llama : change starcoder2 rope type

.clang-tidy

@@ -12,7 +12,6 @@ Checks: >
     -readability-implicit-bool-conversion,
     -readability-magic-numbers,
     -readability-uppercase-literal-suffix,
-    -readability-simplify-boolean-expr,
     clang-analyzer-*,
     -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling,
     performance-*,

.devops/cloud-v-pipeline

@@ -15,7 +15,7 @@ node('x86_runner1'){            // Running on x86 runner containing latest vecto
     stage('Running llama.cpp'){
         sh'''#!/bin/bash
             module load gnu-bin2/0.1            # loading latest versions of vector qemu and vector gcc
-            qemu-riscv64 -L /softwares/gnu-bin2/sysroot  -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./llama-cli -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt            # Running llama.cpp on vector qemu-riscv64
+            qemu-riscv64 -L /softwares/gnu-bin2/sysroot  -cpu rv64,v=true,vlen=256,elen=64,vext_spec=v1.0 ./main -m /home/alitariq/codellama-7b.Q4_K_M.gguf -p "Anything" -n 9 > llama_log.txt            # Running llama.cpp on vector qemu-riscv64
             cat llama_log.txt                   # Printing results
         '''
     }

.devops/full-cuda.Dockerfile

@@ -6,13 +6,13 @@ ARG CUDA_VERSION=11.7.1
 # Target the CUDA build image
 ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION}
 
-FROM ${BASE_CUDA_DEV_CONTAINER} AS build
+FROM ${BASE_CUDA_DEV_CONTAINER} as build
 
 # Unless otherwise specified, we make a fat build.
 ARG CUDA_DOCKER_ARCH=all
 
 RUN apt-get update && \
-    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1
+    apt-get install -y build-essential python3 python3-pip git
 
 COPY requirements.txt   requirements.txt
 COPY requirements       requirements
@@ -26,11 +26,9 @@ COPY . .
 
 # Set nvcc architecture
 ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
-# Enable CUDA
-ENV GGML_CUDA=1
-# Enable cURL
-ENV LLAMA_CURL=1
+# Enable cuBLAS
+ENV LLAMA_CUBLAS=1
 
-RUN make -j$(nproc)
+RUN make
 
 ENTRYPOINT ["/app/.devops/tools.sh"]

.devops/full-rocm.Dockerfile

@@ -6,7 +6,7 @@ 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
+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
@@ -36,15 +36,10 @@ COPY . .
 # Set nvcc architecture
 ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
 # Enable ROCm
-ENV GGML_HIPBLAS=1
+ENV LLAMA_HIPBLAS=1
 ENV CC=/opt/rocm/llvm/bin/clang
 ENV CXX=/opt/rocm/llvm/bin/clang++
 
-# Enable cURL
-ENV LLAMA_CURL=1
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev
-
-RUN make -j$(nproc)
+RUN make
 
 ENTRYPOINT ["/app/.devops/tools.sh"]

.devops/full.Dockerfile

@@ -1,9 +1,9 @@
 ARG UBUNTU_VERSION=22.04
 
-FROM ubuntu:$UBUNTU_VERSION AS build
+FROM ubuntu:$UBUNTU_VERSION as build
 
 RUN apt-get update && \
-    apt-get install -y build-essential python3 python3-pip git libcurl4-openssl-dev libgomp1
+    apt-get install -y build-essential python3 python3-pip git
 
 COPY requirements.txt   requirements.txt
 COPY requirements       requirements
@@ -15,10 +15,7 @@ WORKDIR /app
 
 COPY . .
 
-ENV LLAMA_CURL=1
-
-
-RUN make -j$(nproc)
+RUN make
 
 ENV LC_ALL=C.utf8
 

.devops/llama-cli-intel.Dockerfile

@@ -1,28 +0,0 @@
-ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
-
-ARG GGML_SYCL_F16=OFF
-RUN apt-get update && \
-    apt-get install -y git
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
-        echo "GGML_SYCL_F16 is set" && \
-        export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
-    fi && \
-    echo "Building with static libs" && \
-    cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx \
-    ${OPT_SYCL_F16} -DBUILD_SHARED_LIBS=OFF && \
-    cmake --build build --config Release --target llama-cli
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
-
-COPY --from=build /app/build/bin/llama-cli /llama-cli
-
-ENV LC_ALL=C.utf8
-
-ENTRYPOINT [ "/llama-cli" ]

.devops/llama-cli.Dockerfile

@@ -1,23 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-FROM ubuntu:$UBUNTU_VERSION AS build
-
-RUN apt-get update && \
-    apt-get install -y build-essential git
-
-WORKDIR /app
-
-COPY . .
-
-RUN make -j$(nproc) llama-cli
-
-FROM ubuntu:$UBUNTU_VERSION AS runtime
-
-RUN apt-get update && \
-    apt-get install -y libgomp1
-
-COPY --from=build /app/llama-cli /llama-cli
-
-ENV LC_ALL=C.utf8
-
-ENTRYPOINT [ "/llama-cli" ]

.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

@@ -1,5 +1,5 @@
 # SRPM for building from source and packaging an RPM for RPM-based distros.
-# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
+# 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
 
@@ -12,7 +12,7 @@
 # 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-cuda
+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)
@@ -32,16 +32,16 @@ CPU inference for Meta's Lllama2 models using default options.
 %setup -n llama.cpp-master
 
 %build
-make -j GGML_CUDA=1
+make -j LLAMA_CUBLAS=1
 
 %install
 mkdir -p %{buildroot}%{_bindir}/
-cp -p llama-cli %{buildroot}%{_bindir}/llama-cuda-cli
-cp -p llama-server %{buildroot}%{_bindir}/llama-cuda-server
-cp -p llama-simple %{buildroot}%{_bindir}/llama-cuda-simple
+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/llamacuda.service
+%{__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
@@ -49,7 +49,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t
 [Service]
 Type=simple
 EnvironmentFile=/etc/sysconfig/llama
-ExecStart=/usr/bin/llama-cuda-server $LLAMA_ARGS
+ExecStart=/usr/bin/llamacppcublasserver $LLAMA_ARGS
 ExecReload=/bin/kill -s HUP $MAINPID
 Restart=never
 
@@ -67,10 +67,10 @@ rm -rf %{buildroot}
 rm -rf %{_builddir}/*
 
 %files
-%{_bindir}/llama-cuda-cli
-%{_bindir}/llama-cuda-server
-%{_bindir}/llama-cuda-simple
-/usr/lib/systemd/system/llamacuda.service
+%{_bindir}/llamacppcublas
+%{_bindir}/llamacppcublasserver
+%{_bindir}/llamacppcublassimple
+/usr/lib/systemd/system/llamacublas.service
 %config /etc/sysconfig/llama
 
 %pre

.devops/llama-cpp.srpm.spec

@@ -1,5 +1,5 @@
 # SRPM for building from source and packaging an RPM for RPM-based distros.
-# https://docs.fedoraproject.org/en-US/quick-docs/creating-rpm-packages
+# 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
 
@@ -38,9 +38,9 @@ make -j
 
 %install
 mkdir -p %{buildroot}%{_bindir}/
-cp -p llama-cli %{buildroot}%{_bindir}/llama-cli
-cp -p llama-server %{buildroot}%{_bindir}/llama-server
-cp -p llama-simple %{buildroot}%{_bindir}/llama-simple
+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
@@ -51,7 +51,7 @@ After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.t
 [Service]
 Type=simple
 EnvironmentFile=/etc/sysconfig/llama
-ExecStart=/usr/bin/llama-server $LLAMA_ARGS
+ExecStart=/usr/bin/llamaserver $LLAMA_ARGS
 ExecReload=/bin/kill -s HUP $MAINPID
 Restart=never
 
@@ -69,9 +69,9 @@ rm -rf %{buildroot}
 rm -rf %{_builddir}/*
 
 %files
-%{_bindir}/llama-cli
-%{_bindir}/llama-server
-%{_bindir}/llama-simple
+%{_bindir}/llama
+%{_bindir}/llamaserver
+%{_bindir}/llamasimple
 /usr/lib/systemd/system/llama.service
 %config /etc/sysconfig/llama
 

.devops/llama-server-intel.Dockerfile

@@ -1,32 +0,0 @@
-ARG ONEAPI_VERSION=2024.1.1-devel-ubuntu22.04
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS build
-
-ARG GGML_SYCL_F16=OFF
-RUN apt-get update && \
-    apt-get install -y git libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-RUN if [ "${GGML_SYCL_F16}" = "ON" ]; then \
-        echo "GGML_SYCL_F16 is set" && \
-        export OPT_SYCL_F16="-DGGML_SYCL_F16=ON"; \
-    fi && \
-    echo "Building with dynamic libs" && \
-    cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \
-    cmake --build build --config Release --target llama-server
-
-FROM intel/oneapi-basekit:$ONEAPI_VERSION AS runtime
-
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev curl
-
-COPY --from=build /app/build/bin/llama-server /llama-server
-
-ENV LC_ALL=C.utf8
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/llama-server" ]

.devops/llama-server.Dockerfile

@@ -1,27 +0,0 @@
-ARG UBUNTU_VERSION=22.04
-
-FROM ubuntu:$UBUNTU_VERSION AS build
-
-RUN apt-get update && \
-    apt-get install -y build-essential git libcurl4-openssl-dev
-
-WORKDIR /app
-
-COPY . .
-
-ENV LLAMA_CURL=1
-
-RUN make -j$(nproc) llama-server
-
-FROM ubuntu:$UBUNTU_VERSION AS runtime
-
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev libgomp1 curl
-
-COPY --from=build /app/llama-server /llama-server
-
-ENV LC_ALL=C.utf8
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/llama-server" ]

.devops/main-cuda.Dockerfile

@@ -6,7 +6,7 @@ ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VER
 # Target the CUDA runtime image
 ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
 
-FROM ${BASE_CUDA_DEV_CONTAINER} AS build
+FROM ${BASE_CUDA_DEV_CONTAINER} as build
 
 # Unless otherwise specified, we make a fat build.
 ARG CUDA_DOCKER_ARCH=all
@@ -20,16 +20,13 @@ COPY . .
 
 # Set nvcc architecture
 ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
-# Enable CUDA
-ENV GGML_CUDA=1
+# Enable cuBLAS
+ENV LLAMA_CUBLAS=1
 
-RUN make -j$(nproc) llama-cli
+RUN make
 
-FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
+FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
 
-RUN apt-get update && \
-    apt-get install -y libgomp1
+COPY --from=build /app/main /main
 
-COPY --from=build /app/llama-cli /llama-cli
-
-ENTRYPOINT [ "/llama-cli" ]
+ENTRYPOINT [ "/main" ]

.devops/main-intel.Dockerfile

@@ -0,0 +1,28 @@
+ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
+
+ARG LLAMA_SYCL_F16=OFF
+RUN apt-get update && \
+    apt-get install -y git
+
+WORKDIR /app
+
+COPY . .
+
+RUN mkdir build && \
+    cd build && \
+    if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
+        echo "LLAMA_SYCL_F16 is set" && \
+        export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
+    fi && \
+    cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
+    cmake --build . --config Release --target main
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
+
+COPY --from=build /app/build/bin/main /main
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/main" ]

.devops/main-rocm.Dockerfile

@@ -6,7 +6,7 @@ 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
+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
@@ -36,10 +36,10 @@ COPY . .
 # Set nvcc architecture
 ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
 # Enable ROCm
-ENV GGML_HIPBLAS=1
+ENV LLAMA_HIPBLAS=1
 ENV CC=/opt/rocm/llvm/bin/clang
 ENV CXX=/opt/rocm/llvm/bin/clang++
 
-RUN make -j$(nproc) llama-cli
+RUN make
 
-ENTRYPOINT [ "/app/llama-cli" ]
+ENTRYPOINT [ "/app/main" ]

.devops/main-vulkan.Dockerfile

@@ -1,9 +1,9 @@
 ARG UBUNTU_VERSION=jammy
 
-FROM ubuntu:$UBUNTU_VERSION AS build
+FROM ubuntu:$UBUNTU_VERSION as build
 
 # Install build tools
-RUN apt update && apt install -y git build-essential cmake wget libgomp1
+RUN apt update && apt install -y git build-essential cmake wget
 
 # Install Vulkan SDK
 RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
@@ -14,14 +14,16 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key
 # Build it
 WORKDIR /app
 COPY . .
-RUN cmake -B build -DGGML_VULKAN=1 && \
-    cmake --build build --config Release --target llama-cli
+RUN mkdir build && \
+    cd build && \
+    cmake .. -DLLAMA_VULKAN=1 && \
+    cmake --build . --config Release --target main
 
 # Clean up
 WORKDIR /
-RUN cp /app/build/bin/llama-cli /llama-cli && \
+RUN cp /app/build/bin/main /main && \
     rm -rf /app
 
 ENV LC_ALL=C.utf8
 
-ENTRYPOINT [ "/llama-cli" ]
+ENTRYPOINT [ "/main" ]

.devops/main.Dockerfile

@@ -0,0 +1,20 @@
+ARG UBUNTU_VERSION=22.04
+
+FROM ubuntu:$UBUNTU_VERSION as build
+
+RUN apt-get update && \
+    apt-get install -y build-essential git
+
+WORKDIR /app
+
+COPY . .
+
+RUN make
+
+FROM ubuntu:$UBUNTU_VERSION as runtime
+
+COPY --from=build /app/main /main
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/main" ]

.devops/nix/apps.nix

@@ -6,10 +6,11 @@
         let
           inherit (config.packages) default;
           binaries = [
-            "llama-cli"
+            "llama"
             "llama-embedding"
             "llama-server"
-            "llama-quantize"
+            "quantize"
+            "train-text-from-scratch"
           ];
           mkApp = name: {
             type = "app";

.devops/nix/package.nix

@@ -1,43 +1,35 @@
 {
   lib,
-  glibc,
   config,
   stdenv,
   mkShell,
-  runCommand,
   cmake,
   ninja,
   pkg-config,
   git,
   python3,
   mpi,
-  blas,
+  openblas, # TODO: Use the generic `blas` so users could switch between alternative implementations
   cudaPackages,
   darwin,
   rocmPackages,
   vulkan-headers,
   vulkan-loader,
-  curl,
-  shaderc,
+  clblast,
   useBlas ? builtins.all (x: !x) [
     useCuda
     useMetalKit
+    useOpenCL
     useRocm
     useVulkan
-  ] && blas.meta.available,
+  ],
   useCuda ? config.cudaSupport,
-  useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin,
+  useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL,
   useMpi ? false, # Increases the runtime closure size by ~700M
+  useOpenCL ? false,
   useRocm ? config.rocmSupport,
-  enableCurl ? true,
   useVulkan ? false,
   llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake
-
-  # It's necessary to consistently use backendStdenv when building with CUDA support,
-  # otherwise we get libstdc++ errors downstream.
-  effectiveStdenv ? if useCuda then cudaPackages.backendStdenv else stdenv,
-  enableStatic ? effectiveStdenv.hostPlatform.isStatic,
-  precompileMetalShaders ? false
 }@inputs:
 
 let
@@ -49,13 +41,17 @@ let
     versionOlder
     ;
 
+  # It's necessary to consistently use backendStdenv when building with CUDA support,
+  # otherwise we get libstdc++ errors downstream.
   stdenv = throw "Use effectiveStdenv instead";
+  effectiveStdenv = if useCuda then cudaPackages.backendStdenv else inputs.stdenv;
 
   suffices =
     lib.optionals useBlas [ "BLAS" ]
     ++ lib.optionals useCuda [ "CUDA" ]
     ++ lib.optionals useMetalKit [ "MetalKit" ]
     ++ lib.optionals useMpi [ "MPI" ]
+    ++ lib.optionals useOpenCL [ "OpenCL" ]
     ++ lib.optionals useRocm [ "ROCm" ]
     ++ lib.optionals useVulkan [ "Vulkan" ];
 
@@ -66,15 +62,10 @@ let
     strings.optionalString (suffices != [ ])
       ", accelerated with ${strings.concatStringsSep ", " suffices}";
 
-  executableSuffix = effectiveStdenv.hostPlatform.extensions.executable;
-
   # TODO: package the Python in this repository in a Nix-like way.
   # It'd be nice to migrate to buildPythonPackage, as well as ensure this repo
   # is PEP 517-compatible, and ensure the correct .dist-info is generated.
   # https://peps.python.org/pep-0517/
-  #
-  # TODO: Package up each Python script or service appropriately, by making
-  # them into "entrypoints"
   llama-python = python3.withPackages (
     ps: [
       ps.numpy
@@ -90,30 +81,9 @@ let
       ps.tiktoken
       ps.torchWithoutCuda
       ps.transformers
-
-      # server bench
-      ps.matplotlib
-
-      # server tests
-      ps.openai
-      ps.behave
-      ps.prometheus-client
-
-      # for examples/pydantic-models-to-grammar-examples.py
-      ps.docstring-parser
-      ps.pydantic
-
-      # for scripts/compare-llama-bench.py
-      ps.gitpython
-      ps.tabulate
     ]
   );
 
-  xcrunHost = runCommand "xcrunHost" {} ''
-    mkdir -p $out/bin
-    ln -s /usr/bin/xcrun $out/bin
-  '';
-
   # apple_sdk is supposed to choose sane defaults, no need to handle isAarch64
   # separately
   darwinBuildInputs =
@@ -126,9 +96,16 @@ let
     ++ optionals useMetalKit [ MetalKit ];
 
   cudaBuildInputs = with cudaPackages; [
-    cuda_cudart
-    cuda_cccl # <nv/target>
-    libcublas
+    cuda_cccl.dev # <nv/target>
+
+    # A temporary hack for reducing the closure size, remove once cudaPackages
+    # have stopped using lndir: https://github.com/NixOS/nixpkgs/issues/271792
+    cuda_cudart.dev
+    cuda_cudart.lib
+    cuda_cudart.static
+    libcublas.dev
+    libcublas.lib
+    libcublas.static
   ];
 
   rocmBuildInputs = with rocmPackages; [
@@ -140,7 +117,6 @@ let
   vulkanBuildInputs = [
     vulkan-headers
     vulkan-loader
-    shaderc
   ];
 in
 
@@ -169,19 +145,14 @@ effectiveStdenv.mkDerivation (
     };
 
     postPatch = ''
-      substituteInPlace ./ggml/src/ggml-metal.m \
+      substituteInPlace ./ggml-metal.m \
         --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
-      substituteInPlace ./ggml/src/ggml-metal.m \
-        --replace '[bundle pathForResource:@"default" ofType:@"metallib"];' "@\"$out/bin/default.metallib\";"
-    '';
 
-    # With PR#6015 https://github.com/ggerganov/llama.cpp/pull/6015,
-    # `default.metallib` may be compiled with Metal compiler from XCode
-    # and we need to escape sandbox on MacOS to access Metal compiler.
-    # `xcrun` is used find the path of the Metal compiler, which is varible
-    # and not on $PATH
-    # see https://github.com/ggerganov/llama.cpp/pull/6118 for discussion
-    __noChroot = effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders;
+      # TODO: Package up each Python script or service appropriately.
+      # If we were to migrate to buildPythonPackage and prepare the `pyproject.toml`,
+      # we could make those *.py into setuptools' entrypoints
+      substituteInPlace ./*.py --replace "/usr/bin/env python" "${llama-python}/bin/python"
+    '';
 
     nativeBuildInputs =
       [
@@ -196,35 +167,29 @@ effectiveStdenv.mkDerivation (
         # TODO: Replace with autoAddDriverRunpath
         # once https://github.com/NixOS/nixpkgs/pull/275241 has been merged
         cudaPackages.autoAddOpenGLRunpathHook
-      ]
-      ++ optionals (effectiveStdenv.hostPlatform.isGnu && enableStatic) [
-        glibc.static
-      ] ++ optionals (effectiveStdenv.isDarwin && useMetalKit && precompileMetalShaders) [
-        xcrunHost
       ];
 
     buildInputs =
       optionals effectiveStdenv.isDarwin darwinBuildInputs
       ++ optionals useCuda cudaBuildInputs
       ++ optionals useMpi [ mpi ]
+      ++ optionals useOpenCL [ clblast ]
       ++ optionals useRocm rocmBuildInputs
-      ++ optionals useBlas [ blas ]
-      ++ optionals useVulkan vulkanBuildInputs
-      ++ optionals enableCurl [ curl ];
+      ++ optionals useVulkan vulkanBuildInputs;
 
     cmakeFlags =
       [
+        (cmakeBool "LLAMA_NATIVE" false)
         (cmakeBool "LLAMA_BUILD_SERVER" true)
-        (cmakeBool "BUILD_SHARED_LIBS" (!enableStatic))
+        (cmakeBool "BUILD_SHARED_LIBS" true)
         (cmakeBool "CMAKE_SKIP_BUILD_RPATH" true)
-        (cmakeBool "LLAMA_CURL" enableCurl)
-        (cmakeBool "GGML_NATIVE" false)
-        (cmakeBool "GGML_BLAS" useBlas)
-        (cmakeBool "GGML_CUDA" useCuda)
-        (cmakeBool "GGML_HIPBLAS" useRocm)
-        (cmakeBool "GGML_METAL" useMetalKit)
-        (cmakeBool "GGML_VULKAN" useVulkan)
-        (cmakeBool "GGML_STATIC" enableStatic)
+        (cmakeBool "LLAMA_BLAS" useBlas)
+        (cmakeBool "LLAMA_CLBLAST" useOpenCL)
+        (cmakeBool "LLAMA_CUBLAS" useCuda)
+        (cmakeBool "LLAMA_HIPBLAS" useRocm)
+        (cmakeBool "LLAMA_METAL" useMetalKit)
+        (cmakeBool "LLAMA_MPI" useMpi)
+        (cmakeBool "LLAMA_VULKAN" useVulkan)
       ]
       ++ optionals useCuda [
         (
@@ -235,25 +200,25 @@ effectiveStdenv.mkDerivation (
         )
       ]
       ++ optionals useRocm [
-        (cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang")
-        (cmakeFeature "CMAKE_HIP_ARCHITECTURES" (builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets))
-      ]
-      ++ optionals useMetalKit [
-        (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1")
-        (cmakeBool "GGML_METAL_EMBED_LIBRARY" (!precompileMetalShaders))
-      ];
+        (cmakeFeature "CMAKE_C_COMPILER" "hipcc")
+        (cmakeFeature "CMAKE_CXX_COMPILER" "hipcc")
 
-    # Environment variables needed for ROCm
-    env = optionals useRocm {
-      ROCM_PATH = "${rocmPackages.clr}";
-      HIP_DEVICE_LIB_PATH = "${rocmPackages.rocm-device-libs}/amdgcn/bitcode";
-    };
+        # Build all targets supported by rocBLAS. When updating search for TARGET_LIST_ROCM
+        # in https://github.com/ROCmSoftwarePlatform/rocBLAS/blob/develop/CMakeLists.txt
+        # and select the line that matches the current nixpkgs version of rocBLAS.
+        # Should likely use `rocmPackages.clr.gpuTargets`.
+        "-DAMDGPU_TARGETS=gfx803;gfx900;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102"
+      ]
+      ++ optionals useMetalKit [ (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1") ]
+      ++ optionals useBlas [ (lib.cmakeFeature "LLAMA_BLAS_VENDOR" "OpenBLAS") ];
 
     # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level,
     # if they haven't been added yet.
     postInstall = ''
+      mv $out/bin/main $out/bin/llama
+      mv $out/bin/server $out/bin/llama-server
       mkdir -p $out/include
-      cp $src/include/llama.h $out/include/
+      cp $src/llama.h $out/include/
     '';
 
     # Define the shells here, but don't add in the inputsFrom to avoid recursion.
@@ -263,6 +228,7 @@ effectiveStdenv.mkDerivation (
         useCuda
         useMetalKit
         useMpi
+        useOpenCL
         useRocm
         useVulkan
         ;
@@ -289,7 +255,7 @@ effectiveStdenv.mkDerivation (
       # Configurations we don't want even the CI to evaluate. Results in the
       # "unsupported platform" messages. This is mostly a no-op, because
       # cudaPackages would've refused to evaluate anyway.
-      badPlatforms = optionals useCuda lib.platforms.darwin;
+      badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin;
 
       # Configurations that are known to result in build failures. Can be
       # overridden by importing Nixpkgs with `allowBroken = true`.
@@ -300,7 +266,7 @@ effectiveStdenv.mkDerivation (
       license = lib.licenses.mit;
 
       # Accommodates `nix run` and `lib.getExe`
-      mainProgram = "llama-cli";
+      mainProgram = "llama";
 
       # These people might respond, on the best effort basis, if you ping them
       # in case of Nix-specific regressions or for reviewing Nix-specific PRs.

.devops/nix/sif.nix

@@ -7,7 +7,7 @@
 }:
 
 let
-  optionalInt = cond: x: if cond then x else 0;
+    optionalInt = cond: x: if cond then x else 0;
 in
 singularity-tools.buildImage rec {
   inherit (llama-cpp) name;

.devops/server-cuda.Dockerfile

@@ -6,13 +6,13 @@ ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VER
 # Target the CUDA runtime image
 ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION}
 
-FROM ${BASE_CUDA_DEV_CONTAINER} AS build
+FROM ${BASE_CUDA_DEV_CONTAINER} as build
 
 # Unless otherwise specified, we make a fat build.
 ARG CUDA_DOCKER_ARCH=all
 
 RUN apt-get update && \
-    apt-get install -y build-essential git libcurl4-openssl-dev
+    apt-get install -y build-essential git
 
 WORKDIR /app
 
@@ -20,20 +20,13 @@ COPY . .
 
 # Set nvcc architecture
 ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH}
-# Enable CUDA
-ENV GGML_CUDA=1
-# Enable cURL
-ENV LLAMA_CURL=1
+# Enable cuBLAS
+ENV LLAMA_CUBLAS=1
 
-RUN make -j$(nproc) llama-server
+RUN make
 
-FROM ${BASE_CUDA_RUN_CONTAINER} AS runtime
+FROM ${BASE_CUDA_RUN_CONTAINER} as runtime
 
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev libgomp1 curl
+COPY --from=build /app/server /server
 
-COPY --from=build /app/llama-server /llama-server
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/llama-server" ]
+ENTRYPOINT [ "/server" ]

.devops/server-intel.Dockerfile

@@ -0,0 +1,28 @@
+ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as build
+
+ARG LLAMA_SYCL_F16=OFF
+RUN apt-get update && \
+    apt-get install -y git
+
+WORKDIR /app
+
+COPY . .
+
+RUN mkdir build && \
+    cd build && \
+    if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \
+        echo "LLAMA_SYCL_F16 is set" && \
+        export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \
+    fi && \
+    cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \
+    cmake --build . --config Release --target server
+
+FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime
+
+COPY --from=build /app/build/bin/server /server
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/server" ]

.devops/server-rocm.Dockerfile

@@ -6,7 +6,7 @@ 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
+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
@@ -36,17 +36,10 @@ COPY . .
 # Set nvcc architecture
 ENV GPU_TARGETS=${ROCM_DOCKER_ARCH}
 # Enable ROCm
-ENV GGML_HIPBLAS=1
+ENV LLAMA_HIPBLAS=1
 ENV CC=/opt/rocm/llvm/bin/clang
 ENV CXX=/opt/rocm/llvm/bin/clang++
 
-# Enable cURL
-ENV LLAMA_CURL=1
-RUN apt-get update && \
-    apt-get install -y libcurl4-openssl-dev curl
+RUN make
 
-RUN make -j$(nproc) llama-server
-
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/app/llama-server" ]
+ENTRYPOINT [ "/app/server" ]

.devops/server-vulkan.Dockerfile

@@ -1,29 +1,29 @@
 ARG UBUNTU_VERSION=jammy
 
-FROM ubuntu:$UBUNTU_VERSION AS build
+FROM ubuntu:$UBUNTU_VERSION as build
 
 # Install build tools
 RUN apt update && apt install -y git build-essential cmake wget
 
-# Install Vulkan SDK and cURL
+# Install Vulkan SDK
 RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \
     wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \
     apt update -y && \
-    apt-get install -y vulkan-sdk libcurl4-openssl-dev curl
+    apt-get install -y vulkan-sdk
 
 # Build it
 WORKDIR /app
 COPY . .
-RUN cmake -B build -DGGML_VULKAN=1 -DLLAMA_CURL=1 && \
-    cmake --build build --config Release --target llama-server
+RUN mkdir build && \
+    cd build && \
+    cmake .. -DLLAMA_VULKAN=1 && \
+    cmake --build . --config Release --target server
 
 # Clean up
 WORKDIR /
-RUN cp /app/build/bin/llama-server /llama-server && \
+RUN cp /app/build/bin/server /server && \
     rm -rf /app
 
 ENV LC_ALL=C.utf8
 
-HEALTHCHECK CMD [ "curl", "-f", "http://localhost:8080/health" ]
-
-ENTRYPOINT [ "/llama-server" ]
+ENTRYPOINT [ "/server" ]

.devops/server.Dockerfile

@@ -0,0 +1,20 @@
+ARG UBUNTU_VERSION=22.04
+
+FROM ubuntu:$UBUNTU_VERSION as build
+
+RUN apt-get update && \
+    apt-get install -y build-essential git
+
+WORKDIR /app
+
+COPY . .
+
+RUN make
+
+FROM ubuntu:$UBUNTU_VERSION as runtime
+
+COPY --from=build /app/server /server
+
+ENV LC_ALL=C.utf8
+
+ENTRYPOINT [ "/server" ]

.devops/tools.sh

@@ -8,11 +8,13 @@ arg1="$1"
 shift
 
 if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
-    python3 ./convert_hf_to_gguf.py "$@"
+    python3 ./convert.py "$@"
 elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
-    ./llama-quantize "$@"
+    ./quantize "$@"
 elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
-    ./llama-cli "$@"
+    ./main "$@"
+elif [[ "$arg1" == '--finetune' || "$arg1" == '-f' ]]; then
+    ./finetune "$@"
 elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
     echo "Converting PTH to GGML..."
     for i in `ls $1/$2/ggml-model-f16.bin*`; do
@@ -20,11 +22,11 @@ elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
             echo "Skip model quantization, it already exists: ${i/f16/q4_0}"
         else
             echo "Converting PTH to GGML: $i into ${i/f16/q4_0}..."
-            ./llama-quantize "$i" "${i/f16/q4_0}" q4_0
+            ./quantize "$i" "${i/f16/q4_0}" q4_0
         fi
     done
 elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then
-    ./llama-server "$@"
+    ./server "$@"
 else
     echo "Unknown command: $arg1"
     echo "Available commands: "
@@ -34,6 +36,8 @@ else
     echo "              ex: --outtype f16 \"/models/7B/\" "
     echo "  --quantize (-q): Optimize with quantization process ggml"
     echo "              ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
+    echo "  --finetune (-f): Run finetune command to create a lora finetune of the model"
+    echo "              See documentation for finetune for command-line parameters"
     echo "  --all-in-one (-a): Execute --convert & --quantize"
     echo "              ex: \"/models/\" 7B"
     echo "  --server (-s): Run a model on the server"

.dockerignore

@@ -12,8 +12,8 @@ build*/
 
 models/*
 
-/llama-cli
-/llama-quantize
+/main
+/quantize
 
 arm_neon.h
 compile_commands.json

.editorconfig

@@ -26,7 +26,3 @@ indent_size = 2
 
 [examples/llama.swiftui/llama.swiftui.xcodeproj/*]
 indent_style = tab
-
-[examples/cvector-generator/*.txt]
-trim_trailing_whitespace = unset
-insert_final_newline = unset

.flake8

@@ -1,17 +1,3 @@
 [flake8]
 max-line-length = 125
-ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503
-exclude =
-    # Do not traverse examples
-    examples,
-    # Do not include package initializers
-    __init__.py,
-    # No need to traverse our git directory
-    .git,
-    # There's no value in checking cache directories
-    __pycache__,
-    # No need to include the build path
-    build,
-    # This contains builds that we don't want to check
-    dist  # This is generated with `python build .` for package releases
-# max-complexity = 10
+ignore = W503

.github/ISSUE_TEMPLATE/01-bug-low.yml

@@ -1,50 +0,0 @@
-name: Low Severity Bugs
-description: Used to report low severity bugs in llama.cpp (e.g. cosmetic issues, non critical UI glitches)
-title: "Bug: "
-labels: ["bug-unconfirmed", "low severity"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to fill out this bug report!
-        Please include information about your system, the steps to reproduce the bug,
-        and the version of llama.cpp that you are using.
-        If possible, please provide a minimal code example that reproduces the bug.
-  - type: textarea
-    id: what-happened
-    attributes:
-      label: What happened?
-      description: Also tell us, what did you expect to happen?
-      placeholder: Tell us what you see!
-    validations:
-      required: true
-  - type: textarea
-    id: version
-    attributes:
-      label: Name and Version
-      description: Which executable and which version of our software are you running? (use `--version` to get a version string)
-      placeholder: |
-        $./llama-cli --version
-        version: 2999 (42b4109e)
-        built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
-    validations:
-      required: true
-  - type: dropdown
-    id: operating-system
-    attributes:
-      label: What operating system are you seeing the problem on?
-      multiple: true
-      options:
-        - Linux
-        - Mac
-        - Windows
-        - BSD
-        - Other? (Please let us know in description)
-    validations:
-      required: false
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant log output
-      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
-      render: shell

.github/ISSUE_TEMPLATE/02-bug-medium.yml

@@ -1,50 +0,0 @@
-name: Medium Severity Bug
-description: Used to report medium severity bugs in llama.cpp (e.g. Malfunctioning Features but generally still useable)
-title: "Bug: "
-labels: ["bug-unconfirmed", "medium severity"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to fill out this bug report!
-        Please include information about your system, the steps to reproduce the bug,
-        and the version of llama.cpp that you are using.
-        If possible, please provide a minimal code example that reproduces the bug.
-  - type: textarea
-    id: what-happened
-    attributes:
-      label: What happened?
-      description: Also tell us, what did you expect to happen?
-      placeholder: Tell us what you see!
-    validations:
-      required: true
-  - type: textarea
-    id: version
-    attributes:
-      label: Name and Version
-      description: Which executable and which version of our software are you running? (use `--version` to get a version string)
-      placeholder: |
-        $./llama-cli --version
-        version: 2999 (42b4109e)
-        built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
-    validations:
-      required: true
-  - type: dropdown
-    id: operating-system
-    attributes:
-      label: What operating system are you seeing the problem on?
-      multiple: true
-      options:
-        - Linux
-        - Mac
-        - Windows
-        - BSD
-        - Other? (Please let us know in description)
-    validations:
-      required: false
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant log output
-      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
-      render: shell

.github/ISSUE_TEMPLATE/03-bug-high.yml

@@ -1,50 +0,0 @@
-name: High Severity Bug
-description: Used to report high severity bugs in llama.cpp (e.g. Malfunctioning features hindering important common workflow)
-title: "Bug: "
-labels: ["bug-unconfirmed", "high severity"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to fill out this bug report!
-        Please include information about your system, the steps to reproduce the bug,
-        and the version of llama.cpp that you are using.
-        If possible, please provide a minimal code example that reproduces the bug.
-  - type: textarea
-    id: what-happened
-    attributes:
-      label: What happened?
-      description: Also tell us, what did you expect to happen?
-      placeholder: Tell us what you see!
-    validations:
-      required: true
-  - type: textarea
-    id: version
-    attributes:
-      label: Name and Version
-      description: Which executable and which version of our software are you running? (use `--version` to get a version string)
-      placeholder: |
-        $./llama-cli --version
-        version: 2999 (42b4109e)
-        built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
-    validations:
-      required: true
-  - type: dropdown
-    id: operating-system
-    attributes:
-      label: What operating system are you seeing the problem on?
-      multiple: true
-      options:
-        - Linux
-        - Mac
-        - Windows
-        - BSD
-        - Other? (Please let us know in description)
-    validations:
-      required: false
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant log output
-      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
-      render: shell

.github/ISSUE_TEMPLATE/04-bug-critical.yml

@@ -1,50 +0,0 @@
-name: Critical Severity Bug
-description: Used to report critical severity bugs in llama.cpp (e.g. Crashing, Corrupted, Dataloss)
-title: "Bug: "
-labels: ["bug-unconfirmed", "critical severity"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Thanks for taking the time to fill out this bug report!
-        Please include information about your system, the steps to reproduce the bug,
-        and the version of llama.cpp that you are using.
-        If possible, please provide a minimal code example that reproduces the bug.
-  - type: textarea
-    id: what-happened
-    attributes:
-      label: What happened?
-      description: Also tell us, what did you expect to happen?
-      placeholder: Tell us what you see!
-    validations:
-      required: true
-  - type: textarea
-    id: version
-    attributes:
-      label: Name and Version
-      description: Which executable and which version of our software are you running? (use `--version` to get a version string)
-      placeholder: |
-        $./llama-cli --version
-        version: 2999 (42b4109e)
-        built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu
-    validations:
-      required: true
-  - type: dropdown
-    id: operating-system
-    attributes:
-      label: What operating system are you seeing the problem on?
-      multiple: true
-      options:
-        - Linux
-        - Mac
-        - Windows
-        - BSD
-        - Other? (Please let us know in description)
-    validations:
-      required: false
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant log output
-      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
-      render: shell

.github/ISSUE_TEMPLATE/05-enhancement.yml

@@ -1,51 +0,0 @@
-name: Enhancement
-description: Used to request enhancements for llama.cpp
-title: "Feature Request: "
-labels: ["enhancement"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggerganov/llama.cpp/discussions/categories/ideas)
-
-  - type: checkboxes
-    id: prerequisites
-    attributes:
-      label: Prerequisites
-      description: Please confirm the following before submitting your enhancement request.
-      options:
-        - label: I am running the latest code. Mention the version if possible as well.
-          required: true
-        - label: I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
-          required: true
-        - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed).
-          required: true
-        - label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new and useful enhancement to share.
-          required: true
-
-  - type: textarea
-    id: feature-description
-    attributes:
-      label: Feature Description
-      description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement.
-      placeholder: Detailed description of the enhancement
-    validations:
-      required: true
-
-  - type: textarea
-    id: motivation
-    attributes:
-      label: Motivation
-      description: Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users.
-      placeholder: Explanation of why this feature is needed and its benefits
-    validations:
-      required: true
-
-  - type: textarea
-    id: possible-implementation
-    attributes:
-      label: Possible Implementation
-      description: If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.
-      placeholder: Detailed description of potential implementation
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/06-research.yml

@@ -1,52 +0,0 @@
-name: Research
-description: Track new technical research area
-title: "Research: "
-labels: ["research 🔬"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Don't forget to check for any [duplicate research issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22)
-
-  - type: checkboxes
-    id: research-stage
-    attributes:
-      label: Research Stage
-      description: Track general state of this research ticket
-      options:
-        - label: Background Research (Let's try to avoid reinventing the wheel)
-        - label: Hypothesis Formed (How do you think this will work and it's effect?)
-        - label: Strategy / Implementation Forming
-        - label: Analysis of results
-        - label: Debrief / Documentation (So people in the future can learn from us)
-
-  - type: textarea
-    id: background
-    attributes:
-      label: Previous existing literature and research
-      description: Whats the current state of the art and whats the motivation for this research?
-
-  - type: textarea
-    id: hypothesis
-    attributes:
-      label: Hypothesis
-      description: How do you think this will work and it's effect?
-
-  - type: textarea
-    id: implementation
-    attributes:
-      label: Implementation
-      description: Got an approach? e.g. a PR ready to go?
-
-  - type: textarea
-    id: analysis
-    attributes:
-      label: Analysis
-      description: How does the proposed implementation behave?
-
-  - type: textarea
-    id: logs
-    attributes:
-      label: Relevant log output
-      description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks.
-      render: shell

.github/ISSUE_TEMPLATE/07-refactor.yml

@@ -1,28 +0,0 @@
-name: Refactor (Maintainers)
-description: Used to track refactoring opportunities
-title: "Refactor: "
-labels: ["refactor"]
-body:
-  - type: markdown
-    attributes:
-      value: |
-        Don't forget to [check for existing refactor issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered.
-        Also you may want to check [Pull request refactor label as well](https://github.com/ggerganov/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too.
-
-  - type: textarea
-    id: background-description
-    attributes:
-      label: Background Description
-      description: Please provide a detailed written description of the pain points you are trying to solve.
-      placeholder: Detailed description behind your motivation to request refactor
-    validations:
-      required: true
-
-  - type: textarea
-    id: possible-approaches
-    attributes:
-      label: Possible Refactor Approaches
-      description: If you have some idea of possible approaches to solve this problem. You may want to make it a todo list.
-      placeholder: Your idea of possible refactoring opportunity/approaches
-    validations:
-      required: false

.github/ISSUE_TEMPLATE/bug.md

@@ -0,0 +1,11 @@
+---
+name: Bug template
+about: Used to report bugs in llama.cpp
+labels: ["bug-unconfirmed"]
+assignees: ''
+
+---
+
+Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug.
+
+If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests).

.github/ISSUE_TEMPLATE/config.yml

@@ -1,11 +0,0 @@
-blank_issues_enabled: true
-contact_links:
-  - name: Got an idea?
-    url: https://github.com/ggerganov/llama.cpp/discussions/categories/ideas
-    about: Pop it there. It may then become an enhancement ticket.
-  - name: Got a question?
-    url: https://github.com/ggerganov/llama.cpp/discussions/categories/q-a
-    about: Ask a question there!
-  - name: Want to contribute?
-    url: https://github.com/ggerganov/llama.cpp/wiki/contribute
-    about: Head to the contribution guide page of the wiki for areas you can help with

.github/ISSUE_TEMPLATE/enhancement.md

@@ -0,0 +1,28 @@
+---
+name: Enhancement template
+about: Used to request enhancements for llama.cpp
+labels: ["enhancement"]
+assignees: ''
+
+---
+
+# Prerequisites
+
+Please answer the following questions for yourself before submitting an issue.
+
+- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
+- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
+- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
+- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share.
+
+# Feature Description
+
+Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement.
+
+# Motivation
+
+Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users.
+
+# Possible Implementation
+
+If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better.

.github/labeler.yml

@@ -1,91 +0,0 @@
-# https://github.com/actions/labeler
-Kompute:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-kompute.h
-            - ggml/src/ggml-kompute.cpp
-            - README-kompute.md
-Apple Metal:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-metal.h
-            - ggml/src/ggml-metal.cpp
-            - README-metal.md
-SYCL:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-sycl.h
-            - ggml/src/ggml-sycl.cpp
-            - ggml/src/ggml-sycl/**
-            - docs/backend/SYCL.md
-            - examples/sycl/**
-Nvidia GPU:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml-cuda.h
-            - ggml/src/ggml-cuda/**
-Vulkan:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/ggml_vk_generate_shaders.py
-            - ggml/src/ggml-vulkan*
-documentation:
-    - changed-files:
-        - any-glob-to-any-file:
-            - docs/**
-            - media/**
-testing:
-    - changed-files:
-        - any-glob-to-any-file:
-            - tests/**
-build:
-    - changed-files:
-        - any-glob-to-any-file:
-            - cmake/**
-            - CMakeLists.txt
-            - CMakePresets.json
-examples:
-    - changed-files:
-        - any-glob-to-any-file: examples/**
-devops:
-    - changed-files:
-        - any-glob-to-any-file:
-            - .devops/**
-            - .github/**
-            - ci/**
-python:
-    - changed-files:
-        - any-glob-to-any-file:
-            - "**/*.py"
-            - requirements/**
-            - gguf-py/**
-            - .flake8
-script:
-    - changed-files:
-        - any-glob-to-any-file:
-            - scripts/**
-android:
-    - changed-files:
-        - any-glob-to-any-file:
-            - examples/llama.android/**
-server:
-    - changed-files:
-        - any-glob-to-any-file:
-            - examples/server/**
-ggml:
-    - changed-files:
-        - any-glob-to-any-file:
-            - ggml/include/ggml*.h
-            - ggml/src/ggml*.c
-            - ggml/src/ggml*.cpp
-            - ggml/src/ggml*.h
-            - ggml-cuda/**
-nix:
-    - changed-files:
-        - any-glob-to-any-file:
-            - "**/*.nix"
-            - .github/workflows/nix-*.yml
-            - .devops/nix/nixpkgs-instances.nix
-embedding:
-    - changed-files:
-        - any-glob-to-any-file: examples/embedding/

.github/pull_request_template.md

@@ -1,7 +0,0 @@
-
-
-- [x] I have read the [contributing guidelines](https://github.com/ggerganov/llama.cpp/blob/master/CONTRIBUTING.md)
-- Self-reported review complexity:
-  - [ ] Low
-  - [ ] Medium
-  - [ ] High

.github/workflows/bench.yml

@@ -1,310 +0,0 @@
-# Benchmark
-name: Benchmark
-
-on:
-  workflow_dispatch:
-    inputs:
-      gpu-series:
-        description: 'Azure GPU series to run with'
-        required: true
-        type: choice
-        options:
-          - Standard_NC4as_T4_v3
-          - Standard_NC24ads_A100_v4
-          - Standard_NC80adis_H100_v5
-      sha:
-        description: 'Commit SHA1 to build'
-        required: false
-        type: string
-      duration:
-        description: 'Duration of the bench'
-        type: string
-        default: 10m
-
-  push:
-    branches:
-      - master
-    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.c', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
-  pull_request_target:
-    types: [opened, synchronize, reopened]
-    paths: ['llama.cpp', 'ggml.c', 'ggml-backend.c', 'ggml-quants.c', '**/*.cu', 'examples/server/*.h*', 'examples/server/*.cpp']
-  schedule:
-    -  cron: '04 2 * * *'
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}-${{ github.event.inputs.sha }}
-  cancel-in-progress: true
-
-jobs:
-  bench-server-baseline:
-    runs-on: Standard_NC4as_T4_v3
-    env:
-      RUNNER_LABEL: Standard_NC4as_T4_v3 # FIXME Do not find a way to not duplicate it
-      N_USERS: 8
-      DURATION: 10m
-
-    strategy:
-      matrix:
-        model: [phi-2]
-        ftype: [q4_0, q8_0, f16]
-        include:
-          - model: phi-2
-            ftype: q4_0
-            pr_comment_enabled: "true"
-
-    if: |
-      inputs.gpu-series == 'Standard_NC4as_T4_v3'
-      || (
-        github.event_name == 'schedule'
-        && github.ref_name == 'master'
-        && github.repository_owner == 'ggerganov'
-      )
-      || github.event_name == 'pull_request_target'
-      || (
-        github.event_name == 'push'
-        && github.event.ref == 'refs/heads/master'
-        && github.repository_owner == 'ggerganov'
-      )
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Install python env
-        id: pipenv
-        run: |
-          cd examples/server/bench
-          python3 -m venv venv
-          source venv/bin/activate
-          pip install -r requirements.txt
-
-      - name: Prometheus
-        id: install_prometheus
-        run: |
-          wget --quiet https://github.com/prometheus/prometheus/releases/download/v2.51.0/prometheus-2.51.0.linux-amd64.tar.gz
-          tar xzf prometheus*.tar.gz --strip-components=1
-          ./prometheus --config.file=examples/server/bench/prometheus.yml &
-          while ! nc -z localhost 9090; do
-            sleep 0.1
-          done
-
-      - name: Set up Go
-        uses: actions/setup-go@v5
-        with:
-          go-version: '1.21'
-
-      - name: Install k6 and xk6-sse
-        id: k6_installation
-        run: |
-          cd examples/server/bench
-          go install go.k6.io/xk6/cmd/xk6@latest
-          xk6 build master \
-              --with github.com/phymbert/xk6-sse
-
-      - name: Build
-        id: cmake_build
-        run: |
-          set -eux
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DLLAMA_CURL=ON \
-              -DLLAMA_CUBLAS=ON \
-              -DCUDAToolkit_ROOT=/usr/local/cuda \
-              -DCMAKE_CUDA_COMPILER=/usr/local/cuda/bin/nvcc \
-              -DCMAKE_CUDA_ARCHITECTURES=75 \
-              -DLLAMA_FATAL_WARNINGS=OFF \
-              -DLLAMA_ALL_WARNINGS=OFF \
-              -DCMAKE_BUILD_TYPE=Release;
-          cmake --build build --config Release -j $(nproc) --target llama-server
-
-      - name: Download the dataset
-        id: download_dataset
-        run: |
-          cd examples/server/bench
-          wget --quiet https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-
-      - name: Server bench
-        id: server_bench
-        run: |
-          set -eux
-
-          cd examples/server/bench
-          source venv/bin/activate
-          python bench.py \
-              --runner-label ${{ env.RUNNER_LABEL }} \
-              --name ${{ github.job }} \
-              --branch ${{ github.head_ref || github.ref_name }} \
-              --commit ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha }} \
-              --scenario script.js \
-              --duration ${{ github.event.inputs.duration || env.DURATION }} \
-              --hf-repo ggml-org/models	 \
-              --hf-file ${{ matrix.model }}/ggml-model-${{ matrix.ftype }}.gguf \
-              --model-path-prefix /models \
-              --parallel ${{ env.N_USERS }} \
-              -ngl 33 \
-              --batch-size 2048 \
-              --ubatch-size	256 \
-              --ctx-size 16384 \
-              --n-prompts 1000 \
-              --max-prompt-tokens 1024 \
-              --max-tokens 2048
-
-          cat results.github.env >> $GITHUB_ENV
-
-          # Remove dataset as we do not want it in the artefact
-          rm ShareGPT_V3_unfiltered_cleaned_split.json
-
-      - uses: actions/upload-artifact@v4
-        with:
-          name: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
-          compression-level: 9
-          path: |
-            examples/server/bench/*.jpg
-            examples/server/bench/*.json
-            examples/server/bench/*.log
-
-      - name: Commit status
-        uses: Sibz/github-status-action@v1
-        with:
-          authToken: ${{secrets.GITHUB_TOKEN}}
-          sha: ${{ inputs.sha || github.event.pull_request.head.sha || github.sha }}
-          context: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
-          description: |
-            ${{ env.BENCH_RESULTS }}
-          state: 'success'
-
-      - name: Upload benchmark images
-        uses: devicons/public-upload-to-imgur@v2.2.2
-        continue-on-error: true # Important as it looks unstable: 503
-        id: imgur_step
-        with:
-          client_id: ${{secrets.IMGUR_CLIENT_ID}}
-          path: |
-            examples/server/bench/prompt_tokens_seconds.jpg
-            examples/server/bench/predicted_tokens_seconds.jpg
-            examples/server/bench/kv_cache_usage_ratio.jpg
-            examples/server/bench/requests_processing.jpg
-
-      - name: Extract mermaid
-        id: set_mermaid
-        run: |
-          set -eux
-
-          cd examples/server/bench
-          PROMPT_TOKENS_SECONDS=$(cat prompt_tokens_seconds.mermaid)
-          echo "PROMPT_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
-          echo "$PROMPT_TOKENS_SECONDS" >> $GITHUB_ENV
-          echo "EOF" >> $GITHUB_ENV
-
-          PREDICTED_TOKENS_SECONDS=$(cat predicted_tokens_seconds.mermaid)
-          echo "PREDICTED_TOKENS_SECONDS<<EOF" >> $GITHUB_ENV
-          echo "$PREDICTED_TOKENS_SECONDS" >> $GITHUB_ENV
-          echo "EOF" >> $GITHUB_ENV
-
-          KV_CACHE_USAGE_RATIO=$(cat kv_cache_usage_ratio.mermaid)
-          echo "KV_CACHE_USAGE_RATIO<<EOF" >> $GITHUB_ENV
-          echo "$KV_CACHE_USAGE_RATIO" >> $GITHUB_ENV
-          echo "EOF" >> $GITHUB_ENV
-
-          REQUESTS_PROCESSING=$(cat requests_processing.mermaid)
-          echo "REQUESTS_PROCESSING<<EOF" >> $GITHUB_ENV
-          echo "$REQUESTS_PROCESSING" >> $GITHUB_ENV
-          echo "EOF" >> $GITHUB_ENV
-
-      - name: Extract image url
-        id: extract_image_url
-        continue-on-error: true
-        run: |
-          set -eux
-
-          echo "IMAGE_O=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[0] }}" >> $GITHUB_ENV
-          echo "IMAGE_1=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[1] }}" >> $GITHUB_ENV
-          echo "IMAGE_2=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[2] }}" >> $GITHUB_ENV
-          echo "IMAGE_3=${{ fromJSON(steps.imgur_step.outputs.imgur_urls)[3] }}" >> $GITHUB_ENV
-
-      - name: Comment PR
-        uses: mshick/add-pr-comment@v2
-        id: comment_pr
-        if: ${{ github.event.pull_request != '' && matrix.pr_comment_enabled == 'true' }}
-        with:
-          message-id: bench-server-${{ github.job }}-${{ env.RUNNER_LABEL }}-${{ matrix.model }}-${{ matrix.ftype }}
-          message: |
-            <p align="center">
-
-            📈 **llama.cpp server** for _${{ github.job }}_ on _${{ env.RUNNER_LABEL }}_ for `${{ matrix.model }}`-`${{ matrix.ftype }}`: **${{ env.BENCH_ITERATIONS}} iterations** 🚀
-
-            </p>
-
-            <details>
-
-            <summary>Expand details for performance related PR only</summary>
-
-            - Concurrent users: ${{ env.N_USERS }}, duration: ${{ github.event.inputs.duration || env.DURATION }}
-            - HTTP request          : avg=${{ env.HTTP_REQ_DURATION_AVG }}ms        p(95)=${{ env.HTTP_REQ_DURATION_P_95_ }}ms fails=${{ env.HTTP_REQ_FAILED_PASSES }}, finish reason: stop=${{ env.LLAMACPP_COMPLETIONS_STOP_RATE_PASSES }} truncated=${{ env.LLAMACPP_COMPLETIONS_TRUNCATED_RATE_PASSES }}
-            - Prompt processing (pp): avg=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_PROMPT_PROCESSING_SECOND_P_95_ }}tk/s
-            - Token generation  (tg): avg=${{ env.LLAMACPP_TOKENS_SECOND_AVG }}tk/s p(95)=${{ env.LLAMACPP_TOKENS_SECOND_P_95_ }}tk/s
-            - ${{ env.BENCH_GRAPH_XLABEL }}
-
-
-            <p align="center">
-
-            <img width="100%" height="100%" src="${{ env.IMAGE_O }}" alt="prompt_tokens_seconds" />
-
-            <details>
-
-            <summary>More</summary>
-
-            ```mermaid
-            ${{ env.PROMPT_TOKENS_SECONDS }}
-            ```
-
-            </details>
-
-            <img width="100%" height="100%" src="${{ env.IMAGE_1 }}" alt="predicted_tokens_seconds"/>
-
-            <details>
-                <summary>More</summary>
-
-            ```mermaid
-            ${{ env.PREDICTED_TOKENS_SECONDS }}
-            ```
-
-            </details>
-
-            </p>
-
-            <details>
-
-            <summary>Details</summary>
-
-            <p align="center">
-
-            <img width="100%" height="100%" src="${{ env.IMAGE_2 }}" alt="kv_cache_usage_ratio" />
-
-            <details>
-                <summary>More</summary>
-
-            ```mermaid
-            ${{ env.KV_CACHE_USAGE_RATIO }}
-            ```
-
-            </details>
-
-            <img width="100%" height="100%" src="${{ env.IMAGE_3 }}" alt="requests_processing"/>
-
-            <details>
-                <summary>More</summary>
-
-            ```mermaid
-            ${{ env.REQUESTS_PROCESSING }}
-            ```
-
-            </details>
-
-            </p>
-            </details>
-            </details>

.github/workflows/close-issue.yml

@@ -1,23 +0,0 @@
-name: Close inactive issues
-on:
-  schedule:
-    - cron: "42 0 * * *"
-
-jobs:
-  close-issues:
-    runs-on: ubuntu-latest
-    permissions:
-      issues: write
-      pull-requests: write
-    steps:
-      - uses: actions/stale@v5
-        with:
-          exempt-issue-labels: "refactor,help wanted,good first issue,research,bug"
-          days-before-issue-stale: 30
-          days-before-issue-close: 14
-          stale-issue-label: "stale"
-          close-issue-message: "This issue was closed because it has been inactive for 14 days since being marked as stale."
-          days-before-pr-stale: -1
-          days-before-pr-close: -1
-          operations-per-run: 10000
-          repo-token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/code-coverage.yml

@@ -0,0 +1,36 @@
+name: Code Coverage
+on: [push, pull_request]
+
+env:
+  GGML_NLOOP: 3
+  GGML_N_THREADS: 1
+
+jobs:
+  run:
+    runs-on: ubuntu-20.04
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install build-essential gcc-8 lcov
+
+      - name: Build
+        run: CC=gcc-8 make -j LLAMA_CODE_COVERAGE=1 tests
+
+      - name: Run tests
+        run: CC=gcc-8 make test
+
+      - name: Generate coverage report
+        run: |
+          make coverage
+          make lcov-report
+
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v3
+        env:
+           CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+        with:
+          files: lcov-report/coverage.info

.github/workflows/docker.yml

@@ -10,20 +10,15 @@
 name: Publish Docker image
 
 on:
-  #pull_request:
+  pull_request:
   push:
     branches:
       - master
-    paths: ['.github/workflows/docker.yml', '.devops/*.Dockerfile', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.cuh', '**/*.swift', '**/*.m', '**/*.metal']
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
 
 jobs:
   push_to_registry:
     name: Push Docker image to Docker Hub
-    #if: github.event.pull_request.draft == false
+    if: github.event.pull_request.draft == false
 
     runs-on: ubuntu-latest
     env:
@@ -31,21 +26,23 @@ jobs:
     strategy:
       matrix:
         config:
-          - { tag: "light", dockerfile: ".devops/llama-cli.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "server", dockerfile: ".devops/llama-server.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "light", dockerfile: ".devops/main.Dockerfile", platforms: "linux/amd64,linux/arm64" }
           - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "light-cuda", dockerfile: ".devops/llama-cli-cuda.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "server-cuda", dockerfile: ".devops/llama-server-cuda.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "server", dockerfile: ".devops/server.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          # NOTE(canardletter): The CUDA builds on arm64 are very slow, so I
+          #                     have disabled them for now until the reason why
+          #                     is understood.
+          - { tag: "light-cuda", dockerfile: ".devops/main-cuda.Dockerfile", platforms: "linux/amd64" }
           - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "light-rocm", dockerfile: ".devops/llama-cli-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "server-rocm", dockerfile: ".devops/llama-server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          # Note: the full-rocm image is failing due to a "no space left on device" error. It is disabled for now to allow the workflow to complete.
-          #- { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
-          - { tag: "light-intel", dockerfile: ".devops/llama-cli-intel.Dockerfile", platforms: "linux/amd64" }
-          - { tag: "server-intel", dockerfile: ".devops/llama-server-intel.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "server-cuda", dockerfile: ".devops/server-cuda.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "server-rocm", dockerfile: ".devops/server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" }
+          - { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" }
+          - { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" }
     steps:
       - name: Check out the repo
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
 
       - name: Set up QEMU
         uses: docker/setup-qemu-action@v2
@@ -90,12 +87,6 @@ jobs:
             echo "name=${SAFE_NAME}-b${BUILD_NUMBER}-${SHORT_HASH}" >> $GITHUB_OUTPUT
           fi
 
-      - name: Downcase github.repository_owner
-        run: |
-          echo "repository_owner_lowercase=${GITHUB_REPOSITORY_OWNER@L}" >> $GITHUB_ENV
-        env:
-          GITHUB_REPOSITORY_OWNER: '${{ github.repository_owner }}'
-
       - name: Build and push Docker image (versioned)
         if: github.event_name == 'push'
         uses: docker/build-push-action@v4
@@ -103,7 +94,7 @@ jobs:
           context: .
           push: true
           platforms: ${{ matrix.config.platforms }}
-          tags: "ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}"
+          tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ env.COMMIT_SHA }}"
           file: ${{ matrix.config.dockerfile }}
 
       - name: Build and push Docker image (tagged)
@@ -112,5 +103,5 @@ jobs:
           context: .
           push: ${{ github.event_name == 'push' }}
           platforms: ${{ matrix.config.platforms }}
-          tags: "ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ env.repository_owner_lowercase }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}"
+          tags: "ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }},ghcr.io/${{ github.repository_owner }}/llama.cpp:${{ matrix.config.tag }}-${{ steps.tag.outputs.name }}"
           file: ${{ matrix.config.dockerfile }}

.github/workflows/editorconfig.yml

@@ -14,14 +14,10 @@ on:
     branches:
       - master
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
 jobs:
   editorconfig:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v3
       - uses: editorconfig-checker/action-editorconfig-checker@main
       - run: editorconfig-checker

.github/workflows/gguf-publish.yml

@@ -24,9 +24,9 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v3
     - name: Set up Python
-      uses: actions/setup-python@v5
+      uses: actions/setup-python@v2
       with:
         python-version: '3.9.x'
     - name: Install dependencies

.github/workflows/labeler.yml

@@ -1,17 +0,0 @@
-name: "Pull Request Labeler"
-on:
-- pull_request_target
-
-jobs:
-  labeler:
-    permissions:
-      contents: read
-      pull-requests: write
-    runs-on: ubuntu-latest
-    steps:
-    - uses: actions/checkout@v4
-      with:
-        repository: "ggerganov/llama.cpp"
-    - uses: actions/labeler@v5
-      with:
-        configuration-path: '.github/labeler.yml'

.github/workflows/nix-ci-aarch64.yml

@@ -17,10 +17,6 @@ on:
     types: [opened, synchronize, reopened]
     paths: ['**/*.nix', 'flake.lock']
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
 jobs:
   nix-build-aarch64:
     runs-on: ubuntu-latest

.github/workflows/nix-ci.yml

@@ -8,10 +8,6 @@ on:
   pull_request:
     types: [opened, synchronize, reopened]
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
 jobs:
   nix-eval:
     strategy:

.github/workflows/python-check-requirements.yml

@@ -3,33 +3,27 @@ name: Python check requirements.txt
 on:
   push:
     paths:
-      - '.github/workflows/python-check-requirements.yml'
       - 'scripts/check-requirements.sh'
       - 'convert*.py'
       - 'requirements.txt'
       - 'requirements/*.txt'
   pull_request:
     paths:
-      - '.github/workflows/python-check-requirements.yml'
       - 'scripts/check-requirements.sh'
       - 'convert*.py'
       - 'requirements.txt'
       - 'requirements/*.txt'
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
 jobs:
   python-check-requirements:
     runs-on: ubuntu-latest
     name: check-requirements
     steps:
       - name: Check out source repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
       - name: Set up Python environment
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: "3.11"
       - name: Run check-requirements.sh script
-        run:  bash scripts/check-requirements.sh
+        run:  bash scripts/check-requirements.sh nocleanup

.github/workflows/python-lint.yml

@@ -2,22 +2,19 @@ name: flake8 Lint
 
 on: [push, pull_request]
 
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
 jobs:
   flake8-lint:
     runs-on: ubuntu-latest
     name: Lint
     steps:
       - name: Check out source repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v3
       - name: Set up Python environment
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v4
         with:
           python-version: "3.11"
       - name: flake8 Lint
         uses: py-actions/flake8@v2
         with:
-            plugins: "flake8-no-print"
+            ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503"
+            exclude: "examples/*,examples/*/**,*/**/__init__.py"

.github/workflows/python-type-check.yml

@@ -1,38 +0,0 @@
-name: Python Type-Check
-
-on:
-  push:
-    paths:
-      - '.github/workflows/python-type-check.yml'
-      - '**.py'
-      - '**/requirements*.txt'
-  pull_request:
-    paths:
-      - '.github/workflows/python-type-check.yml'
-      - '**.py'
-      - '**/requirements*.txt'
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }}
-  cancel-in-progress: true
-
-jobs:
-  python-type-check:
-    runs-on: ubuntu-latest
-    name: pyright type-check
-    steps:
-      - name: Check out source repository
-        uses: actions/checkout@v4
-      - name: Set up Python environment
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.11"
-      - name: Install Python dependencies
-        # TODO: use a venv
-        run: pip install -r requirements/requirements-all.txt
-      - name: Type-check with Pyright
-        uses: jakebailey/pyright-action@v2
-        with:
-          version: 1.1.370
-          level: warning
-          warnings: true

.github/workflows/server.yml

@@ -3,26 +3,13 @@ name: Server
 
 on:
   workflow_dispatch: # allows manual triggering
-    inputs:
-      sha:
-        description: 'Commit SHA1 to build'
-        required: false
-        type: string
-      slow_tests:
-        description: 'Run slow tests'
-        required: true
-        type: boolean
   push:
     branches:
       - master
-    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*']
   pull_request:
     types: [opened, synchronize, reopened]
-    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/**.*']
-
-concurrency:
-  group: ${{ github.workflow }}-${{ github.ref }}-${{ github.head_ref || github.run_id }}
-  cancel-in-progress: true
+    paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*']
 
 jobs:
   server:
@@ -30,154 +17,67 @@ jobs:
 
     strategy:
       matrix:
-        sanitizer: [ADDRESS, UNDEFINED] # THREAD is broken
-        build_type: [RelWithDebInfo]
+        sanitizer: [ADDRESS, THREAD, UNDEFINED]
+        build_type: [Debug, Release]
         include:
           - build_type: Release
             sanitizer: ""
-      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
+        exclude:
+          - build_type: Release
+            sanitizer: ADDRESS
+          - build_type: Release
+            sanitizer: THREAD
+          - build_type: Release
+            sanitizer: UNDEFINED
+
+    container:
+      image: ubuntu:latest
+      ports:
+        - 8888
+      options: --cpus 4
 
     steps:
+      - name: Clone
+        id: checkout
+        uses: actions/checkout@v3
+
       - name: Dependencies
         id: depends
         run: |
-          sudo apt-get update
-          sudo apt-get -y install \
+          apt-get update
+          apt-get -y install \
             build-essential \
-            xxd \
             git \
             cmake \
-            curl \
+            python3-pip \
             wget \
-            language-pack-en \
-            libcurl4-openssl-dev
+            psmisc
 
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: Python setup
-        id: setup_python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
+      - name: Build
+        id: cmake_build
+        run: |
+          mkdir build
+          cd build
+          cmake .. \
+              -DLLAMA_NATIVE=OFF \
+              -DLLAMA_BUILD_SERVER=ON \
+              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
+              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
+          cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server
 
       - name: Tests dependencies
         id: test_dependencies
         run: |
           pip install -r examples/server/tests/requirements.txt
 
-      - name: Verify server deps
-        id: verify_server_deps
+      - name: Download models
+        id: download_models
         run: |
-          git config --global --add safe.directory $(realpath .)
-          cd examples/server
-          git ls-files --others --modified
-          git status
-          ./deps.sh
-          git status
-          not_ignored_files="$(git ls-files --others --modified)"
-          echo "Modified files: ${not_ignored_files}"
-          if [ -n "${not_ignored_files}" ]; then
-            echo "Repository is dirty or server deps are not built as expected"
-            echo "${not_ignored_files}"
-            exit 1
-          fi
-
-      - name: Build (no OpenMP)
-        id: cmake_build_no_openmp
-        if: ${{ matrix.sanitizer == 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DLLAMA_CURL=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON \
-              -DGGML_OPENMP=OFF ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
-
-      - name: Build
-        id: cmake_build
-        if: ${{ matrix.sanitizer != 'THREAD' }}
-        run: |
-          cmake -B build \
-              -DGGML_NATIVE=OFF \
-              -DLLAMA_BUILD_SERVER=ON \
-              -DLLAMA_CURL=ON \
-              -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \
-              -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ;
-          cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target llama-server
+          cd examples/server/tests
+          ../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf
 
       - name: Tests
-        id: server_integration_tests
+        id: server_integration_test
         run: |
           cd examples/server/tests
           PORT=8888 ./tests.sh
-
-      - name: Slow tests
-        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
-        run: |
-          cd examples/server/tests
-          PORT=8888 ./tests.sh --stop --no-skipped --no-capture --tags slow
-
-
-  server-windows:
-    runs-on: windows-2019
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v4
-        with:
-          fetch-depth: 0
-          ref: ${{ github.event.inputs.sha || github.event.pull_request.head.sha || github.sha || github.head_ref || github.ref_name }}
-
-      - name: libCURL
-        id: get_libcurl
-        env:
-          CURL_VERSION: 8.6.0_6
-        run: |
-          curl.exe -o $env:RUNNER_TEMP/curl.zip -L "https://curl.se/windows/dl-${env:CURL_VERSION}/curl-${env:CURL_VERSION}-win64-mingw.zip"
-          mkdir $env:RUNNER_TEMP/libcurl
-          tar.exe -xvf $env:RUNNER_TEMP/curl.zip --strip-components=1 -C $env:RUNNER_TEMP/libcurl
-
-      - name: Build
-        id: cmake_build
-        run: |
-          cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include"
-          cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target llama-server
-
-      - name: Python setup
-        id: setup_python
-        uses: actions/setup-python@v5
-        with:
-          python-version: '3.11'
-
-      - name: Tests dependencies
-        id: test_dependencies
-        run: |
-          pip install -r examples/server/tests/requirements.txt
-
-      - name: Copy Libcurl
-        id: prepare_libcurl
-        run: |
-          cp $env:RUNNER_TEMP/libcurl/bin/libcurl-x64.dll ./build/bin/Release/libcurl-x64.dll
-
-      - name: Tests
-        id: server_integration_tests
-        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
-        run: |
-          cd examples/server/tests
-          behave.exe --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
-
-      - name: Slow tests
-        id: server_integration_tests_slow
-        if: ${{ (github.event.schedule || github.event.inputs.slow_tests == 'true') && matrix.build_type == 'Release' }}
-        run: |
-          cd examples/server/tests
-          behave.exe --stop --no-skipped --no-capture --tags slow

.github/workflows/tidy-post.yml

@@ -0,0 +1,20 @@
+name: clang-tidy review post comments
+
+on:
+  workflow_dispatch:
+    workflows: ["clang-tidy-review"]
+    types:
+      - completed
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: ZedThree/clang-tidy-review/post@v0.13.0
+        # lgtm_comment_body, max_comments, and annotations need to be set on the posting workflow in a split setup
+        with:
+          # adjust options as necessary
+          lgtm_comment_body: ''
+          annotations: false
+          max_comments: 25

.github/workflows/tidy-review.yml

@@ -0,0 +1,23 @@
+name: clang-tidy-review
+
+on:
+  pull_request:
+    branches:
+      - master
+
+jobs:
+  clang-tidy-review:
+    runs-on: ubuntu-latest
+
+    steps:
+    - uses: actions/checkout@v3
+
+    - uses: ZedThree/clang-tidy-review@v0.13.0
+      id: review
+      with:
+        lgtm_comment_body: ''
+        build_dir: build
+        cmake_command: cmake . -B build -DCMAKE_EXPORT_COMPILE_COMMANDS=on
+        split_workflow: true
+
+    - uses: ZedThree/clang-tidy-review/upload@v0.13.0

.github/workflows/zig-build.yml

@@ -0,0 +1,25 @@
+name: Zig CI
+
+on:
+  pull_request:
+  push:
+    branches:
+      - master
+
+jobs:
+  build:
+    strategy:
+      fail-fast: false
+      matrix:
+        runs-on: [ubuntu-latest, macos-latest, windows-latest]
+    runs-on: ${{ matrix.runs-on }}
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          submodules: recursive
+          fetch-depth: 0
+      - uses: goto-bus-stop/setup-zig@v2
+        with:
+          version: 0.11.0
+      - name: Build Summary
+        run: zig build --summary all -freference-trace

.gitignore

@@ -1,132 +1,94 @@
-# Extensions
-
-*.a
-*.bat
-*.bin
-*.dll
-*.dot
-*.etag
-*.exe
-*.gcda
-*.gcno
-*.gcov
-*.gguf
-*.gguf.json
-*.lastModified
-*.log
-*.metallib
 *.o
+*.a
 *.so
-*.tmp
-
-# IDE / OS
-
+*.gguf
+*.bin
+*.exe
+*.dll
+*.log
+*.gcov
+*.gcno
+*.gcda
+*.dot
+*.bat
+*.metallib
+.DS_Store
+.build/
 .cache/
 .ccls-cache/
 .direnv/
-.DS_Store
 .envrc
-.idea/
 .swiftpm
+.venv
+.clang-tidy
 .vs/
 .vscode/
-nppBackup
+.idea/
 
-
-# Coverage
-
-gcovr-report/
 lcov-report/
+gcovr-report/
 
-# Build Artifacts
-
-tags
-.build/
 build*
-!build-info.cmake
-!build-info.cpp.in
-!build-info.sh
-!build.zig
-!docs/build.md
-/libllama.so
-/llama-*
-/vulkan-shaders-gen
-android-ndk-*
-arm_neon.h
 cmake-build-*
-CMakeSettings.json
-compile_commands.json
-ggml-metal-embed.metal
-llama-batched-swift
-/rpc-server
 out/
 tmp/
 
-# Deprecated
-
-/main
-/server
-
-# CI
-
-!.github/workflows/*.yml
-
-# Models
-
 models/*
 models-mnt
-!models/.editorconfig
-!models/ggml-vocab-*.gguf*
 
-# Zig
+/Pipfile
+/baby-llama
+/beam-search
+/benchmark-matmult
+/convert-llama2c-to-ggml
+/embd-input-test
+/embedding
+/gguf
+/gguf-llama-simple
+/imatrix
+/infill
+/libllama.so
+/llama-bench
+/llava-cli
+/lookahead
+/lookup
+/main
+/metal
+/passkey
+/perplexity
+/q8dot
+/quantize
+/quantize-stats
+/result
+/save-load-state
+/server
+/simple
+/batched
+/batched-bench
+/export-lora
+/finetune
+/speculative
+/parallel
+/train-text-from-scratch
+/tokenize
+/vdot
+/common/build-info.cpp
+arm_neon.h
+compile_commands.json
+CMakeSettings.json
+
+__pycache__
+dist
 
 zig-out/
 zig-cache/
 
-# Logs
-
 ppl-*.txt
 qnt-*.txt
 perf-*.txt
 
-# Examples
-
 examples/jeopardy/results.txt
-examples/server/*.css.hpp
-examples/server/*.html.hpp
-examples/server/*.js.hpp
-examples/server/*.mjs.hpp
-!build_64.sh
-!examples/*.bat
-!examples/*/*.kts
-!examples/*/*/*.kts
-!examples/sycl/*.bat
-!examples/sycl/*.sh
 
-# Python
-
-/.venv
-__pycache__/
-*/poetry.lock
+poetry.lock
 poetry.toml
-
-# Nix
-/result
-
-# Test binaries
-/tests/test-backend-ops
-/tests/test-double-float
-/tests/test-grad0
-/tests/test-grammar-parser
-/tests/test-llama-grammar
-/tests/test-opt
-/tests/test-quantize-fns
-/tests/test-quantize-perf
-/tests/test-rope
-/tests/test-sampling
-/tests/test-tokenizer-0
-/tests/test-tokenizer-1-bpe
-/tests/test-tokenizer-1-spm
-
-# Scripts
-!/scripts/install-oneapi.bat
+nppBackup

.gitmodules

@@ -1,3 +1,3 @@
 [submodule "kompute"]
-	path = ggml/src/kompute
+	path = kompute
 	url = https://github.com/nomic-ai/kompute.git

.pre-commit-config.yaml

@@ -3,14 +3,13 @@
 exclude: prompts/.*.txt
 repos:
 - repo: https://github.com/pre-commit/pre-commit-hooks
-  rev: v4.6.0
+  rev: v3.2.0
   hooks:
   - id: trailing-whitespace
   - id: end-of-file-fixer
   - id: check-yaml
   - id: check-added-large-files
 - repo: https://github.com/PyCQA/flake8
-  rev: 7.0.0
+  rev: 6.0.0
   hooks:
   -   id: flake8
-      additional_dependencies: [flake8-no-print]

AUTHORS

@@ -1,782 +0,0 @@
-# date: Wed Jun 26 19:36:34 EEST 2024
-# this file is auto-generated by scripts/gen-authors.sh
-
-0cc4m <picard12@live.de>
-0xspringtime <110655352+0xspringtime@users.noreply.github.com>
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-44670 <44670@users.noreply.github.com>
-AN Long <aisk@users.noreply.github.com>
-AT <manyoso@users.noreply.github.com>
-Aarni Koskela <akx@iki.fi>
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-Aaryaman Vasishta <aaryaman.vasishta@amd.com>
-Abheek Gulati <abheekg@hotmail.com>
-Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com>
-Abhishek Gopinath K <31348521+overtunned@users.noreply.github.com>
-Adithya Balaji <adithya.b94@gmail.com>
-AdithyanI <adithyan.i4internet@gmail.com>
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-Adrian Hesketh <a-h@users.noreply.github.com>
-Ahmet Zeer <ahmed.zeer@std.yildiz.edu.tr>
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-Aisuko <urakiny@gmail.com>
-Akarshan Biswas <akarshanbiswas@fedoraproject.org>
-Albert Jin <albert.jin@gmail.com>
-Alberto <57916483+albbus-stack@users.noreply.github.com>
-Alex <awhill19@icloud.com>
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-Alex Klinkhamer <from.github.com.917@grencez.dev>
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-Ali Nehzat <ali.nehzat@thanks.dev>
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-Amir <amir_zia@outlook.com>
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-Ido S <ido.pluto@gmail.com>
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-Mateusz Charytoniuk <mateusz.charytoniuk@protonmail.com>
-Matheus C. França <matheus-catarino@hotmail.com>
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-Mattheus Chediak <shammcity00@gmail.com>
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-strawberrymelonpanda <152940198+strawberrymelonpanda@users.noreply.github.com>
-swittk <switt1995@gmail.com>
-takov751 <40316768+takov751@users.noreply.github.com>
-tarcey <cey.tarik@gmail.com>
-texmex76 <40733439+texmex76@users.noreply.github.com>
-thement <40525767+thement@users.noreply.github.com>
-tjohnman <tjohnman@users.noreply.github.com>
-tslmy <tslmy@users.noreply.github.com>
-ubik2 <ubik2@users.noreply.github.com>
-uint256_t <konndennsa@gmail.com>
-uint256_t <maekawatoshiki1017@gmail.com>
-unbounded <haakon@likedan.net>
-valiray <133289098+valiray@users.noreply.github.com>
-vik <vikhyatk@gmail.com>
-viric <viric@viric.name>
-vodkaslime <646329483@qq.com>
-vvhg1 <94630311+vvhg1@users.noreply.github.com>
-vxiiduu <73044267+vxiiduu@users.noreply.github.com>
-wbpxre150 <100937007+wbpxre150@users.noreply.github.com>
-whoreson <139810751+whoreson@users.noreply.github.com>
-woachk <24752637+woachk@users.noreply.github.com>
-wonjun Jang <strutive07@gmail.com>
-woodx <124784234+woodx9@users.noreply.github.com>
-wzy <32936898+Freed-Wu@users.noreply.github.com>
-xaedes <xaedes@gmail.com>
-xaedes <xaedes@googlemail.com>
-xloem <0xloem@gmail.com>
-yangli2 <yangli2@gmail.com>
-yuiseki <yuiseki@gmail.com>
-zakkor <edward.partenie@gmail.com>
-zhangkaihuo <zhangkaihuo@gmail.com>
-zhouwg <6889919+zhouwg@users.noreply.github.com>
-zhouwg <zhouwg2000@gmail.com>
-zrm <trustiosity.zrm@gmail.com>
-Ștefan-Gabriel Muscalu <legraphista@users.noreply.github.com>
-源文雨 <41315874+fumiama@users.noreply.github.com>
-Нияз Гарифзянов <112617865+garrnizon@users.noreply.github.com>

CMakePresets.json

@@ -1,65 +0,0 @@
-{
-  "version": 4,
-  "configurePresets": [
-    {
-        "name":  "base",
-        "hidden": true,
-        "generator":   "Ninja",
-        "binaryDir":   "${sourceDir}/build-${presetName}",
-        "cacheVariables": {
-            "CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
-            "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
-        }
-    },
-    {
-        "name": "sycl-base",
-        "hidden": true,
-        "generator": "Ninja",
-        "binaryDir": "${sourceDir}/build-${presetName}",
-        "cacheVariables": {
-            "CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
-            "CMAKE_CXX_COMPILER": "icx",
-            "CMAKE_C_COMPILER": "cl",
-            "GGML_SYCL": "ON",
-            "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
-        }
-    },
-    { "name": "debug",   "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } },
-    { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } },
-    { "name": "reldbg",  "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
-    { "name": "static",  "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } },
-
-    {
-        "name": "arm64-windows-msvc", "hidden": true,
-        "architecture": { "value": "arm64",       "strategy": "external" },
-        "toolset":      { "value": "host=x86_64", "strategy": "external" },
-        "cacheVariables": {
-            "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-msvc.cmake"
-        }
-    },
-
-    {
-        "name": "arm64-windows-llvm", "hidden": true,
-        "architecture": { "value": "arm64",       "strategy": "external" },
-        "toolset":      { "value": "host=x86_64", "strategy": "external" },
-        "cacheVariables": {
-            "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-llvm.cmake"
-        }
-    },
-
-    { "name": "arm64-windows-llvm-debug"  , "inherits": [ "base", "arm64-windows-llvm",  "debug"   ] },
-    { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm",  "reldbg" ] },
-    { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm",  "reldbg", "static" ] },
-
-    { "name": "arm64-windows-msvc-debug"  , "inherits": [ "base", "arm64-windows-msvc",  "debug"   ] },
-    { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg" ] },
-    { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc",  "reldbg", "static" ] },
-
-    { "name": "x64-windows-msvc-debug"  , "inherits": [ "base", "debug"   ] },
-    { "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] },
-    { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] },
-
-    { "name": "x64-windows-sycl-debug"  , "inherits": [ "sycl-base", "debug"   ] },
-    { "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] }
-  ]
-}

CONTRIBUTING.md

@@ -1,29 +0,0 @@
-# Pull requests (for contributors)
-
-- Test your changes:
-  - Using the commands in the [`tests`](tests) folder. For instance, running the `./tests/test-backend-ops` command tests different backend implementations of the GGML library
-  - Execute [the full CI locally on your machine](ci/README.md) before publishing
-- Please rate the complexity of your PR (i.e. `Review Complexity : Low`, `Review Complexity : Medium`, `Review Complexity : High`). This makes it easier for maintainers to triage the PRs.
-  - The PR template has a series of review complexity checkboxes `[ ]` that [you can mark as](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/about-task-lists) `[X]` for your convenience
-- Consider allowing write access to your branch for faster review
-- If your PR becomes stale, don't hesitate to ping the maintainers in the comments
-
-# Pull requests (for collaborators)
-
-- Squash-merge PRs
-- Use the following format for the squashed commit title: `<module> : <commit title> (#<issue_number>)`. For example: `utils : fix typo in utils.py (#1234)`
-- Optionally, pick a `<module>` from here: https://github.com/ggerganov/llama.cpp/wiki/Modules
-
-# Coding guidelines
-
-- Avoid adding third-party dependencies, extra files, extra headers, etc.
-- Always consider cross-compatibility with other operating systems and architectures
-- Avoid fancy looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple
-- There are no strict rules for the code style, but try to follow the patterns in the code (indentation, spaces, etc.). Vertical alignment makes things more readable and easier to batch edit
-- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
-- Naming usually optimizes for common prefix (see https://github.com/ggerganov/ggml/pull/302#discussion_r1243240963)
-- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
-- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggerganov/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$
-
-![matmul](media/matmul.png)
-

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2023-2024 The ggml authors
+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

Package.swift

@@ -2,48 +2,6 @@
 
 import PackageDescription
 
-var sources = [
-    "src/llama.cpp",
-    "src/llama-vocab.cpp",
-    "src/llama-grammar.cpp",
-    "src/llama-sampling.cpp",
-    "src/unicode.cpp",
-    "src/unicode-data.cpp",
-    "ggml/src/ggml.c",
-    "ggml/src/ggml-alloc.c",
-    "ggml/src/ggml-backend.c",
-    "ggml/src/ggml-quants.c",
-    "ggml/src/ggml-aarch64.c",
-]
-
-var resources: [Resource] = []
-var linkerSettings: [LinkerSetting] = []
-var cSettings: [CSetting] =  [
-    .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
-    .unsafeFlags(["-fno-objc-arc"]),
-    // NOTE: NEW_LAPACK will required iOS version 16.4+
-    // We should consider add this in the future when we drop support for iOS 14
-    // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
-    // .define("ACCELERATE_NEW_LAPACK"),
-    // .define("ACCELERATE_LAPACK_ILP64")
-]
-
-#if canImport(Darwin)
-sources.append("ggml/src/ggml-metal.m")
-resources.append(.process("ggml/src/ggml-metal.metal"))
-linkerSettings.append(.linkedFramework("Accelerate"))
-cSettings.append(
-    contentsOf: [
-        .define("GGML_USE_ACCELERATE"),
-        .define("GGML_USE_METAL")
-    ]
-)
-#endif
-
-#if os(Linux)
-    cSettings.append(.define("_GNU_SOURCE"))
-#endif
-
 let package = Package(
     name: "llama",
     platforms: [
@@ -66,13 +24,36 @@ let package = Package(
                "models",
                "tests",
                "CMakeLists.txt",
+               "ggml-cuda.cu",
+               "ggml-cuda.h",
                "Makefile"
             ],
-            sources: sources,
-            resources: resources,
+            sources: [
+                "ggml.c",
+                "llama.cpp",
+                "ggml-alloc.c",
+                "ggml-backend.c",
+                "ggml-quants.c",
+                "ggml-metal.m",
+            ],
+            resources: [
+                .process("ggml-metal.metal")
+            ],
             publicHeadersPath: "spm-headers",
-            cSettings: cSettings,
-            linkerSettings: linkerSettings
+            cSettings: [
+                .unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
+                .define("GGML_USE_ACCELERATE"),
+                .unsafeFlags(["-fno-objc-arc"]),
+                .define("GGML_USE_METAL"),
+                // NOTE: NEW_LAPACK will required iOS version 16.4+
+                // We should consider add this in the future when we drop support for iOS 14
+                // (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
+                // .define("ACCELERATE_NEW_LAPACK"),
+                // .define("ACCELERATE_LAPACK_ILP64")
+            ],
+            linkerSettings: [
+                .linkedFramework("Accelerate")
+            ]
         )
     ],
     cxxLanguageStandard: .cxx11

README-sycl.md

@@ -0,0 +1,494 @@
+# llama.cpp for SYCL
+
+- [Background](#background)
+- [OS](#os)
+- [Intel GPU](#intel-gpu)
+- [Docker](#docker)
+- [Linux](#linux)
+- [Windows](#windows)
+- [Environment Variable](#environment-variable)
+- [Known Issue](#known-issue)
+- [Q&A](#q&a)
+- [Todo](#todo)
+
+## Background
+
+SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators—such as CPUs, GPUs, and FPGAs. It is a single-source embedded domain-specific language based on pure C++17.
+
+oneAPI is a specification that is open and standards-based, supporting multiple architecture types including but not limited to GPU, CPU, and FPGA. The spec has both direct programming and API-based programming paradigms.
+
+Intel uses the SYCL as direct programming language to support CPU, GPUs and FPGAs.
+
+To avoid to re-invent the wheel, this code refer other code paths in llama.cpp (like OpenBLAS, cuBLAS, CLBlast). We use a open-source tool [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) migrate to SYCL.
+
+The llama.cpp for SYCL is used to support Intel GPUs.
+
+For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
+
+## OS
+
+|OS|Status|Verified|
+|-|-|-|
+|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39|
+|Windows|Support|Windows 11|
+
+
+## Intel GPU
+
+### Verified
+
+|Intel GPU| Status | Verified Model|
+|-|-|-|
+|Intel Data Center Max Series| Support| Max 1550|
+|Intel Data Center Flex Series| Support| Flex 170|
+|Intel Arc Series| Support| Arc 770, 730M|
+|Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake|
+|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7|
+
+Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use.
+
+### Memory
+
+The memory is a limitation to run LLM on GPUs.
+
+When run llama.cpp, there is print log to show the applied memory on GPU. You could know how much memory to be used in your case. Like `llm_load_tensors:            buffer size =  3577.56 MiB`.
+
+For iGPU, please make sure the shared memory from host memory is enough. For llama-2-7b.Q4_0, recommend the host memory is 8GB+.
+
+For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+.
+
+## Docker
+
+Note:
+- Only docker on Linux is tested. Docker on WSL may not work.
+- You may need to install Intel GPU driver on the host machine (See the [Linux](#linux) section to know how to do that)
+
+### Build the image
+
+You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
+
+
+```sh
+# For F16:
+#docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile .
+
+# Or, for F32:
+docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile .
+
+# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example
+```
+
+### Run
+
+```sh
+# Firstly, find all the DRI cards:
+ls -la /dev/dri
+# Then, pick the card that you want to use.
+
+# For example with "/dev/dri/card1"
+docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+```
+
+## Linux
+
+### Setup Environment
+
+1. Install Intel GPU driver.
+
+a. Please install Intel GPU driver by official guide: [Install GPU Drivers](https://dgpu-docs.intel.com/driver/installation.html).
+
+Note: for iGPU, please install the client GPU driver.
+
+b. Add user to group: video, render.
+
+```sh
+sudo usermod -aG render username
+sudo usermod -aG video username
+```
+
+Note: re-login to enable it.
+
+c. Check
+
+```sh
+sudo apt install clinfo
+sudo clinfo -l
+```
+
+Output (example):
+
+```
+Platform #0: Intel(R) OpenCL Graphics
+ `-- Device #0: Intel(R) Arc(TM) A770 Graphics
+
+
+Platform #0: Intel(R) OpenCL HD Graphics
+ `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]
+```
+
+2. Install Intel® oneAPI Base toolkit.
+
+a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
+
+Recommend to install to default folder: **/opt/intel/oneapi**.
+
+Following guide use the default folder as example. If you use other folder, please modify the following guide info with your folder.
+
+b. Check
+
+```sh
+source /opt/intel/oneapi/setvars.sh
+
+sycl-ls
+```
+
+There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**.
+
+Output (example):
+```
+[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
+[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
+[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO  [23.30.26918.50]
+[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918]
+
+```
+
+2. Build locally:
+
+Note:
+- You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference.
+- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
+
+```sh
+mkdir -p build
+cd build
+source /opt/intel/oneapi/setvars.sh
+
+# For FP16:
+#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON
+
+# Or, for FP32:
+cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
+
+# Build example/main only
+#cmake --build . --config Release --target main
+
+# Or, build all binary
+cmake --build . --config Release -v
+
+cd ..
+```
+
+or
+
+```sh
+./examples/sycl/build.sh
+```
+
+### Run
+
+1. Put model file to folder **models**
+
+You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example.
+
+2. Enable oneAPI running environment
+
+```
+source /opt/intel/oneapi/setvars.sh
+```
+
+3. List device ID
+
+Run without parameter:
+
+```sh
+./build/bin/ls-sycl-device
+
+# or running the "main" executable and look at the output log:
+
+./build/bin/main
+```
+
+Check the ID in startup log, like:
+
+```
+found 4 SYCL devices:
+  Device 0: Intel(R) Arc(TM) A770 Graphics,	compute capability 1.3,
+    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+  Device 1: Intel(R) FPGA Emulation Device,	compute capability 1.2,
+    max compute_units 24,	max work group size 67108864,	max sub group size 64,	global mem size 67065057280
+  Device 2: 13th Gen Intel(R) Core(TM) i7-13700K,	compute capability 3.0,
+    max compute_units 24,	max work group size 8192,	max sub group size 64,	global mem size 67065057280
+  Device 3: Intel(R) Arc(TM) A770 Graphics,	compute capability 3.0,
+    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+
+```
+
+|Attribute|Note|
+|-|-|
+|compute capability 1.3|Level-zero running time, recommended |
+|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
+
+4. Set device ID and execute llama.cpp
+
+Set device ID = 0 by **GGML_SYCL_DEVICE=0**
+
+```sh
+GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
+```
+or run by script:
+
+```sh
+./examples/sycl/run_llama2.sh
+```
+
+Note:
+
+- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue.
+
+
+5. Check the device ID in output
+
+Like:
+```
+Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
+```
+
+## Windows
+
+### Setup Environment
+
+1. Install Intel GPU driver.
+
+Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
+
+Note: **The driver is mandatory for compute function**.
+
+2. Install Visual Studio.
+
+Please install [Visual Studio](https://visualstudio.microsoft.com/) which impact oneAPI environment enabling in Windows.
+
+3. Install Intel® oneAPI Base toolkit.
+
+a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html).
+
+Recommend to install to default folder: **C:\Program Files (x86)\Intel\oneAPI**.
+
+Following guide uses the default folder as example. If you use other folder, please modify the following guide info with your folder.
+
+b. Enable oneAPI running environment:
+
+- In Search, input 'oneAPI'.
+
+Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022"
+
+- In Run:
+
+In CMD:
+```
+"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
+```
+
+c. Check GPU
+
+In oneAPI command line:
+
+```
+sycl-ls
+```
+
+There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**.
+
+Output (example):
+```
+[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2  [2023.16.10.0.17_160000]
+[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
+[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO  [31.0.101.5186]
+[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044]
+```
+
+4. Install cmake & make
+
+a. Download & install cmake for Windows: https://cmake.org/download/
+
+b. Download & install mingw-w64 make for Windows provided by w64devkit
+
+- Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases).
+
+- Extract `w64devkit` on your pc.
+
+- Add the **bin** folder path in the Windows system PATH environment, like `C:\xxx\w64devkit\bin\`.
+
+### Build locally:
+
+In oneAPI command line window:
+
+```
+mkdir -p build
+cd build
+@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
+
+::  for FP16
+::  faster for long-prompt inference
+::  cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON
+
+::  for FP32
+cmake -G "MinGW Makefiles" ..  -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx  -DCMAKE_BUILD_TYPE=Release
+
+
+::  build example/main only
+::  make main
+
+::  build all binary
+make -j
+cd ..
+```
+
+or
+
+```
+.\examples\sycl\win-build-sycl.bat
+```
+
+Note:
+
+- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only.
+
+### Run
+
+1. Put model file to folder **models**
+
+You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example.
+
+2. Enable oneAPI running environment
+
+- In Search, input 'oneAPI'.
+
+Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022"
+
+- In Run:
+
+In CMD:
+```
+"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
+```
+
+3. List device ID
+
+Run without parameter:
+
+```
+build\bin\ls-sycl-device.exe
+
+or
+
+build\bin\main.exe
+```
+
+Check the ID in startup log, like:
+
+```
+found 4 SYCL devices:
+  Device 0: Intel(R) Arc(TM) A770 Graphics,	compute capability 1.3,
+    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+  Device 1: Intel(R) FPGA Emulation Device,	compute capability 1.2,
+    max compute_units 24,	max work group size 67108864,	max sub group size 64,	global mem size 67065057280
+  Device 2: 13th Gen Intel(R) Core(TM) i7-13700K,	compute capability 3.0,
+    max compute_units 24,	max work group size 8192,	max sub group size 64,	global mem size 67065057280
+  Device 3: Intel(R) Arc(TM) A770 Graphics,	compute capability 3.0,
+    max compute_units 512,	max work group size 1024,	max sub group size 32,	global mem size 16225243136
+
+```
+
+|Attribute|Note|
+|-|-|
+|compute capability 1.3|Level-zero running time, recommended |
+|compute capability 3.0|OpenCL running time, slower than level-zero in most cases|
+
+4. Set device ID and execute llama.cpp
+
+Set device ID = 0 by **set GGML_SYCL_DEVICE=0**
+
+```
+set GGML_SYCL_DEVICE=0
+build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0
+```
+or run by script:
+
+```
+.\examples\sycl\win-run-llama2.bat
+```
+
+Note:
+
+- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue.
+
+
+5. Check the device ID in output
+
+Like:
+```
+Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
+```
+
+## Environment Variable
+
+#### Build
+
+|Name|Value|Function|
+|-|-|-|
+|LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path. <br>For FP32/FP16, LLAMA_SYCL=ON is mandatory.|
+|LLAMA_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path. Faster for long-prompt inference. <br>For FP32, not set it.|
+|CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path|
+|CMAKE_CXX_COMPILER|icpx (Linux), icx (Windows)|use icpx/icx for SYCL code path|
+
+#### Running
+
+
+|Name|Value|Function|
+|-|-|-|
+|GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output|
+|GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG|
+
+## Known Issue
+
+- Hang during startup
+
+  llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block.
+
+  Solution: add **--no-mmap** or **--mmap 0**.
+
+## Q&A
+
+- Error:  `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.
+
+  Miss to enable oneAPI running environment.
+
+  Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`.
+
+- In Windows, no result, not error.
+
+  Miss to enable oneAPI running environment.
+
+- Meet compile error.
+
+  Remove folder **build** and try again.
+
+- I can **not** see **[ext_oneapi_level_zero:gpu:0]** afer install GPU driver in Linux.
+
+  Please run **sudo sycl-ls**.
+
+  If you see it in result, please add video/render group to your ID:
+
+  ```
+  sudo usermod -aG render username
+  sudo usermod -aG video username
+  ```
+
+  Then **relogin**.
+
+  If you do not see it, please check the installation GPU steps again.
+
+## Todo
+
+- Support multiple cards.

SECURITY.md

@@ -1,67 +0,0 @@
-# Security Policy
-
- - [**Using llama.cpp securely**](#using-llamacpp-securely)
-   - [Untrusted models](#untrusted-models)
-   - [Untrusted inputs](#untrusted-inputs)
-   - [Data privacy](#data-privacy)
-   - [Untrusted environments or networks](#untrusted-environments-or-networks)
-   - [Multi-Tenant environments](#multi-tenant-environments)
- - [**Reporting a vulnerability**](#reporting-a-vulnerability)
-
-## Using llama.cpp securely
-
-### Untrusted models
-Be careful when running untrusted models. This classification includes models created by unknown developers or utilizing data obtained from unknown sources.
-
-*Always execute untrusted models within a secure, isolated environment such as a sandbox* (e.g., containers, virtual machines). This helps protect your system from potentially malicious code.
-
-> [!NOTE]
-> The trustworthiness of a model is not binary. You must always determine the proper level of caution depending on the specific model and how it matches your use case and risk tolerance.
-
-### Untrusted inputs
-
-Some models accept various input formats (text, images, audio, etc.). The libraries converting these inputs have varying security levels, so it's crucial to isolate the model and carefully pre-process inputs to mitigate script injection risks.
-
-For maximum security when handling untrusted inputs, you may need to employ the following:
-
-* Sandboxing: Isolate the environment where the inference happens.
-* Pre-analysis: Check how the model performs by default when exposed to prompt injection (e.g. using [fuzzing for prompt injection](https://github.com/FonduAI/awesome-prompt-injection?tab=readme-ov-file#tools)). This will give you leads on how hard you will have to work on the next topics.
-* Updates: Keep both LLaMA C++ and your libraries updated with the latest security patches.
-* Input Sanitation: Before feeding data to the model, sanitize inputs rigorously. This involves techniques such as:
-    * Validation: Enforce strict rules on allowed characters and data types.
-    * Filtering: Remove potentially malicious scripts or code fragments.
-    * Encoding: Convert special characters into safe representations.
-    * Verification: Run tooling that identifies potential script injections (e.g. [models that detect prompt injection attempts](https://python.langchain.com/docs/guides/safety/hugging_face_prompt_injection)).
-
-### Data privacy
-
-To protect sensitive data from potential leaks or unauthorized access, it is crucial to sandbox the model execution. This means running the model in a secure, isolated environment, which helps mitigate many attack vectors.
-
-### Untrusted environments or networks
-
-If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions:
-* Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value
-* Encrypt your data if sending it over the network.
-
-### Multi-Tenant environments
-
-If you intend to run multiple models in parallel with shared memory, it is your responsibility to ensure the models do not interact or access each other's data. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks.
-
-1. Tenant Isolation: Models should run separately with strong isolation methods to prevent unwanted data access. Separating networks is crucial for isolation, as it prevents unauthorized access to data or models and malicious users from sending graphs to execute under another tenant's identity.
-
-2. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring.
-
-3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk.
-
-4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time.
-
-## Reporting a vulnerability
-
-Beware that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities of LLaMA C++.
-
-<!-- normal version -->
-However, If you have discovered a security vulnerability in this project, please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
-
-Please disclose it as a private [security advisory](https://github.com/ggerganov/llama.cpp/security/advisories/new).
-
-A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.

build.zig

@@ -0,0 +1,139 @@
+// Compatible with Zig Version 0.11.0
+const std = @import("std");
+const ArrayList = std.ArrayList;
+const Compile = std.Build.Step.Compile;
+const ConfigHeader = std.Build.Step.ConfigHeader;
+const Mode = std.builtin.Mode;
+const CrossTarget = std.zig.CrossTarget;
+
+const Maker = struct {
+    builder: *std.build.Builder,
+    target: CrossTarget,
+    optimize: Mode,
+    enable_lto: bool,
+
+    include_dirs: ArrayList([]const u8),
+    cflags: ArrayList([]const u8),
+    cxxflags: ArrayList([]const u8),
+    objs: ArrayList(*Compile),
+
+    fn addInclude(m: *Maker, dir: []const u8) !void {
+        try m.include_dirs.append(dir);
+    }
+    fn addProjectInclude(m: *Maker, path: []const []const u8) !void {
+        try m.addInclude(try m.builder.build_root.join(m.builder.allocator, path));
+    }
+    fn addCFlag(m: *Maker, flag: []const u8) !void {
+        try m.cflags.append(flag);
+    }
+    fn addCxxFlag(m: *Maker, flag: []const u8) !void {
+        try m.cxxflags.append(flag);
+    }
+    fn addFlag(m: *Maker, flag: []const u8) !void {
+        try m.addCFlag(flag);
+        try m.addCxxFlag(flag);
+    }
+
+    fn init(builder: *std.build.Builder) !Maker {
+        const target = builder.standardTargetOptions(.{});
+        const zig_version = @import("builtin").zig_version_string;
+        const commit_hash = try std.ChildProcess.exec(
+            .{ .allocator = builder.allocator, .argv = &.{ "git", "rev-parse", "HEAD" } },
+        );
+        try std.fs.cwd().writeFile("common/build-info.cpp", builder.fmt(
+            \\int LLAMA_BUILD_NUMBER = {};
+            \\char const *LLAMA_COMMIT = "{s}";
+            \\char const *LLAMA_COMPILER = "Zig {s}";
+            \\char const *LLAMA_BUILD_TARGET = "{s}";
+            \\
+        , .{ 0, commit_hash.stdout[0 .. commit_hash.stdout.len - 1], zig_version, try target.allocDescription(builder.allocator) }));
+        var m = Maker{
+            .builder = builder,
+            .target = target,
+            .optimize = builder.standardOptimizeOption(.{}),
+            .enable_lto = false,
+            .include_dirs = ArrayList([]const u8).init(builder.allocator),
+            .cflags = ArrayList([]const u8).init(builder.allocator),
+            .cxxflags = ArrayList([]const u8).init(builder.allocator),
+            .objs = ArrayList(*Compile).init(builder.allocator),
+        };
+
+        try m.addCFlag("-std=c11");
+        try m.addCxxFlag("-std=c++11");
+        try m.addProjectInclude(&.{});
+        try m.addProjectInclude(&.{"common"});
+        return m;
+    }
+
+    fn obj(m: *const Maker, name: []const u8, src: []const u8) *Compile {
+        const o = m.builder.addObject(.{ .name = name, .target = m.target, .optimize = m.optimize });
+        if (o.target.getAbi() != .msvc)
+            o.defineCMacro("_GNU_SOURCE", null);
+
+        if (std.mem.endsWith(u8, src, ".c")) {
+            o.addCSourceFiles(&.{src}, m.cflags.items);
+            o.linkLibC();
+        } else {
+            o.addCSourceFiles(&.{src}, m.cxxflags.items);
+            if (o.target.getAbi() == .msvc) {
+                o.linkLibC(); // need winsdk + crt
+            } else {
+                // linkLibCpp already add (libc++ + libunwind + libc)
+                o.linkLibCpp();
+            }
+        }
+        for (m.include_dirs.items) |i| o.addIncludePath(.{ .path = i });
+        o.want_lto = m.enable_lto;
+        return o;
+    }
+
+    fn exe(m: *const Maker, name: []const u8, src: []const u8, deps: []const *Compile) *Compile {
+        const e = m.builder.addExecutable(.{ .name = name, .target = m.target, .optimize = m.optimize });
+        e.addCSourceFiles(&.{src}, m.cxxflags.items);
+        for (deps) |d| e.addObject(d);
+        for (m.objs.items) |o| e.addObject(o);
+        for (m.include_dirs.items) |i| e.addIncludePath(.{ .path = i });
+
+        // https://github.com/ziglang/zig/issues/15448
+        if (e.target.getAbi() == .msvc) {
+            e.linkLibC(); // need winsdk + crt
+        } else {
+            // linkLibCpp already add (libc++ + libunwind + libc)
+            e.linkLibCpp();
+        }
+        m.builder.installArtifact(e);
+        e.want_lto = m.enable_lto;
+        return e;
+    }
+};
+
+pub fn build(b: *std.build.Builder) !void {
+    var make = try Maker.init(b);
+    make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false;
+
+    const ggml = make.obj("ggml", "ggml.c");
+    const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c");
+    const ggml_backend = make.obj("ggml-backend", "ggml-backend.c");
+    const ggml_quants = make.obj("ggml-quants", "ggml-quants.c");
+    const llama = make.obj("llama", "llama.cpp");
+    const buildinfo = make.obj("common", "common/build-info.cpp");
+    const common = make.obj("common", "common/common.cpp");
+    const console = make.obj("console", "common/console.cpp");
+    const sampling = make.obj("sampling", "common/sampling.cpp");
+    const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp");
+    const train = make.obj("train", "common/train.cpp");
+    const clip = make.obj("clip", "examples/llava/clip.cpp");
+    const llava = make.obj("llava", "examples/llava/llava.cpp");
+
+    _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, console, grammar_parser });
+    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
+    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
+    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo });
+    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
+    _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, train });
+
+    const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, common, buildinfo, sampling, grammar_parser, clip, llava });
+    if (server.target.isWindows()) {
+        server.linkSystemLibrary("ws2_32");
+    }
+}

ci/run.sh

@@ -36,21 +36,20 @@ SRC=`pwd`
 CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=ON"
 
 if [ ! -z ${GG_BUILD_METAL} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_METAL_SHADER_DEBUG=ON"
 fi
 
 if [ ! -z ${GG_BUILD_CUDA} ]; then
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=1"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_CUBLAS=1"
 fi
 
 if [ ! -z ${GG_BUILD_SYCL} ]; then
     if [ -z ${ONEAPI_ROOT} ]; then
-        echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:"
-        echo "source /opt/intel/oneapi/setvars.sh"
+        echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:\n source /opt/intel/oneapi/setvars.sh"
         exit 1
     fi
 
-    CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
+    CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON"
 fi
 ## helpers
 
@@ -103,9 +102,6 @@ function gg_run_ctest_debug {
 
     set -e
 
-    # Check cmake, make and ctest are installed
-    gg_check_build_requirements
-
     (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j                                          ) 2>&1 | tee -a $OUT/${ci}-make.log
 
@@ -134,9 +130,6 @@ function gg_run_ctest_release {
 
     set -e
 
-    # Check cmake, make and ctest are installed
-    gg_check_build_requirements
-
     (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
 
@@ -159,64 +152,13 @@ function gg_sum_ctest_release {
     gg_printf '```\n'
 }
 
-# test_scripts_debug
-
-function gg_run_test_scripts_debug {
-    cd ${SRC}
-
-    set -e
-
-    (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
-    (cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
-
-    set +e
-}
-
-function gg_sum_test_scripts_debug {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Runs test scripts in debug mode\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '```\n'
-    gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
-    gg_printf '```\n'
-    gg_printf '\n'
-}
-
-# test_scripts_release
-
-function gg_run_test_scripts_release {
-    cd ${SRC}
-
-    set -e
-
-    (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
-    (cd ./examples/quantize   && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
-
-    set +e
-}
-
-function gg_sum_test_scripts_release {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'Runs test scripts in release mode\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '```\n'
-    gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
-    gg_printf '```\n'
-    gg_printf '\n'
-}
-
 function gg_get_model {
-    local gguf_0="$MNT/models/pythia/1.4B/ggml-model-f16.gguf"
-    local gguf_1="$MNT/models/pythia/2.8B/ggml-model-f16.gguf"
-    local gguf_2="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
-    if [[ -s $gguf_0 ]]; then
-        echo -n "$gguf_0"
-    elif [[ -s $gguf_1 ]]; then
-        echo -n "$gguf_1"
-    elif [[ -s $gguf_2 ]]; then
-        echo -n "$gguf_2"
+    local gguf_3b="$MNT/models/open-llama/3B-v2/ggml-model-f16.gguf"
+    local gguf_7b="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
+    if [[ -s $gguf_3b ]]; then
+        echo -n "$gguf_3b"
+    elif [[ -s $gguf_7b ]]; then
+        echo -n "$gguf_7b"
     else
         echo >&2 "No model found. Can't run gg_run_ctest_with_model."
         exit 1
@@ -265,35 +207,33 @@ function gg_sum_ctest_with_model_release {
     gg_printf '```\n'
 }
 
-# open_llama_7b_v2
-# requires: GG_BUILD_CUDA
+# open_llama_3b_v2
 
-function gg_run_open_llama_7b_v2 {
+function gg_run_open_llama_3b_v2 {
     cd ${SRC}
 
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/config.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/tokenizer.model
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/tokenizer_config.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/special_tokens_map.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/pytorch_model.bin.index.json
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00001-of-00002.bin
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin
-    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/config.json
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/tokenizer.model
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/tokenizer_config.json
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/special_tokens_map.json
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/pytorch_model.bin
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/generation_config.json
 
     gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
     unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
+    head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw
 
-    path_models="../models-mnt/open-llama/7B-v2"
+    path_models="../models-mnt/open-llama/3B-v2"
     path_wiki="../models-mnt/wikitext/wikitext-2-raw"
 
     rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
 
     set -e
 
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                                           ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                                             ) 2>&1 | tee -a $OUT/${ci}-make.log
 
-    python3 ../examples/convert_legacy_llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
+    python3 ../convert.py ${path_models}
 
     model_f16="${path_models}/ggml-model-f16.gguf"
     model_q8_0="${path_models}/ggml-model-q8_0.gguf"
@@ -307,49 +247,46 @@ function gg_run_open_llama_7b_v2 {
     model_q5_k="${path_models}/ggml-model-q5_k.gguf"
     model_q6_k="${path_models}/ggml-model-q6_k.gguf"
 
-    wiki_test="${path_wiki}/wiki.test.raw"
+    wiki_test_60="${path_wiki}/wiki.test-60.raw"
 
-    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
-    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
-    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
-    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
-    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
-    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
-    ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
-    ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
+    ./bin/quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/quantize ${model_f16} ${model_q4_0} q4_0
+    ./bin/quantize ${model_f16} ${model_q4_1} q4_1
+    ./bin/quantize ${model_f16} ${model_q5_0} q5_0
+    ./bin/quantize ${model_f16} ${model_q5_1} q5_1
+    ./bin/quantize ${model_f16} ${model_q2_k} q2_k
+    ./bin/quantize ${model_f16} ${model_q3_k} q3_k
+    ./bin/quantize ${model_f16} ${model_q4_k} q4_k
+    ./bin/quantize ${model_f16} ${model_q5_k} q5_k
+    ./bin/quantize ${model_f16} ${model_q6_k} q6_k
 
-    (time ./bin/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/main --model ${model_f16}  -s 1234 -n 64 --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} -s 1234 -n 64 --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} -s 1234 -n 64 --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} -s 1234 -n 64 --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} -s 1234 -n 64 --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} -s 1234 -n 64 --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} -s 1234 -n 64 --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} -s 1234 -n 64 --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} -s 1234 -n 64 --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} -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/llama-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/llama-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
-    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 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 1 ) 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 1 ) 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 1 ) 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 1 ) 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 1 ) 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 1 ) 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 1 ) 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 1 ) 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 1 ) 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 1 ) 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 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
-    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/llama-save-load-state     -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state     -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -378,148 +315,58 @@ function gg_run_open_llama_7b_v2 {
 
     cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
 
-    set +e
-}
-
-function gg_sum_open_llama_7b_v2 {
-    gg_printf '### %s\n\n' "${ci}"
-
-    gg_printf 'OpenLLaMA 7B-v2:\n'
-    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
-    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
-    gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.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)"
-    gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
-    gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
-    gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
-    gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
-    gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
-    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 '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
-}
-
-# pythia_1.4b
-
-function gg_run_pythia_1_4b {
-    cd ${SRC}
-
-    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/config.json
-    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer.json
-    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer_config.json
-    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/special_tokens_map.json
-    gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/resolve/main/pytorch_model.bin
-
-    gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
-    unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
-    head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw
-
-    path_models="../models-mnt/pythia/1.4B"
-    path_wiki="../models-mnt/wikitext/wikitext-2-raw"
-
-    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
-
-    set -e
-
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
-
-    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
-
-    model_f16="${path_models}/ggml-model-f16.gguf"
-    model_q8_0="${path_models}/ggml-model-q8_0.gguf"
-    model_q4_0="${path_models}/ggml-model-q4_0.gguf"
-    model_q4_1="${path_models}/ggml-model-q4_1.gguf"
-    model_q5_0="${path_models}/ggml-model-q5_0.gguf"
-    model_q5_1="${path_models}/ggml-model-q5_1.gguf"
-    model_q2_k="${path_models}/ggml-model-q2_k.gguf"
-    model_q3_k="${path_models}/ggml-model-q3_k.gguf"
-    model_q4_k="${path_models}/ggml-model-q4_k.gguf"
-    model_q5_k="${path_models}/ggml-model-q5_k.gguf"
-    model_q6_k="${path_models}/ggml-model-q6_k.gguf"
-
-    wiki_test_60="${path_wiki}/wiki.test-60.raw"
-
-    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
-    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
-    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
-    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
-    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
-    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
-    ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
-    ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
-
-    (time ./bin/llama-cli --model ${model_f16}  -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-cli --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-cli --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-cli --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-cli --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-cli --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-cli --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-cli --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-cli --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-cli --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/llama-cli --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/llama-perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
-
-    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
-
-    (time ./bin/llama-save-load-state     --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-
-    function check_ppl {
+    # lora
+    function compare_ppl {
         qnt="$1"
-        ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+        ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+        ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
 
-        if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
-            printf '  - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
+        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 OK\n' "$qnt" "$ppl"
+        printf '  - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2"
         return 0
     }
 
-    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-   #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model
-    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-    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
+    path_lora="../models-mnt/open-llama/3B-v2/lora"
+    path_shakespeare="../models-mnt/shakespeare"
 
-    cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
+    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 1 ) 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 1 ) 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 1 ) 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 1 ) 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 1 ) 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
 }
 
-function gg_sum_pythia_1_4b {
+function gg_sum_open_llama_3b_v2 {
     gg_printf '### %s\n\n' "${ci}"
 
-    gg_printf 'Pythia 1.4B:\n'
+    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 '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.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)"
@@ -532,34 +379,42 @@ function gg_sum_pythia_1_4b {
     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 '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.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)"
 }
 
-# pythia_2_8b
+# open_llama_7b_v2
 # requires: GG_BUILD_CUDA
 
-function gg_run_pythia_2_8b {
+function gg_run_open_llama_7b_v2 {
     cd ${SRC}
 
-    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/config.json
-    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer.json
-    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer_config.json
-    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/special_tokens_map.json
-    gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/resolve/main/pytorch_model.bin
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/config.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/tokenizer.model
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/tokenizer_config.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/special_tokens_map.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/pytorch_model.bin.index.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00001-of-00002.bin
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json
 
     gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
     unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
 
-    path_models="../models-mnt/pythia/2.8B"
+    path_models="../models-mnt/open-llama/7B-v2"
     path_wiki="../models-mnt/wikitext/wikitext-2-raw"
 
     rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
 
     set -e
 
-    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DGGML_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
-    (time make -j                                                           ) 2>&1 | tee -a $OUT/${ci}-make.log
+    (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUBLAS=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                                             ) 2>&1 | tee -a $OUT/${ci}-make.log
 
-    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
+    python3 ../convert.py ${path_models}
 
     model_f16="${path_models}/ggml-model-f16.gguf"
     model_q8_0="${path_models}/ggml-model-q8_0.gguf"
@@ -575,47 +430,44 @@ function gg_run_pythia_2_8b {
 
     wiki_test="${path_wiki}/wiki.test.raw"
 
-    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
-    ./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
-    ./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
-    ./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
-    ./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
-    ./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
-    ./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
-    ./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
-    ./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
+    ./bin/quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/quantize ${model_f16} ${model_q4_0} q4_0
+    ./bin/quantize ${model_f16} ${model_q4_1} q4_1
+    ./bin/quantize ${model_f16} ${model_q5_0} q5_0
+    ./bin/quantize ${model_f16} ${model_q5_1} q5_1
+    ./bin/quantize ${model_f16} ${model_q2_k} q2_k
+    ./bin/quantize ${model_f16} ${model_q3_k} q3_k
+    ./bin/quantize ${model_f16} ${model_q4_k} q4_k
+    ./bin/quantize ${model_f16} ${model_q5_k} q5_k
+    ./bin/quantize ${model_f16} ${model_q6_k} q6_k
 
-    (time ./bin/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/llama-cli --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/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/llama-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/llama-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
-    (time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
-    (time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
-    (time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
-    (time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
-    (time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
-    (time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
-    (time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
-    (time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
-    (time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 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
+    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
 
-    (time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
+    (time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
 
-    (time ./bin/llama-save-load-state     -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state     -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
-    (time ./bin/llama-save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
+    (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
 
     function check_ppl {
         qnt="$1"
@@ -636,7 +488,7 @@ function gg_run_pythia_2_8b {
     check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
-   #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model
+    check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
     check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
@@ -644,16 +496,59 @@ function gg_run_pythia_2_8b {
 
     cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.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
 }
 
-function gg_sum_pythia_2_8b {
+function gg_sum_open_llama_7b_v2 {
     gg_printf '### %s\n\n' "${ci}"
 
-    gg_printf 'Pythia 2.8B:\n'
+    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 '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.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)"
@@ -666,6 +561,11 @@ function gg_sum_pythia_2_8b {
     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 '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.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)"
 }
 
 # bge-small
@@ -674,7 +574,7 @@ function gg_run_embd_bge_small {
     cd ${SRC}
 
     gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json
-    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer.json
+    gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/tokenizer.model
     gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer_config.json
     gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/special_tokens_map.json
     gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/pytorch_model.bin
@@ -694,33 +594,19 @@ function gg_run_embd_bge_small {
     (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
     (time make -j                                            ) 2>&1 | tee -a $OUT/${ci}-make.log
 
-    python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
+    python3 ../convert-hf-to-gguf.py ${path_models}
 
     model_f16="${path_models}/ggml-model-f16.gguf"
     model_q8_0="${path_models}/ggml-model-q8_0.gguf"
 
-    ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/quantize ${model_f16} ${model_q8_0} q8_0
 
-    (time ./bin/llama-embedding --model ${model_f16}  -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
-    (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/embedding --model ${model_f16}  -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/embedding --model ${model_q8_0} -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
 
     set +e
 }
 
-function gg_check_build_requirements {
-    if ! command -v cmake &> /dev/null; then
-        gg_printf 'cmake not found, please install'
-    fi
-
-    if ! command -v make &> /dev/null; then
-        gg_printf 'make not found, please install'
-    fi
-
-    if ! command -v ctest &> /dev/null; then
-        gg_printf 'ctest not found, please install'
-    fi
-}
-
 function gg_sum_embd_bge_small {
     gg_printf '### %s\n\n' "${ci}"
 
@@ -755,17 +641,11 @@ test $ret -eq 0 && gg_run ctest_release
 if [ -z ${GG_BUILD_LOW_PERF} ]; then
     test $ret -eq 0 && gg_run embd_bge_small
 
-    if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then
-        test $ret -eq 0 && gg_run test_scripts_debug
-        test $ret -eq 0 && gg_run test_scripts_release
-    fi
-
     if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
         if [ -z ${GG_BUILD_CUDA} ]; then
-            test $ret -eq 0 && gg_run pythia_1_4b
+            test $ret -eq 0 && gg_run open_llama_3b_v2
         else
-            test $ret -eq 0 && gg_run pythia_2_8b
-            #test $ret -eq 0 && gg_run open_llama_7b_v2
+            test $ret -eq 0 && gg_run open_llama_7b_v2
         fi
         test $ret -eq 0 && gg_run ctest_with_model_debug
         test $ret -eq 0 && gg_run ctest_with_model_release

cmake/FindSIMD.cmake

@@ -79,22 +79,22 @@ endmacro()
 # flags are for MSVC only!
 check_sse("AVX" " ;/arch:AVX")
 if (NOT ${AVX_FOUND})
-    set(GGML_AVX OFF)
+    set(LLAMA_AVX OFF)
 else()
-    set(GGML_AVX ON)
+    set(LLAMA_AVX ON)
 endif()
 
 check_sse("AVX2" " ;/arch:AVX2")
 check_sse("FMA" " ;/arch:AVX2")
 if ((NOT ${AVX2_FOUND}) OR (NOT ${FMA_FOUND}))
-    set(GGML_AVX2 OFF)
+    set(LLAMA_AVX2 OFF)
 else()
-    set(GGML_AVX2 ON)
+    set(LLAMA_AVX2 ON)
 endif()
 
 check_sse("AVX512" " ;/arch:AVX512")
 if (NOT ${AVX512_FOUND})
-    set(GGML_AVX512 OFF)
+    set(LLAMA_AVX512 OFF)
 else()
-    set(GGML_AVX512 ON)
+    set(LLAMA_AVX512 ON)
 endif()

cmake/arm64-windows-llvm.cmake

@@ -1,16 +0,0 @@
-set( CMAKE_SYSTEM_NAME Windows )
-set( CMAKE_SYSTEM_PROCESSOR arm64 )
-
-set( target arm64-pc-windows-msvc )
-
-set( CMAKE_C_COMPILER    clang )
-set( CMAKE_CXX_COMPILER  clang++ )
-
-set( CMAKE_C_COMPILER_TARGET   ${target} )
-set( CMAKE_CXX_COMPILER_TARGET ${target} )
-
-set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only" )
-set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" )
-
-set( CMAKE_C_FLAGS_INIT   "${arch_c_flags} ${warn_c_flags}" )
-set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" )

cmake/arm64-windows-msvc.cmake

@@ -1,6 +0,0 @@
-set( CMAKE_SYSTEM_NAME Windows )
-set( CMAKE_SYSTEM_PROCESSOR arm64 )
-
-set( target arm64-pc-windows-msvc )
-set( CMAKE_C_COMPILER_TARGET   ${target} )
-set( CMAKE_CXX_COMPILER_TARGET ${target} )

cmake/git-vars.cmake

@@ -1,22 +0,0 @@
-find_package(Git)
-
-# the commit's SHA1
-execute_process(COMMAND
-    "${GIT_EXECUTABLE}" describe --match=NeVeRmAtCh --always --abbrev=8
-    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
-    OUTPUT_VARIABLE GIT_SHA1
-    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
-
-# the date of the commit
-execute_process(COMMAND
-    "${GIT_EXECUTABLE}" log -1 --format=%ad --date=local
-    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
-    OUTPUT_VARIABLE GIT_DATE
-    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
-
-# the subject of the commit
-execute_process(COMMAND
-    "${GIT_EXECUTABLE}" log -1 --format=%s
-    WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
-    OUTPUT_VARIABLE GIT_COMMIT_SUBJECT
-    ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)

cmake/llama.pc.in

@@ -1,10 +0,0 @@
-prefix=@CMAKE_INSTALL_PREFIX@
-exec_prefix=${prefix}
-libdir=${exec_prefix}/lib
-includedir=${prefix}/include
-
-Name: llama
-Description: Port of Facebook's LLaMA model in C/C++
-Version: @PROJECT_VERSION@
-Libs: -L${libdir} -lllama
-Cflags: -I${includedir}

codecov.yml

@@ -0,0 +1,14 @@
+comment: off
+
+coverage:
+  status:
+    project:
+      default:
+        target: auto
+        threshold: 0
+        base: auto
+    patch:
+      default:
+        target: auto
+        threshold: 0
+        base: auto

common/CMakeLists.txt

@@ -1,6 +1,5 @@
 # common
 
-find_package(Threads REQUIRED)
 
 # Build info header
 #
@@ -20,12 +19,7 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
         endif()
     endif()
 
-    if(EXISTS "${GIT_DIR}/index")
-        set(GIT_INDEX "${GIT_DIR}/index")
-    else()
-        message(WARNING "Git index not found in git repository.")
-        set(GIT_INDEX "")
-    endif()
+    set(GIT_INDEX "${GIT_DIR}/index")
 else()
     message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.")
     set(GIT_INDEX "")
@@ -37,7 +31,7 @@ add_custom_command(
     COMMENT "Generating build details from Git"
     COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
             -DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
-            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info-gen-cpp.cmake"
+            -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake"
     WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
     DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
     VERBATIM
@@ -48,6 +42,7 @@ if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()
 
+
 set(TARGET common)
 
 add_library(${TARGET} STATIC
@@ -60,29 +55,14 @@ add_library(${TARGET} STATIC
     console.cpp
     grammar-parser.h
     grammar-parser.cpp
-    json.hpp
-    json-schema-to-grammar.cpp
     train.h
     train.cpp
-    ngram-cache.h
-    ngram-cache.cpp
     )
 
 if (BUILD_SHARED_LIBS)
     set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
 endif()
 
-set(LLAMA_COMMON_EXTRA_LIBS build_info)
-
-# Use curl to download model url
-if (LLAMA_CURL)
-    find_package(CURL REQUIRED)
-    add_definitions(-DLLAMA_USE_CURL)
-    include_directories(${CURL_INCLUDE_DIRS})
-    find_library(CURL_LIBRARY curl REQUIRED)
-    set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} ${CURL_LIBRARY})
-endif ()
-
 target_include_directories(${TARGET} PUBLIC .)
-target_compile_features   (${TARGET} PUBLIC cxx_std_11)
-target_link_libraries     (${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
+target_compile_features(${TARGET} PUBLIC cxx_std_11)
+target_link_libraries(${TARGET} PRIVATE build_info PUBLIC llama)

common/common.h

@@ -27,155 +27,112 @@
 #define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0)
 
 #define print_build_info() do {                                                                     \
-    fprintf(stderr, "%s: build = %d (%s)\n",      __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);      \
+    fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT);           \
     fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET);    \
 } while(0)
 
-#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf"
-
-struct llama_lora_adapter_info {
-    std::string path;
-    float scale;
-};
-
-struct llama_lora_adapter_container : llama_lora_adapter_info {
-    struct llama_lora_adapter * adapter;
-};
-
 // build info
 extern int LLAMA_BUILD_NUMBER;
-extern char const * LLAMA_COMMIT;
-extern char const * LLAMA_COMPILER;
-extern char const * LLAMA_BUILD_TARGET;
-
-struct llama_control_vector_load_info;
-
-//
-// CPU utils
-//
-
-int32_t cpu_get_num_physical_cores();
-int32_t cpu_get_num_math();
+extern char const *LLAMA_COMMIT;
+extern char const *LLAMA_COMPILER;
+extern char const *LLAMA_BUILD_TARGET;
 
 //
 // CLI argument parsing
 //
-
-// dimensionality reduction methods, used by cvector-generator
-enum dimre_method {
-    DIMRE_METHOD_PCA,
-    DIMRE_METHOD_MEAN,
-};
+int32_t get_num_physical_cores();
 
 struct gpt_params {
-    uint32_t seed                 = LLAMA_DEFAULT_SEED; // RNG seed
+    uint32_t seed                 = -1;    // RNG seed
 
-    int32_t n_threads             = cpu_get_num_math();
-    int32_t n_threads_draft       =    -1;
-    int32_t n_threads_batch       =    -1; // number of threads to use for batch processing (-1 = use n_threads)
-    int32_t n_threads_batch_draft =    -1;
-    int32_t n_predict             =    -1; // new tokens to predict
-    int32_t n_ctx                 =     0; // context size
-    int32_t n_batch               =  2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
-    int32_t n_ubatch              =   512; // physical batch size for prompt processing (must be >=32 to use BLAS)
-    int32_t n_keep                =     0; // number of tokens to keep from initial prompt
-    int32_t n_draft               =     5; // number of tokens to draft during speculative decoding
-    int32_t n_chunks              =    -1; // max number of chunks to process (-1 = unlimited)
-    int32_t n_parallel            =     1; // number of parallel sequences to decode
-    int32_t n_sequences           =     1; // number of sequences to decode
-    float   p_split               =  0.1f; // speculative decoding split probability
-    int32_t n_gpu_layers          =    -1; // number of layers to store in VRAM (-1 - use default)
-    int32_t n_gpu_layers_draft    =    -1; // number of layers to store in VRAM for the draft model (-1 - use default)
-    int32_t main_gpu              =     0; // the GPU that is used for scratch and small tensors
-    float   tensor_split[128]     =   {0}; // how split tensors should be distributed across GPUs
-    int32_t grp_attn_n            =     1; // group-attention factor
-    int32_t grp_attn_w            =   512; // group-attention width
-    int32_t n_print               =    -1; // print token count every n tokens (-1 = disabled)
-    float   rope_freq_base        =  0.0f; // RoPE base frequency
-    float   rope_freq_scale       =  0.0f; // RoPE frequency scaling factor
+    int32_t n_threads             = get_num_physical_cores();
+    int32_t n_threads_draft       = -1;
+    int32_t n_threads_batch       = -1;    // number of threads to use for batch processing (-1 = use n_threads)
+    int32_t n_threads_batch_draft = -1;
+    int32_t n_predict             = -1;    // new tokens to predict
+    int32_t n_ctx                 = 512;   // context size
+    int32_t n_batch               = 512;   // batch size for prompt processing (must be >=32 to use BLAS)
+    int32_t n_keep                = 0;     // number of tokens to keep from initial prompt
+    int32_t n_draft               = 8;     // number of tokens to draft during speculative decoding
+    int32_t n_chunks              = -1;    // max number of chunks to process (-1 = unlimited)
+    int32_t n_parallel            = 1;     // number of parallel sequences to decode
+    int32_t n_sequences           = 1;     // number of sequences to decode
+    float   p_accept              = 0.5f;  // speculative decoding accept probability
+    float   p_split               = 0.1f;  // speculative decoding split probability
+    int32_t n_gpu_layers          = -1;    // number of layers to store in VRAM (-1 - use default)
+    int32_t n_gpu_layers_draft    = -1;    // number of layers to store in VRAM for the draft model (-1 - use default)
+    llama_split_mode split_mode   = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
+    int32_t main_gpu              = 0;     // the GPU that is used for scratch and small tensors
+    float   tensor_split[128]     = {0};   // how split tensors should be distributed across GPUs
+    int32_t n_beams               = 0;     // if non-zero then use beam search of given width.
+    int32_t grp_attn_n            = 1;     // group-attention factor
+    int32_t grp_attn_w            = 512;   // group-attention width
+    int32_t n_print               = -1;    // print token count every n tokens (-1 = disabled)
+    float   rope_freq_base        = 0.0f;  // RoPE base frequency
+    float   rope_freq_scale       = 0.0f;  // RoPE frequency scaling factor
     float   yarn_ext_factor       = -1.0f; // YaRN extrapolation mix factor
-    float   yarn_attn_factor      =  1.0f; // YaRN magnitude scaling factor
+    float   yarn_attn_factor      = 1.0f;  // YaRN magnitude scaling factor
     float   yarn_beta_fast        = 32.0f; // YaRN low correction dim
-    float   yarn_beta_slow        =  1.0f; // YaRN high correction dim
-    int32_t yarn_orig_ctx         =     0; // YaRN original context length
+    float   yarn_beta_slow        = 1.0f;  // YaRN high correction dim
+    int32_t yarn_orig_ctx         = 0;     // YaRN original context length
     float   defrag_thold          = -1.0f; // KV cache defragmentation threshold
-
-    ggml_backend_sched_eval_callback cb_eval = nullptr;
-    void * cb_eval_user_data                 = nullptr;
-
-    ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
-
-    enum llama_split_mode        split_mode        = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
-    enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
-    enum llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
-    enum llama_attention_type    attention_type    = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
+    int32_t rope_scaling_type     = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
+    ggml_numa_strategy numa       = GGML_NUMA_STRATEGY_DISABLED;
 
     // // sampling parameters
     struct llama_sampling_params sparams;
 
-    std::string model                = ""; // model path
-    std::string model_draft          = ""; // draft model for speculative decoding
-    std::string model_alias          = "unknown"; // model alias
-    std::string model_url            = ""; // model url to download
-    std::string hf_token             = ""; // HF token
-    std::string hf_repo              = ""; // HF repo
-    std::string hf_file              = ""; // HF file
-    std::string prompt               = "";
-    std::string prompt_file          = ""; // store the external prompt file name
-    std::string path_prompt_cache    = ""; // path to file for saving/loading prompt eval state
-    std::string input_prefix         = ""; // string to prefix user inputs with
-    std::string input_suffix         = ""; // string to suffix user inputs with
-    std::string logdir               = ""; // directory in which to save YAML log files
-    std::string lookup_cache_static  = ""; // path of static ngram cache file for lookup decoding
-    std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding
-    std::string logits_file          = ""; // file for saving *all* logits
-    std::string rpc_servers          = ""; // comma separated list of RPC servers
+    std::string model             = "models/7B/ggml-model-f16.gguf"; // model path
+    std::string model_draft       = "";                              // draft model for speculative decoding
+    std::string model_alias       = "unknown"; // model alias
+    std::string prompt            = "";
+    std::string prompt_file       = "";  // store the external prompt file name
+    std::string path_prompt_cache = "";  // path to file for saving/loading prompt eval state
+    std::string input_prefix      = "";  // string to prefix user inputs with
+    std::string input_suffix      = "";  // string to suffix user inputs with
+    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 logits_file       = "";  // file for saving *all* logits
 
-    std::vector<std::string> in_files;   // all input files
-    std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
     std::vector<llama_model_kv_override> kv_overrides;
 
-    bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_lora_adapter_apply)
-    std::vector<llama_lora_adapter_info> lora_adapters; // lora adapter path with user defined scale
+    // TODO: avoid tuple, use struct
+    std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
+    std::string lora_base  = "";                              // base model path for the lora adapter
 
-    std::vector<llama_control_vector_load_info> control_vectors; // control vector with user defined scale
+    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
 
-    int32_t verbosity                  = 0;
-    int32_t control_vector_layer_start = -1; // layer range for control vector
-    int32_t control_vector_layer_end   = -1; // layer range for control vector
+    bool   winogrande      = false; // compute Winogrande score over random tasks from datafile supplied in prompt
+    size_t winogrande_tasks= 0;     // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
 
-    int32_t ppl_stride      = 0;     // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
-    int32_t 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   multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
+    size_t multiple_choice_tasks = 0;     // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
 
-    bool   winogrande       = false; // compute Winogrande score over random tasks from datafile supplied in prompt
-    size_t winogrande_tasks = 0;     // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
+    bool   kl_divergence   = false; // compute KL-divergence
 
-    bool   multiple_choice  = false;  // compute TruthfulQA score over random tasks from datafile supplied in prompt
-    size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
-
-    bool   kl_divergence    = false; // compute KL divergence
-
-    bool usage             = false; // print usage
+    bool mul_mat_q         = true;  // if true, use mul_mat_q kernels instead of cuBLAS
+    bool random_prompt     = false; // do not randomize prompt if none provided
     bool use_color         = false; // use color to distinguish generations and inputs
-    bool special           = false; // enable special token output
     bool interactive       = false; // interactive mode
-    bool interactive_first = false; // wait for user input immediately
-    bool conversation      = false; // conversation mode (does not print special tokens and suffix/prefix)
+    bool chatml            = false; // chatml mode (used for models trained on chatml syntax)
     bool prompt_cache_all  = false; // save user input and generations to prompt cache
     bool prompt_cache_ro   = false; // open the prompt cache read-only and do not update it
 
-    bool escape            = true;  // escape "\n", "\r", "\t", "\'", "\"", and "\\"
+    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
-    bool cont_batching     = true;  // insert new sequences for decoding on-the-fly
-    bool flash_attn        = false; // flash attention
+    bool cont_batching     = false; // insert new sequences for decoding on-the-fly
 
     bool input_prefix_bos  = false; // prefix BOS to user inputs, preceding input_prefix
     bool ignore_eos        = false; // ignore generated EOS tokens
+    bool instruct          = false; // instruction mode (used for Alpaca models)
     bool logits_all        = false; // return logits for all tokens in the batch
     bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
@@ -184,156 +141,46 @@ struct gpt_params {
     bool infill            = false; // use infill mode
     bool dump_kv_cache     = false; // dump the KV cache contents for debugging purposes
     bool no_kv_offload     = false; // disable KV offloading
-    bool warmup            = true;  // warmup run
-    bool check_tensors     = false; // validate tensor data
 
     std::string cache_type_k = "f16"; // KV cache data type for the K
     std::string cache_type_v = "f16"; // KV cache data type for the V
 
     // multimodal models (see examples/llava)
-    std::string mmproj = "";        // path to multimodal projector
-    std::vector<std::string> image; // path to image file(s)
-
-    // embedding
-    bool embedding         = false; // get only sentence embedding
-    int32_t embd_normalize = 2;     // normalisation for embendings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
-    std::string embd_out   = "";    // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
-    std::string embd_sep   = "\n";  // separator of embendings
-
-    // server params
-    int32_t port           = 8080;         // server listens on this network port
-    int32_t timeout_read   = 600;          // http read timeout in seconds
-    int32_t timeout_write  = timeout_read; // http write timeout in seconds
-    int32_t n_threads_http = -1;           // number of threads to process HTTP requests
-
-    std::string hostname      = "127.0.0.1";
-    std::string public_path   = "";
-    std::string chat_template = "";
-    std::string system_prompt = "";
-    bool enable_chat_template = true;
-
-    std::vector<std::string> api_keys;
-
-    std::string ssl_file_key  = "";
-    std::string ssl_file_cert = "";
-
-    bool endpoint_slots   = true;
-    bool endpoint_metrics = false;
-
-    bool log_json = false;
-
-    std::string slot_save_path;
-
-    float slot_prompt_similarity = 0.5f;
-
-    // batched-bench params
-    bool is_pp_shared = false;
-
-    std::vector<int32_t> n_pp;
-    std::vector<int32_t> n_tg;
-    std::vector<int32_t> n_pl;
-
-    // retrieval params
-    std::vector<std::string> context_files; // context files to embed
-
-    int32_t chunk_size = 64; // chunk size for context embedding
-
-    std::string chunk_separator = "\n"; // chunk separator for context embedding
-
-    // passkey params
-    int32_t n_junk = 250; // number of times to repeat the junk text
-    int32_t i_pos  = -1;  // position of the passkey in the junk text
-
-    // imatrix params
-    std::string out_file = "imatrix.dat"; // save the resulting imatrix to this file
-
-    int32_t n_out_freq  = 10; // output the imatrix every n_out_freq iterations
-    int32_t n_save_freq =  0; // save the imatrix every n_save_freq iterations
-    int32_t i_chunk     =  0; // start processing from this chunk
-
-    bool process_output = false; // collect data for the output tensor
-    bool compute_ppl    = true;  // whether to compute perplexity
-
-    // cvector-generator params
-    int n_pca_batch = 100;
-    int n_pca_iterations = 1000;
-    dimre_method cvector_dimre_method = DIMRE_METHOD_PCA;
-    std::string cvector_outfile       = "control_vector.gguf";
-    std::string cvector_positive_file = "examples/cvector-generator/positive.txt";
-    std::string cvector_negative_file = "examples/cvector-generator/negative.txt";
-
-    bool spm_infill = false; // suffix/prefix/middle pattern for infill
-
-    std::string lora_outfile = "ggml-lora-merged-f16.gguf";
+    std::string mmproj = ""; // path to multimodal projector
+    std::string image  = ""; // path to an image file
 };
 
-void gpt_params_handle_hf_token(gpt_params & params);
-void gpt_params_handle_model_default(gpt_params & params);
+bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params);
 
-bool gpt_params_parse_ex   (int argc, char ** argv, gpt_params & params);
-bool gpt_params_parse      (int argc, char ** argv, gpt_params & params);
-bool gpt_params_find_arg   (int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param);
-void gpt_params_print_usage(int argc, char ** argv, const gpt_params & params);
+bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
 
-std::string gpt_params_get_system_info(const gpt_params & params);
+void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
+
+std::string get_system_info(const gpt_params & params);
+
+std::string gpt_random_prompt(std::mt19937 & rng);
+
+void process_escapes(std::string& input);
 
 //
 // String utils
 //
 
+std::vector<llama_sampler_type> sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
+std::vector<llama_sampler_type> sampler_types_from_chars(const std::string & names_string);
 std::vector<std::string> string_split(std::string input, char separator);
-
-std::string string_strip(const std::string & str);
-std::string string_get_sortable_timestamp();
-
-template<class T>
-static std::vector<T> string_split(const std::string & str, char delim) {
-    std::vector<T> values;
-    std::istringstream str_stream(str);
-    std::string token;
-    while (std::getline(str_stream, token, delim)) {
-        T value;
-        std::istringstream token_stream(token);
-        token_stream >> value;
-        values.push_back(value);
-    }
-    return values;
-}
-
-bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
-void string_process_escapes(std::string & input);
-
-//
-// Filesystem utils
-//
-
-bool fs_validate_filename(const std::string & filename);
-bool fs_create_directory_with_parents(const std::string & path);
-
-std::string fs_get_cache_directory();
-std::string fs_get_cache_file(const std::string & filename);
+std::string sampler_type_to_name_string(llama_sampler_type sampler_type);
 
 //
 // Model utils
 //
 
-struct llama_init_result {
-    struct llama_model   * model   = nullptr;
-    struct llama_context * context = nullptr;
-    std::vector<llama_lora_adapter_container> lora_adapters;
-};
-
-struct llama_init_result    llama_init_from_gpt_params(gpt_params & params);
+// TODO: avoid tuplue, use struct
+std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(gpt_params & params);
 
 struct llama_model_params   llama_model_params_from_gpt_params  (const gpt_params & params);
 struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params);
 
-struct llama_model * llama_load_model_from_url(const char * model_url, const char * path_model, const char * hf_token, const struct llama_model_params & params);
-struct llama_model * llama_load_model_from_hf(const char * repo, const char * file, const char * path_model, const char * hf_token, const struct llama_model_params & params);
-
-// clear LoRA adapters from context, then apply new list of adapters
-void llama_lora_adapters_apply(struct llama_context * ctx, std::vector<llama_lora_adapter_container> & lora_adapters);
-
 // Batch utils
 
 void llama_batch_clear(struct llama_batch & batch);
@@ -354,121 +201,61 @@ void llama_batch_add(
 std::vector<llama_token> llama_tokenize(
   const struct llama_context * ctx,
            const std::string & text,
-                        bool   add_special,
-                        bool   parse_special = false);
+                        bool   add_bos,
+                        bool   special = false);
 
 std::vector<llama_token> llama_tokenize(
     const struct llama_model * model,
            const std::string & text,
-                        bool   add_special,
-                        bool   parse_special = false);
+                        bool   add_bos,
+                        bool   special = false);
 
-// tokenizes a token into a piece, optionally renders special/control tokens
+// 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,
-                       bool          special = true);
+                       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`
-// optionally renders special/control tokens
-std::string llama_detokenize(
+std::string llama_detokenize_bpe(
                          llama_context * ctx,
-        const std::vector<llama_token> & tokens,
-                                  bool   special = true);
+        const std::vector<llama_token> & tokens);
 
 // Uses the value from the model metadata if possible, otherwise
 // defaults to true when model type is SPM, otherwise false.
 bool llama_should_add_bos_token(const llama_model * model);
 
 //
-// Chat template utils
+// YAML utils
 //
 
-// same with llama_chat_message, but uses std::string
-struct llama_chat_msg {
-    std::string role;
-    std::string content;
-};
+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();
 
-// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
-bool llama_chat_verify_template(const std::string & tmpl);
-
-// CPP wrapper for llama_chat_apply_template
-// If the built-in template is not supported, we default to chatml
-// If the custom "tmpl" is not supported, we throw an error
-std::string llama_chat_apply_template(const struct llama_model * model,
-        const std::string & tmpl,
-        const std::vector<llama_chat_msg> & chat,
-        bool add_ass);
-
-// Format single message, while taking into account the position of that message in chat history
-std::string llama_chat_format_single(const struct llama_model * model,
-        const std::string & tmpl,
-        const std::vector<llama_chat_msg> & past_msg,
-        const llama_chat_msg & new_msg,
-        bool add_ass);
-
-// Returns an example of formatted chat
-std::string llama_chat_format_example(const struct llama_model * model,
-        const std::string & tmpl);
+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);
 
 //
 // KV cache utils
 //
 
 // Dump the KV cache view with the number of sequences per cell.
-void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80);
+void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80);
 
 // Dump the KV cache view showing individual sequences in each cell (long output).
-void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40);
-
-//
-// Embedding utils
-//
-
-void llama_embd_normalize(const float * inp, float * out, int n, int embd_norm = 2);
-
-float llama_embd_similarity_cos(const float * embd1, const float * embd2, int n);
-
-//
-// Control vector utils
-//
-
-struct llama_control_vector_data {
-    int n_embd;
-
-    // stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
-    std::vector<float> data;
-};
-
-struct llama_control_vector_load_info {
-    float strength;
-
-    std::string fname;
-};
-
-// Load control vectors, scale each by strength, and add them together.
-// On error, returns {-1, empty}
-llama_control_vector_data llama_control_vector_load(const std::vector<llama_control_vector_load_info> & load_infos);
-
-//
-// Split utils
-//
-
-static const char * const LLM_KV_SPLIT_NO            = "split.no";
-static const char * const LLM_KV_SPLIT_COUNT         = "split.count";
-static const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
-
-//
-// YAML utils
-//
-
-void yaml_dump_vector_float    (FILE * stream, const char * prop_name, const std::vector<float> & data);
-void yaml_dump_vector_int      (FILE * stream, const char * prop_name, const std::vector<int> & data);
-void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data);
-
-void yaml_dump_non_result_info(
-    FILE * stream, const gpt_params & params, const llama_context * lctx,
-    const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);
+void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40);

common/grammar-parser.cpp

@@ -26,7 +26,7 @@ namespace grammar_parser {
 
     static uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) {
         uint32_t next_id = static_cast<uint32_t>(state.symbol_ids.size());
-        auto result = state.symbol_ids.emplace(std::string(src, len), next_id);
+        auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id));
         return result.first->second;
     }
 
@@ -46,12 +46,8 @@ namespace grammar_parser {
         state.rules[rule_id] = rule;
     }
 
-    static bool is_digit_char(char c) {
-        return '0' <= c && c <= '9';
-    }
-
     static bool is_word_char(char c) {
-        return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || is_digit_char(c);
+        return ('a' <= c && c <= 'z') || ('A' <= c && c <= 'Z') || c == '-' || ('0' <= c && c <= '9');
     }
 
     static std::pair<uint32_t, const char *> parse_hex(const char * src, int size) {
@@ -103,17 +99,6 @@ namespace grammar_parser {
         return pos;
     }
 
-    static const char * parse_int(const char * src) {
-        const char * pos = src;
-        while (is_digit_char(*pos)) {
-            pos++;
-        }
-        if (pos == src) {
-            throw std::runtime_error(std::string("expecting integer at ") + src);
-        }
-        return pos;
-    }
-
     static std::pair<uint32_t, const char *> parse_char(const char * src) {
         if (*src == '\\') {
             switch (src[1]) {
@@ -152,68 +137,11 @@ namespace grammar_parser {
             bool                                 is_nested) {
         size_t last_sym_start = out_elements.size();
         const char * pos = src;
-
-        auto handle_repetitions = [&](int min_times, int max_times) {
-
-            if (last_sym_start == out_elements.size()) {
-                throw std::runtime_error(std::string("expecting preceding item to */+/?/{ at ") + pos);
-            }
-
-            // apply transformation to previous symbol (last_sym_start to end) according to
-            // the following rewrite rules:
-            // S{m,n} --> S S S (m times) S'(n-m)
-            //            S'(x)   ::= S S'(x-1) |
-            //            (... n-m definitions of these S' rules ...)
-            //            S'(1)   ::= S |
-            // S{m,} -->  S S S (m times) S'
-            //            S'     ::= S S' |
-            // S*     --> S{0,}
-            //        --> S'     ::= S S' |
-            // S+     --> S{1,}
-            //        --> S S'
-            //            S'     ::= S S' |
-            // S?     --> S{0,1}
-            //        --> S'
-            //            S'     ::= S |
-
-            std::vector<llama_grammar_element> previous_elements(out_elements.begin() + last_sym_start, out_elements.end());
-            if (min_times == 0) {
-                out_elements.resize(last_sym_start);
-            } else {
-                // Repeat the previous elements (min_times - 1) times
-                for (int i = 1; i < min_times; i++) {
-                    out_elements.insert(out_elements.end(), previous_elements.begin(), previous_elements.end());
-                }
-            }
-
-            uint32_t last_rec_rule_id = 0;
-            auto n_opt = max_times < 0 ? 1 : max_times - min_times;
-
-            std::vector<llama_grammar_element> rec_rule(previous_elements);
-            for (int i = 0; i < n_opt; i++) {
-                rec_rule.resize(previous_elements.size());
-                uint32_t rec_rule_id = generate_symbol_id(state, rule_name);
-                if (i > 0 || max_times < 0) {
-                    rec_rule.push_back({LLAMA_GRETYPE_RULE_REF, max_times < 0 ? rec_rule_id : last_rec_rule_id});
-                }
-                rec_rule.push_back({LLAMA_GRETYPE_ALT, 0});
-                rec_rule.push_back({LLAMA_GRETYPE_END, 0});
-                add_rule(state, rec_rule_id, rec_rule);
-                last_rec_rule_id = rec_rule_id;
-            }
-            if (n_opt > 0) {
-                out_elements.push_back({LLAMA_GRETYPE_RULE_REF, last_rec_rule_id});
-            }
-        };
-
         while (*pos) {
             if (*pos == '"') { // literal string
                 pos++;
                 last_sym_start = out_elements.size();
                 while (*pos != '"') {
-                    if (!*pos) {
-                        throw std::runtime_error("unexpected end of input");
-                    }
                     auto char_pair = parse_char(pos);
                          pos       = char_pair.second;
                     out_elements.push_back({LLAMA_GRETYPE_CHAR, char_pair.first});
@@ -228,9 +156,6 @@ namespace grammar_parser {
                 }
                 last_sym_start = out_elements.size();
                 while (*pos != ']') {
-                    if (!*pos) {
-                        throw std::runtime_error("unexpected end of input");
-                    }
                     auto char_pair = parse_char(pos);
                          pos       = char_pair.second;
                     enum llama_gretype type = last_sym_start < out_elements.size()
@@ -239,9 +164,6 @@ namespace grammar_parser {
 
                     out_elements.push_back({type, char_pair.first});
                     if (pos[0] == '-' && pos[1] != ']') {
-                        if (!pos[1]) {
-                            throw std::runtime_error("unexpected end of input");
-                        }
                         auto endchar_pair = parse_char(pos + 1);
                              pos          = endchar_pair.second;
                         out_elements.push_back({LLAMA_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first});
@@ -266,51 +188,40 @@ namespace grammar_parser {
                     throw std::runtime_error(std::string("expecting ')' at ") + pos);
                 }
                 pos = parse_space(pos + 1, is_nested);
-            } else if (*pos == '.') { // any char
-                last_sym_start = out_elements.size();
-                out_elements.push_back({LLAMA_GRETYPE_CHAR_ANY, 0});
-                pos = parse_space(pos + 1, is_nested);
-            } else if (*pos == '*') {
-                pos = parse_space(pos + 1, is_nested);
-                handle_repetitions(0, -1);
-            } else if (*pos == '+') {
-                pos = parse_space(pos + 1, is_nested);
-                handle_repetitions(1, -1);
-            } else if (*pos == '?') {
-                pos = parse_space(pos + 1, is_nested);
-                handle_repetitions(0, 1);
-            } else if (*pos == '{') {
-                pos = parse_space(pos + 1, is_nested);
-
-                if (!is_digit_char(*pos)) {
-                    throw std::runtime_error(std::string("expecting an int at ") + pos);
+            } else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator
+                if (last_sym_start == out_elements.size()) {
+                    throw std::runtime_error(std::string("expecting preceding item to */+/? at ") + pos);
                 }
-                const char * int_end = parse_int(pos);
-                int min_times = std::stoul(std::string(pos, int_end - pos));
-                pos = parse_space(int_end, is_nested);
 
-                int max_times = -1;
-
-                if (*pos == '}') {
-                    max_times = min_times;
-                    pos = parse_space(pos + 1, is_nested);
-                } else if (*pos == ',') {
-                    pos = parse_space(pos + 1, is_nested);
-
-                    if (is_digit_char(*pos)) {
-                        const char * int_end = parse_int(pos);
-                        max_times = std::stoul(std::string(pos, int_end - pos));
-                        pos = parse_space(int_end, is_nested);
-                    }
-
-                    if (*pos != '}') {
-                        throw std::runtime_error(std::string("expecting '}' at ") + pos);
-                    }
-                    pos = parse_space(pos + 1, is_nested);
-                } else {
-                    throw std::runtime_error(std::string("expecting ',' at ") + pos);
+                // apply transformation to previous symbol (last_sym_start to end) according to
+                // rewrite rules:
+                // S* --> S' ::= S S' |
+                // S+ --> S' ::= S S' | S
+                // S? --> S' ::= S |
+                uint32_t sub_rule_id = generate_symbol_id(state, rule_name);
+                std::vector<llama_grammar_element> sub_rule;
+                // add preceding symbol to generated rule
+                sub_rule.insert(
+                    sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());
+                if (*pos == '*' || *pos == '+') {
+                    // cause generated rule to recurse
+                    sub_rule.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});
                 }
-                handle_repetitions(min_times, max_times);
+                // mark start of alternate def
+                sub_rule.push_back({LLAMA_GRETYPE_ALT, 0});
+                if (*pos == '+') {
+                    // add preceding symbol as alternate only for '+' (otherwise empty)
+                    sub_rule.insert(
+                        sub_rule.end(), out_elements.begin() + last_sym_start, out_elements.end());
+                }
+                sub_rule.push_back({LLAMA_GRETYPE_END, 0});
+                add_rule(state, sub_rule_id, sub_rule);
+
+                // in original rule, replace previous symbol with reference to generated rule
+                out_elements.resize(last_sym_start);
+                out_elements.push_back({LLAMA_GRETYPE_RULE_REF, sub_rule_id});
+
+                pos = parse_space(pos + 1, is_nested);
             } else {
                 break;
             }
@@ -367,22 +278,6 @@ namespace grammar_parser {
             while (*pos) {
                 pos = parse_rule(state, pos);
             }
-            // Validate the state to ensure that all rules are defined
-            for (const auto & rule : state.rules) {
-                for (const auto & elem : rule) {
-                    if (elem.type == LLAMA_GRETYPE_RULE_REF) {
-                        // Ensure that the rule at that location exists
-                        if (elem.value >= state.rules.size() || state.rules[elem.value].empty()) {
-                            // Get the name of the rule that is missing
-                            for (const auto & kv : state.symbol_ids) {
-                                if (kv.second == elem.value) {
-                                    throw std::runtime_error("Undefined rule identifier '" + kv.first + "'");
-                                }
-                            }
-                        }
-                    }
-                }
-            }
             return state;
         } catch (const std::exception & err) {
             fprintf(stderr, "%s: error parsing grammar: %s\n", __func__, err.what());
@@ -405,7 +300,6 @@ namespace grammar_parser {
             case LLAMA_GRETYPE_CHAR_NOT:       return true;
             case LLAMA_GRETYPE_CHAR_ALT:       return true;
             case LLAMA_GRETYPE_CHAR_RNG_UPPER: return true;
-            case LLAMA_GRETYPE_CHAR_ANY:       return true;
             default:                           return false;
         }
     }
@@ -420,7 +314,6 @@ namespace grammar_parser {
                 case LLAMA_GRETYPE_CHAR_NOT:       fprintf(file, "CHAR_NOT");       break;
                 case LLAMA_GRETYPE_CHAR_RNG_UPPER: fprintf(file, "CHAR_RNG_UPPER"); break;
                 case LLAMA_GRETYPE_CHAR_ALT:       fprintf(file, "CHAR_ALT");       break;
-                case LLAMA_GRETYPE_CHAR_ANY:       fprintf(file, "CHAR_ANY");       break;
             }
             switch (elem.type) {
                 case LLAMA_GRETYPE_END:
@@ -432,7 +325,6 @@ namespace grammar_parser {
                 case LLAMA_GRETYPE_CHAR_NOT:
                 case LLAMA_GRETYPE_CHAR_RNG_UPPER:
                 case LLAMA_GRETYPE_CHAR_ALT:
-                case LLAMA_GRETYPE_CHAR_ANY:
                     fprintf(file, "(\"");
                     print_grammar_char(file, elem.value);
                     fprintf(file, "\") ");
@@ -490,15 +382,11 @@ namespace grammar_parser {
                     }
                     print_grammar_char(file, elem.value);
                     break;
-                case LLAMA_GRETYPE_CHAR_ANY:
-                    fprintf(file, ".");
-                    break;
             }
             if (is_char_element(elem)) {
                 switch (rule[i + 1].type) {
                     case LLAMA_GRETYPE_CHAR_ALT:
                     case LLAMA_GRETYPE_CHAR_RNG_UPPER:
-                    case LLAMA_GRETYPE_CHAR_ANY:
                         break;
                     default:
                         fprintf(file, "] ");

common/json-schema-to-grammar.h

@@ -1,8 +0,0 @@
-#pragma once
-
-#include "ggml.h"
-// Change JSON_ASSERT from assert() to GGML_ASSERT:
-#define JSON_ASSERT GGML_ASSERT
-#include "json.hpp"
-
-std::string json_schema_to_grammar(const nlohmann::ordered_json& schema);

common/log.h

@@ -211,7 +211,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
         #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
     #else
         #define LOG_FLF_FMT "[%24s:%5ld][%24s] "
-        #define LOG_FLF_VAL , __FILE__, (long)__LINE__, __FUNCTION__
+        #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
     #endif
 #else
     #define LOG_FLF_FMT "%s"
@@ -224,7 +224,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
         #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
     #else
         #define LOG_TEE_FLF_FMT "[%24s:%5ld][%24s] "
-        #define LOG_TEE_FLF_VAL , __FILE__, (long)__LINE__, __FUNCTION__
+        #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__
     #endif
 #else
     #define LOG_TEE_FLF_FMT "%s"
@@ -234,7 +234,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // INTERNAL, DO NOT USE
 //  USE LOG() INSTEAD
 //
-#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__)
+#ifndef _MSC_VER
     #define LOG_IMPL(str, ...)                                                                                      \
     do {                                                                                                            \
         if (LOG_TARGET != nullptr)                                                                                  \
@@ -257,7 +257,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // INTERNAL, DO NOT USE
 //  USE LOG_TEE() INSTEAD
 //
-#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__)
+#ifndef _MSC_VER
     #define LOG_TEE_IMPL(str, ...)                                                                                                      \
     do {                                                                                                                                \
         if (LOG_TARGET != nullptr)                                                                                                      \
@@ -294,10 +294,10 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // Main LOG macro.
 //  behaves like printf, and supports arguments the exact same way.
 //
-#if !defined(_MSC_VER) || defined(__clang__)
+#ifndef _MSC_VER
     #define LOG(...) LOG_IMPL(__VA_ARGS__, "")
 #else
-    #define LOG(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "")
+    #define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "")
 #endif
 
 // Main TEE macro.
@@ -308,19 +308,19 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 // Secondary target can be changed just like LOG_TARGET
 //  by defining LOG_TEE_TARGET
 //
-#if !defined(_MSC_VER) || defined(__clang__)
+#ifndef _MSC_VER
     #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "")
 #else
-    #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "")
+    #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "")
 #endif
 
 // LOG macro variants with auto endline.
-#if !defined(_MSC_VER) || defined(__clang__)
+#ifndef _MSC_VER
     #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")
+    #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
@@ -566,7 +566,6 @@ inline void log_print_usage()
     printf("  --log-new             Create a separate new log file on start. "
                                    "Each log file will have unique name: \"<name>.<ID>.log\"\n");
     printf("  --log-append          Don't truncate the old log file.\n");
-    printf("\n");
 }
 
 #define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv)
@@ -630,7 +629,7 @@ inline std::string LOG_TOKENS_TOSTR_PRETTY(const C & ctx, const T & tokens)
     buf << "[ ";
 
     bool first = true;
-    for (const auto & token : tokens)
+    for (const auto &token : tokens)
     {
         if (!first) {
             buf << ", ";

common/ngram-cache.cpp

@@ -1,282 +0,0 @@
-#include "ngram-cache.h"
-#include "common.h"
-#include "log.h"
-
-#include <cstdint>
-#include <fstream>
-
-void llama_ngram_cache_update(llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max,
-                              std::vector<llama_token> & inp, int nnew, bool print_progress) {
-    const int64_t t_start_ms = ggml_time_ms();
-    const int64_t inp_size = inp.size();
-
-    const int64_t n_todo = inp_size * (ngram_max - ngram_min + 1);
-    int64_t n_done = 0;
-
-    for (int64_t ngram_size = ngram_min; ngram_size <= ngram_max; ++ngram_size) {
-        const int64_t i_start = std::max(inp_size - nnew, ngram_size);
-        for (int64_t i = i_start; i < inp_size; ++i) {
-            const int64_t ngram_start = i - ngram_size;
-            llama_ngram ngram(&inp[ngram_start], ngram_size);
-            const llama_token token = inp[i];
-
-            llama_ngram_cache::iterator part_it = ngram_cache.find(ngram);
-            if (part_it == ngram_cache.end()) {
-                llama_ngram_cache_part part;
-                part.emplace(token, 1);
-                ngram_cache.emplace(ngram, part);
-            } else {
-                llama_ngram_cache_part::iterator token_count_it = part_it->second.find(token);
-                if (token_count_it == part_it->second.end()) {
-                    part_it->second.emplace(token, 1);
-                } else {
-                    token_count_it->second++;
-                }
-            }
-            ++n_done;
-
-            if (print_progress && n_done % 10000000 == 0) {
-                const int64_t t_now_ms = ggml_time_ms();
-                const int64_t eta_ms   = (inp_size*(ngram_max-ngram_min+1) - n_done) * (t_now_ms - t_start_ms) / n_done;
-                const int64_t eta_min  = eta_ms / (60*1000);
-                const int64_t eta_s    = (eta_ms - 60*1000*eta_min) / 1000;
-
-                fprintf(stderr, "%s: %" PRId64 "/%" PRId64 " done, ETA: %02" PRId64 ":%02" PRId64 "\n", __func__, n_done, n_todo, eta_min, eta_s);
-            }
-        }
-    }
-}
-
-// Helper function to get a token from the combined, speculative sequence of inp and draft.
-static llama_token get_token(const std::vector<llama_token> & inp, const std::vector<llama_token> & draft, const size_t i) {
-    return i < inp.size() ? inp[i] : draft[1 + i - inp.size()];
-}
-
-// If sample size or percentage are below these thresholds the draft is aborted early:
-constexpr int    draft_min_sample_size_lax[LLAMA_NGRAM_MAX] = { 2,  2,  1,  1};
-constexpr int        draft_min_percent_lax[LLAMA_NGRAM_MAX] = {66, 50, 50, 50};
-constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4,  3,  2,  2};
-constexpr int     draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66};
-
-// Helper function that tries to draft a token from only the static ngram cache:
-static llama_token try_draft(llama_ngram_cache & nc_static, const llama_ngram ngram_static) {
-    llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
-    if (part_static_it == nc_static.end()) {
-        return -1;
-    }
-    const llama_ngram_cache_part part_static = part_static_it->second;
-
-    int max_count_static  = 0;
-    int sum_count_static  = 0;
-    llama_token max_token = -1;
-
-    for (std::pair<llama_token, int> token_count_static : part_static) {
-        const llama_token token = token_count_static.first;
-        const int32_t count_static  = token_count_static.second;
-
-        if (count_static > max_count_static) {
-            max_token        = token;
-            max_count_static = count_static;
-        }
-        sum_count_static += count_static;
-    }
-
-    if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) {
-        return -1;
-    }
-    if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) {
-        return -1;
-    }
-    return max_token;
-}
-
-// Try to draft a token from primary cache (context/dynamic), validate with static cache:
-static llama_token try_draft(
-    llama_ngram_cache & nc_primary, const std::vector<llama_ngram> & ngrams_primary, llama_ngram_cache_part & part_static,
-    const int * min_sample_size, const int * min_percent) {
-
-    llama_token drafted_token = -1;
-
-    for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == -1; --i) {
-        const llama_ngram ngram_primary = ngrams_primary[i];
-
-        llama_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
-        if (part_primary_it == nc_primary.end()) {
-            continue;
-        }
-        const llama_ngram_cache_part part_primary = part_primary_it->second;
-
-        int max_count_primary = 0;
-        int max_count_static  = 0;
-        int sum_count_primary = 0;
-        llama_token max_token = -1;
-
-        for (std::pair<llama_token, int> token_count_primary : part_primary) {
-            const llama_token token = token_count_primary.first;
-
-            llama_ngram_cache_part::iterator token_count_static_it = part_static.find(token);
-
-            const int32_t count_primary = token_count_primary.second;
-            const int32_t count_static  = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1;
-
-            if (count_primary*count_static > max_count_primary*max_count_static) {
-                max_token         = token;
-                max_count_primary = count_primary;
-                max_count_static  = count_static;
-            }
-            sum_count_primary += count_primary;
-        }
-
-        if (sum_count_primary < min_sample_size[i]) {
-            continue;
-        }
-        if (100*max_count_primary < min_percent[i]*sum_count_primary) {
-            continue;;
-        }
-        drafted_token = max_token;
-    }
-
-    return drafted_token;
-}
-
-void llama_ngram_cache_draft(
-    std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
-    llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static
-) {
-    GGML_ASSERT(draft.size() == 1);
-    const int inp_size = inp.size();
-
-    if (inp_size < LLAMA_NGRAM_STATIC) {
-        return;
-    }
-
-    while ((int) draft.size()-1 < n_draft) {
-        llama_token drafted_token = -1;
-
-        const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1;
-        llama_ngram ngram_static;
-        for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) {
-            ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j);
-        }
-        llama_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
-        llama_ngram_cache_part part_static;
-        if (part_static_it != nc_static.end()) {
-            part_static = part_static_it->second;
-        }
-
-        // cd = context + dynamic
-        std::vector<llama_ngram> ngrams_cd;
-        for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) {
-            const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1;
-            llama_ngram ngram_cd;
-            for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) {
-                ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j);
-            }
-            ngrams_cd.push_back(ngram_cd);
-        }
-        if (drafted_token == -1) {
-            drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax);
-        }
-        if (drafted_token == -1) {
-            drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict);
-        }
-        if (drafted_token == -1) {
-            drafted_token = try_draft(nc_static, ngram_static);
-        }
-
-        if (drafted_token == -1) {
-            break;
-        }
-
-        LOG(" - draft candidate: token=%d\n", drafted_token);
-        draft.push_back(drafted_token);
-    }
-}
-
-void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename) {
-    std::ofstream file_out(filename, std::ios::binary);
-    for (std::pair<llama_ngram, llama_ngram_cache_part> item : ngram_cache) {
-        const llama_ngram      ngram        = item.first;
-        llama_ngram_cache_part token_counts = item.second;
-        GGML_ASSERT(!token_counts.empty());
-        const int32_t ntokens = token_counts.size();
-        GGML_ASSERT(ntokens > 0);
-
-        file_out.write(reinterpret_cast<const char *>(&ngram),   sizeof(llama_ngram));
-        file_out.write(reinterpret_cast<const char *>(&ntokens), sizeof(int32_t));
-        for (std::pair<llama_token, int32_t> item2 : token_counts) {
-            const llama_token token = item2.first;
-            const int32_t     count = item2.second;
-            GGML_ASSERT(count > 0);
-
-            file_out.write(reinterpret_cast<const char *>(&token), sizeof(llama_token));
-            file_out.write(reinterpret_cast<const char *>(&count), sizeof(int32_t));
-        }
-    }
-
-}
-
-llama_ngram_cache llama_ngram_cache_load(std::string & filename) {
-    std::ifstream hashmap_file(filename, std::ios::binary);
-    if (!hashmap_file) {
-        throw std::ifstream::failure("Unable to open file " + filename);
-    }
-    llama_ngram_cache ngram_cache;
-
-    llama_ngram ngram;
-    int32_t     ntokens;
-    llama_token token;
-    int32_t     count;
-
-    char * ngramc   = reinterpret_cast<char*>(&ngram);
-    char * ntokensc = reinterpret_cast<char*>(&ntokens);
-    char * tokenc   = reinterpret_cast<char*>(&token);
-    char * countc   = reinterpret_cast<char*>(&count);
-    while(hashmap_file.read(ngramc, sizeof(llama_ngram))) {
-        GGML_ASSERT(!hashmap_file.eof());
-        GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t)));
-        GGML_ASSERT(ntokens > 0);
-        llama_ngram_cache_part token_counts;
-
-        for (int i = 0; i < ntokens; ++i) {
-            GGML_ASSERT(!hashmap_file.eof());
-            GGML_ASSERT(hashmap_file.read(tokenc, sizeof(llama_token)));
-            GGML_ASSERT(!hashmap_file.eof());
-            GGML_ASSERT(hashmap_file.read(countc, sizeof(int32_t)));
-            GGML_ASSERT(count > 0);
-            token_counts.emplace(token, count);
-        }
-
-        ngram_cache.emplace(ngram, token_counts);
-    }
-    GGML_ASSERT(hashmap_file.eof());
-
-    return ngram_cache;
-}
-
-void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add) {
-    for (std::pair<llama_ngram, llama_ngram_cache_part> ngram_part : ngram_cache_add) {
-        const llama_ngram      ngram = ngram_part.first;
-        llama_ngram_cache_part  part = ngram_part.second;
-
-        llama_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram);
-        if (part_merged_it == ngram_cache_target.end()) {
-            ngram_cache_target.emplace(ngram, part);
-            continue;
-        }
-
-        for (std::pair<llama_token, int32_t> token_count : part) {
-            const llama_token token = token_count.first;
-            const int32_t     count = token_count.second;
-            GGML_ASSERT(count > 0);
-
-            llama_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token);
-            if (token_count_merged_it == part_merged_it->second.end()) {
-                part_merged_it->second.emplace(token, count);
-                continue;
-            }
-
-            token_count_merged_it->second += count;
-        }
-    }
-}

common/ngram-cache.h

@@ -1,101 +0,0 @@
-#pragma once
-
-#include "llama.h"
-
-#include <unordered_map>
-#include <string>
-#include <vector>
-
-#define LLAMA_NGRAM_MIN    1
-#define LLAMA_NGRAM_MAX    4
-#define LLAMA_NGRAM_STATIC 2
-
-// Data structures to map n-grams to empirical token probabilities:
-
-struct llama_ngram {
-    llama_token tokens[LLAMA_NGRAM_MAX];
-
-    llama_ngram() {
-        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            tokens[i] = -1;
-        }
-    }
-
-    llama_ngram(const llama_token * input, const int ngram_size) {
-        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            tokens[i] = i < ngram_size ? input[i] : -1;
-        }
-    }
-
-    bool operator==(const llama_ngram & other) const {
-        for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
-            if (tokens[i] != other.tokens[i]) {
-                return false;
-            }
-        }
-        return true;
-    }
-};
-
-struct llama_token_hash_function {
-    size_t operator()(const llama_token token) const {
-        // see https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo/
-        return token * 11400714819323198485llu;
-    }
-};
-
-struct llama_ngram_hash_function {
-    size_t operator()(const llama_ngram & ngram) const {
-        size_t hash = llama_token_hash_function{}(ngram.tokens[0]);
-        for (int i = 1; i < LLAMA_NGRAM_MAX; ++i) {
-            hash ^= llama_token_hash_function{}(ngram.tokens[i]);
-        }
-        return hash;
-    }
-};
-
-// token -> number of times token has been seen
-typedef std::unordered_map<llama_token, int32_t> llama_ngram_cache_part;
-
-// n-gram -> empirical distribution of following tokens
-typedef std::unordered_map<llama_ngram, llama_ngram_cache_part, llama_ngram_hash_function> llama_ngram_cache;
-
-
-// Update an ngram cache with tokens.
-// ngram_cache:         the cache to modify.
-// ngram_min/ngram_max: the min/max size of the ngrams to extract from inp_data.
-// inp_data:            the token sequence with which to update ngram_cache.
-// nnew:                how many new tokens have been appended to inp_data since the last call to this function.
-// print_progress:      whether to print progress to stderr.
-//
-// In order to get correct results inp_data can ONLY BE APPENDED TO.
-// Changes in the middle need a complete rebuild.
-void llama_ngram_cache_update(
-    llama_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector<llama_token> & inp_data, int nnew, bool print_progress);
-
-// Try to draft tokens from ngram caches.
-// inp:                the tokens generated so far.
-// draft:              the token sequence to draft. Expected to initially contain the previously sampled token.
-// n_draft:            maximum number of tokens to add to draft.
-// ngram_min/gram_max: the min/max size of the ngrams in nc_context and nc_dynamic.
-// nc_context:         ngram cache based on current context.
-// nc_dynamic:         ngram cache based on previous user generations.
-// nc_static:          ngram cache generated from a large text corpus, used for validation.
-void llama_ngram_cache_draft(
-    std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
-    llama_ngram_cache & nc_context, llama_ngram_cache & nc_dynamic, llama_ngram_cache & nc_static);
-
-// Save an ngram cache to a file.
-// ngram_cache: the ngram cache to save.
-// filename:    the path under which to save the ngram cache.
-void llama_ngram_cache_save(llama_ngram_cache & ngram_cache, std::string & filename);
-
-// Load an ngram cache saved with llama_ngram_cache_save.
-// filename: the path from which to load the ngram cache.
-// returns:  an ngram cache containing the information saved to filename.
-llama_ngram_cache llama_ngram_cache_load(std::string & filename);
-
-// Merge two ngram caches.
-// ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add.
-// ngram_cache_add:    the ngram cache to add to ngram_cache_target.
-void llama_ngram_cache_merge(llama_ngram_cache & ngram_cache_target, llama_ngram_cache & ngram_cache_add);

common/sampling.cpp

@@ -1,6 +1,4 @@
-#define LLAMA_API_INTERNAL
 #include "sampling.h"
-#include <random>
 
 struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_params & params) {
     struct llama_sampling_context * result = new llama_sampling_context();
@@ -19,30 +17,15 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_
             return nullptr;
         }
 
-        // Ensure that there is a "root" node.
-        if (result->parsed_grammar.symbol_ids.find("root") == result->parsed_grammar.symbol_ids.end()) {
-            fprintf(stderr, "%s: grammar does not contain a 'root' symbol\n", __func__);
-            delete result;
-            return nullptr;
-        }
-
         std::vector<const llama_grammar_element *> grammar_rules(result->parsed_grammar.c_rules());
 
-        struct llama_grammar * grammar = llama_grammar_init(
+        result->grammar = llama_grammar_init(
                 grammar_rules.data(),
                 grammar_rules.size(), result->parsed_grammar.symbol_ids.at("root"));
-        if (grammar == nullptr) {
-            throw std::runtime_error("Failed to initialize llama_grammar");
-        }
-        result->grammar = grammar;
     }
 
     result->prev.resize(params.n_prev);
 
-    result->n_valid = 0;
-
-    llama_sampling_set_rng_seed(result, params.seed);
-
     return result;
 }
 
@@ -63,25 +46,13 @@ void llama_sampling_reset(llama_sampling_context * ctx) {
     if (!ctx->parsed_grammar.rules.empty()) {
         std::vector<const llama_grammar_element *> grammar_rules(ctx->parsed_grammar.c_rules());
 
-        struct llama_grammar * grammar = llama_grammar_init(
+        ctx->grammar = llama_grammar_init(
                 grammar_rules.data(),
                 grammar_rules.size(), ctx->parsed_grammar.symbol_ids.at("root"));
-        if (grammar == nullptr) {
-            throw std::runtime_error("Failed to initialize llama_grammar");
-        }
-        ctx->grammar = grammar;
     }
 
     std::fill(ctx->prev.begin(), ctx->prev.end(), 0);
     ctx->cur.clear();
-    ctx->n_valid = 0;
-}
-
-void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) {
-    if (seed == LLAMA_DEFAULT_SEED) {
-        seed = std::random_device{}();
-    }
-    ctx->rng.seed(seed);
 }
 
 void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst) {
@@ -133,7 +104,7 @@ std::string llama_sampling_order_print(const llama_sampling_params & params) {
     std::string result = "CFG -> Penalties ";
     if (params.mirostat == 0) {
         for (auto sampler_type : params.samplers_sequence) {
-            const auto sampler_type_name = llama_sampling_type_to_str(sampler_type);
+            const auto sampler_type_name = sampler_type_to_name_string(sampler_type);
             if (!sampler_type_name.empty()) {
                 result += "-> " + sampler_type_name + " ";
             }
@@ -145,87 +116,6 @@ std::string llama_sampling_order_print(const llama_sampling_params & params) {
     return result;
 }
 
-std::string llama_sampling_type_to_str(llama_sampler_type sampler_type) {
-    switch (sampler_type) {
-        case llama_sampler_type::TOP_K:       return "top_k";
-        case llama_sampler_type::TFS_Z:       return "tfs_z";
-        case llama_sampler_type::TYPICAL_P:   return "typical_p";
-        case llama_sampler_type::TOP_P:       return "top_p";
-        case llama_sampler_type::MIN_P:       return "min_p";
-        case llama_sampler_type::TEMPERATURE: return "temperature";
-        default : return "";
-    }
-}
-
-std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
-    std::unordered_map<std::string, llama_sampler_type> sampler_canonical_name_map {
-        {"top_k",       llama_sampler_type::TOP_K},
-        {"top_p",       llama_sampler_type::TOP_P},
-        {"typical_p",   llama_sampler_type::TYPICAL_P},
-        {"min_p",       llama_sampler_type::MIN_P},
-        {"tfs_z",       llama_sampler_type::TFS_Z},
-        {"temperature", llama_sampler_type::TEMPERATURE}
-    };
-
-    // since samplers names are written multiple ways
-    // make it ready for both system names and input names
-    std::unordered_map<std::string, llama_sampler_type> sampler_alt_name_map {
-        {"top-k",       llama_sampler_type::TOP_K},
-        {"top-p",       llama_sampler_type::TOP_P},
-        {"nucleus",     llama_sampler_type::TOP_P},
-        {"typical-p",   llama_sampler_type::TYPICAL_P},
-        {"typical",     llama_sampler_type::TYPICAL_P},
-        {"min-p",       llama_sampler_type::MIN_P},
-        {"tfs-z",       llama_sampler_type::TFS_Z},
-        {"tfs",         llama_sampler_type::TFS_Z},
-        {"temp",        llama_sampler_type::TEMPERATURE}
-    };
-
-    std::vector<llama_sampler_type> sampler_types;
-    sampler_types.reserve(names.size());
-    for (const auto & name : names)
-    {
-        auto sampler_item = sampler_canonical_name_map.find(name);
-        if (sampler_item != sampler_canonical_name_map.end())
-        {
-            sampler_types.push_back(sampler_item->second);
-        }
-        else
-        {
-            if (allow_alt_names)
-            {
-                sampler_item = sampler_alt_name_map.find(name);
-                if (sampler_item != sampler_alt_name_map.end())
-                {
-                    sampler_types.push_back(sampler_item->second);
-                }
-            }
-        }
-    }
-    return sampler_types;
-}
-
-std::vector<llama_sampler_type> llama_sampling_types_from_chars(const std::string & names_string) {
-    std::unordered_map<char, llama_sampler_type> sampler_name_map {
-        {'k', llama_sampler_type::TOP_K},
-        {'p', llama_sampler_type::TOP_P},
-        {'y', llama_sampler_type::TYPICAL_P},
-        {'m', llama_sampler_type::MIN_P},
-        {'f', llama_sampler_type::TFS_Z},
-        {'t', llama_sampler_type::TEMPERATURE}
-    };
-
-    std::vector<llama_sampler_type> sampler_types;
-    sampler_types.reserve(names_string.size());
-    for (const auto & c : names_string) {
-        const auto sampler_item = sampler_name_map.find(c);
-        if (sampler_item != sampler_name_map.end()) {
-            sampler_types.push_back(sampler_item->second);
-        }
-    }
-    return sampler_types;
-}
-
 // no reasons to expose this function in header
 static void sampler_queue(
                    struct llama_context * ctx_main,
@@ -268,117 +158,35 @@ static llama_token llama_sampling_sample_impl(
                   struct llama_context * ctx_main,
                   struct llama_context * ctx_cfg,
                   const int idx,
-                  bool is_resampling) {
-    const llama_sampling_params & params = ctx_sampling->params;
-
-    const float   temp            = params.temp;
-    const int     mirostat        = params.mirostat;
-    const float   mirostat_tau    = params.mirostat_tau;
-    const float   mirostat_eta    = params.mirostat_eta;
-
-    std::vector<float> original_logits;
-    auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, /* apply_grammar= */ is_resampling, &original_logits);
-    if (ctx_sampling->grammar != NULL && !is_resampling) {
-        GGML_ASSERT(!original_logits.empty());
-    }
-    llama_token id = 0;
-
-    if (temp < 0.0) {
-        // greedy sampling, with probs
-        llama_sample_softmax(ctx_main, &cur_p);
-        id = cur_p.data[0].id;
-    } else if (temp == 0.0) {
-        // greedy sampling, no probs
-        id = llama_sample_token_greedy(ctx_main, &cur_p);
-    } else {
-        if (mirostat == 1) {
-            const int mirostat_m = 100;
-            llama_sample_temp(ctx_main, &cur_p, temp);
-            id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu);
-        } else if (mirostat == 2) {
-            llama_sample_temp(ctx_main, &cur_p, temp);
-            id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
-        } else {
-            // temperature sampling
-            size_t min_keep = std::max(1, params.min_keep);
-
-            sampler_queue(ctx_main, params, cur_p, min_keep);
-
-            id = llama_sample_token_with_rng(ctx_main, &cur_p, ctx_sampling->rng);
-
-            //{
-            //    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;
-            //        (void)id; // To avoid a warning that id is unused when logging is disabled.
-            //        LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p);
-            //    }
-            //}
-
-            //LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
-        }
-    }
-
-    if (ctx_sampling->grammar != NULL && !is_resampling) {
-        // Get a pointer to the logits
-        float * logits = llama_get_logits_ith(ctx_main, idx);
-
-        // Create an array with a single token data element for the sampled id
-        llama_token_data single_token_data = {id, logits[id], 0.0f};
-        llama_token_data_array single_token_data_array = { &single_token_data, 1, false };
-
-        // Apply grammar constraints to the single token
-        llama_grammar_sample(ctx_sampling->grammar, ctx_main, &single_token_data_array);
-
-        // Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY
-        bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
-
-        // If the token is not valid according to the grammar, perform resampling
-        if (!is_valid) {
-            LOG("Resampling because token %d: '%s' does not meet grammar rules\n", id, llama_token_to_piece(ctx_main, id).c_str());
-
-            // Restore logits from the copy
-            std::copy(original_logits.begin(), original_logits.end(), logits);
-
-            return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, /* is_resampling= */ true);
-        }
-    }
-
-    ctx_sampling->n_valid = temp == 0.0f ? 0 : cur_p.size;
-
-    return id;
-}
-
-static llama_token_data_array llama_sampling_prepare_impl(
-                  struct llama_sampling_context * ctx_sampling,
-                  struct llama_context * ctx_main,
-                  struct llama_context * ctx_cfg,
-                  const int idx,
-                  bool apply_grammar,
-                  std::vector<float> * original_logits) {
+                  bool is_resampling) {  // Add a parameter to indicate if we are resampling
     const llama_sampling_params & params = ctx_sampling->params;
 
     const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
 
+    const float   temp            = params.temp;
     const int32_t penalty_last_n  = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
     const float   penalty_repeat  = params.penalty_repeat;
     const float   penalty_freq    = params.penalty_freq;
     const float   penalty_present = params.penalty_present;
-
+    const int     mirostat        = params.mirostat;
+    const float   mirostat_tau    = params.mirostat_tau;
+    const float   mirostat_eta    = params.mirostat_eta;
     const bool    penalize_nl     = params.penalize_nl;
 
     auto & prev = ctx_sampling->prev;
     auto & cur  = ctx_sampling->cur;
 
+    llama_token id = 0;
+
     // Get a pointer to the logits
     float * logits = llama_get_logits_ith(ctx_main, idx);
 
-    if (ctx_sampling->grammar != NULL && !apply_grammar) {
-        GGML_ASSERT(original_logits != NULL);
-        // Only make a copy of the original logits if we are not applying grammar checks, not sure if I actually have to do this.
-        *original_logits = {logits, logits + n_vocab};
+    // Declare original_logits at the beginning of the function scope
+    std::vector<float> original_logits;
+
+    if (!is_resampling) {
+        // Only make a copy of the original logits if we are not in the resampling phase, not sure if I actually have to do this.
+        original_logits = std::vector<float>(logits, logits + llama_n_vocab(llama_get_model(ctx_main)));
     }
 
     // apply params.logit_bias map
@@ -391,10 +199,10 @@ static llama_token_data_array llama_sampling_prepare_impl(
         llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
     }
 
-    cur.resize(n_vocab);
+    cur.clear();
 
     for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-        cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
+        cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
     }
 
     llama_token_data_array cur_p = { cur.data(), cur.size(), false };
@@ -419,12 +227,72 @@ static llama_token_data_array llama_sampling_prepare_impl(
         }
     }
 
-    // apply grammar checks before sampling logic
-    if (apply_grammar && ctx_sampling->grammar != NULL) {
-        llama_grammar_sample(ctx_sampling->grammar, ctx_main, &cur_p);
+    // If we are in the resampling phase, apply grammar checks before sampling logic
+    if (is_resampling && ctx_sampling->grammar != NULL) {
+        llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
     }
 
-    return cur_p;
+    if (temp < 0.0) {
+        // greedy sampling, with probs
+        llama_sample_softmax(ctx_main, &cur_p);
+        id = cur_p.data[0].id;
+    } else if (temp == 0.0) {
+        // greedy sampling, no probs
+        id = llama_sample_token_greedy(ctx_main, &cur_p);
+    } else {
+        if (mirostat == 1) {
+            const int mirostat_m = 100;
+            llama_sample_temp(ctx_main, &cur_p, temp);
+            id = llama_sample_token_mirostat(ctx_main, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &ctx_sampling->mirostat_mu);
+        } else if (mirostat == 2) {
+            llama_sample_temp(ctx_main, &cur_p, temp);
+            id = llama_sample_token_mirostat_v2(ctx_main, &cur_p, mirostat_tau, mirostat_eta, &ctx_sampling->mirostat_mu);
+        } else {
+            // temperature sampling
+            size_t min_keep = std::max(1, params.min_keep);
+
+            sampler_queue(ctx_main, params, cur_p, min_keep);
+
+            id = llama_sample_token(ctx_main, &cur_p);
+
+            //{
+            //    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;
+            //        (void)id; // To avoid a warning that id is unused when logging is disabled.
+            //        LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx_main, id).c_str(), cur_p.data[i].p);
+            //    }
+            //}
+
+            //LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx_main, id).c_str());
+        }
+    }
+
+    if (ctx_sampling->grammar != NULL && !is_resampling) {
+        // Create an array with a single token data element for the sampled id
+        llama_token_data single_token_data = {id, logits[id], 0.0f};
+        llama_token_data_array single_token_data_array = { &single_token_data, 1, false };
+
+        // Apply grammar constraints to the single token
+        llama_sample_grammar(ctx_main, &single_token_data_array, ctx_sampling->grammar);
+
+        // Check if the token is valid according to the grammar by seeing if its logit has been set to -INFINITY
+        bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
+
+        // If the token is not valid according to the grammar, perform resampling
+        if (!is_valid) {
+            LOG("Resampling because token %d: '%s' does not meet grammar rules\n", id, llama_token_to_piece(ctx_main, id).c_str());
+
+            // Restore logits from the copy
+            std::copy(original_logits.begin(), original_logits.end(), logits);
+
+            return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, true);  // Pass true for is_resampling
+        }
+    }
+
+    return id;
 }
 
 llama_token llama_sampling_sample(
@@ -433,17 +301,7 @@ llama_token llama_sampling_sample(
                   struct llama_context * ctx_cfg,
                   const int idx) {
     // Call the implementation function with is_resampling set to false by default
-    return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, /* is_resampling= */ false);
-}
-
-llama_token_data_array llama_sampling_prepare(
-                  struct llama_sampling_context * ctx_sampling,
-                  struct llama_context * ctx_main,
-                  struct llama_context * ctx_cfg,
-                  const int idx,
-                  bool apply_grammar,
-                  std::vector<float> * original_logits) {
-    return llama_sampling_prepare_impl(ctx_sampling,ctx_main, ctx_cfg, idx, apply_grammar, original_logits);
+    return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false);
 }
 
 void llama_sampling_accept(
@@ -455,6 +313,6 @@ void llama_sampling_accept(
     ctx_sampling->prev.push_back(id);
 
     if (ctx_sampling->grammar != NULL && apply_grammar) {
-        llama_grammar_accept_token(ctx_sampling->grammar, ctx_main, id);
+        llama_grammar_accept_token(ctx_main, ctx_sampling->grammar, id);
     }
 }

common/sampling.h

@@ -4,10 +4,9 @@
 
 #include "grammar-parser.h"
 
-#include <random>
 #include <string>
-#include <unordered_map>
 #include <vector>
+#include <unordered_map>
 
 // sampler types
 enum class llama_sampler_type : char {
@@ -21,26 +20,25 @@ enum class llama_sampler_type : char {
 
 // sampling parameters
 typedef struct llama_sampling_params {
-    int32_t     n_prev                = 64;                 // number of previous tokens to remember
-    int32_t     n_probs               = 0;                  // if greater than 0, output the probabilities of top n_probs tokens.
-    int32_t     min_keep              = 0;                  // 0 = disabled, otherwise samplers should return at least min_keep tokens
-    int32_t     top_k                 = 40;                 // <= 0 to use vocab size
-    float       top_p                 = 0.95f;              // 1.0 = disabled
-    float       min_p                 = 0.05f;              // 0.0 = disabled
-    float       tfs_z                 = 1.00f;              // 1.0 = disabled
-    float       typical_p             = 1.00f;              // 1.0 = disabled
-    float       temp                  = 0.80f;              // <= 0.0 to sample greedily, 0.0 to not output probabilities
-    float       dynatemp_range        = 0.00f;              // 0.0 = disabled
-    float       dynatemp_exponent     = 1.00f;              // controls how entropy maps to temperature in dynamic temperature sampler
-    int32_t     penalty_last_n        = 64;                 // last n tokens to penalize (0 = disable penalty, -1 = context size)
-    float       penalty_repeat        = 1.00f;              // 1.0 = disabled
-    float       penalty_freq          = 0.00f;              // 0.0 = disabled
-    float       penalty_present       = 0.00f;              // 0.0 = disabled
-    int32_t     mirostat              = 0;                  // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
-    float       mirostat_tau          = 5.00f;              // target entropy
-    float       mirostat_eta          = 0.10f;              // learning rate
-    bool        penalize_nl           = false;              // consider newlines as a repeatable token
-    uint32_t    seed                  = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampling_context
+    int32_t     n_prev                = 64;       // number of previous tokens to remember
+    int32_t     n_probs               = 0;        // if greater than 0, output the probabilities of top n_probs tokens.
+    int32_t     min_keep              = 0;        // 0 = disabled, otherwise samplers should return at least min_keep tokens
+    int32_t     top_k                 = 40;       // <= 0 to use vocab size
+    float       top_p                 = 0.95f;    // 1.0 = disabled
+    float       min_p                 = 0.05f;    // 0.0 = disabled
+    float       tfs_z                 = 1.00f;    // 1.0 = disabled
+    float       typical_p             = 1.00f;    // 1.0 = disabled
+    float       temp                  = 0.80f;    // <= 0.0 to sample greedily, 0.0 to not output probabilities
+    float       dynatemp_range        = 0.00f;    // 0.0 = disabled
+    float       dynatemp_exponent     = 1.00f;    // controls how entropy maps to temperature in dynamic temperature sampler
+    int32_t     penalty_last_n        = 64;       // last n tokens to penalize (0 = disable penalty, -1 = context size)
+    float       penalty_repeat        = 1.10f;    // 1.0 = disabled
+    float       penalty_freq          = 0.00f;    // 0.0 = disabled
+    float       penalty_present       = 0.00f;    // 0.0 = disabled
+    int32_t     mirostat              = 0;        // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
+    float       mirostat_tau          = 5.00f;    // target entropy
+    float       mirostat_eta          = 0.10f;    // learning rate
+    bool        penalize_nl           = true;     // consider newlines as a repeatable token
 
     std::vector<llama_sampler_type> samplers_sequence = {
         llama_sampler_type::TOP_K,
@@ -81,9 +79,6 @@ struct llama_sampling_context {
     // TODO: replace with ring-buffer
     std::vector<llama_token>      prev;
     std::vector<llama_token_data> cur;
-    size_t n_valid; // Number of correct top tokens with correct probabilities.
-
-    std::mt19937 rng;
 };
 
 #include "common.h"
@@ -98,9 +93,6 @@ void llama_sampling_free(struct llama_sampling_context * ctx);
 // - reset grammar
 void llama_sampling_reset(llama_sampling_context * ctx);
 
-// Set the sampler seed
-void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed);
-
 // Copy the sampler context
 void llama_sampling_cp(llama_sampling_context * src, llama_sampling_context * dst);
 
@@ -116,11 +108,6 @@ std::string llama_sampling_print(const llama_sampling_params & params);
 // Print sampling order into a string
 std::string llama_sampling_order_print(const llama_sampling_params & params);
 
-std::string llama_sampling_type_to_str(llama_sampler_type sampler_type);
-
-std::vector<llama_sampler_type> llama_sampling_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
-std::vector<llama_sampler_type> llama_sampling_types_from_chars(const std::string & names_string);
-
 // this is a common sampling function used across the examples for convenience
 // it can serve as a starting point for implementing your own sampling function
 // Note: When using multiple sequences, it is the caller's responsibility to call
@@ -142,16 +129,7 @@ llama_token llama_sampling_sample(
         struct llama_sampling_context * ctx_sampling,
         struct llama_context * ctx_main,
         struct llama_context * ctx_cfg,
-        int idx = -1);
-
-// Prepares and adjusts the set of token candidates for sampling based on penalties, biases, and sampling parameters.
-llama_token_data_array llama_sampling_prepare(
-        struct llama_sampling_context * ctx_sampling,
-        struct llama_context * ctx_main,
-        struct llama_context * ctx_cfg,
-        int idx = 0,
-        bool apply_grammar = true,
-        std::vector<float> * original_logits = nullptr);
+        int idx = 0);
 
 void llama_sampling_accept(
         struct llama_sampling_context * ctx_sampling,

common/train.cpp

@@ -1052,7 +1052,7 @@ struct train_params_common get_default_train_params_common() {
 
     params.custom_n_ctx = false;
 
-    params.use_flash              = false;
+    params.use_flash              = true;
     params.use_checkpointing      = true;
 
     params.sample_start           = "";
@@ -1380,7 +1380,7 @@ bool consume_common_train_arg(
 
 void finish_processing_train_args(struct train_params_common * params) {
     if (params->escape) {
-        string_process_escapes(params->sample_start);
+        process_escapes(params->sample_start);
     }
 }
 

convert-llama-ggml-to-gguf.py

@@ -1,7 +1,6 @@
 #!/usr/bin/env python3
 from __future__ import annotations
 
-import logging
 import argparse
 import os
 import struct
@@ -15,8 +14,6 @@ if 'NO_LOCAL_GGUF' not in os.environ:
     sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
 import gguf
 
-logger = logging.getLogger("ggml-to-gguf")
-
 
 class GGMLFormat(IntEnum):
     GGML = 0
@@ -128,14 +125,11 @@ class Tensor:
         self.start_offset = offset
         self.len_bytes = n_bytes
         offset += n_bytes
+        # print(n_dims, name_len, dtype, self.dims, self.name, pad)
         return offset - orig_offset
 
 
 class GGMLModel:
-
-    file_format: GGMLFormat
-    format_version: int
-
     def __init__(self):
         self.hyperparameters = None
         self.vocab = None
@@ -181,7 +175,7 @@ class GGMLModel:
         offset += self.validate_header(data, offset)
         hp = Hyperparameters()
         offset += hp.load(data, offset)
-        logger.info(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}')
+        print(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}')
         self.validate_conversion(hp.ftype)
         vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML)
         offset += vocab.load(data, offset, hp.n_vocab)
@@ -221,12 +215,12 @@ class GGMLToGGUF:
                     if float(hp.n_head) / float(x) == gqa:
                         n_kv_head = x
                 assert n_kv_head is not None, "Couldn't determine n_kv_head from GQA param"
-                logger.info(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}')
+                print(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}')
         self.n_kv_head = n_kv_head
         self.name_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAMA, ggml_model.hyperparameters.n_layer)
 
     def save(self):
-        logger.info('* Preparing to save GGUF file')
+        print('* Preparing to save GGUF file')
         gguf_writer = gguf.GGUFWriter(
             self.cfg.output,
             gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA],
@@ -236,11 +230,11 @@ class GGMLToGGUF:
         if self.special_vocab is not None:
             self.special_vocab.add_to_gguf(gguf_writer)
         self.add_tensors(gguf_writer)
-        logger.info("    gguf: write header")
+        print("    gguf: write header")
         gguf_writer.write_header_to_file()
-        logger.info("    gguf: write metadata")
+        print("    gguf: write metadata")
         gguf_writer.write_kv_data_to_file()
-        logger.info("    gguf: write tensors")
+        print("    gguf: write tensors")
         gguf_writer.write_tensors_to_file()
         gguf_writer.close()
 
@@ -256,7 +250,7 @@ class GGMLToGGUF:
             name = cfg.name if cfg.name is not None else cfg.input.name
         except UnicodeDecodeError:
             name = None
-        logger.info('* Adding model parameters and KV items')
+        print('* Adding model parameters and KV items')
         if name is not None:
             gguf_writer.add_name(name)
         gguf_writer.add_description(desc)
@@ -287,14 +281,13 @@ class GGMLToGGUF:
     def add_vocab(self, gguf_writer):
         hp = self.model.hyperparameters
         gguf_writer.add_tokenizer_model('llama')
-        gguf_writer.add_tokenizer_pre('default')
         tokens = []
         scores = []
         toktypes = []
         if self.vocab_override is not None:
             vo = self.vocab_override
-            logger.info('* Adding vocab item(s)')
-            for (_, (vbytes, score, ttype)) in enumerate(vo.all_tokens()):
+            print('* Adding vocab item(s)')
+            for (idx, (vbytes, score, ttype)) in enumerate(vo.all_tokens()):
                 tokens.append(vbytes)
                 scores.append(score)
                 toktypes.append(ttype)
@@ -305,7 +298,7 @@ class GGMLToGGUF:
             if len(toktypes) > 0:
                 gguf_writer.add_token_types(toktypes)
             return
-        logger.info(f'* Adding {hp.n_vocab} vocab item(s)')
+        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
@@ -340,7 +333,7 @@ class GGMLToGGUF:
     def add_tensors(self, gguf_writer):
         tensor_map = self.name_map
         data = self.data
-        logger.info(f'* Adding {len(self.model.tensors)} tensor(s)')
+        print(f'* Adding {len(self.model.tensors)} tensor(s)')
         for tensor in self.model.tensors:
             name = str(tensor.name, 'UTF-8')
             mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
@@ -350,6 +343,7 @@ class GGMLToGGUF:
                 temp = tempdims[1]
                 tempdims[1] = tempdims[0]
                 tempdims[0] = temp
+            # print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}')
             gguf_writer.add_tensor(
                 mapped_name,
                 data[tensor.start_offset:tensor.start_offset + tensor.len_bytes],
@@ -358,8 +352,7 @@ class GGMLToGGUF:
 
 
 def handle_metadata(cfg, hp):
-    import examples.convert_legacy_llama as convert
-
+    import convert
     assert cfg.model_metadata_dir.is_dir(), 'Metadata dir is not a directory'
     hf_config_path   = cfg.model_metadata_dir / "config.json"
     orig_config_path = cfg.model_metadata_dir / "params.json"
@@ -380,7 +373,7 @@ def handle_metadata(cfg, hp):
         raise ValueError('Unable to load metadata')
     vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir)
     vocab_factory = convert.VocabFactory(vocab_path)
-    vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir)
+    vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype, cfg.model_metadata_dir)
     convert.check_vocab_size(params, vocab)
     return params, vocab, special_vocab
 
@@ -405,37 +398,35 @@ def handle_args():
                         help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
     parser.add_argument("--vocab-dir", type=Path,
                         help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
-    parser.add_argument("--vocabtype", default="spm,hfft",
-                        help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)")
-    parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
+    parser.add_argument("--vocabtype", choices=["spm", "bpe"], default="spm",
+                        help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)")
     return parser.parse_args()
 
 
 def main():
     cfg = handle_args()
-    logging.basicConfig(level=logging.DEBUG if cfg.verbose else logging.INFO)
-    logger.info(f'* Using config: {cfg}')
-    logger.warning('=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===')
+    print(f'* Using config: {cfg}')
+    print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n')
     if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'):
-        logger.info('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".')
+        print('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".')
     data = np.memmap(cfg.input, mode = 'r')
     model = GGMLModel()
-    logger.info('* Scanning GGML input file')
+    print('* Scanning GGML input file')
     offset = model.load(data, 0)  # noqa
-    logger.info(f'* GGML model hyperparameters: {model.hyperparameters}')
+    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, special_vocab) = handle_metadata(cfg, model.hyperparameters)
-        logger.info('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.')
-        logger.info(f'* Overriding params: {params_override}')
-        logger.info(f'* Overriding vocab: {vocab_override}')
-        logger.info(f'* Special vocab: {special_vocab}')
+        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:
-        logger.warning('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
+        print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
         if model.file_format == GGMLFormat.GGML:
-            logger.info('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
+            print('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
     converter = GGMLToGGUF(
         model, data, cfg,
         params_override = params_override,
@@ -443,7 +434,7 @@ def main():
         special_vocab = special_vocab
     )
     converter.save()
-    logger.info(f'* Successful completion. Output saved to: {cfg.output}')
+    print(f'* Successful completion. Output saved to: {cfg.output}')
 
 
 if __name__ == '__main__':

convert-lora-to-ggml.py

@@ -0,0 +1,148 @@
+#!/usr/bin/env python3
+from __future__ import annotations
+
+import json
+import os
+import struct
+import sys
+from pathlib import Path
+from typing import Any, BinaryIO, Sequence
+
+import numpy as np
+import torch
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf'))
+import gguf
+
+NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1}
+
+
+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"
+    fout.write(struct.pack("i", int(params["lora_alpha"])))
+
+
+def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None:
+    sname = name.encode("utf-8")
+    fout.write(
+        struct.pack(
+            "iii",
+            len(shape),
+            len(sname),
+            NUMPY_TYPE_TO_FTYPE[data_type.name],
+        )
+    )
+    fout.write(struct.pack("i" * len(shape), *shape[::-1]))
+    fout.write(sname)
+    fout.seek((fout.tell() + 31) & -32)
+
+
+if __name__ == '__main__':
+    if len(sys.argv) < 2:
+        print(f"Usage: python {sys.argv[0]} <path> [arch]")
+        print(
+            "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'"
+        )
+        print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)")
+        sys.exit(1)
+
+    input_json = os.path.join(sys.argv[1], "adapter_config.json")
+    input_model = os.path.join(sys.argv[1], "adapter_model.bin")
+    output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin")
+
+    if os.path.exists(input_model):
+        model = torch.load(input_model, map_location="cpu")
+    else:
+        input_model = os.path.join(sys.argv[1], "adapter_model.safetensors")
+        # lazy import load_file only if lora is in safetensors format.
+        from safetensors.torch import load_file
+        model = load_file(input_model, device="cpu")
+
+    arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama"
+
+    if arch_name not in gguf.MODEL_ARCH_NAMES.values():
+        print(f"Error: unsupported architecture {arch_name}")
+        sys.exit(1)
+
+    arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)]
+    name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone
+
+    with open(input_json, "r") as f:
+        params = json.load(f)
+
+    if params["peft_type"] != "LORA":
+        print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA")
+        sys.exit(1)
+
+    if params["fan_in_fan_out"] is True:
+        print("Error: param fan_in_fan_out is not supported")
+        sys.exit(1)
+
+    if params["bias"] is not None and params["bias"] != "none":
+        print("Error: param bias is not supported")
+        sys.exit(1)
+
+    # TODO: these seem to be layers that have been trained but without lora.
+    # doesn't seem widely used but eventually should be supported
+    if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0:
+        print("Error: param modules_to_save is not supported")
+        sys.exit(1)
+
+    with open(output_path, "wb") as fout:
+        fout.truncate()
+
+        write_file_header(fout, params)
+        for k, v in model.items():
+            orig_k = k
+            if k.endswith(".default.weight"):
+                k = k.replace(".default.weight", ".weight")
+            if k in ["llama_proj.weight", "llama_proj.bias"]:
+                continue
+            if k.endswith("lora_A.weight"):
+                if v.dtype != torch.float16 and v.dtype != torch.float32:
+                    v = v.float()
+                v = v.T
+            else:
+                v = v.float()
+
+            t = v.detach().numpy()
+
+            prefix = "base_model.model."
+            if k.startswith(prefix):
+                k = k[len(prefix) :]
+
+            lora_suffixes = (".lora_A.weight", ".lora_B.weight")
+            if k.endswith(lora_suffixes):
+                suffix = k[-len(lora_suffixes[0]):]
+                k = k[: -len(lora_suffixes[0])]
+            else:
+                print(f"Error: unrecognized tensor name {orig_k}")
+                sys.exit(1)
+
+            tname = name_map.get_name(k)
+            if tname is None:
+                print(f"Error: could not map tensor name {orig_k}")
+                print(" Note: the arch parameter must be specified if the model is not llama")
+                sys.exit(1)
+
+            if suffix == ".lora_A.weight":
+                tname += ".weight.loraA"
+            elif suffix == ".lora_B.weight":
+                tname += ".weight.loraB"
+            else:
+                assert False
+
+            print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
+            write_tensor_header(fout, tname, t.shape, t.dtype)
+            t.tofile(fout)
+
+    print(f"Converted {input_json} and {input_model} to {output_path}")

convert-persimmon-to-gguf.py

@@ -0,0 +1,136 @@
+#!/usr/bin/env python3
+import argparse
+import os
+import sys
+from pathlib import Path
+from pprint import pprint
+
+import torch
+from sentencepiece import SentencePieceProcessor
+
+if 'NO_LOCAL_GGUF' not in os.environ:
+    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
+import gguf
+
+
+def _flatten_dict(dct, tensors, prefix=None):
+    assert isinstance(dct, dict)
+    for key in dct.keys():
+        new_prefix = prefix + '.' + key if prefix is not None else key
+        if isinstance(dct[key], torch.Tensor):
+            tensors[new_prefix] = dct[key]
+        elif isinstance(dct[key], dict):
+            _flatten_dict(dct[key], tensors, new_prefix)
+        else:
+            raise ValueError(type(dct[key]))
+    return None
+
+
+def _get_sentencepiece_tokenizer_info(dir_model: Path):
+    tokenizer_path = dir_model / 'adept_vocab.model'
+    print('gguf: getting sentencepiece tokenizer from', tokenizer_path)
+    tokenizer = SentencePieceProcessor(str(tokenizer_path))
+    print('gguf: adding tokens')
+    tokens: list[bytes] = []
+    scores: list[float] = []
+    toktypes: list[int] = []
+
+    for i in range(tokenizer.vocab_size()):
+        text: bytes
+        score: float
+
+        piece = tokenizer.id_to_piece(i)
+        text = piece.encode("utf-8")
+        score = tokenizer.get_score(i)
+
+        toktype = 1
+        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
+
+        tokens.append(text)
+        scores.append(score)
+        toktypes.append(toktype)
+        pass
+    return tokens, scores, toktypes
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file")
+    parser.add_argument("--outfile",             type=Path, help="path to write to; default: based on input")
+    parser.add_argument("--ckpt-path",           type=Path, help="path to persimmon checkpoint .pt file")
+    parser.add_argument("--model-dir",           type=Path, help="directory containing model e.g. 8b_chat_model_release")
+    parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory")
+    args = parser.parse_args()
+    sys.path.append(str(args.adept_inference_dir))
+    persimmon_model = torch.load(args.ckpt_path)
+    hparams = persimmon_model['args']
+    pprint(hparams)
+    tensors: dict[str, torch.Tensor] = {}
+    _flatten_dict(persimmon_model['model'], tensors, None)
+
+    arch = gguf.MODEL_ARCH.PERSIMMON
+    gguf_writer = gguf.GGUFWriter(args.outfile, gguf.MODEL_ARCH_NAMES[arch])
+
+    block_count = hparams.num_layers
+    head_count = hparams.num_attention_heads
+    head_count_kv = head_count
+    ctx_length = hparams.seq_length
+    hidden_size = hparams.hidden_size
+
+    gguf_writer.add_name('persimmon-8b-chat')
+    gguf_writer.add_context_length(ctx_length)
+    gguf_writer.add_embedding_length(hidden_size)
+    gguf_writer.add_block_count(block_count)
+    gguf_writer.add_feed_forward_length(hparams.ffn_hidden_size)
+    # ref: https://github.com/ggerganov/llama.cpp/pull/4889/commits/eea19039fc52ea2dbd1aab45b59ab4e3e29a3443
+    gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2)
+    gguf_writer.add_head_count(head_count)
+    gguf_writer.add_head_count_kv(head_count_kv)
+    gguf_writer.add_rope_freq_base(hparams.rotary_emb_base)
+    gguf_writer.add_layer_norm_eps(hparams.layernorm_epsilon)
+
+    tokens, scores, toktypes = _get_sentencepiece_tokenizer_info(args.model_dir)
+    gguf_writer.add_tokenizer_model('llama')
+    gguf_writer.add_token_list(tokens)
+    gguf_writer.add_token_scores(scores)
+    gguf_writer.add_token_types(toktypes)
+    gguf_writer.add_bos_token_id(71013)
+    gguf_writer.add_eos_token_id(71013)
+
+    tensor_map = gguf.get_tensor_name_map(arch, block_count)
+    print(tensor_map)
+    for name in tensors.keys():
+        data = tensors[name]
+        if name.endswith(".self_attention.rotary_emb.inv_freq"):
+            continue
+        old_dtype = data.dtype
+        # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?)
+        data = data.to(torch.float32).squeeze().numpy()
+        new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
+        if new_name is None:
+            print("Can not map tensor '" + name + "'")
+            sys.exit()
+        n_dims = len(data.shape)
+        print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
+        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()
+
+    gguf_writer.close()
+
+    print(f"gguf: model successfully exported to '{args.outfile}'")
+    print("")
+
+
+if __name__ == '__main__':
+    main()

convert_hf_to_gguf_update.py

@@ -1,351 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-# This script downloads the tokenizer models of the specified models from Huggingface and
-# generates the get_vocab_base_pre() function for convert_hf_to_gguf.py
-#
-# This is necessary in order to analyze the type of pre-tokenizer used by the model and
-# provide the necessary information to llama.cpp via the GGUF header in order to implement
-# the same pre-tokenizer.
-#
-# ref: https://github.com/ggerganov/llama.cpp/pull/6920
-#
-# Instructions:
-#
-# - Add a new model to the "models" list
-# - Run the script with your huggingface token:
-#
-#   python3 convert_hf_to_gguf_update.py <huggingface_token>
-#
-# - Copy-paste the generated get_vocab_base_pre() function into convert_hf_to_gguf.py
-# - Update llama.cpp with the new pre-tokenizer if necessary
-#
-# TODO: generate tokenizer tests for llama.cpp
-#
-
-import logging
-import os
-import pathlib
-import re
-
-import requests
-import sys
-import json
-
-from hashlib import sha256
-from enum import IntEnum, auto
-from transformers import AutoTokenizer
-
-logging.basicConfig(level=logging.DEBUG)
-logger = logging.getLogger("convert_hf_to_gguf_update")
-sess = requests.Session()
-
-
-class TOKENIZER_TYPE(IntEnum):
-    SPM = auto()
-    BPE = auto()
-    WPM = auto()
-    UGM = auto()
-
-
-# TODO: this string has to exercise as much pre-tokenizer functionality as possible
-#       will be updated with time - contributions welcome
-CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n  \n   \n    \n     \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
-
-if len(sys.argv) == 2:
-    token = sys.argv[1]
-    if not token.startswith("hf_"):
-        logger.info("Huggingface token seems invalid")
-        logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>")
-        sys.exit(1)
-else:
-    logger.info("Usage: python convert_hf_to_gguf_update.py <huggingface_token>")
-    sys.exit(1)
-
-# TODO: add models here, base models preferred
-models = [
-    {"name": "llama-spm",      "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
-    {"name": "llama-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
-    {"name": "phi-3",          "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
-    {"name": "deepseek-llm",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
-    {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
-    {"name": "falcon",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
-    {"name": "bert-bge",       "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
-    {"name": "mpt",            "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
-    {"name": "starcoder",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
-    {"name": "gpt-2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
-    {"name": "stablelm2",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", },
-    {"name": "refact",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
-    {"name": "command-r",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
-    {"name": "qwen2",          "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
-    {"name": "olmo",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
-    {"name": "dbrx",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
-    {"name": "jina-v2-en",     "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
-    {"name": "jina-v2-es",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
-    {"name": "jina-v2-de",     "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
-    {"name": "smaug-bpe",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
-    {"name": "poro-chat",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
-    {"name": "jina-v2-code",   "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
-    {"name": "viking",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
-    {"name": "gemma",          "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", },
-    {"name": "gemma-2",        "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
-    {"name": "jais",           "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
-    {"name": "t5",             "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
-    {"name": "codeshell",      "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", },
-    {"name": "tekken",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", },
-    {"name": "smollm",         "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
-]
-
-
-def download_file_with_auth(url, token, save_path):
-    headers = {"Authorization": f"Bearer {token}"}
-    response = sess.get(url, headers=headers)
-    response.raise_for_status()
-    os.makedirs(os.path.dirname(save_path), exist_ok=True)
-    with open(save_path, 'wb') as downloaded_file:
-        downloaded_file.write(response.content)
-    logger.info(f"File {save_path} downloaded successfully")
-
-
-def download_model(model):
-    name = model["name"]
-    repo = model["repo"]
-    tokt = model["tokt"]
-
-    os.makedirs(f"models/tokenizers/{name}", exist_ok=True)
-
-    files = ["config.json", "tokenizer.json", "tokenizer_config.json"]
-
-    if tokt == TOKENIZER_TYPE.SPM:
-        files.append("tokenizer.model")
-
-    if tokt == TOKENIZER_TYPE.UGM:
-        files.append("spiece.model")
-
-    for file in files:
-        save_path = f"models/tokenizers/{name}/{file}"
-        if os.path.isfile(save_path):
-            logger.info(f"{name}: File {save_path} already exists - skipping")
-            continue
-        download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path)
-
-
-for model in models:
-    try:
-        download_model(model)
-    except Exception as e:
-        logger.error(f"Failed to download model {model['name']}. Error: {e}")
-
-
-# generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function:
-
-src_ifs = ""
-for model in models:
-    name = model["name"]
-    tokt = model["tokt"]
-
-    if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM:
-        continue
-
-    # Skip if the tokenizer folder does not exist or there are other download issues previously
-    if not os.path.exists(f"models/tokenizers/{name}"):
-        logger.warning(f"Directory for tokenizer {name} not found. Skipping...")
-        continue
-
-    # create the tokenizer
-    try:
-        if name == "t5":
-            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
-        else:
-            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
-    except OSError as e:
-        logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}")
-        continue  # Skip to the next model if the tokenizer can't be loaded
-
-    chktok = tokenizer.encode(CHK_TXT)
-    chkhsh = sha256(str(chktok).encode()).hexdigest()
-
-    logger.info(f"model: {name}")
-    logger.info(f"tokt: {tokt}")
-    logger.info(f"repo: {model['repo']}")
-    logger.info(f"chktok: {chktok}")
-    logger.info(f"chkhsh: {chkhsh}")
-
-    # print the "pre_tokenizer" content from the tokenizer.json
-    with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f:
-        cfg = json.load(f)
-        normalizer = cfg["normalizer"]
-        logger.info("normalizer: " + json.dumps(normalizer, indent=4))
-        pre_tokenizer = cfg["pre_tokenizer"]
-        logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
-        if "ignore_merges" in cfg["model"]:
-            logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4))
-
-    logger.info("")
-
-    src_ifs += f"        if chkhsh == \"{chkhsh}\":\n"
-    src_ifs += f"            # ref: {model['repo']}\n"
-    src_ifs += f"            res = \"{name}\"\n"
-
-src_func = f"""
-    def get_vocab_base_pre(self, tokenizer) -> str:
-        # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
-        # is specific for the BPE pre-tokenizer used by the model
-        # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
-        # use in llama.cpp to implement the same pre-tokenizer
-
-        chktxt = {repr(CHK_TXT)}
-
-        chktok = tokenizer.encode(chktxt)
-        chkhsh = sha256(str(chktok).encode()).hexdigest()
-
-        logger.debug(f"chktok: {{chktok}}")
-        logger.debug(f"chkhsh: {{chkhsh}}")
-
-        res = None
-
-        # NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script
-        #       or pull the latest version of the model from Huggingface
-        #       don't edit the hashes manually!
-{src_ifs}
-        if res is None:
-            logger.warning("\\n")
-            logger.warning("**************************************************************************************")
-            logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
-            logger.warning("**          There are 2 possible reasons for this:")
-            logger.warning("**          - the model has not been added to convert_hf_to_gguf_update.py yet")
-            logger.warning("**          - the pre-tokenization config has changed upstream")
-            logger.warning("**          Check your model files and convert_hf_to_gguf_update.py and update them accordingly.")
-            logger.warning("** ref:     https://github.com/ggerganov/llama.cpp/pull/6920")
-            logger.warning("**")
-            logger.warning(f"** chkhsh:  {{chkhsh}}")
-            logger.warning("**************************************************************************************")
-            logger.warning("\\n")
-            raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
-
-        logger.debug(f"tokenizer.ggml.pre: {{repr(res)}}")
-        logger.debug(f"chkhsh: {{chkhsh}}")
-
-        return res
-"""
-
-convert_py_pth = pathlib.Path("convert_hf_to_gguf.py")
-convert_py = convert_py_pth.read_text(encoding="utf-8")
-convert_py = re.sub(
-    r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)",
-    lambda m: m.group(1) + src_func + m.group(3),
-    convert_py,
-    flags=re.DOTALL | re.MULTILINE,
-)
-
-convert_py_pth.write_text(convert_py, encoding="utf-8")
-
-logger.info("+++ convert_hf_to_gguf.py was updated")
-
-# generate tests for each tokenizer model
-
-tests = [
-    "ied 4 ½ months",
-    "Führer",
-    "",
-    " ",
-    "  ",
-    "   ",
-    "\t",
-    "\n",
-    "\n\n",
-    "\n\n\n",
-    "\t\n",
-    "Hello world",
-    " Hello world",
-    "Hello World",
-    " Hello World",
-    " Hello World!",
-    "Hello, world!",
-    " Hello, world!",
-    " this is 🦙.cpp",
-    "w048 7tuijk dsdfhu",
-    "нещо на Български",
-    "កាន់តែពិសេសអាចខលចេញ",
-    "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
-    "Hello",
-    " Hello",
-    "  Hello",
-    "   Hello",
-    "    Hello",
-    "    Hello\n    Hello",
-    " (",
-    "\n =",
-    "' era",
-    "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
-    "!!!!!!",
-    "3",
-    "33",
-    "333",
-    "3333",
-    "33333",
-    "333333",
-    "3333333",
-    "33333333",
-    "333333333",
-    "Cửa Việt", # llama-bpe fails on this
-    " discards",
-    CHK_TXT,
-]
-
-# write the tests to ./models/ggml-vocab-{name}.gguf.inp
-# the format is:
-#
-# test0
-# __ggml_vocab_test__
-# test1
-# __ggml_vocab_test__
-# ...
-#
-
-# with each model, encode all tests and write the results in ./models/ggml-vocab-{name}.gguf.out
-# for each test, write the resulting tokens on a separate line
-
-for model in models:
-    name = model["name"]
-    tokt = model["tokt"]
-
-    # Skip if the tokenizer folder does not exist or there are other download issues previously
-    if not os.path.exists(f"models/tokenizers/{name}"):
-        logger.warning(f"Directory for tokenizer {name} not found. Skipping...")
-        continue
-
-    # create the tokenizer
-    try:
-        if name == "t5":
-            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
-        else:
-            tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
-    except OSError as e:
-        logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
-        continue  # Skip this model and continue with the next one in the loop
-
-    with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f:
-        for text in tests:
-            f.write(f"{text}")
-            f.write("\n__ggml_vocab_test__\n")
-
-    with open(f"models/ggml-vocab-{name}.gguf.out", "w") as f:
-        for text in tests:
-            res = tokenizer.encode(text, add_special_tokens=False)
-            for r in res:
-                f.write(f" {r}")
-            f.write("\n")
-
-    logger.info(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*")
-
-# generate commands for creating vocab files
-
-logger.info("\nRun the following commands to generate the vocab files for testing:\n")
-
-for model in models:
-    name = model["name"]
-
-    print(f"python3 convert_hf_to_gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100
-
-logger.info("\n")

convert_lora_to_gguf.py

@@ -1,393 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-
-from __future__ import annotations
-
-from dataclasses import dataclass
-import logging
-import argparse
-import os
-import sys
-import json
-from math import prod
-from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, Iterable, Iterator, Sequence, SupportsIndex, cast
-
-import torch
-
-if TYPE_CHECKING:
-    from torch import Tensor
-
-if 'NO_LOCAL_GGUF' not in os.environ:
-    sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
-import gguf
-
-# reuse model definitions from convert_hf_to_gguf.py
-from convert_hf_to_gguf import LazyTorchTensor, Model
-
-logger = logging.getLogger("lora-to-gguf")
-
-
-@dataclass
-class PartialLoraTensor:
-    A: Tensor | None = None
-    B: Tensor | None = None
-
-
-# magic to support tensor shape modifications and splitting
-class LoraTorchTensor:
-    _lora_A: Tensor  # (n_rank, row_size)
-    _lora_B: Tensor  # (col_size, n_rank)
-    _rank: int
-
-    def __init__(self, A: Tensor, B: Tensor):
-        assert len(A.shape) == len(B.shape)
-        assert A.shape[-2] == B.shape[-1]
-        if A.dtype != B.dtype:
-            A = A.to(torch.float32)
-            B = B.to(torch.float32)
-        self._lora_A = A
-        self._lora_B = B
-        self._rank = B.shape[-1]
-
-    def get_lora_A_B(self) -> tuple[Tensor, Tensor]:
-        return (self._lora_A, self._lora_B)
-
-    def __getitem__(
-        self,
-        indices: (
-            SupportsIndex
-            | slice
-            | tuple[SupportsIndex | slice | Tensor, ...]  # TODO: add ellipsis in the type signature
-        ),
-    ) -> LoraTorchTensor:
-        shape = self.shape
-        if isinstance(indices, SupportsIndex):
-            if len(shape) > 2:
-                return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices])
-            else:
-                raise NotImplementedError  # can't return a vector
-        elif isinstance(indices, slice):
-            if len(shape) > 2:
-                return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices])
-            else:
-                return LoraTorchTensor(self._lora_A, self._lora_B[indices])
-        elif isinstance(indices, tuple):
-            assert len(indices) > 0
-            if indices[-1] is Ellipsis:
-                return self[indices[:-1]]
-            # expand ellipsis
-            indices = tuple(
-                u
-                for v in (
-                    (
-                        (slice(None, None) for _ in range(len(indices) - 1))
-                        if i is Ellipsis
-                        else (i,)
-                    )
-                    for i in indices
-                )
-                for u in v
-            )
-
-            if len(indices) < len(shape):
-                indices = (*indices, *(slice(None, None) for _ in range(len(indices), len(shape))))
-
-            # TODO: make sure this is correct
-            indices_A = (
-                *(
-                    (
-                        j.__index__() % self._lora_A.shape[i]
-                        if isinstance(j, SupportsIndex)
-                        else slice(None, None)
-                    )
-                    for i, j in enumerate(indices[:-2])
-                ),
-                slice(None, None),
-                indices[-1],
-            )
-            indices_B = indices[:-1]
-            return LoraTorchTensor(self._lora_A[indices_A], self._lora_B[indices_B])
-        else:
-            raise NotImplementedError  # unknown indice type
-
-    @property
-    def dtype(self) -> torch.dtype:
-        assert self._lora_A.dtype == self._lora_B.dtype
-        return self._lora_A.dtype
-
-    @property
-    def shape(self) -> tuple[int, ...]:
-        assert len(self._lora_A.shape) == len(self._lora_B.shape)
-        return (*self._lora_B.shape[:-1], self._lora_A.shape[-1])
-
-    def size(self, dim=None):
-        assert dim is None
-        return self.shape
-
-    def reshape(self, *shape: int | tuple[int, ...]) -> LoraTorchTensor:
-        if isinstance(shape[0], tuple):
-            new_shape: tuple[int, ...] = shape[0]
-        else:
-            new_shape = cast(tuple[int, ...], shape)
-        orig_shape = self.shape
-        if len(new_shape) < 2:
-            raise NotImplementedError  # can't become a vector
-
-        # expand -1 in the shape
-        if any(dim == -1 for dim in new_shape):
-            n_elems = prod(orig_shape)
-            n_new_elems = prod(dim if dim != -1 else 1 for dim in new_shape)
-            assert n_elems % n_new_elems == 0
-            new_shape = (*(dim if dim != -1 else n_elems // n_new_elems for dim in new_shape),)
-
-        if new_shape[-1] != orig_shape[-1]:
-            raise NotImplementedError  # can't reshape the row size trivially
-
-        shape_A = (*(1 for _ in new_shape[:-2]), self._rank, orig_shape[-1])
-        shape_B = (*new_shape[:-1], self._rank)
-        return LoraTorchTensor(
-            self._lora_A.reshape(shape_A),
-            self._lora_B.reshape(shape_B),
-        )
-
-    def reshape_as(self, other: Tensor) -> LoraTorchTensor:
-        return self.reshape(*other.shape)
-
-    def view(self, *size: int) -> LoraTorchTensor:
-        return self.reshape(*size)
-
-    def permute(self, *dims: int) -> LoraTorchTensor:
-        shape = self.shape
-        dims = tuple(dim - len(shape) if dim >= 0 else dim for dim in dims)
-        if dims[-1] == -1:
-            # TODO: support higher dimensional A shapes bigger than 1
-            assert all(dim == 1 for dim in self._lora_A.shape[:-2])
-            return LoraTorchTensor(self._lora_A, self._lora_B.permute(*dims))
-        if len(shape) == 2 and dims[-1] == -2 and dims[-2] == -1:
-            return LoraTorchTensor(self._lora_B.permute(*dims), self._lora_A.permute(*dims))
-        else:
-            # TODO: compose the above two
-            raise NotImplementedError
-
-    def transpose(self, dim0: int, dim1: int) -> LoraTorchTensor:
-        shape = self.shape
-        dims = [i for i in range(len(shape))]
-        dims[dim0], dims[dim1] = dims[dim1], dims[dim0]
-        return self.permute(*dims)
-
-    def swapaxes(self, axis0: int, axis1: int) -> LoraTorchTensor:
-        return self.transpose(axis0, axis1)
-
-    def to(self, *args, **kwargs):
-        return LoraTorchTensor(self._lora_A.to(*args, **kwargs), self._lora_B.to(*args, **kwargs))
-
-    @classmethod
-    def __torch_function__(cls, func: Callable, types, args=(), kwargs=None):
-        del types  # unused
-
-        if kwargs is None:
-            kwargs = {}
-
-        if func is torch.permute:
-            return type(args[0]).permute(*args, **kwargs)
-        elif func is torch.reshape:
-            return type(args[0]).reshape(*args, **kwargs)
-        elif func is torch.stack:
-            assert isinstance(args[0], Sequence)
-            dim = kwargs.get("dim", 0)
-            assert dim == 0
-            return LoraTorchTensor(
-                torch.stack([a._lora_A for a in args[0]], dim),
-                torch.stack([b._lora_B for b in args[0]], dim),
-            )
-        elif func is torch.cat:
-            assert isinstance(args[0], Sequence)
-            dim = kwargs.get("dim", 0)
-            assert dim == 0
-            if len(args[0][0].shape) > 2:
-                return LoraTorchTensor(
-                    torch.cat([a._lora_A for a in args[0]], dim),
-                    torch.cat([b._lora_B for b in args[0]], dim),
-                )
-            elif all(torch.equal(args[0][0]._lora_A, t._lora_A) for t in args[0][1:]):
-                return LoraTorchTensor(
-                    args[0][0]._lora_A,
-                    torch.cat([b._lora_B for b in args[0]], dim),
-                )
-            else:
-                raise NotImplementedError
-        else:
-            raise NotImplementedError
-
-
-def get_base_tensor_name(lora_tensor_name: str) -> str:
-    base_name = lora_tensor_name.replace("base_model.model.", "")
-    base_name = base_name.replace(".lora_A.weight", ".weight")
-    base_name = base_name.replace(".lora_B.weight", ".weight")
-    return base_name
-
-
-def parse_args() -> argparse.Namespace:
-    parser = argparse.ArgumentParser(
-        description="Convert a huggingface PEFT LoRA adapter to a GGML compatible file")
-    parser.add_argument(
-        "--outfile", type=Path,
-        help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
-    )
-    parser.add_argument(
-        "--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f16",
-        help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
-    )
-    parser.add_argument(
-        "--bigendian", action="store_true",
-        help="model is executed on big endian machine",
-    )
-    parser.add_argument(
-        "--no-lazy", action="store_true",
-        help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)",
-    )
-    parser.add_argument(
-        "--verbose", action="store_true",
-        help="increase output verbosity",
-    )
-    parser.add_argument(
-        "--dry-run", action="store_true",
-        help="only print out what will be done, without writing any new files",
-    )
-    parser.add_argument(
-        "--base", type=Path, required=True,
-        help="directory containing base model file",
-    )
-    parser.add_argument(
-        "lora_path", type=Path,
-        help="directory containing LoRA adapter file",
-    )
-
-    return parser.parse_args()
-
-
-if __name__ == '__main__':
-    args = parse_args()
-    logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
-
-    ftype_map: dict[str, gguf.LlamaFileType] = {
-        "f32": gguf.LlamaFileType.ALL_F32,
-        "f16": gguf.LlamaFileType.MOSTLY_F16,
-        "bf16": gguf.LlamaFileType.MOSTLY_BF16,
-        "q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
-        "auto": gguf.LlamaFileType.GUESSED,
-    }
-
-    ftype = ftype_map[args.outtype]
-
-    dir_base_model: Path = args.base
-    dir_lora: Path = args.lora_path
-    lora_config = dir_lora / "adapter_config.json"
-    input_model = dir_lora / "adapter_model.safetensors"
-
-    if args.outfile is not None:
-        fname_out = args.outfile
-    else:
-        # output in the same directory as the model by default
-        fname_out = dir_lora
-
-    if os.path.exists(input_model):
-        # lazy import load_file only if lora is in safetensors format.
-        from safetensors.torch import load_file
-
-        lora_model = load_file(input_model, device="cpu")
-    else:
-        input_model = os.path.join(dir_lora, "adapter_model.bin")
-        lora_model = torch.load(input_model, map_location="cpu", weights_only=True)
-
-    # load base model
-    logger.info(f"Loading base model: {dir_base_model.name}")
-    hparams = Model.load_hparams(dir_base_model)
-    with torch.inference_mode():
-        try:
-            model_class = Model.from_model_architecture(hparams["architectures"][0])
-        except NotImplementedError:
-            logger.error(f"Model {hparams['architectures'][0]} is not supported")
-            sys.exit(1)
-
-        class LoraModel(model_class):
-            model_arch = model_class.model_arch
-
-            lora_alpha: float
-
-            def __init__(self, *args, dir_lora_model: Path, lora_alpha: float, **kwargs):
-
-                super().__init__(*args, **kwargs)
-
-                self.dir_model_card = dir_lora_model
-                self.lora_alpha = float(lora_alpha)
-
-            def set_type(self):
-                self.gguf_writer.add_type(gguf.GGUFType.ADAPTER)
-                self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
-
-            def set_gguf_parameters(self):
-                self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
-                super().set_gguf_parameters()
-
-            def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
-                tensor_map: dict[str, PartialLoraTensor] = {}
-
-                for name, tensor in lora_model.items():
-                    if self.lazy:
-                        tensor = LazyTorchTensor.from_eager(tensor)
-                    base_name = get_base_tensor_name(name)
-                    is_lora_a = ".lora_A.weight" in name
-                    is_lora_b = ".lora_B.weight" in name
-                    if not is_lora_a and not is_lora_b:
-                        if ".base_layer.weight" in name:
-                            continue
-                        logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
-                        sys.exit(1)
-
-                    if base_name in tensor_map:
-                        if is_lora_a:
-                            tensor_map[base_name].A = tensor
-                        else:
-                            tensor_map[base_name].B = tensor
-                    else:
-                        if is_lora_a:
-                            tensor_map[base_name] = PartialLoraTensor(A=tensor)
-                        else:
-                            tensor_map[base_name] = PartialLoraTensor(B=tensor)
-
-                for name, tensor in tensor_map.items():
-                    assert tensor.A is not None
-                    assert tensor.B is not None
-                    yield (name, cast(torch.Tensor, LoraTorchTensor(tensor.A, tensor.B)))
-
-            def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
-                dest = super().modify_tensors(data_torch, name, bid)
-                for dest_name, dest_data in dest:
-                    assert isinstance(dest_data, LoraTorchTensor)
-                    lora_a, lora_b = dest_data.get_lora_A_B()
-
-                    yield (dest_name + ".lora_a", lora_a)
-                    yield (dest_name + ".lora_b", lora_b)
-
-        with open(lora_config, "r") as f:
-            lparams: dict[str, Any] = json.load(f)
-
-        alpha: float = lparams["lora_alpha"]
-
-        model_instance = LoraModel(
-            dir_base_model,
-            ftype,
-            fname_out,
-            is_big_endian=args.bigendian,
-            use_temp_file=False,
-            eager=args.no_lazy,
-            dry_run=args.dry_run,
-            dir_lora_model=dir_lora,
-            lora_alpha=alpha,
-        )
-
-        logger.info("Exporting model...")
-        model_instance.write()
-        logger.info(f"Model successfully exported to {model_instance.fname_out}")

docs/BLIS.md

@@ -23,15 +23,15 @@ Install BLIS:
 sudo make install
 ```
 
-We recommend using openmp since it's easier to modify the cores being used.
+We recommend using openmp since it's easier to modify the cores been used.
 
 ### llama.cpp compilation
 
 Makefile:
 
 ```bash
-make GGML_BLIS=1 -j
-# make GGML_BLIS=1 llama-benchmark-matmult
+make LLAMA_BLIS=1 -j
+# make LLAMA_BLIS=1 benchmark-matmult
 ```
 
 CMake:
@@ -39,7 +39,7 @@ CMake:
 ```bash
 mkdir build
 cd build
-cmake -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=FLAME ..
+cmake -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=FLAME ..
 make -j
 ```