llama_free_model(model);
+    llama_free(ctx);
+
+    llama_backend_free();
+
+    return success ? 0 : 3;
+}

tests/test-tokenizer-0-falcon.py

@@ -0,0 +1,83 @@
+# tests with BPE tokenizer
+
+import os
+import sys
+import argparse
+
+from transformers import AutoTokenizer
+
+parser = argparse.ArgumentParser()
+parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file")
+parser.add_argument("--fname-tok",   help="path to a text file to tokenize")
+args = parser.parse_args()
+
+dir_tokenizer = args.dir_tokenizer
+
+tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer)
+
+tests = [
+        "",
+        " ",
+        "  ",
+        "   ",
+        "\t",
+        "\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",
+    ]
+
+for text in tests:
+    print('text: ', text)
+    print(tokenizer.encode(text))
+    print(tokenizer.decode(tokenizer.encode(text)))
+
+print("\n\ntests for C++:\n")
+for text in tests:
+    res = tokenizer.encode(text)
+
+    k = text.replace('\n', '\\n')
+    k = k.replace('\t', '\\t')
+    k = '"' + k + '"'
+    print("{ %-24s, { " % k, end='')
+    for x in res:
+        print("%7d," % x, end='')
+    print(" }, },")
+
+print(tokenizer.encode('hello'))
+print(tokenizer.encode('world'))
+print(tokenizer.encode(' world'))
+print(tokenizer.encode('hello world'))
+
+fname_tok = args.fname_tok
+if fname_tok:
+    print('tokenizing file: ', fname_tok)
+    fname_out = fname_tok + '.tok'
+    with open(fname_tok, 'r') as f:
+        lines = f.readlines()
+        s = ''.join(lines)
+        res = tokenizer.encode(s)
+        # write to file
+        with open(fname_out, 'w') as f:
+            for x in res:
+                f.write(str(x) + ' ')
+            f.write('\n')
+        print('len(res): ', len(res))
+        print('len(lines): ', len(lines))
+    print('results written to: ', fname_out)