llama : change starcoder2 rope type

.devops/nix/package.nix

@@ -1,6 +1,5 @@
 {
   lib,
-  glibc,
   config,
   stdenv,
   mkShell,
@@ -31,11 +30,6 @@
   useRocm ? config.rocmSupport,
   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
 }@inputs:
 
 let
@@ -47,7 +41,10 @@ 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" ]
@@ -170,9 +167,6 @@ 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
       ];
 
     buildInputs =
@@ -187,7 +181,7 @@ effectiveStdenv.mkDerivation (
       [
         (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_BLAS" useBlas)
         (cmakeBool "LLAMA_CLBLAST" useOpenCL)
@@ -196,7 +190,6 @@ effectiveStdenv.mkDerivation (
         (cmakeBool "LLAMA_METAL" useMetalKit)
         (cmakeBool "LLAMA_MPI" useMpi)
         (cmakeBool "LLAMA_VULKAN" useVulkan)
-        (cmakeBool "LLAMA_STATIC" enableStatic)
       ]
       ++ optionals useCuda [
         (

.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;

.github/workflows/build.yml

@@ -145,28 +145,6 @@ jobs:
           cd build
           ctest -L main --verbose
 
-  ubuntu-22-cmake-vulkan:
-    runs-on: ubuntu-22.04
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v3
-
-      - name: Dependencies
-        id: depends
-        run: |
-          sudo apt-get update
-          sudo apt-get install build-essential libvulkan-dev
-
-      - name: Build
-        id: cmake_build
-        run: |
-          mkdir build
-          cd build
-          cmake -DLLAMA_VULKAN=ON ..
-          cmake --build . --config Release -j $(nproc)
-
   ubuntu-22-cmake-sycl:
     runs-on: ubuntu-22.04
 
@@ -333,7 +311,6 @@ jobs:
           mkdir build
           cd build
           cmake -G Xcode .. \
-            -DLLAMA_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
             -DLLAMA_BUILD_SERVER=OFF \
@@ -362,7 +339,6 @@ jobs:
           mkdir build
           cd build
           cmake -G Xcode .. \
-            -DLLAMA_METAL_EMBED_LIBRARY=ON \
             -DLLAMA_BUILD_EXAMPLES=OFF \
             -DLLAMA_BUILD_TESTS=OFF \
             -DLLAMA_BUILD_SERVER=OFF \
@@ -427,8 +403,6 @@ jobs:
             defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON'
           - build: 'vulkan'
             defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON'
-          - build: 'arm64'
-            defines: '-A ARM64 -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON'
 
     steps:
       - name: Clone
@@ -524,7 +498,7 @@ jobs:
       - name: Test
         id: cmake_test
         # not all machines have native AVX-512
-        if: ${{ matrix.build != 'arm64' && matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }}
+        if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }}
         run: |
           cd build
           ctest -L main -C Release --verbose --timeout 900

.github/workflows/close-issue.yml

@@ -1,22 +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:
-          days-before-issue-stale: 30
-          days-before-issue-close: 14
-          stale-issue-label: "stale"
-          stale-issue-message: "This issue is stale because it has been open for 30 days with no activity."
-          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
-          repo-token: ${{ secrets.GITHUB_TOKEN }}

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

@@ -3,14 +3,12 @@ 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'
@@ -28,4 +26,4 @@ jobs:
         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/server.yml

@@ -3,11 +3,6 @@ name: Server
 
 on:
   workflow_dispatch: # allows manual triggering
-    inputs:
-      slow_tests:
-        description: 'Run slow tests'
-        required: true
-        type: boolean
   push:
     branches:
       - master
@@ -15,8 +10,6 @@ on:
   pull_request:
     types: [opened, synchronize, reopened]
     paths: ['.github/workflows/server.yml', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu', '**/*.swift', '**/*.m', 'examples/server/tests/**.*']
-  schedule:
-    -  cron: '0 0 * * *'
 
 jobs:
   server:
@@ -25,14 +18,17 @@ jobs:
     strategy:
       matrix:
         sanitizer: [ADDRESS, THREAD, UNDEFINED]
-        build_type: [Debug]
+        build_type: [Debug, Release]
         include:
           - build_type: Release
             sanitizer: ""
-          - build_type: Debug
+        exclude:
+          - build_type: Release
+            sanitizer: ADDRESS
+          - build_type: Release
             sanitizer: THREAD
-            disabled_on_pr: true
-      fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken
+          - build_type: Release
+            sanitizer: UNDEFINED
 
     container:
       image: ubuntu:latest
@@ -44,8 +40,6 @@ jobs:
       - name: Clone
         id: checkout
         uses: actions/checkout@v3
-        with:
-          fetch-depth: 0
 
       - name: Dependencies
         id: depends
@@ -57,7 +51,7 @@ jobs:
             cmake \
             python3-pip \
             wget \
-            language-pack-en
+            psmisc
 
       - name: Build
         id: cmake_build
@@ -76,60 +70,14 @@ jobs:
         run: |
           pip install -r examples/server/tests/requirements.txt
 
+      - name: Download models
+        id: download_models
+        run: |
+          cd examples/server/tests
+          ../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf
+
       - name: Tests
-        id: server_integration_tests
-        if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }}
+        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-latest
-
-    steps:
-      - name: Clone
-        id: checkout
-        uses: actions/checkout@v3
-        with:
-          fetch-depth: 0
-
-      - name: Build
-        id: cmake_build
-        run: |
-          mkdir build
-          cd build
-          cmake ..  -DLLAMA_BUILD_SERVER=ON -DCMAKE_BUILD_TYPE=Release ;
-          cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} --target 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: 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

.gitignore

@@ -25,8 +25,6 @@
 .vscode/
 .idea/
 
-ggml-metal-embed.metal
-
 lcov-report/
 gcovr-report/
 
@@ -47,7 +45,6 @@ models-mnt
 /embedding
 /gguf
 /gguf-llama-simple
-/gritlm
 /imatrix
 /infill
 /libllama.so

CMakeLists.txt

@@ -116,9 +116,7 @@ option(LLAMA_MPI                             "llama: use MPI"
 option(LLAMA_QKK_64                          "llama: use super-block size of 64 for k-quants"   OFF)
 option(LLAMA_SYCL                            "llama: use SYCL"                                  OFF)
 option(LLAMA_SYCL_F16                        "llama: use 16 bit floats for sycl calculations"   OFF)
-set(LLAMA_SYCL_TARGET   "INTEL" CACHE STRING "llama: sycl target device")
 option(LLAMA_CPU_HBM                         "llama: use memkind for CPU HBM"                   OFF)
-set(LLAMA_SCHED_MAX_COPIES  "4" CACHE STRING "llama: max input copies for pipeline parallelism")
 
 option(LLAMA_BUILD_TESTS                     "llama: build tests"    ${LLAMA_STANDALONE})
 option(LLAMA_BUILD_EXAMPLES                  "llama: build examples" ${LLAMA_STANDALONE})
@@ -148,8 +146,6 @@ set(THREADS_PREFER_PTHREAD_FLAG ON)
 find_package(Threads REQUIRED)
 include(CheckCXXCompilerFlag)
 
-add_compile_definitions(GGML_SCHED_MAX_COPIES=${LLAMA_SCHED_MAX_COPIES})
-
 # enable libstdc++ assertions for debug builds
 if (CMAKE_SYSTEM_NAME MATCHES "Linux")
     add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
@@ -200,70 +196,63 @@ if (LLAMA_METAL)
         add_compile_definitions(GGML_METAL_NDEBUG)
     endif()
 
-    # copy ggml-common.h and ggml-metal.metal to bin directory
-    configure_file(ggml-common.h    ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h    COPYONLY)
+    # get full path to the file
+    #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
+
+    # copy ggml-metal.metal to bin directory
     configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
 
     if (LLAMA_METAL_EMBED_LIBRARY)
         enable_language(ASM)
         add_compile_definitions(GGML_METAL_EMBED_LIBRARY)
 
-        set(METALLIB_COMMON "${CMAKE_CURRENT_SOURCE_DIR}/ggml-common.h")
-        set(METALLIB_SOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
-
+        set(METALLIB_SOURCE "${CMAKE_SOURCE_DIR}/ggml-metal.metal")
         file(MAKE_DIRECTORY "${CMAKE_BINARY_DIR}/autogenerated")
-
-        # merge ggml-common.h and ggml-metal.metal into a single file
-        set(METALLIB_EMBED_ASM    "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.s")
-        set(METALLIB_SOURCE_EMBED "${CMAKE_BINARY_DIR}/autogenerated/ggml-metal-embed.metal")
+        set(EMBED_METALLIB_ASSEMBLY "${CMAKE_BINARY_DIR}/autogenerated/ggml-embed-metallib.s")
 
         add_custom_command(
-            OUTPUT ${METALLIB_EMBED_ASM}
-            COMMAND echo "Embedding Metal library"
-            COMMAND sed -e '/\#include \"ggml-common.h\"/r ${METALLIB_COMMON}' -e '/\#include \"ggml-common.h\"/d' < ${METALLIB_SOURCE} > ${METALLIB_SOURCE_EMBED}
-            COMMAND echo ".section __DATA,__ggml_metallib"          >  ${METALLIB_EMBED_ASM}
-            COMMAND echo ".globl _ggml_metallib_start"              >> ${METALLIB_EMBED_ASM}
-            COMMAND echo "_ggml_metallib_start:"                    >> ${METALLIB_EMBED_ASM}
-            COMMAND echo ".incbin \\\"${METALLIB_SOURCE_EMBED}\\\"" >> ${METALLIB_EMBED_ASM}
-            COMMAND echo ".globl _ggml_metallib_end"                >> ${METALLIB_EMBED_ASM}
-            COMMAND echo "_ggml_metallib_end:"                      >> ${METALLIB_EMBED_ASM}
-            DEPENDS ggml-metal.metal ggml-common.h
+            OUTPUT ${EMBED_METALLIB_ASSEMBLY}
+            COMMAND echo ".section __DATA,__ggml_metallib" > ${EMBED_METALLIB_ASSEMBLY}
+            COMMAND echo ".globl _ggml_metallib_start" >> ${EMBED_METALLIB_ASSEMBLY}
+            COMMAND echo "_ggml_metallib_start:" >> ${EMBED_METALLIB_ASSEMBLY}
+            COMMAND echo ".incbin \\\"${METALLIB_SOURCE}\\\"" >> ${EMBED_METALLIB_ASSEMBLY}
+            COMMAND echo ".globl _ggml_metallib_end" >> ${EMBED_METALLIB_ASSEMBLY}
+            COMMAND echo "_ggml_metallib_end:" >> ${EMBED_METALLIB_ASSEMBLY}
+            DEPENDS ${METALLIB_SOURCE}
             COMMENT "Generate assembly for embedded Metal library"
         )
 
-        set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${METALLIB_EMBED_ASM})
-    else()
-        if (LLAMA_METAL_SHADER_DEBUG)
-            # custom command to do the following:
-            #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
-            #   xcrun -sdk macosx metallib                   ggml-metal.air   -o default.metallib
-            #
-            # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
-            #       disabling fast math is needed in order to pass tests/test-backend-ops
-            # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
-            # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
-            #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
-            set(XC_FLAGS -fno-fast-math -fno-inline -g)
-        else()
-            set(XC_FLAGS -O3)
+        set(GGML_SOURCES_METAL ${GGML_SOURCES_METAL} ${EMBED_METALLIB_ASSEMBLY})
+    endif()
+
+    if (LLAMA_METAL_SHADER_DEBUG)
+        # custom command to do the following:
+        #   xcrun -sdk macosx metal    -fno-fast-math -c ggml-metal.metal -o ggml-metal.air
+        #   xcrun -sdk macosx metallib                   ggml-metal.air   -o default.metallib
+        #
+        # note: this is the only way I found to disable fast-math in Metal. it's ugly, but at least it works
+        #       disabling fast math is needed in order to pass tests/test-backend-ops
+        # note: adding -fno-inline fixes the tests when using MTL_SHADER_VALIDATION=1
+        # note: unfortunately, we have to call it default.metallib instead of ggml.metallib
+        #       ref: https://github.com/ggerganov/whisper.cpp/issues/1720
+        set(XC_FLAGS -fno-fast-math -fno-inline -g)
+        if (LLAMA_QKK_64)
+            set(XC_FLAGS ${XC_FLAGS} -DQK_K=64)
         endif()
 
         add_custom_command(
             OUTPUT ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
             COMMAND xcrun -sdk macosx metal    ${XC_FLAGS} -c ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
             COMMAND xcrun -sdk macosx metallib                ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air   -o ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.air
-            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-common.h
-            COMMAND rm -f ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal
-            DEPENDS ggml-metal.metal ggml-common.h
+            DEPENDS ggml-metal.metal
             COMMENT "Compiling Metal kernels"
-            )
+        )
 
         add_custom_target(
             ggml-metal ALL
             DEPENDS ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/default.metallib
-            )
-    endif() # LLAMA_METAL_EMBED_LIBRARY
+        )
+    endif()
 
     set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
         ${FOUNDATION_LIBRARY}
@@ -544,10 +533,6 @@ if (LLAMA_HIPBLAS)
 endif()
 
 if (LLAMA_SYCL)
-    if (NOT LLAMA_SYCL_TARGET MATCHES "^(INTEL|NVIDIA)$")
-        message(FATAL_ERROR "Invalid backend chosen, supported options are INTEL or NVIDIA")
-    endif()
-
     if ( NOT DEFINED ENV{ONEAPI_ROOT})
         message(FATAL_ERROR "Not detect ENV {ONEAPI_ROOT}, please install oneAPI & source it, like: source /opt/intel/oneapi/setvars.sh")
     endif()
@@ -569,9 +554,6 @@ if (LLAMA_SYCL)
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
     set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib")
-    if (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl-targets=nvptx64-nvidia-cuda")
-    endif()
 
     set(GGML_HEADERS_SYCL ggml-sycl.h)
     set(GGML_SOURCES_SYCL ggml-sycl.cpp)
@@ -579,11 +561,7 @@ if (LLAMA_SYCL)
     if (WIN32)
         set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib)
     else()
-        if (LLAMA_SYCL_TARGET STREQUAL "INTEL")
-            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
-        elseif (LLAMA_SYCL_TARGET STREQUAL "NVIDIA")
-            set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl pthread m dl onemkl)
-        endif()
+        set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread)
     endif()
 endif()
 
@@ -1150,8 +1128,6 @@ endif()
 add_library(llama
             llama.cpp
             llama.h
-            unicode.h
-            unicode.cpp
             )
 
 target_include_directories(llama PUBLIC .)

Makefile

@@ -2,7 +2,7 @@
 BUILD_TARGETS = \
 	main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
 	simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search  \
-	speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o
+	speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey tests/test-c.o
 
 # Binaries only useful for tests
 TEST_TARGETS = \
@@ -167,10 +167,6 @@ ifeq ($(UNAME_S),OpenBSD)
 	MK_CPPFLAGS += -D_BSD_SOURCE
 endif
 
-ifdef LLAMA_SCHED_MAX_COPIES
-	MK_CPPFLAGS += -DGGML_SCHED_MAX_COPIES=$(LLAMA_SCHED_MAX_COPIES)
-endif
-
 ifdef LLAMA_DEBUG
 	MK_CFLAGS   += -O0 -g
 	MK_CXXFLAGS += -O0 -g
@@ -205,10 +201,6 @@ ifdef LLAMA_SERVER_VERBOSE
 	MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
 endif
 
-ifdef LLAMA_SERVER_SSL
-	MK_CPPFLAGS += -DCPPHTTPLIB_OPENSSL_SUPPORT
-	MK_LDFLAGS += -lssl -lcrypto
-endif
 
 ifdef LLAMA_CODE_COVERAGE
 	MK_CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase ''
@@ -457,7 +449,7 @@ endif # LLAMA_CUDA_PEER_MAX_BATCH_SIZE
 ifdef LLAMA_CUDA_CCBIN
 	MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
 endif
-ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml-common.h
+ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
 ifdef JETSON_EOL_MODULE_DETECT
 	$(NVCC) -I. -Icommon -D_XOPEN_SOURCE=600 -D_GNU_SOURCE -DNDEBUG -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I/usr/local/cuda/targets/aarch64-linux/include -std=c++11 -O3 $(NVCCFLAGS) $(CPPFLAGS) -Xcompiler "$(CUDA_CXXFLAGS)" -c $< -o $@
 else
@@ -553,20 +545,19 @@ endif
 endif # LLAMA_METAL
 
 ifdef LLAMA_METAL
-ggml-metal.o: ggml-metal.m ggml-metal.h ggml.h
+ggml-metal.o: ggml-metal.m ggml-metal.h
 	$(CC) $(CFLAGS) -c $< -o $@
 
 ifdef LLAMA_METAL_EMBED_LIBRARY
-ggml-metal-embed.o: ggml-metal.metal ggml-common.h
+ggml-metal-embed.o: ggml-metal.metal
 	@echo "Embedding Metal library"
-	@sed -e '/#include "ggml-common.h"/r ggml-common.h' -e '/#include "ggml-common.h"/d' < ggml-metal.metal > ggml-metal-embed.metal
 	$(eval TEMP_ASSEMBLY=$(shell mktemp))
-	@echo ".section __DATA, __ggml_metallib"   >  $(TEMP_ASSEMBLY)
-	@echo ".globl _ggml_metallib_start"        >> $(TEMP_ASSEMBLY)
-	@echo "_ggml_metallib_start:"              >> $(TEMP_ASSEMBLY)
-	@echo ".incbin \"ggml-metal-embed.metal\"" >> $(TEMP_ASSEMBLY)
-	@echo ".globl _ggml_metallib_end"          >> $(TEMP_ASSEMBLY)
-	@echo "_ggml_metallib_end:"                >> $(TEMP_ASSEMBLY)
+	@echo ".section __DATA, __ggml_metallib" > $(TEMP_ASSEMBLY)
+	@echo ".globl _ggml_metallib_start" >> $(TEMP_ASSEMBLY)
+	@echo "_ggml_metallib_start:" >> $(TEMP_ASSEMBLY)
+	@echo ".incbin \"$<\"" >> $(TEMP_ASSEMBLY)
+	@echo ".globl _ggml_metallib_end" >> $(TEMP_ASSEMBLY)
+	@echo "_ggml_metallib_end:" >> $(TEMP_ASSEMBLY)
 	@$(AS) $(TEMP_ASSEMBLY) -o $@
 	@rm -f ${TEMP_ASSEMBLY}
 endif
@@ -635,15 +626,12 @@ ggml-alloc.o: ggml-alloc.c ggml.h ggml-alloc.h
 ggml-backend.o: ggml-backend.c ggml.h ggml-backend.h
 	$(CC)  $(CFLAGS)   -c $< -o $@
 
-ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h ggml-common.h
+ggml-quants.o: ggml-quants.c ggml.h ggml-quants.h
 	$(CC) $(CFLAGS)    -c $< -o $@
 
-unicode.o: unicode.cpp unicode.h
-	$(CXX) $(CXXFLAGS) -c $< -o $@
+OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o
 
-OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o
-
-llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
+llama.o: llama.cpp ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h
 	$(CXX) $(CXXFLAGS) -c $< -o $@
 
 COMMON_H_DEPS = common/common.h common/sampling.h common/log.h
@@ -732,17 +720,14 @@ embedding: examples/embedding/embedding.cpp                   ggml.o llama.o $(C
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-gritlm: examples/gritlm/gritlm.cpp                         ggml.o llama.o $(COMMON_DEPS) $(OBJS)
-	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
-
 save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS)
 
-server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
+server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h examples/llava/llava.h examples/llava/llava.cpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)
-	$(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2)
+	$(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual
+	$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2)
 
 gguf: examples/gguf/gguf.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<)

Package.swift

@@ -31,7 +31,6 @@ let package = Package(
             sources: [
                 "ggml.c",
                 "llama.cpp",
-                "unicode.cpp",
                 "ggml-alloc.c",
                 "ggml-backend.c",
                 "ggml-quants.c",

README-sycl.md

@@ -1,7 +1,6 @@
 # llama.cpp for SYCL
 
 - [Background](#background)
-- [News](#news)
 - [OS](#os)
 - [Intel GPU](#intel-gpu)
 - [Docker](#docker)
@@ -26,21 +25,6 @@ The llama.cpp for SYCL is used to support Intel GPUs.
 
 For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building).
 
-## News
-
-- 2024.3
-  - Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing.
-  - Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE.
-  - Support detecting all GPUs with level-zero and same top **Max compute units**.
-  - Support OPs
-    - hardsigmoid
-    - hardswish
-    - pool2d
-
-- 2024.1
-  - Create SYCL backend for Intel GPU.
-  - Support Windows build
-
 ## OS
 
 |OS|Status|Verified|
@@ -73,29 +57,6 @@ For iGPU, please make sure the shared memory from host memory is enough. For lla
 
 For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+.
 
-## Nvidia GPU
-
-### Verified
-
-|Intel GPU| Status | Verified Model|
-|-|-|-|
-|Ampere Series| Support| A100|
-
-### oneMKL
-
-The current oneMKL release does not contain the oneMKL cuBlas backend.
-As a result for Nvidia GPU's oneMKL must be built from source.
-
-```
-git clone https://github.com/oneapi-src/oneMKL
-cd oneMKL
-mkdir build
-cd build
-cmake -G Ninja .. -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON
-ninja
-// Add paths as necessary
-```
-
 ## Docker
 
 Note:
@@ -209,9 +170,6 @@ source /opt/intel/oneapi/setvars.sh
 # Or, for FP32:
 cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
-# For Nvidia GPUs
-cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
-
 # Build example/main only
 #cmake --build . --config Release --target main
 
@@ -491,7 +449,6 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
 |-|-|-|
 |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|
-|ZES_ENABLE_SYSMAN| 0 (default) or 1|Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer|
 
 ## Known Issue
 
@@ -501,10 +458,6 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device
 
   Solution: add **--no-mmap** or **--mmap 0**.
 
-- Split-mode: [row] is not supported
-
-  It's on developing.
-
 ## Q&A
 
 - Error:  `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`.

README.md

@@ -8,19 +8,12 @@
 
 Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++
 
-### Recent API changes
-
-- [2024 Mar 13] Add `llama_synchronize()` + `llama_context_params.n_ubatch` https://github.com/ggerganov/llama.cpp/pull/6017
-- [2024 Mar 8] `llama_kv_cache_seq_rm()` returns a `bool` instead of `void`, and new `llama_n_seq_max()` returns the upper limit of acceptable `seq_id` in batches (relevant when dealing with multiple sequences) https://github.com/ggerganov/llama.cpp/pull/5328
-- [2024 Mar 4] Embeddings API updated https://github.com/ggerganov/llama.cpp/pull/5796
-- [2024 Mar 3] `struct llama_context_params` https://github.com/ggerganov/llama.cpp/pull/5849
-
 ### Hot topics
 
-- Multi-GPU pipeline parallelizm support https://github.com/ggerganov/llama.cpp/pull/6017
-- Looking for contributions to add Deepseek support: https://github.com/ggerganov/llama.cpp/issues/5981
-- Quantization blind testing: https://github.com/ggerganov/llama.cpp/discussions/5962
-- Initial Mamba support has been added: https://github.com/ggerganov/llama.cpp/pull/5328
+- Support for chat templates: [Wiki (contributions welcome)](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template)
+- Support for Gemma models: https://github.com/ggerganov/llama.cpp/pull/5631
+- Non-linear quantization IQ4_NL: https://github.com/ggerganov/llama.cpp/pull/5590
+- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
 
 ----
 
@@ -111,8 +104,6 @@ Typically finetunes of the base models below are supported as well.
 - [x] [InternLM2](https://huggingface.co/models?search=internlm2)
 - [x] [CodeShell](https://github.com/WisdomShell/codeshell)
 - [x] [Gemma](https://ai.google.dev/gemma)
-- [x] [Mamba](https://github.com/state-spaces/mamba)
-- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
 
 **Multimodal models:**
 
@@ -794,7 +785,7 @@ And after 4.45 hours, you will have the final perplexity.
 ### Interactive mode
 
 If you want a more ChatGPT-like experience, you can run in interactive mode by passing `-i` as a parameter.
-In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMA emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
+In this mode, you can always interrupt generation by pressing Ctrl+C and entering one or more lines of text, which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt that makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
 
 Here is an example of a few-shot interaction, invoked with the command
 
@@ -858,7 +849,7 @@ Sample run:
 ```
 == Running in interactive mode. ==
  - Press Ctrl+C to interject at any time.
- - Press Return to return control to LLaMA.
+ - Press Return to return control to LLaMa.
  - If you want to submit another line, end your input in '\'.
 
  Below is an instruction that describes a task. Write a response that appropriately completes the request.
@@ -905,9 +896,6 @@ First, install the essential packages for termux:
 pkg install clang wget git cmake
 ```
 Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake:
-
-You can execute the following commands on your computer to avoid downloading the NDK to your mobile. Of course, you can also do this in Termux.
-
 ```
 $ mkdir build-android
 $ cd build-android
@@ -916,28 +904,7 @@ $ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROI
 $ make
 ```
 Install [termux](https://termux.dev/) on your device and run `termux-setup-storage` to get access to your SD card.
-Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission:
-
-(Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`)
-```
-$cp -r /sdcard/llama.cpp/bin /data/data/com.termux/files/home/
-$cd /data/data/com.termux/files/home/bin
-$chmod +x ./*
-```
-
-Download model [llama-2-7b-chat.Q4_K_M.gguf](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGUF/blob/main/llama-2-7b-chat.Q4_K_M.gguf), and push it to `/sdcard/llama.cpp/`, then move it to `/data/data/com.termux/files/home/model/`
-
-```
-$mv /sdcard/llama.cpp/llama-2-7b-chat.Q4_K_M.gguf /data/data/com.termux/files/home/model/
-```
-
-Now, you can start chatting:
-```
-$cd /data/data/com.termux/files/home/bin
-$./main -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml
-```
-
-Here is a demo of an interactive session running on Pixel 5 phone:
+Finally, copy the `llama` binary and the model files to your device storage. Here is a demo of an interactive session running on Pixel 5 phone:
 
 https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
 

build.zig

@@ -115,7 +115,6 @@ pub fn build(b: *std.build.Builder) !void {
     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 unicode = make.obj("unicode", "unicode.cpp");
     const llama = make.obj("llama", "llama.cpp");
     const buildinfo = make.obj("common", "common/build-info.cpp");
     const common = make.obj("common", "common/common.cpp");
@@ -126,14 +125,14 @@ pub fn build(b: *std.build.Builder) !void {
     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, unicode, common, buildinfo, sampling, console, grammar_parser });
-    _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
-    _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
-    _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, common, buildinfo });
-    _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, 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, unicode, common, buildinfo, train });
+    _ = 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, unicode, common, buildinfo, sampling, grammar_parser, clip, llava });
+    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

@@ -45,8 +45,7 @@ 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
 

common/CMakeLists.txt

@@ -19,12 +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 "")

common/common.h

@@ -37,16 +37,13 @@ extern char const *LLAMA_COMMIT;
 extern char const *LLAMA_COMPILER;
 extern char const *LLAMA_BUILD_TARGET;
 
-struct llama_control_vector_load_info;
-
-int32_t get_num_physical_cores();
-
 //
 // CLI argument parsing
 //
+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             = get_num_physical_cores();
     int32_t n_threads_draft       = -1;
@@ -54,13 +51,13 @@ struct gpt_params {
     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               = 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_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               = 5;     // number of tokens to draft during speculative decoding
+    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)
@@ -79,11 +76,8 @@ struct gpt_params {
     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_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
-
-    llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
-    llama_pooling_type      pooling_type      = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
+    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;
@@ -106,11 +100,6 @@ struct gpt_params {
     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
-
-    int32_t control_vector_layer_start = -1; // layer range for control vector
-    int32_t control_vector_layer_end   = -1; // layer range for control vector
-
     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)
@@ -126,6 +115,7 @@ struct gpt_params {
 
     bool   kl_divergence   = false; // compute KL-divergence
 
+    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 interactive       = false; // interactive mode
@@ -269,32 +259,3 @@ 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 dump_kv_cache_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);
-
-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);

common/grammar-parser.cpp

@@ -278,22 +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());

common/log.h

@@ -297,7 +297,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 #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.
@@ -311,7 +311,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
 #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.
@@ -319,8 +319,8 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std::
     #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

common/sampling.cpp

@@ -17,13 +17,6 @@ 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());
 
         result->grammar = llama_grammar_init(
@@ -302,77 +295,6 @@ static llama_token llama_sampling_sample_impl(
     return id;
 }
 
-static llama_token_data_array llama_sample_probability_distribution_impl(
-                  struct llama_sampling_context * ctx_sampling,
-                  struct llama_context * ctx_main,
-                  struct llama_context * ctx_cfg,
-                  const int idx) {
-    const llama_sampling_params & params = ctx_sampling->params;
-
-    const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
-
-    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 bool    penalize_nl     = params.penalize_nl;
-
-    auto & prev = ctx_sampling->prev;
-    auto & cur  = ctx_sampling->cur;
-
-    // Get a pointer to the logits
-    float * logits = llama_get_logits_ith(ctx_main, idx);
-
-    // Declare original_logits at the beginning of the function scope
-    std::vector<float> original_logits;
-
-    // apply params.logit_bias map
-    for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
-        logits[it->first] += it->second;
-    }
-
-    if (ctx_cfg) {
-        float * logits_guidance = llama_get_logits_ith(ctx_cfg, idx);
-        llama_sample_apply_guidance(ctx_main, logits, logits_guidance, params.cfg_scale);
-    }
-
-    cur.clear();
-
-    for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
-        cur.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
-    }
-
-    llama_token_data_array cur_p = { cur.data(), cur.size(), false };
-
-    // apply penalties
-    const auto& penalty_tokens = params.use_penalty_prompt_tokens ? params.penalty_prompt_tokens : prev;
-    const int penalty_tokens_used_size = std::min((int)penalty_tokens.size(), penalty_last_n);
-    if (penalty_tokens_used_size) {
-        const float nl_logit = logits[llama_token_nl(llama_get_model(ctx_main))];
-
-        llama_sample_repetition_penalties(ctx_main, &cur_p,
-                penalty_tokens.data() + penalty_tokens.size() - penalty_tokens_used_size,
-                penalty_tokens_used_size, penalty_repeat, penalty_freq, penalty_present);
-
-        if (!penalize_nl) {
-            for (size_t idx = 0; idx < cur_p.size; idx++) {
-                if (cur_p.data[idx].id == llama_token_nl(llama_get_model(ctx_main))) {
-                    cur_p.data[idx].logit = nl_logit;
-                    break;
-                }
-            }
-        }
-    }
-
-    // apply grammar checks
-    if (ctx_sampling->grammar != NULL) {
-        llama_sample_grammar(ctx_main, &cur_p, ctx_sampling->grammar);
-    }
-
-    llama_sample_softmax(ctx_main, &cur_p);
-    return cur_p;
-}
-
 llama_token llama_sampling_sample(
                   struct llama_sampling_context * ctx_sampling,
                   struct llama_context * ctx_main,
@@ -382,14 +304,6 @@ llama_token llama_sampling_sample(
     return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false);
 }
 
-llama_token_data_array llama_sampling_probability_distribution(
-                  struct llama_sampling_context * ctx_sampling,
-                  struct llama_context * ctx_main,
-                  struct llama_context * ctx_cfg,
-                  const int idx) {
-    return llama_sample_probability_distribution_impl(ctx_sampling,ctx_main, ctx_cfg, idx);
-}
-
 void llama_sampling_accept(
         struct llama_sampling_context * ctx_sampling,
         struct llama_context * ctx_main,

common/sampling.h

@@ -131,13 +131,6 @@ llama_token llama_sampling_sample(
         struct llama_context * ctx_cfg,
         int idx = 0);
 
-// returns the probability that token of given id will be sampled
-llama_token_data_array llama_sampling_probability_distribution(
-        struct llama_sampling_context * ctx_sampling,
-        struct llama_context * ctx_main,
-        struct llama_context * ctx_cfg,
-        int idx = 0);
-
 void llama_sampling_accept(
         struct llama_sampling_context * ctx_sampling,
         struct llama_context * ctx_main,

convert-hf-to-gguf.py

@@ -8,10 +8,9 @@ import json
 import os
 import re
 import sys
-from abc import ABC, abstractmethod
 from enum import IntEnum
 from pathlib import Path
-from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterator, Sequence, TypeVar, cast
+from typing import TYPE_CHECKING, Any, ContextManager, Iterator, Sequence, cast
 
 import numpy as np
 import torch
@@ -37,12 +36,7 @@ class SentencePieceTokenTypes(IntEnum):
     BYTE = 6
 
 
-AnyModel = TypeVar("AnyModel", bound="type[Model]")
-
-
-class Model(ABC):
-    _model_classes: dict[str, type[Model]] = {}
-
+class Model:
     def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool):
         self.dir_model = dir_model
         self.ftype = ftype
@@ -53,14 +47,10 @@ class Model(ABC):
         self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin")
         self.part_names = self._get_part_names()
         self.hparams = Model.load_hparams(self.dir_model)
+        self.model_arch = self._get_model_architecture()
         self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=False)
         self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"])
 
-    @property
-    @abstractmethod
-    def model_arch(self) -> gguf.MODEL_ARCH:
-        pass
-
     def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any:
         key = next((k for k in keys if k in self.hparams), None)
         if key is not None:
@@ -186,22 +176,53 @@ class Model(ABC):
         with open(dir_model / "config.json", "r", encoding="utf-8") as f:
             return json.load(f)
 
-    @classmethod
-    def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]:
-        assert names
-
-        def func(modelcls: type[Model]):
-            for name in names:
-                cls._model_classes[name] = modelcls
-            return modelcls
-        return func
-
-    @classmethod
-    def from_model_architecture(cls, arch):
-        try:
-            return cls._model_classes[arch]
-        except KeyError:
-            raise NotImplementedError(f'Architecture {arch!r} not supported!') from None
+    @staticmethod
+    def from_model_architecture(model_architecture):
+        if model_architecture == "GPTNeoXForCausalLM":
+            return GPTNeoXModel
+        if model_architecture == "BloomForCausalLM":
+            return BloomModel
+        if model_architecture == "MPTForCausalLM":
+            return MPTModel
+        if model_architecture in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
+            return BaichuanModel
+        if model_architecture in ("FalconForCausalLM", "RWForCausalLM"):
+            return FalconModel
+        if model_architecture == "GPTBigCodeForCausalLM":
+            return StarCoderModel
+        if model_architecture == "GPTRefactForCausalLM":
+            return RefactModel
+        if model_architecture == "PersimmonForCausalLM":
+            return PersimmonModel
+        if model_architecture in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
+            return StableLMModel
+        if model_architecture == "QWenLMHeadModel":
+            return QwenModel
+        if model_architecture == "Qwen2ForCausalLM":
+            return Model
+        if model_architecture == "MixtralForCausalLM":
+            return MixtralModel
+        if model_architecture == "GPT2LMHeadModel":
+            return GPT2Model
+        if model_architecture == "PhiForCausalLM":
+            return Phi2Model
+        if model_architecture == "PlamoForCausalLM":
+            return PlamoModel
+        if model_architecture == "CodeShellForCausalLM":
+            return CodeShellModel
+        if model_architecture == "OrionForCausalLM":
+            return OrionModel
+        if model_architecture == "InternLM2ForCausalLM":
+            return InternLM2Model
+        if model_architecture == "MiniCPMForCausalLM":
+            return MiniCPMModel
+        if model_architecture == "BertModel":
+            return BertModel
+        if model_architecture == "NomicBertModel":
+            return NomicBertModel
+        if model_architecture == "GemmaForCausalLM":
+            return GemmaModel
+        return Model
 
     def _is_model_safetensors(self) -> bool:
         return Model.count_model_parts(self.dir_model, ".safetensors") > 0
@@ -216,6 +237,57 @@ class Model(ABC):
             return ("pytorch_model.bin",)
         return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1))
 
+    def _get_model_architecture(self) -> gguf.MODEL_ARCH:
+        arch = self.hparams["architectures"][0]
+        if arch == "GPTNeoXForCausalLM":
+            return gguf.MODEL_ARCH.GPTNEOX
+        if arch == "BloomForCausalLM":
+            return gguf.MODEL_ARCH.BLOOM
+        if arch == "MPTForCausalLM":
+            return gguf.MODEL_ARCH.MPT
+        if arch in ("BaichuanForCausalLM", "BaiChuanForCausalLM"):
+            return gguf.MODEL_ARCH.BAICHUAN
+        if arch in ("FalconForCausalLM", "RWForCausalLM"):
+            return gguf.MODEL_ARCH.FALCON
+        if arch == "GPTBigCodeForCausalLM":
+            return gguf.MODEL_ARCH.STARCODER
+        if arch == "GPTRefactForCausalLM":
+            return gguf.MODEL_ARCH.REFACT
+        if arch == "PersimmonForCausalLM":
+            return gguf.MODEL_ARCH.PERSIMMON
+        if arch in ("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
+            return gguf.MODEL_ARCH.STABLELM
+        if arch == "QWenLMHeadModel":
+            return gguf.MODEL_ARCH.QWEN
+        if arch == "Qwen2ForCausalLM":
+            return gguf.MODEL_ARCH.QWEN2
+        if arch == "MixtralForCausalLM":
+            return gguf.MODEL_ARCH.LLAMA
+        if arch == "GPT2LMHeadModel":
+            return gguf.MODEL_ARCH.GPT2
+        if arch == "PhiForCausalLM":
+            return gguf.MODEL_ARCH.PHI2
+        if arch == "PlamoForCausalLM":
+            return gguf.MODEL_ARCH.PLAMO
+        if arch == "CodeShellForCausalLM":
+            return gguf.MODEL_ARCH.CODESHELL
+        if arch == "OrionForCausalLM":
+            return gguf.MODEL_ARCH.ORION
+        if arch == "InternLM2ForCausalLM":
+            return gguf.MODEL_ARCH.INTERNLM2
+        if arch == "MiniCPMForCausalLM":
+            return gguf.MODEL_ARCH.MINICPM
+        if arch == "BertModel":
+            return gguf.MODEL_ARCH.BERT
+        if arch == "NomicBertModel":
+            return gguf.MODEL_ARCH.NOMIC_BERT
+        if arch == "GemmaForCausalLM":
+            return gguf.MODEL_ARCH.GEMMA
+        if arch == "Starcoder2ForCausalLM":
+            return gguf.MODEL_ARCH.STARCODER2
+
+        raise NotImplementedError(f'Architecture "{arch}" not supported!')
+
     def _set_vocab_gpt2(self):
         dir_model = self.dir_model
         hparams = self.hparams
@@ -383,10 +455,7 @@ class Model(ABC):
         special_vocab.add_to_gguf(self.gguf_writer)
 
 
-@Model.register("GPTNeoXForCausalLM")
 class GPTNeoXModel(Model):
-    model_arch = gguf.MODEL_ARCH.GPTNEOX
-
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
 
@@ -403,10 +472,7 @@ class GPTNeoXModel(Model):
         self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"])
 
 
-@Model.register("BloomForCausalLM")
 class BloomModel(Model):
-    model_arch = gguf.MODEL_ARCH.BLOOM
-
     def set_gguf_parameters(self):
         self.gguf_writer.add_name("Bloom")
         n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed"))
@@ -498,10 +564,7 @@ class BloomModel(Model):
                 print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
 
 
-@Model.register("MPTForCausalLM")
 class MPTModel(Model):
-    model_arch = gguf.MODEL_ARCH.MPT
-
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layers"]
         self.gguf_writer.add_name(self.dir_model.name)
@@ -564,10 +627,7 @@ class MPTModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
-@Model.register("OrionForCausalLM")
 class OrionModel(Model):
-    model_arch = gguf.MODEL_ARCH.ORION
-
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
@@ -646,10 +706,7 @@ class OrionModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
-@Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM")
 class BaichuanModel(Model):
-    model_arch = gguf.MODEL_ARCH.BAICHUAN
-
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
@@ -764,10 +821,7 @@ class BaichuanModel(Model):
         return weights[r * n_part:r * n_part + r, ...]
 
 
-@Model.register("FalconForCausalLM", "RWForCausalLM")
 class FalconModel(Model):
-    model_arch = gguf.MODEL_ARCH.FALCON
-
     def set_gguf_parameters(self):
         block_count = self.hparams.get("num_hidden_layers")
         if block_count is None:
@@ -860,10 +914,7 @@ class FalconModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
-@Model.register("GPTBigCodeForCausalLM")
 class StarCoderModel(Model):
-    model_arch = gguf.MODEL_ARCH.STARCODER
-
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layer"]
 
@@ -878,10 +929,7 @@ class StarCoderModel(Model):
         self.gguf_writer.add_file_type(self.ftype)
 
 
-@Model.register("GPTRefactForCausalLM")
 class RefactModel(Model):
-    model_arch = gguf.MODEL_ARCH.REFACT
-
     def set_gguf_parameters(self):
         hidden_dim = self.hparams["n_embd"]
         inner_dim = 4 * hidden_dim
@@ -965,10 +1013,7 @@ class RefactModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
-@Model.register("PersimmonForCausalLM")
 class PersimmonModel(Model):
-    model_arch = gguf.MODEL_ARCH.PERSIMMON
-
     def set_gguf_parameters(self):
         block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers"))
         head_count = self.hparams["num_attention_heads"]
@@ -1016,10 +1061,7 @@ class PersimmonModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
-@Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM")
 class StableLMModel(Model):
-    model_arch = gguf.MODEL_ARCH.STABLELM
-
     def set_vocab(self):
         if (self.dir_model / "tokenizer.json").is_file():
             self._set_vocab_gpt2()
@@ -1043,18 +1085,12 @@ class StableLMModel(Model):
         self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"]))
 
 
-@Model.register("MixtralForCausalLM")
 class MixtralModel(Model):
-    model_arch = gguf.MODEL_ARCH.LLAMA
-
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
 
-@Model.register("MiniCPMForCausalLM")
 class MiniCPMModel(Model):
-    model_arch = gguf.MODEL_ARCH.MINICPM
-
     def set_gguf_parameters(self):
         block_count = self.hparams["num_hidden_layers"]
         self.gguf_writer.add_name("MiniCPM")
@@ -1131,10 +1167,7 @@ class MiniCPMModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
-@Model.register("QWenLMHeadModel")
 class QwenModel(Model):
-    model_arch = gguf.MODEL_ARCH.QWEN
-
     @staticmethod
     def token_bytes_to_string(b):
         from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
@@ -1214,15 +1247,7 @@ class QwenModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
-@Model.register("Qwen2ForCausalLM")
-class Qwen2Model(Model):
-    model_arch = gguf.MODEL_ARCH.QWEN2
-
-
-@Model.register("GPT2LMHeadModel")
 class GPT2Model(Model):
-    model_arch = gguf.MODEL_ARCH.GPT2
-
     def set_gguf_parameters(self):
         self.gguf_writer.add_name(self.dir_model.name)
         self.gguf_writer.add_block_count(self.hparams["n_layer"])
@@ -1284,10 +1309,7 @@ class GPT2Model(Model):
                 self.gguf_writer.add_tensor("output.weight", data)
 
 
-@Model.register("PhiForCausalLM")
 class Phi2Model(Model):
-    model_arch = gguf.MODEL_ARCH.PHI2
-
     def set_gguf_parameters(self):
         block_count = self.find_hparam(["num_hidden_layers", "n_layer"])
 
@@ -1309,10 +1331,7 @@ class Phi2Model(Model):
         self.gguf_writer.add_add_bos_token(False)
 
 
-@Model.register("PlamoForCausalLM")
 class PlamoModel(Model):
-    model_arch = gguf.MODEL_ARCH.PLAMO
-
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
@@ -1391,10 +1410,7 @@ class PlamoModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
-@Model.register("CodeShellForCausalLM")
 class CodeShellModel(Model):
-    model_arch = gguf.MODEL_ARCH.CODESHELL
-
     def set_gguf_parameters(self):
         block_count = self.hparams["n_layer"]
 
@@ -1459,10 +1475,7 @@ class CodeShellModel(Model):
                 print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}")
 
 
-@Model.register("InternLM2ForCausalLM")
 class InternLM2Model(Model):
-    model_arch = gguf.MODEL_ARCH.INTERNLM2
-
     def set_vocab(self):
         # (TODO): Is there a better way?
         # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character
@@ -1634,10 +1647,7 @@ in chat mode so that the conversation can end normally.")
                 self.post_write_tensors(tensor_map, name, data_torch)
 
 
-@Model.register("BertModel")
 class BertModel(Model):
-    model_arch = gguf.MODEL_ARCH.BERT
-
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.vocab_size = None
@@ -1647,17 +1657,16 @@ class BertModel(Model):
         self.gguf_writer.add_causal_attention(False)
 
         # get pooling path
+        with open(self.dir_model / "modules.json", encoding="utf-8") as f:
+            modules = json.load(f)
         pooling_path = None
-        module_path = self.dir_model / "modules.json"
-        if module_path.is_file():
-            with open(module_path, encoding="utf-8") as f:
-                modules = json.load(f)
-            for mod in modules:
-                if mod["type"] == "sentence_transformers.models.Pooling":
-                    pooling_path = mod["path"]
-                    break
+        for mod in modules:
+            if mod["type"] == "sentence_transformers.models.Pooling":
+                pooling_path = mod["path"]
+                break
 
         # get pooling type
+        pooling_type = gguf.PoolingType.NONE
         if pooling_path is not None:
             with open(self.dir_model / pooling_path / "config.json", encoding="utf-8") as f:
                 pooling = json.load(f)
@@ -1667,7 +1676,8 @@ class BertModel(Model):
                 pooling_type = gguf.PoolingType.CLS
             else:
                 raise NotImplementedError("Only MEAN and CLS pooling types supported")
-            self.gguf_writer.add_pooling_type(pooling_type)
+
+        self.gguf_writer.add_pooling_type(pooling_type.value)
 
     def set_vocab(self):
         path = self.dir_model
@@ -1743,10 +1753,7 @@ class BertModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
-@Model.register("NomicBertModel")
 class NomicBertModel(BertModel):
-    model_arch = gguf.MODEL_ARCH.NOMIC_BERT
-
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
@@ -1783,10 +1790,7 @@ class NomicBertModel(BertModel):
             yield name, data
 
 
-@Model.register("GemmaForCausalLM")
 class GemmaModel(Model):
-    model_arch = gguf.MODEL_ARCH.GEMMA
-
     def set_vocab(self):
         self._set_vocab_sentencepiece()
 
@@ -1811,15 +1815,16 @@ class GemmaModel(Model):
         tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
 
         for name, data_torch in self.get_tensors():
+            # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
+            if name.endswith("norm.weight"):
+                data_torch = data_torch + 1
+
             old_dtype = data_torch.dtype
 
             # convert any unsupported data types to float32
             if data_torch.dtype not in (torch.float16, torch.float32):
                 data_torch = data_torch.to(torch.float32)
 
-            # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89
-            if name.endswith("norm.weight"):
-                data_torch = data_torch + 1
             data = data_torch.squeeze().numpy()
 
             # map tensor names
@@ -1842,146 +1847,6 @@ class GemmaModel(Model):
             self.gguf_writer.add_tensor(new_name, data)
 
 
-@Model.register("Starcoder2ForCausalLM")
-class StarCoder2Model(Model):
-    model_arch = gguf.MODEL_ARCH.STARCODER2
-
-
-@Model.register("MambaForCausalLM", "MambaLMHeadModel")
-class MambaModel(Model):
-    model_arch = gguf.MODEL_ARCH.MAMBA
-
-    def set_vocab(self):
-        vocab_size = self.hparams["vocab_size"]
-        # Round vocab size to next multiple of 8
-        pad_vocab = self.hparams.get("pad_vocab_size_multiple", 8)
-        # pad using ceiling division
-        # ref: https://stackoverflow.com/a/17511341/22827863
-        vocab_size = -(vocab_size // -pad_vocab) * pad_vocab
-        self.hparams["vocab_size"] = vocab_size
-
-        if (self.dir_model / "tokenizer.json").is_file():
-            self._set_vocab_gpt2()
-        else:
-            # Use the GPT-NeoX tokenizer when no tokenizer files are present
-            tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf"
-            print(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'")
-            neox_reader = gguf.GGUFReader(tokenizer_path, "r")
-
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL)
-            self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]))
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST)
-            self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size])
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE)
-            self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size])
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES)
-            self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data])
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID)
-            self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0])
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID)
-            self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0])
-            field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID)
-            self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0])
-
-    def set_gguf_parameters(self):
-        d_model = self.find_hparam(["hidden_size", "d_model"])
-        d_conv  = self.find_hparam(["conv_kernel", "d_conv"], optional=True) or 4
-        d_inner = self.find_hparam(["intermediate_size", "d_inner"], optional=True) or 2 * d_model
-        d_state = self.find_hparam(["state_size", "d_state"], optional=True) or 16
-        # ceiling division
-        # ref: https://stackoverflow.com/a/17511341/22827863
-        # ref: https://github.com/state-spaces/mamba/blob/ce59daea3a090d011d6476c6e5b97f6d58ddad8b/mamba_ssm/modules/mamba_simple.py#L58
-        dt_rank = self.find_hparam(["time_step_rank", "dt_rank"], optional=True) or -(d_model // -16)
-        rms_norm_eps = self.find_hparam(["layer_norm_epsilon", "rms_norm_eps"], optional=True) or 1e-5
-
-        # Fail early for models which don't have a block expansion factor of 2
-        assert d_inner == 2 * d_model
-
-        self.gguf_writer.add_name(self.dir_model.name)
-        self.gguf_writer.add_context_length(2**20) # arbitrary value; for those who use the default
-        self.gguf_writer.add_embedding_length(d_model)
-        self.gguf_writer.add_feed_forward_length(0) # unused, but seemingly required when loading
-        self.gguf_writer.add_head_count(0) # unused, but seemingly required when loading
-        self.gguf_writer.add_block_count(self.hparams["n_layer"])
-        self.gguf_writer.add_ssm_conv_kernel(d_conv)
-        self.gguf_writer.add_ssm_inner_size(d_inner)
-        self.gguf_writer.add_ssm_state_size(d_state)
-        self.gguf_writer.add_ssm_time_step_rank(dt_rank)
-        self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps)
-        self.gguf_writer.add_file_type(self.ftype)
-
-    def write_tensors(self):
-        block_count = self.hparams["n_layer"]
-        tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
-
-        tok_embd = None
-        tok_embd_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.TOKEN_EMBD] + ".weight"
-        output_name   = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.OUTPUT]     + ".weight"
-
-        for name, data_torch in self.get_tensors():
-            old_dtype = data_torch.dtype
-
-            # convert any unsupported data types to float32
-            if data_torch.dtype not in (torch.float16, torch.float32):
-                data_torch = data_torch.to(torch.float32)
-
-            # map tensor names
-            new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
-            if new_name is None:
-                print(f"Can not map tensor {name!r}")
-                sys.exit()
-
-            if name.endswith(".A_log"):
-                print("A_log --> A ==> " + new_name)
-                data_torch = -torch.exp(data_torch)
-
-            # assuming token_embd.weight is seen before output.weight
-            if tok_embd is not None and new_name == output_name:
-                if torch.equal(tok_embd, data_torch):
-                    print(f"{output_name} is equivalent to {tok_embd_name}, omitting")
-                    continue
-            if new_name == tok_embd_name:
-                tok_embd = data_torch
-
-            data = data_torch.squeeze().numpy()
-
-            n_dims = len(data.shape)
-            data_dtype = data.dtype
-
-            # if f32 desired, convert any float16 to float32
-            if self.ftype == 0 and data_dtype == np.float16:
-                data = data.astype(np.float32)
-
-            # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
-            if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
-                data = data.astype(np.float32)
-
-            # if f16 desired, convert big float32 2-dim weight tensors to float16
-            if self.ftype == 1 and data_dtype == np.float32 and new_name.removesuffix(".weight").endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2:
-                data = data.astype(np.float16)
-
-            print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
-
-            self.gguf_writer.add_tensor(new_name, data)
-
-
-@Model.register("CohereForCausalLM")
-class CommandR2Model(Model):
-    model_arch = gguf.MODEL_ARCH.COMMAND_R
-
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-
-        # max_position_embeddings = 8192 in config.json but model was actually
-        # trained on 128k context length
-        self.hparams["max_position_embeddings"] = self.hparams["model_max_length"]
-
-    def set_gguf_parameters(self):
-        super().set_gguf_parameters()
-        self.gguf_writer.add_logit_scale(self.hparams["logit_scale"])
-        self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE)
-
-
 ###### CONVERSION LOGIC ######
 
 

convert-llama-ggml-to-gguf.py

@@ -373,7 +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
 
@@ -398,8 +398,8 @@ 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("--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()
 
 

convert.py

@@ -332,9 +332,6 @@ class Params:
 #
 
 class BpeVocab:
-    tokenizer_model = "gpt2"
-    name = "bpe"
-
     def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
         self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read())
         if isinstance(self.bpe_tokenizer.get('model'), dict):
@@ -393,9 +390,6 @@ class BpeVocab:
 
 
 class SentencePieceVocab:
-    tokenizer_model = "llama"
-    name = "spm"
-
     def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None:
         self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer))
         added_tokens: dict[str, int]
@@ -459,9 +453,6 @@ class SentencePieceVocab:
 
 
 class HfVocab:
-    tokenizer_model = "llama"
-    name = "hfft"
-
     def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None = None) -> None:
         try:
             from transformers import AutoTokenizer
@@ -562,15 +553,7 @@ class HfVocab:
         return f"<HfVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
 
 
-class NoVocab:
-    tokenizer_model = "no_vocab"
-    name = "no_vocab"
-
-    def __repr__(self) -> str:
-        return "<NoVocab for a model without integrated vocabulary>"
-
-
-Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab | NoVocab"
+Vocab: TypeAlias = "BpeVocab | SentencePieceVocab | HfVocab"
 
 
 #
@@ -952,10 +935,8 @@ def check_vocab_size(params: Params, vocab: Vocab, pad_vocab: bool = False) -> N
     # Handle special case where the model's vocab size is not set
     if params.n_vocab == -1:
         raise ValueError(
-            f"The model's vocab size is set to -1 in params.json. Please update it manually.{f' Maybe {vocab.vocab_size}?' if hasattr(vocab, 'vocab_size') else ''}"
+            f"The model's vocab size is set to -1 in params.json. Please update it manually. Maybe {vocab.vocab_size}?"
         )
-    if isinstance(vocab, NoVocab):
-        return  # model has no vocab
 
     # Check for a vocab size mismatch
     if params.n_vocab == vocab.vocab_size:
@@ -996,7 +977,6 @@ class OutputFile:
             name = str(params.path_model.parent).split('/')[-1]
 
         self.gguf.add_name                (name)
-        self.gguf.add_vocab_size          (params.n_vocab)
         self.gguf.add_context_length      (params.n_ctx)
         self.gguf.add_embedding_length    (params.n_embd)
         self.gguf.add_block_count         (params.n_layer)
@@ -1033,9 +1013,21 @@ class OutputFile:
         if params.ftype is not None:
             self.gguf.add_file_type(params.ftype)
 
-    def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]:
-        assert not isinstance(vocab, NoVocab)
+    def handle_tokenizer_model(self, vocab: Vocab) -> str:
+        # Map the vocab types to the supported tokenizer models
+        tokenizer_model = {
+            SentencePieceVocab: "llama",
+            HfVocab: "llama",
+            BpeVocab: "gpt2",
+        }.get(type(vocab))
 
+        # Block if vocab type is not predefined
+        if tokenizer_model is None:
+            raise ValueError("Unknown vocab type: Not supported")
+
+        return tokenizer_model
+
+    def extract_vocabulary_from_model(self, vocab: Vocab) -> tuple[list[bytes], list[float], list[gguf.TokenType]]:
         tokens = []
         scores = []
         toktypes = []
@@ -1051,8 +1043,11 @@ class OutputFile:
         return tokens, scores, toktypes
 
     def add_meta_vocab(self, vocab: Vocab) -> None:
+        # Handle the tokenizer model
+        tokenizer_model = self.handle_tokenizer_model(vocab)
+
         # Ensure that tokenizer_model is added to the GGUF model
-        self.gguf.add_tokenizer_model(vocab.tokenizer_model)
+        self.gguf.add_tokenizer_model(tokenizer_model)
 
         # Extract model vocabulary for model conversion
         tokens, scores, toktypes = self.extract_vocabulary_from_model(vocab)
@@ -1079,26 +1074,6 @@ class OutputFile:
     def write_tensor_info(self) -> None:
         self.gguf.write_ti_data_to_file()
 
-    def write_tensor_data(self, ftype: GGMLFileType, model: LazyModel, concurrency: int) -> None:
-        ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency=concurrency)
-        if ftype == GGMLFileType.MostlyQ8_0:
-            ndarrays = bounded_parallel_map(
-                OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
-                use_processpool_executor=True,
-            )
-        else:
-            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
-
-        start = time.time()
-        for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
-            elapsed = time.time() - start
-            size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
-            padi = len(str(len(model)))
-            print(
-                f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
-            )
-            self.gguf.write_tensor_data(ndarray)
-
     def close(self) -> None:
         self.gguf.close()
 
@@ -1107,7 +1082,7 @@ class OutputFile:
         fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab,
         endianess: gguf.GGUFEndian = gguf.GGUFEndian.LITTLE, pad_vocab: bool = False,
     ) -> None:
-        check_vocab_size(params, vocab, pad_vocab=pad_vocab)
+        check_vocab_size(params, vocab, pad_vocab = pad_vocab)
 
         of = OutputFile(fname_out, endianess=endianess)
 
@@ -1145,11 +1120,8 @@ class OutputFile:
 
         # meta data
         of.add_meta_arch(params)
-        if isinstance(vocab, NoVocab):
-            of.gguf.add_tokenizer_model(vocab.tokenizer_model)
-        else:
-            of.add_meta_vocab(vocab)
-            of.add_meta_special_vocab(svocab)
+        of.add_meta_vocab(vocab)
+        of.add_meta_special_vocab(svocab)
 
         # tensor info
         for name, lazy_tensor in model.items():
@@ -1159,7 +1131,24 @@ class OutputFile:
         of.write_tensor_info()
 
         # tensor data
-        of.write_tensor_data(ftype, model, concurrency)
+        ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency)
+        if ftype == GGMLFileType.MostlyQ8_0:
+            ndarrays = bounded_parallel_map(
+                OutputFile.maybe_do_quantize, ndarrays_inner, concurrency=concurrency, max_workers=concurrency,
+                use_processpool_executor=True,
+            )
+        else:
+            ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner)
+
+        start = time.time()
+        for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)):
+            elapsed = time.time() - start
+            size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape)
+            padi = len(str(len(model)))
+            print(
+                f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}"
+            )
+            of.gguf.write_tensor_data(ndarray)
 
         of.close()
 
@@ -1293,35 +1282,38 @@ def load_some_model(path: Path) -> ModelPlus:
 
 
 class VocabFactory:
-    _FILES = {"spm": "tokenizer.model", "bpe": "vocab.json", "hfft": "tokenizer.json"}
-
     def __init__(self, path: Path):
         self.path = path
-        self.file_paths = self._detect_files()
-        print(f"Found vocab files: {self.file_paths}")
+        self.files: dict[str, Path | None] = {
+            "tokenizer.model": None,
+            "vocab.json": None,
+            "tokenizer.json": None,
+        }
+        self._detect_files()
 
-    def _detect_files(self) -> dict[str, Path | None]:
-        def locate(file: str) -> Path | None:
-            if (path := self.path / file).exists():
-                return path
-            if (path := self.path.parent / file).exists():
-                return path
-            return None
+    def _detect_files(self):
+        for file in self.files.keys():
+            file_path = self.path / file
+            parent_file_path = self.path.parent / file
+            if file_path.exists():
+                self.files[file] = file_path
+            elif parent_file_path.exists():
+                self.files[file] = parent_file_path
+        print(f"Found vocab files: {self.files}")
 
-        return {vt: locate(f) for vt, f in self._FILES.items()}
+    def _select_file(self, vocabtype: str | None) -> Path:
+        if vocabtype in ["spm", "bpe"]:
+            for file_key in self.files.keys():
+                if (file := self.files[file_key]) is not None:
+                    return file
+            raise FileNotFoundError(f"{vocabtype} vocab not found.")
+        if vocabtype == "hfft":
+            # For Hugging Face Fast Tokenizer, return the directory path instead of a specific file
+            return self.path
+        raise ValueError(f"Unsupported vocabulary type {vocabtype}")
 
-    def _select_file(self, vocab_types: list[str]) -> tuple[str, Path]:
-        for vtype in vocab_types:
-            try:
-                path = self.file_paths[vtype]
-            except KeyError:
-                raise ValueError(f"Unsupported vocabulary type {vtype}") from None
-            if path is not None:
-                return vtype, path
-        raise FileNotFoundError(f"Could not find any of {[self._FILES[vt] for vt in vocab_types]}")
-
-    def _create_special_vocab(self, vocab: Vocab, model_parent_path: Path) -> gguf.SpecialVocab:
-        load_merges = vocab.name == "bpe"
+    def _create_special_vocab(self, vocab: Vocab, vocabtype: str, model_parent_path: Path) -> gguf.SpecialVocab:
+        load_merges = vocabtype == "bpe"
         n_vocab = vocab.vocab_size if hasattr(vocab, "vocab_size") else None
         return gguf.SpecialVocab(
             model_parent_path,
@@ -1330,34 +1322,30 @@ class VocabFactory:
             n_vocab=n_vocab,
         )
 
-    def _create_vocab_by_path(self, vocab_types: list[str]) -> Vocab:
-        vocab_type, path = self._select_file(vocab_types)
-        print(f"Loading vocab file {path!r}, type {vocab_type!r}")
+    def load_vocab(self, vocabtype: str, model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
+        path = self._select_file(vocabtype)
+        print(f"Loading vocab file '{path}', type '{vocabtype}'")
 
         added_tokens_path = path.parent / "added_tokens.json"
-        if vocab_type == "bpe":
-            return BpeVocab(
-                path, added_tokens_path if added_tokens_path.exists() else None
-            )
-        if vocab_type == "spm":
-            return SentencePieceVocab(
-                path, added_tokens_path if added_tokens_path.exists() else None
-            )
-        if vocab_type == "hfft":
-            return HfVocab(
-                path.parent, added_tokens_path if added_tokens_path.exists() else None
-            )
-        raise ValueError(vocab_type)
-
-    def load_vocab(self, vocab_types: list[str], model_parent_path: Path) -> tuple[Vocab, gguf.SpecialVocab]:
         vocab: Vocab
-        if len(vocab_types) == 1 and "no_vocab" in vocab_types:
-            vocab = NoVocab()
+        if vocabtype == "bpe":
+            vocab = BpeVocab(
+                path, added_tokens_path if added_tokens_path.exists() else None
+            )
+        elif vocabtype == "spm":
+            vocab = SentencePieceVocab(
+                path, added_tokens_path if added_tokens_path.exists() else None
+            )
+        elif vocabtype == "hfft":
+            vocab = HfVocab(
+                path, added_tokens_path if added_tokens_path.exists() else None
+            )
         else:
-            vocab = self._create_vocab_by_path(vocab_types)
+            raise ValueError(f"Unsupported vocabulary type {vocabtype}")
         # FIXME: Respect --vocab-dir?
         special_vocab = self._create_special_vocab(
             vocab,
+            vocabtype,
             model_parent_path,
         )
         return vocab, special_vocab
@@ -1391,14 +1379,15 @@ def main(args_in: list[str] | None = None) -> None:
     if np.uint32(1) == np.uint32(1).newbyteorder("<"):
         # We currently only support Q8_0 output on little endian systems.
         output_choices.append("q8_0")
-    parser = argparse.ArgumentParser(description="Convert a LLaMA model to a GGML compatible file")
+    vocab_types = ["spm", "bpe", "hfft"]
+    parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file")
+    parser.add_argument("--awq-path",     type=Path,              help="Path to scale awq cache file", default=None)
     parser.add_argument("--dump",         action="store_true",    help="don't convert, just show what's in the model")
     parser.add_argument("--dump-single",  action="store_true",    help="don't convert, just show what's in a single model file")
     parser.add_argument("--vocab-only",   action="store_true",    help="extract only the vocab")
-    parser.add_argument("--no-vocab",     action="store_true",    help="store model without the vocab")
     parser.add_argument("--outtype",      choices=output_choices, help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)")
     parser.add_argument("--vocab-dir",    type=Path,              help="directory containing tokenizer.model, if separate from model file")
-    parser.add_argument("--vocab-type",                           help="vocab types to try in order, choose from 'spm', 'bpe', 'hfft' (default: spm,hfft)", default="spm,hfft")
+    parser.add_argument("--vocab-type",   choices=vocab_types,    help="The vocabulary format used to define the tokenizer model (default: spm)", default="spm")
     parser.add_argument("--outfile",      type=Path,              help="path to write to; default: based on input")
     parser.add_argument("model",          type=Path,              help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
     parser.add_argument("--ctx",          type=int,               help="model training context (default: based on input)")
@@ -1408,10 +1397,18 @@ def main(args_in: list[str] | None = None) -> None:
     parser.add_argument("--skip-unknown", action="store_true",    help="skip unknown tensor names instead of failing")
 
     args = parser.parse_args(args_in)
-    if args.no_vocab:
-        if args.vocab_only:
-            raise ValueError("no need to specify --vocab-only if using --no-vocab")
-        args.vocab_type = "no_vocab"
+    if args.awq_path:
+        sys.path.insert(1, str(Path(__file__).parent / 'awq-py'))
+        from awq.apply_awq import add_scale_weights  # type: ignore[import-not-found]
+        tmp_model_path = args.model / "weighted_model"
+        if tmp_model_path.is_dir():
+            print(f"{tmp_model_path} exists as a weighted model.")
+        else:
+            tmp_model_path.mkdir(parents=True, exist_ok=True)
+            print("Saving new weighted model ...")
+            add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path))
+            print(f"Saved weighted model at {tmp_model_path}.")
+        args.model = tmp_model_path
 
     if args.dump_single:
         model_plus = lazy_load_file(args.model)
@@ -1451,7 +1448,7 @@ def main(args_in: list[str] | None = None) -> None:
     model_parent_path = model_plus.paths[0].parent
     vocab_path = Path(args.vocab_dir or args.model or model_parent_path)
     vocab_factory = VocabFactory(vocab_path)
-    vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type.split(","), model_parent_path)
+    vocab, special_vocab = vocab_factory.load_vocab(args.vocab_type, model_parent_path)
 
     if args.vocab_only:
         if not args.outfile:
@@ -1462,7 +1459,7 @@ def main(args_in: list[str] | None = None) -> None:
         print(f"Wrote {outfile}")
         return
 
-    if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab:
+    if model_plus.vocab is not None and args.vocab_dir is None:
         vocab = model_plus.vocab
 
     print(f"Vocab info: {vocab}")

examples/CMakeLists.txt

@@ -20,7 +20,6 @@ else()
     add_subdirectory(convert-llama2c-to-ggml)
     add_subdirectory(embedding)
     add_subdirectory(finetune)
-    add_subdirectory(gritlm)
     add_subdirectory(infill)
     add_subdirectory(llama-bench)
     add_subdirectory(llava)

examples/batched-bench/batched-bench.cpp

@@ -32,15 +32,16 @@ int main(int argc, char ** argv) {
     gpt_params params;
 
     if (argc == 1 || argv[1][0] == '-') {
-        printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] <PP> <TG> <PL>\n" , argv[0]);
+        printf("usage: %s MODEL_PATH [N_KV_MAX] [IS_PP_SHARED] [NGL] [MMQ] <PP> <TG> <PL>\n" , argv[0]);
         printf("  <PP>, <TG> and PL are comma-separated lists of numbers without spaces\n\n");
-        printf("  example: %s ggml-model-f16.gguf 2048 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
+        printf("  example: %s ggml-model-f16.gguf 2048 0 999 0 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]);
         return 1 ;
     }
 
     int n_kv_max     = 2048;
     int is_pp_shared = 0;
     int n_gpu_layers = 0;
+    int mmq          = 0;
 
     std::vector<int> n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, };
     std::vector<int> n_tg = { 128, 256, };
@@ -64,15 +65,19 @@ int main(int argc, char ** argv) {
     }
 
     if (argc >= 6) {
-        n_pp = parse_list(argv[5]);
+        mmq = std::atoi(argv[5]);
     }
 
     if (argc >= 7) {
-        n_tg = parse_list(argv[6]);
+        n_pp = parse_list(argv[6]);
     }
 
     if (argc >= 8) {
-        n_pl = parse_list(argv[7]);
+        n_tg = parse_list(argv[7]);
+    }
+
+    if (argc >= 9) {
+        n_pl = parse_list(argv[8]);
     }
 
     // init LLM
@@ -101,13 +106,11 @@ int main(int argc, char ** argv) {
     ctx_params.seed      = 1234;
     ctx_params.n_ctx     = n_kv_max;
     ctx_params.n_batch   = 512;
+    ctx_params.mul_mat_q = mmq;
 
     ctx_params.n_threads       = params.n_threads;
     ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
-    // ensure enough sequences are available
-    ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end());
-
     llama_context * ctx = llama_new_context_with_model(model, ctx_params);
 
     if (ctx == NULL) {
@@ -138,8 +141,6 @@ int main(int argc, char ** argv) {
                 LOG_TEE("failed to decode the batch, n_batch = %d, ret = %d\n", n_batch, ret);
                 return false;
             }
-
-            llama_synchronize(ctx);
         }
 
         return true;
@@ -158,7 +159,7 @@ int main(int argc, char ** argv) {
     }
 
     LOG_TEE("\n");
-    LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch);
+    LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch);
     LOG_TEE("\n");
 
     LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP",     "TG",     "B",    "N_KV",     "T_PP s",   "S_PP t/s", "T_TG s",   "S_TG t/s", "T s",      "S t/s");
@@ -179,10 +180,10 @@ int main(int argc, char ** argv) {
 
                 llama_batch_clear(batch);
 
-                for (int i = 0; i < pp; ++i) {
-                    for (int j = 0; j < (is_pp_shared ? 1 : pl); ++j) {
-                        llama_batch_add(batch, 0, i, { j }, false);
-                    }
+                const int n_tokens = is_pp_shared ? pp : pl*pp;
+
+                for (int i = 0; i < n_tokens; ++i) {
+                    llama_batch_add(batch, 0, i, { 0 }, false);
                 }
                 batch.logits[batch.n_tokens - 1] = true;
 
@@ -197,7 +198,7 @@ int main(int argc, char ** argv) {
 
                 if (is_pp_shared) {
                     for (int32_t i = 1; i < pl; ++i) {
-                        llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
+                        llama_kv_cache_seq_cp(ctx, 0, i, 0, pp);
                     }
                 }
 

examples/batched/batched.cpp

@@ -80,7 +80,6 @@ int main(int argc, char ** argv) {
     ctx_params.seed  = 1234;
     ctx_params.n_ctx = n_kv_req;
     ctx_params.n_batch = std::max(n_len, n_parallel);
-    ctx_params.n_seq_max       = n_parallel;
     ctx_params.n_threads       = params.n_threads;
     ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
 
@@ -133,7 +132,7 @@ int main(int argc, char ** argv) {
     // assign the system KV cache to all parallel sequences
     // this way, the parallel sequences will "reuse" the prompt tokens without having to copy them
     for (int32_t i = 1; i < n_parallel; ++i) {
-        llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
+        llama_kv_cache_seq_cp(ctx, 0, i, 0, batch.n_tokens);
     }
 
     if (n_parallel > 1) {

examples/benchmark/benchmark-matmult.cpp

@@ -189,10 +189,12 @@ int main(int argc, char ** argv)  {
 
     int32_t nelements = sizex*sizey;
 
+    std::vector<int64_t> hist_cur(1 << 4, 0);
+
     // Set up a the benchmark matrices
     // printf("Creating new tensor q11 & Running quantize\n");
     struct ggml_tensor * q11 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
-    ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], nullptr);
+    ggml_quantize_chunk(qtype, (const float *) m11->data, q11->data, 0, nelements/m11->ne[0], m11->ne[0], hist_cur.data(), nullptr);
 
     // Set up a the compute graph
     // printf("Creating new tensor q31\n");
@@ -205,7 +207,7 @@ int main(int argc, char ** argv)  {
     // Set up a second graph computation to make sure we override the CPU cache lines
     // printf("Creating new tensor q12 & Running quantize\n");
     struct ggml_tensor * q12 = ggml_new_tensor_2d(ctx, qtype, sizex, sizey);
-    ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], nullptr);
+    ggml_quantize_chunk(qtype, (const float *) m12->data, q12->data, 0, nelements/m12->ne[0], m12->ne[0], hist_cur.data(), nullptr);
 
     // printf("Creating new tensor q32\n");
     struct ggml_tensor * q32 = ggml_mul_mat(ctx, q12, m2);

examples/embedding/embedding.cpp

@@ -19,7 +19,18 @@ static std::vector<std::string> split_lines(const std::string & s) {
 
 static void batch_add_seq(llama_batch & batch, const std::vector<int32_t> & tokens, int seq_id) {
     for (size_t i = 0; i < tokens.size(); i++) {
-        llama_batch_add(batch, tokens[i], i, { seq_id }, i == tokens.size() - 1);
+        llama_batch_add(batch, tokens[i], i, { seq_id }, false);
+    }
+}
+
+static void normalize(float * vec, float * out, int n) {
+    float norm = 0;
+    for (int i = 0; i < n; i++) {
+        norm += vec[i] * vec[i];
+    }
+    norm = sqrt(norm);
+    for (int i = 0; i < n; i++) {
+        out[i] = vec[i] / norm;
     }
 }
 
@@ -33,23 +44,11 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu
         fprintf(stderr, "%s : failed to decode\n", __func__);
     }
 
-    for (int i = 0; i < batch.n_tokens; i++) {
-        if (!batch.logits[i]) {
-            continue;
-        }
-
-        // try to get sequence embeddings - supported only when pooling_type is not NONE
-        const float * embd = llama_get_embeddings_seq(ctx, batch.seq_id[i][0]);
-        if (embd == NULL) {
-            embd = llama_get_embeddings_ith(ctx, i);
-            if (embd == NULL) {
-                fprintf(stderr, "%s: failed to get embeddings for token %d\n", __func__, i);
-                continue;
-            }
-        }
-
-        float * out = output + batch.seq_id[i][0] * n_embd;
-        llama_embd_normalize(embd, out, n_embd);
+    // normalize on copy
+    for (int k = 0; k < n_seq; k++) {
+        float * emb = llama_get_embeddings_ith(ctx, k);
+        float * out = output + k * n_embd;
+        normalize(emb, out, n_embd);
     }
 }
 
@@ -107,25 +106,18 @@ int main(int argc, char ** argv) {
 
     // max batch size
     const uint64_t n_batch = params.n_batch;
-    GGML_ASSERT(params.n_batch >= params.n_ctx);
+    GGML_ASSERT(params.n_batch == params.n_ctx);
 
     // tokenize the prompts and trim
     std::vector<std::vector<int32_t>> inputs;
     for (const auto & prompt : prompts) {
-        auto inp = ::llama_tokenize(ctx, prompt, true, false);
+        auto inp = ::llama_tokenize(ctx, prompt, true);
         if (inp.size() > n_batch) {
             inp.resize(n_batch);
         }
         inputs.push_back(inp);
     }
 
-    // add eos if not present
-    for (auto & inp : inputs) {
-        if (inp.empty() || inp.back() != llama_token_eos(model)) {
-            inp.push_back(llama_token_eos(model));
-        }
-    }
-
     // tokenization stats
     if (params.verbose_prompt) {
         for (int i = 0; i < (int) inputs.size(); i++) {
@@ -140,7 +132,7 @@ int main(int argc, char ** argv) {
 
     // initialize batch
     const int n_prompts = prompts.size();
-    struct llama_batch batch = llama_batch_init(n_batch, 0, 1);
+    struct llama_batch batch = llama_batch_init(n_batch, 0, n_prompts);
 
     // allocate output
     const int n_embd = llama_n_embd(model);
@@ -153,7 +145,6 @@ int main(int argc, char ** argv) {
     for (int k = 0; k < n_prompts; k++) {
         // clamp to n_batch tokens
         auto & inp = inputs[k];
-
         const uint64_t n_toks = inp.size();
 
         // encode if at capacity
@@ -174,26 +165,15 @@ int main(int argc, char ** argv) {
     float * out = emb + p * n_embd;
     batch_decode(ctx, batch, out, s, n_embd);
 
-    // print the first part of the embeddings
-    fprintf(stdout, "\n");
-    for (int j = 0; j < n_prompts; j++) {
-        fprintf(stdout, "embedding %d: ", j);
-        for (int i = 0; i < std::min(16, n_embd); i++) {
-            fprintf(stdout, "%9.6f ", emb[j * n_embd + i]);
+    // print first 3 embeddings
+    for (int j = 0; j < std::min(3, n_prompts); j++) {
+        fprintf(stderr, "embedding %d: ", j);
+        for (int i = 0; i < n_embd; i++) {
+            fprintf(stderr, "%f ", emb[j * n_embd + i]);
         }
-        fprintf(stdout, "\n");
-    }
-
-    // print cosine similarity matrix
-    fprintf(stdout, "\n");
-    printf("cosine similarity matrix:\n\n");
-    for (int i = 0; i < n_prompts; i++) {
-        for (int j = 0; j < n_prompts; j++) {
-            float sim = llama_embd_similarity_cos(emb + i * n_embd, emb + j * n_embd, n_embd);
-            fprintf(stdout, "%6.2f ", sim);
-        }
-        fprintf(stdout, "\n");
+        fprintf(stderr, "\n\n");
     }
+    fprintf(stderr, "\n");
 
     // clean up
     llama_print_timings(ctx);

examples/gguf/gguf.cpp

@@ -211,7 +211,6 @@ static bool gguf_ex_read_1(const std::string & fname) {
                 for (int j = 0; j < ggml_nelements(cur); ++j) {
                     if (data[j] != 100 + i) {
                         fprintf(stderr, "%s: tensor[%d]: data[%d] = %f\n", __func__, i, j, data[j]);
-                        gguf_free(ctx);
                         return false;
                     }
                 }

examples/gritlm/CMakeLists.txt

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

examples/gritlm/gritlm.cpp

@@ -1,215 +0,0 @@
-#include "common.h"
-#include "llama.h"
-
-#include <string>
-#include <vector>
-
-// #define GRIT_DEBUG
-
-static std::vector<std::vector<float>> encode(llama_context * ctx, const std::vector<std::string> & sentences, const std::string & instruction) {
-    std::vector<std::vector<float>> result;
-
-    const llama_model * mdl = llama_get_model(ctx);
-
-    llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1);
-
-    for (uint64_t i = 0; i < sentences.size(); i++) {
-        llama_batch_clear(batch);
-
-        const std::string input_string = instruction + sentences[i];
-
-        std::vector<llama_token> inputs = llama_tokenize(mdl, input_string, true, false);
-
-        const int32_t n_toks = inputs.size();
-
-        // GritLM seems to have EOS = ""
-        // https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18
-        // inputs.push_back(llama_token_eos(mdl));
-
-        // we want to ignore instruction tokens for mean pooling
-        const int32_t n_inst = llama_tokenize(mdl, instruction, true, false).size();
-
-#ifdef GRIT_DEBUG
-        // debug tokens - should be matching as referenced in the GritLM sample
-        std::for_each(inputs.begin(), inputs.end(), [&ctx](llama_token t) {
-            std::printf("[%u:%s]", t, llama_token_to_piece(ctx, t).c_str());
-        });
-        std::printf("\n");
-#endif
-
-        // add input to batch (this increments n_tokens)
-        for (int32_t j = 0; j < n_toks; j++) {
-            llama_batch_add(batch, inputs[j], j, { 0 }, j >= n_inst);
-        }
-
-        // clear previous kv_cache values (irrelevant for embeddings)
-        llama_kv_cache_clear(ctx);
-        llama_set_causal_attn(ctx, false);
-
-        // run model
-        llama_decode(ctx, batch);
-
-        // get embedding dimensions
-        uint64_t n_embd = llama_n_embd(mdl);
-
-        // allocate embedding output
-        std::vector<float> emb_unorm(n_embd, 0.0f);
-
-        // sum up all token embeddings
-        for (int32_t k = n_inst; k < n_toks; k++) {
-            float * emb = llama_get_embeddings_ith(ctx, k);
-            for (uint64_t j = 0; j < n_embd; j++) {
-                emb_unorm[j] += emb[j];
-            }
-        }
-
-        // divide by number of tokens (mean pooling)
-        {
-            const uint64_t n_sent = n_toks - n_inst;
-
-            for (uint64_t j = 0; j < n_embd; j++) {
-                emb_unorm[j] /= n_sent;
-            }
-        }
-
-        std::vector<float> emb_norm(emb_unorm.size());
-        llama_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd);
-        result.push_back(emb_norm);
-
-#ifdef GRIT_DEBUG
-        // print out emb_norm
-        std::printf("embedding %ld: ", i);
-        for (uint64_t j = 0; j < n_embd; j++) {
-            std::printf("%.5f ", emb_norm[j]);
-        }
-        std::printf("\n\n");
-#endif
-    }
-
-    llama_batch_free(batch);
-
-    return result;
-}
-
-static std::string generate(llama_context * ctx, const std::string & prompt, bool stream) {
-    std::string result;
-
-    const llama_model * mdl = llama_get_model(ctx);
-    llama_token eos_token = llama_token_eos(mdl);
-
-    llama_kv_cache_clear(ctx);
-    llama_set_causal_attn(ctx, true);
-    llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
-
-    std::vector<llama_token> inputs = llama_tokenize(mdl, prompt, false, true);
-    int32_t i_current_token = 0;
-
-    while (true) {
-        llama_batch_clear(bat);
-        auto n_inputs = (int32_t)inputs.size();
-        for (int32_t i = 0; i < n_inputs; i++) {
-            llama_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1);
-        }
-        inputs.clear();
-
-        llama_decode(ctx, bat);
-        auto logits = llama_get_logits_ith(ctx, bat.n_tokens - 1);
-
-        auto candidates = std::vector<llama_token_data>(llama_n_vocab(mdl));
-        auto n_candidates = (int32_t)candidates.size();
-        for (int32_t token = 0; token < n_candidates; token++) {
-            candidates[token] = llama_token_data{ token, logits[token], 0.0f };
-        }
-        auto candidates_p = llama_token_data_array{ candidates.data(), candidates.size(), false };
-
-        llama_token token = llama_sample_token_greedy(ctx, &candidates_p);
-        if (token == eos_token) {
-            break;
-        }
-
-        std::string piece = llama_token_to_piece(ctx, token);
-        if (stream) {
-            std::printf("%s", piece.c_str());
-            std::fflush(stdout);
-        }
-
-        inputs.push_back(token);
-
-        result += piece;
-    }
-
-    if (stream) {
-        std::printf("\n");
-    }
-
-    llama_batch_free(bat);
-
-    return result;
-}
-
-static std::string gritlm_instruction(const std::string & instruction) {
-    return !instruction.empty() ? "<|user|>\n" + instruction + "\n<|embed|>\n" : "<|embed|>\n";
-}
-
-int main(int argc, char * argv[]) {
-    gpt_params params;
-    if (!gpt_params_parse(argc, argv, params)) {
-        return 1;
-    }
-
-    llama_model_params mparams = llama_model_params_from_gpt_params(params);
-    llama_context_params cparams = llama_context_params_from_gpt_params(params);
-
-    llama_backend_init();
-
-    llama_model * mdl = llama_load_model_from_file(params.model.c_str(), mparams);
-
-    // create new context - set to embedding mode
-    cparams.embeddings = true;
-    llama_context * ctx = llama_new_context_with_model(mdl, cparams);
-
-    // ### Embedding/Representation ###
-    // samples taken from: https://github.com/ContextualAI/gritlm#basic
-    {
-        const std::string instruction = "Given a scientific paper title, retrieve the paper's abstract";
-
-        const std::vector<std::string> queries = {
-            "Bitcoin: A Peer-to-Peer Electronic Cash System",
-            "Generative Representational Instruction Tuning",
-        };
-
-        const std::vector<std::string> documents = {
-            "A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.",
-            "All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.",
-        };
-
-        // No need to add instruction for retrieval documents
-        const std::vector<std::vector<float>> d_rep = encode(ctx, documents, gritlm_instruction(""));
-        const std::vector<std::vector<float>> q_rep = encode(ctx, queries,   gritlm_instruction(instruction));
-
-        const int n_embd = llama_n_embd(mdl);
-
-        const float cosine_sim_q0_d0 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
-        const float cosine_sim_q0_d1 = llama_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
-        const float cosine_sim_q1_d0 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd);
-        const float cosine_sim_q1_d1 = llama_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd);
-
-        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0);
-        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1);
-        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[0].c_str(), cosine_sim_q1_d0);
-        std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[1].c_str(), cosine_sim_q1_d1);
-    }
-
-    // ### Generation ###
-    // GritLM models are not finetuned with system prompts, as you can just include system-like instructions together with your user instruction
-    {
-        const std::string prompt = "<|user|>\nPlease write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare.\n<|assistant|>\n";
-        std::string response = generate(ctx, prompt, true);
-    }
-
-    llama_free(ctx);
-    llama_free_model(mdl);
-    llama_backend_free();
-
-    return 0;
-}

examples/infill/infill.cpp

@@ -378,10 +378,10 @@ int main(int argc, char ** argv) {
     if (params.interactive) {
         const char *control_message;
         if (params.multiline_input) {
-            control_message = " - To return control to LLaMA, end your input with '\\'.\n"
+            control_message = " - To return control to LLaMa, end your input with '\\'.\n"
                               " - To return control without starting a new line, end your input with '/'.\n";
         } else {
-            control_message = " - Press Return to return control to LLaMA.\n"
+            control_message = " - Press Return to return control to LLaMa.\n"
                               " - To return control without starting a new line, end your input with '/'.\n"
                               " - If you want to submit another line, end your input with '\\'.\n";
         }

examples/llama-bench/README.md

@@ -35,6 +35,7 @@ options:
   -mg, --main-gpu <i>                 (default: 0)
   -nkvo, --no-kv-offload <0|1>        (default: 0)
   -mmp, --mmap <0|1>                  (default: 1)
+  -mmq, --mul-mat-q <0|1>             (default: 1)
   -ts, --tensor_split <ts0/ts1/..>    (default: 0)
   -r, --repetitions <n>               (default: 5)
   -o, --output <csv|json|md|sql>      (default: md)

examples/llama-bench/llama-bench.cpp

@@ -8,7 +8,6 @@
 #include <cstdio>
 #include <cstring>
 #include <ctime>
-#include <cstdlib>
 #include <iterator>
 #include <map>
 #include <numeric>
@@ -104,7 +103,6 @@ static std::string get_cpu_info() {
                 }
             }
         }
-        fclose(f);
     }
 #endif
     // TODO: other platforms
@@ -125,15 +123,20 @@ static std::string get_gpu_info() {
     }
 #endif
 #ifdef GGML_USE_SYCL
-    int count = ggml_backend_sycl_get_device_count();
-    for (int i = 0; i < count; i++) {
-        char buf[128];
-        ggml_sycl_get_device_description(i, buf, sizeof(buf));
-        id += buf;
-        if (i < count - 1) {
+    int device_list[GGML_SYCL_MAX_DEVICES];
+    ggml_sycl_get_gpu_list(device_list, GGML_SYCL_MAX_DEVICES);
+
+    for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) {
+        if (device_list[i] >0 ){
+            char buf[128];
+            ggml_sycl_get_device_description(i, buf, sizeof(buf));
+            id += buf;
             id += "/";
         }
     }
+    if (id.length() >2 ) {
+        id.pop_back();
+    }
 #endif
     // TODO: other backends
     return id;
@@ -166,7 +169,6 @@ struct cmd_params {
     std::vector<int> n_prompt;
     std::vector<int> n_gen;
     std::vector<int> n_batch;
-    std::vector<int> n_ubatch;
     std::vector<ggml_type> type_k;
     std::vector<ggml_type> type_v;
     std::vector<int> n_threads;
@@ -174,9 +176,9 @@ struct cmd_params {
     std::vector<llama_split_mode> split_mode;
     std::vector<int> main_gpu;
     std::vector<bool> no_kv_offload;
+    std::vector<bool> mul_mat_q;
     std::vector<std::vector<float>> tensor_split;
     std::vector<bool> use_mmap;
-    std::vector<bool> embeddings;
     int reps;
     bool verbose;
     output_formats output_format;
@@ -186,8 +188,7 @@ static const cmd_params cmd_params_defaults = {
     /* model         */ {"models/7B/ggml-model-q4_0.gguf"},
     /* n_prompt      */ {512},
     /* n_gen         */ {128},
-    /* n_batch       */ {2048},
-    /* n_ubatch      */ {512},
+    /* n_batch       */ {512},
     /* type_k        */ {GGML_TYPE_F16},
     /* type_v        */ {GGML_TYPE_F16},
     /* n_threads     */ {get_num_physical_cores()},
@@ -195,9 +196,9 @@ static const cmd_params cmd_params_defaults = {
     /* split_mode    */ {LLAMA_SPLIT_MODE_LAYER},
     /* main_gpu      */ {0},
     /* no_kv_offload */ {false},
+    /* mul_mat_q     */ {true},
     /* tensor_split  */ {std::vector<float>(llama_max_devices(), 0.0f)},
     /* use_mmap      */ {true},
-    /* embeddings    */ {false},
     /* reps          */ 5,
     /* verbose       */ false,
     /* output_format */ MARKDOWN
@@ -212,7 +213,6 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -p, --n-prompt <n>                  (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
     printf("  -n, --n-gen <n>                     (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
     printf("  -b, --batch-size <n>                (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
-    printf("  -ub N, --ubatch-size <n>            (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str());
     printf("  -ctk <t>, --cache-type-k <t>        (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
     printf("  -ctv <t>, --cache-type-v <t>        (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
     printf("  -t, --threads <n>                   (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
@@ -221,8 +221,8 @@ static void print_usage(int /* argc */, char ** argv) {
     printf("  -mg, --main-gpu <i>                 (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
     printf("  -nkvo, --no-kv-offload <0|1>        (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str());
     printf("  -mmp, --mmap <0|1>                  (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str());
-    printf("  -embd, --embeddings <0|1>           (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str());
-    printf("  -ts, --tensor-split <ts0/ts1/..>    (default: 0)\n");
+    printf("  -mmq, --mul-mat-q <0|1>             (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str());
+    printf("  -ts, --tensor_split <ts0/ts1/..>    (default: 0)\n");
     printf("  -r, --repetitions <n>               (default: %d)\n", cmd_params_defaults.reps);
     printf("  -o, --output <csv|json|md|sql>      (default: %s)\n", output_format_str(cmd_params_defaults.output_format));
     printf("  -v, --verbose                       (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
@@ -302,13 +302,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             }
             auto p = split<int>(argv[i], split_delim);
             params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
-        } else if (arg == "-ub" || arg == "--ubatch-size") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            auto p = split<int>(argv[i], split_delim);
-            params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end());
         } else if (arg == "-ctk" || arg == "--cache-type-k") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -390,6 +383,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             }
             auto p = split<bool>(argv[i], split_delim);
             params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end());
+        } else if (arg == "-mmq" || arg == "--mul-mat-q") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            auto p = split<bool>(argv[i], split_delim);
+            params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end());
         } else if (arg == "-mmp" || arg == "--mmap") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -397,13 +397,6 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
             }
             auto p = split<bool>(argv[i], split_delim);
             params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end());
-        } else if (arg == "-embd" || arg == "--embeddings") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            auto p = split<bool>(argv[i], split_delim);
-            params.embeddings.insert(params.embeddings.end(), p.begin(), p.end());
         } else if (arg == "-ts" || arg == "--tensor-split") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -467,16 +460,15 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
     if (params.n_prompt.empty())     { params.n_prompt = cmd_params_defaults.n_prompt; }
     if (params.n_gen.empty())        { params.n_gen = cmd_params_defaults.n_gen; }
     if (params.n_batch.empty())      { params.n_batch = cmd_params_defaults.n_batch; }
-    if (params.n_ubatch.empty())     { params.n_ubatch = cmd_params_defaults.n_ubatch; }
     if (params.type_k.empty())       { params.type_k = cmd_params_defaults.type_k; }
     if (params.type_v.empty())       { params.type_v = cmd_params_defaults.type_v; }
     if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
     if (params.split_mode.empty())   { params.split_mode = cmd_params_defaults.split_mode; }
     if (params.main_gpu.empty())     { params.main_gpu = cmd_params_defaults.main_gpu; }
     if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; }
+    if (params.mul_mat_q.empty())    { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
     if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; }
     if (params.use_mmap.empty())     { params.use_mmap = cmd_params_defaults.use_mmap; }
-    if (params.embeddings.empty())   { params.embeddings = cmd_params_defaults.embeddings; }
     if (params.n_threads.empty())    { params.n_threads = cmd_params_defaults.n_threads; }
 
     return params;
@@ -487,7 +479,6 @@ struct cmd_params_instance {
     int n_prompt;
     int n_gen;
     int n_batch;
-    int n_ubatch;
     ggml_type type_k;
     ggml_type type_v;
     int n_threads;
@@ -495,9 +486,9 @@ struct cmd_params_instance {
     llama_split_mode split_mode;
     int main_gpu;
     bool no_kv_offload;
+    bool mul_mat_q;
     std::vector<float> tensor_split;
     bool use_mmap;
-    bool embeddings;
 
     llama_model_params to_llama_mparams() const {
         llama_model_params mparams = llama_model_default_params();
@@ -525,11 +516,10 @@ struct cmd_params_instance {
 
         cparams.n_ctx = n_prompt + n_gen;
         cparams.n_batch = n_batch;
-        cparams.n_ubatch = n_ubatch;
         cparams.type_k = type_k;
         cparams.type_v = type_v;
+        cparams.mul_mat_q = mul_mat_q;
         cparams.offload_kqv = !no_kv_offload;
-        cparams.embeddings = embeddings;
 
         return cparams;
     }
@@ -545,11 +535,10 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
     for (const auto & mg : params.main_gpu)
     for (const auto & ts : params.tensor_split)
     for (const auto & mmp : params.use_mmap)
-    for (const auto & embd : params.embeddings)
     for (const auto & nb : params.n_batch)
-    for (const auto & nub : params.n_ubatch)
     for (const auto & tk : params.type_k)
     for (const auto & tv : params.type_v)
+    for (const auto & mmq : params.mul_mat_q)
     for (const auto & nkvo : params.no_kv_offload)
     for (const auto & nt : params.n_threads) {
         for (const auto & n_prompt : params.n_prompt) {
@@ -561,7 +550,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_prompt     = */ n_prompt,
                 /* .n_gen        = */ 0,
                 /* .n_batch      = */ nb,
-                /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
                 /* .n_threads    = */ nt,
@@ -569,9 +557,9 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .split_mode   = */ sm,
                 /* .main_gpu     = */ mg,
                 /* .no_kv_offload= */ nkvo,
+                /* .mul_mat_q    = */ mmq,
                 /* .tensor_split = */ ts,
                 /* .use_mmap     = */ mmp,
-                /* .embeddings   = */ embd,
             };
             instances.push_back(instance);
         }
@@ -585,7 +573,6 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .n_prompt     = */ 0,
                 /* .n_gen        = */ n_gen,
                 /* .n_batch      = */ nb,
-                /* .n_ubatch     = */ nub,
                 /* .type_k       = */ tk,
                 /* .type_v       = */ tv,
                 /* .n_threads    = */ nt,
@@ -593,9 +580,9 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
                 /* .split_mode   = */ sm,
                 /* .main_gpu     = */ mg,
                 /* .no_kv_offload= */ nkvo,
+                /* .mul_mat_q    = */ mmq,
                 /* .tensor_split = */ ts,
                 /* .use_mmap     = */ mmp,
-                /* .embeddings   = */ embd,
             };
             instances.push_back(instance);
         }
@@ -622,7 +609,6 @@ struct test {
     uint64_t model_size;
     uint64_t model_n_params;
     int n_batch;
-    int n_ubatch;
     int n_threads;
     ggml_type type_k;
     ggml_type type_v;
@@ -630,9 +616,9 @@ struct test {
     llama_split_mode split_mode;
     int main_gpu;
     bool no_kv_offload;
+    bool mul_mat_q;
     std::vector<float> tensor_split;
     bool use_mmap;
-    bool embeddings;
     int n_prompt;
     int n_gen;
     std::string test_time;
@@ -646,7 +632,6 @@ struct test {
         model_size = llama_model_size(lmodel);
         model_n_params = llama_model_n_params(lmodel);
         n_batch = inst.n_batch;
-        n_ubatch = inst.n_ubatch;
         n_threads = inst.n_threads;
         type_k = inst.type_k;
         type_v = inst.type_v;
@@ -654,9 +639,9 @@ struct test {
         split_mode = inst.split_mode;
         main_gpu = inst.main_gpu;
         no_kv_offload = inst.no_kv_offload;
+        mul_mat_q = inst.mul_mat_q;
         tensor_split = inst.tensor_split;
         use_mmap = inst.use_mmap;
-        embeddings = inst.embeddings;
         n_prompt = inst.n_prompt;
         n_gen = inst.n_gen;
         // RFC 3339 date-time format
@@ -725,11 +710,10 @@ struct test {
             "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas",
             "cpu_info", "gpu_info",
             "model_filename", "model_type", "model_size", "model_n_params",
-            "n_batch", "n_ubatch",
-            "n_threads", "type_k", "type_v",
+            "n_batch", "n_threads", "type_k", "type_v",
             "n_gpu_layers", "split_mode",
             "main_gpu", "no_kv_offload",
-            "tensor_split", "use_mmap", "embeddings",
+            "mul_mat_q", "tensor_split", "use_mmap",
             "n_prompt", "n_gen", "test_time",
             "avg_ns", "stddev_ns",
             "avg_ts", "stddev_ts"
@@ -740,8 +724,7 @@ struct test {
     enum field_type {STRING, BOOL, INT, FLOAT};
 
     static field_type get_field_type(const std::string & field) {
-        if (field == "build_number" || field == "n_batch" || field == "n_ubatch" ||
-            field == "n_threads" ||
+        if (field == "build_number" || field == "n_batch" || field == "n_threads" ||
             field == "model_size" || field == "model_n_params" ||
             field == "n_gpu_layers" || field == "main_gpu" ||
             field == "n_prompt" || field == "n_gen" ||
@@ -750,7 +733,7 @@ struct test {
         }
         if (field == "cuda" || field == "opencl"  || field == "vulkan" || field == "kompute" || field == "metal" ||
             field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" ||
-            field == "use_mmap" || field == "embeddings") {
+            field == "mul_mat_q" || field == "use_mmap") {
             return BOOL;
         }
         if (field == "avg_ts" || field == "stddev_ts") {
@@ -781,11 +764,10 @@ struct test {
             std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas),
             cpu_info, gpu_info,
             model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
-            std::to_string(n_batch), std::to_string(n_ubatch),
-            std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
+            std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
             std::to_string(n_gpu_layers), split_mode_str(split_mode),
             std::to_string(main_gpu), std::to_string(no_kv_offload),
-            tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings),
+            std::to_string(mul_mat_q), tensor_split_str, std::to_string(use_mmap),
             std::to_string(n_prompt), std::to_string(n_gen), test_time,
             std::to_string(avg_ns()), std::to_string(stdev_ns()),
             std::to_string(avg_ts()), std::to_string(stdev_ts())
@@ -949,15 +931,15 @@ struct markdown_printer : public printer {
         if (field == "n_threads") {
             return "threads";
         }
+        if (field == "mul_mat_q") {
+            return "mmq";
+        }
         if (field == "no_kv_offload") {
             return "nkvo";
         }
         if (field == "use_mmap") {
             return "mmap";
         }
-        if (field == "embeddings") {
-            return "embd";
-        }
         if (field == "tensor_split") {
             return "ts";
         }
@@ -980,9 +962,6 @@ struct markdown_printer : public printer {
         if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
             fields.emplace_back("n_batch");
         }
-        if (params.n_ubatch.size() > 1 || params.n_ubatch != cmd_params_defaults.n_ubatch) {
-            fields.emplace_back("n_ubatch");
-        }
         if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) {
             fields.emplace_back("type_k");
         }
@@ -995,6 +974,9 @@ struct markdown_printer : public printer {
         if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) {
             fields.emplace_back("split_mode");
         }
+        if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) {
+            fields.emplace_back("mul_mat_q");
+        }
         if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) {
             fields.emplace_back("no_kv_offload");
         }
@@ -1004,9 +986,6 @@ struct markdown_printer : public printer {
         if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) {
             fields.emplace_back("use_mmap");
         }
-        if (params.embeddings.size() > 1 || params.embeddings != cmd_params_defaults.embeddings) {
-            fields.emplace_back("embeddings");
-        }
         fields.emplace_back("test");
         fields.emplace_back("t/s");
 
@@ -1122,40 +1101,25 @@ struct sql_printer : public printer {
 };
 
 static void test_prompt(llama_context * ctx, int n_prompt, int n_past, int n_batch, int n_threads) {
-    llama_set_n_threads(ctx, n_threads, n_threads);
-
-    const llama_model * model = llama_get_model(ctx);
-    const int32_t n_vocab = llama_n_vocab(model);
-
-    std::vector<llama_token> tokens(n_batch);
-
+    std::vector<llama_token> tokens(n_batch, llama_token_bos(llama_get_model(ctx)));
     int n_processed = 0;
 
+    llama_set_n_threads(ctx, n_threads, n_threads);
+
     while (n_processed < n_prompt) {
         int n_tokens = std::min(n_prompt - n_processed, n_batch);
-        tokens[0] = n_processed == 0 && llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
-        for (int i = 1; i < n_tokens; i++) {
-            tokens[i] = std::rand() % n_vocab;
-        }
         llama_decode(ctx, llama_batch_get_one(tokens.data(), n_tokens, n_past + n_processed, 0));
         n_processed += n_tokens;
     }
-
-    llama_synchronize(ctx);
 }
 
 static void test_gen(llama_context * ctx, int n_gen, int n_past, int n_threads) {
+    llama_token token = llama_token_bos(llama_get_model(ctx));
+
     llama_set_n_threads(ctx, n_threads, n_threads);
 
-    const llama_model * model = llama_get_model(ctx);
-    const int32_t n_vocab = llama_n_vocab(model);
-
-    llama_token token = llama_add_bos_token(model) ? llama_token_bos(model) : std::rand() % n_vocab;
-
     for (int i = 0; i < n_gen; i++) {
         llama_decode(ctx, llama_batch_get_one(&token, 1, n_past + i, 0));
-        llama_synchronize(ctx);
-        token = std::rand() % n_vocab;
     }
 }
 
@@ -1244,8 +1208,7 @@ int main(int argc, char ** argv) {
 
         // warmup run
         if (t.n_prompt > 0) {
-            //test_prompt(ctx, std::min(t.n_batch, std::min(t.n_prompt, 32)), 0, t.n_batch, t.n_threads);
-            test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads);
+            test_prompt(ctx, std::min(2, t.n_batch), 0, t.n_batch, t.n_threads);
         }
         if (t.n_gen > 0) {
             test_gen(ctx, 1, 0, t.n_threads);
@@ -1261,7 +1224,6 @@ int main(int argc, char ** argv) {
             if (t.n_gen > 0) {
                 test_gen(ctx, t.n_gen, t.n_prompt, t.n_threads);
             }
-
             uint64_t t_ns = get_time_ns() - t_start;
             t.samples_ns.push_back(t_ns);
         }

examples/llama.android/app/src/main/cpp/llama-android.cpp

@@ -33,45 +33,6 @@ jclass la_int_var;
 jmethodID la_int_var_value;
 jmethodID la_int_var_inc;
 
-std::string cached_token_chars;
-
-bool is_valid_utf8(const char * string) {
-    if (!string) {
-        return true;
-    }
-
-    const unsigned char * bytes = (const unsigned char *)string;
-    int num;
-
-    while (*bytes != 0x00) {
-        if ((*bytes & 0x80) == 0x00) {
-            // U+0000 to U+007F
-            num = 1;
-        } else if ((*bytes & 0xE0) == 0xC0) {
-            // U+0080 to U+07FF
-            num = 2;
-        } else if ((*bytes & 0xF0) == 0xE0) {
-            // U+0800 to U+FFFF
-            num = 3;
-        } else if ((*bytes & 0xF8) == 0xF0) {
-            // U+10000 to U+10FFFF
-            num = 4;
-        } else {
-            return false;
-        }
-
-        bytes += 1;
-        for (int i = 1; i < num; ++i) {
-            if ((*bytes & 0xC0) != 0x80) {
-                return false;
-            }
-            bytes += 1;
-        }
-    }
-
-    return true;
-}
-
 static void log_callback(ggml_log_level level, const char * fmt, void * data) {
     if (level == GGML_LOG_LEVEL_ERROR)     __android_log_print(ANDROID_LOG_ERROR, TAG, fmt, data);
     else if (level == GGML_LOG_LEVEL_INFO) __android_log_print(ANDROID_LOG_INFO, TAG, fmt, data);
@@ -334,8 +295,6 @@ Java_com_example_llama_Llm_completion_1init(
         jint n_len
     ) {
 
-    cached_token_chars.clear();
-
     const auto text = env->GetStringUTFChars(jtext, 0);
     const auto context = reinterpret_cast<llama_context *>(context_pointer);
     const auto batch = reinterpret_cast<llama_batch *>(batch_pointer);
@@ -413,16 +372,8 @@ Java_com_example_llama_Llm_completion_1loop(
     }
 
     auto new_token_chars = llama_token_to_piece(context, new_token_id);
-    cached_token_chars += new_token_chars;
-
-    jstring new_token = nullptr;
-    if (is_valid_utf8(cached_token_chars.c_str())) {
-        new_token = env->NewStringUTF(cached_token_chars.c_str());
-        LOGi("cached: %s, new_token_chars: `%s`, id: %d", cached_token_chars.c_str(), new_token_chars.c_str(), new_token_id);
-        cached_token_chars.clear();
-    } else {
-        new_token = env->NewStringUTF("");
-    }
+    LOGi("new_token_chars: `%s`", new_token_chars.c_str());
+    auto new_token = env->NewStringUTF(new_token_chars.c_str());
 
     llama_batch_clear(*batch);
     llama_batch_add(*batch, new_token_id, n_cur, { 0 }, true);

examples/llama.android/app/src/main/java/com/example/llama/Llm.kt

@@ -71,7 +71,7 @@ class Llm {
         batch: Long,
         nLen: Int,
         ncur: IntVar
-    ): String?
+    ): String
 
     private external fun kv_cache_clear(context: Long)
 
@@ -115,7 +115,7 @@ class Llm {
                 val ncur = IntVar(completion_init(state.context, state.batch, message, nlen))
                 while (ncur.value <= nlen) {
                     val str = completion_loop(state.context, state.batch, nlen, ncur)
-                    if (str == null) {
+                    if (str.isEmpty()) {
                         break
                     }
                     emit(str)

examples/llama.swiftui/llama.cpp.swift/LibLlama.swift

@@ -221,7 +221,6 @@ actor LlamaContext {
             if llama_decode(context, batch) != 0 {
                 print("llama_decode() failed during prompt")
             }
-            llama_synchronize(context)
 
             let t_pp_end = ggml_time_us()
 
@@ -241,7 +240,6 @@ actor LlamaContext {
                 if llama_decode(context, batch) != 0 {
                     print("llama_decode() failed during text generation")
                 }
-                llama_synchronize(context)
             }
 
             let t_tg_end = ggml_time_us()

examples/llava/README.md

@@ -63,20 +63,12 @@ Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` director
 ```console
 git clone https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b
 ```
-
-2) Install the required Python packages:
-
-```sh
-pip install -r examples/llava/requirements.txt
-```
-
-3) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
+2) Use `llava-surgery-v2.py` which also supports llava-1.5 variants pytorch as well as safetensor models:
 ```console
 python examples/llava/llava-surgery-v2.py -C -m ../llava-v1.6-vicuna-7b/
 ```
 - you will find a llava.projector and a llava.clip file in your model directory
-
-4) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory:
+3) Copy the llava.clip file into a subdirectory (like vit), rename it to pytorch_model.bin and add a fitting vit configuration to the directory:
 ```console
 mkdir vit
 cp ../llava-v1.6-vicuna-7b/llava.clip vit/pytorch_model.bin
@@ -84,18 +76,18 @@ cp ../llava-v1.6-vicuna-7b/llava.projector vit/
 curl -s -q https://huggingface.co/cmp-nct/llava-1.6-gguf/raw/main/config_vit.json -o vit/config.json
 ```
 
-5) Create the visual gguf model:
+4) Create the visual gguf model:
 ```console
 python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projector vit/llava.projector --output-dir vit --clip-model-is-vision
 ```
 - This is similar to llava-1.5, the difference is that we tell the encoder that we are working with the pure vision model part of CLIP
 
-6) Then convert the model to gguf format:
+5) Then convert the model to gguf format:
 ```console
 python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown
 ```
 
-7) And finally we can run the llava-cli using the 1.6 model version:
+6) And finally we can run the llava-cli using the 1.6 model version:
 ```console
 ./llava-cli -m ../llava-v1.6-vicuna-7b/ggml-model-f16.gguf --mmproj vit/mmproj-model-f16.gguf --image some-image.jpg -c 4096
 ```

examples/llava/clip.cpp

@@ -995,7 +995,6 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         if (!new_clip->ctx_data) {
             fprintf(stderr, "%s: ggml_init() failed\n", __func__);
             clip_free(new_clip);
-            gguf_free(ctx);
             return nullptr;
         }
 
@@ -1003,7 +1002,6 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
         if (!fin) {
             printf("cannot open model file for loading tensors\n");
             clip_free(new_clip);
-            gguf_free(ctx);
             return nullptr;
         }
 
@@ -1025,7 +1023,6 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) {
             if (!fin) {
                 printf("%s: failed to seek for tensor %s\n", __func__, name);
                 clip_free(new_clip);
-                gguf_free(ctx);
                 return nullptr;
             }
             int num_bytes = ggml_nbytes(cur);
@@ -1235,16 +1232,16 @@ struct clip_image_f32 * clip_image_f32_init() {
 
 void clip_image_u8_free(struct clip_image_u8  * img) { delete img; }
 void clip_image_f32_free(struct clip_image_f32 * img) { delete img; }
-void clip_image_u8_batch_free(struct clip_image_u8_batch  * batch) {
-    if (batch->size > 0) {
-        delete[] batch->data;
-        batch->size = 0;
+void clip_image_u8_batch_free(struct clip_image_u8_batch  & batch) {
+    if (batch.size > 0) {
+        delete[] batch.data;
+        batch.size = 0;
     }
 }
-void clip_image_f32_batch_free(struct clip_image_f32_batch  * batch) {
-    if (batch->size > 0) {
-        delete[] batch->data;
-        batch->size = 0;
+void clip_image_f32_batch_free(struct clip_image_f32_batch  & batch) {
+    if (batch.size > 0) {
+        delete[] batch.data;
+        batch.size = 0;
     }
 }
 
@@ -1497,7 +1494,7 @@ static std::vector<clip_image_u8*> divide_to_patches_u8(const clip_image_u8 & im
 
 // returns the normalized float tensor for llava-1.5, for spatial_unpad with anyres processing for llava-1.6 it returns the normalized image patch tensors as a vector
 // res_imgs memory is being allocated here, previous allocations will be freed if found
-bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch * res_imgs) {
+bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs) {
     bool pad_to_square = true;
     if (!ctx->has_vision_encoder) {
         printf("This gguf file seems to have no vision encoder\n");
@@ -1509,11 +1506,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
         pad_to_square = false;
     }
     // free the previous res_imgs if any set
-    if (res_imgs->size > 0) {
+    if (res_imgs.size > 0) {
         clip_image_f32_batch_free(res_imgs);
     }
-    res_imgs->data = nullptr;
-    res_imgs->size = 0;
+    res_imgs.data = nullptr;
+    res_imgs.size = 0;
 
     // the logic below is to pad the shorter side to the longer side with a background color: rgb(122, 116, 104)
     // see https://github.com/haotian-liu/LLaVA/blob/e854a2bf85118c504f6f16bf5c3c7c92f8fa8c6b/llava/conversation.py#L113-L156
@@ -1568,11 +1565,11 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
             bicubic_resize(*img, *image_original_resize, params.image_size, params.image_size); // in python this is "shortest_edge", but all CLIP are square
             patches.insert(patches.begin(), image_original_resize);
             // clip_image_f32_batch_init(patches.size());
-            res_imgs->size = patches.size();
-            res_imgs->data = new clip_image_f32[res_imgs->size];
+            res_imgs.size = patches.size();
+            res_imgs.data = new clip_image_f32[res_imgs.size];
             int num=0;
             for (auto& patch : patches) {
-                normalize_image_u8_to_f32(patch, &res_imgs->data[num], ctx->image_mean, ctx->image_std);
+                normalize_image_u8_to_f32(patch, &res_imgs.data[num], ctx->image_mean, ctx->image_std);
                 num++;
             }
 
@@ -1660,9 +1657,9 @@ bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, cli
     // }
     // res_imgs.push_back(res);
 
-    res_imgs->size = 1;
-    res_imgs->data = new clip_image_f32[res_imgs->size];
-    res_imgs->data[0] = *res;
+    res_imgs.size = 1;
+    res_imgs.data = new clip_image_f32[res_imgs.size];
+    res_imgs.data[0] = *res;
     clip_image_f32_free(res);
 
     return true;
@@ -1865,6 +1862,7 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
 
     std::vector<uint8_t> work(512);
     std::vector<float> conv_buf(512);
+    std::vector<int64_t> hist_all(1 << 4, 0);
     size_t total_size_org = 0;
     size_t total_size_new = 0;
 
@@ -1911,7 +1909,6 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
                 break;
             default:
                 printf("Please use an input file in f32 or f16\n");
-                gguf_free(ctx_out);
                 return false;
             }
 
@@ -1920,7 +1917,48 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
             }
             new_data = work.data();
 
-            new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, n_elms/cur->ne[0], cur->ne[0], nullptr);
+            std::vector<int64_t> hist_cur(1 << 4, 0);
+
+            switch (new_type) {
+                case GGML_TYPE_Q4_0: {
+                    new_size = ggml_quantize_q4_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
+                } break;
+                case GGML_TYPE_Q4_1: {
+                    new_size = ggml_quantize_q4_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
+                } break;
+                case GGML_TYPE_Q5_0: {
+                    new_size = ggml_quantize_q5_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
+                } break;
+                case GGML_TYPE_Q5_1: {
+                    new_size = ggml_quantize_q5_1(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
+                } break;
+                case GGML_TYPE_Q8_0: {
+                    new_size = ggml_quantize_q8_0(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
+                } break;
+                case GGML_TYPE_Q2_K: {
+                    new_size = ggml_quantize_q2_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
+                } break;
+                case GGML_TYPE_Q3_K: {
+                    new_size = ggml_quantize_q3_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
+                } break;
+                case GGML_TYPE_Q4_K: {
+                    new_size = ggml_quantize_q4_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
+                } break;
+                case GGML_TYPE_Q5_K: {
+                    new_size = ggml_quantize_q5_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
+                } break;
+                case GGML_TYPE_Q6_K: {
+                    new_size = ggml_quantize_q6_K(f32_data, new_data, n_elms, cur->ne[0], hist_cur.data());
+                } break;
+                default: {
+                    fprintf(stderr, "%s: unsupported quantization type %d\n", __func__, new_type);
+                    return false;
+                }
+            }
+
+            for (size_t j = 0; j < hist_cur.size(); ++j) {
+                hist_all[j] += hist_cur[j];
+            }
         } else {
             new_type = cur->type;
             new_data = cur->data;
@@ -1955,6 +1993,17 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i
     {
         printf("%s: original  size = %8.2f MB\n", __func__, total_size_org / 1024.0 / 1024.0);
         printf("%s: quantized size = %8.2f MB\n", __func__, total_size_new / 1024.0 / 1024.0);
+
+        int64_t sum_all = 0;
+        for (size_t i = 0; i < hist_all.size(); ++i) {
+            sum_all += hist_all[i];
+        }
+
+        printf("%s: hist: ", __func__);
+        for (size_t i = 0; i < hist_all.size(); ++i) {
+            printf("%5.3f ", hist_all[i] / (float)sum_all);
+        }
+        printf("\n");
     }
 
     return true;

examples/llava/clip.h

@@ -60,8 +60,8 @@ CLIP_API struct clip_image_f32 * clip_image_f32_init();
 
 CLIP_API void clip_image_u8_free (struct clip_image_u8  * img);
 CLIP_API void clip_image_f32_free(struct clip_image_f32 * img);
-CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch  * batch);
-CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch * batch);
+CLIP_API void clip_image_u8_batch_free (struct clip_image_u8_batch  & batch);
+CLIP_API void clip_image_f32_batch_free(struct clip_image_f32_batch & batch);
 
 CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 * img);
 
@@ -69,7 +69,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8
 CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img);
 
 /** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */
-CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs );
+CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const clip_image_u8 * img, clip_image_f32_batch & res_imgs );
 
 CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx);
 

examples/llava/llava.cpp

@@ -223,7 +223,7 @@ static bool encode_image_with_clip(clip_ctx * ctx_clip, int n_threads, const cli
     clip_image_f32_batch img_res_v;
     img_res_v.size = 0;
     img_res_v.data = nullptr;
-    if (!clip_image_preprocess(ctx_clip, img, &img_res_v)) {
+    if (!clip_image_preprocess(ctx_clip, img, img_res_v)) {
         fprintf(stderr, "%s: unable to preprocess image\n", __func__);
         delete[] img_res_v.data;
         return false;

examples/llava/llava.h

@@ -29,9 +29,9 @@ struct llava_image_embed {
 };
 
 /** sanity check for clip <-> llava embed size match */
-LLAVA_API bool llava_validate_embed_size(const struct llama_context * ctx_llama, const struct clip_ctx * ctx_clip);
+LLAVA_API bool llava_validate_embed_size(const llama_context * ctx_llama, const clip_ctx * ctx_clip);
 
-LLAVA_API bool llava_image_embed_make_with_clip_img(struct clip_ctx * ctx_clip, int n_threads, const struct clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
+LLAVA_API bool llava_image_embed_make_with_clip_img(clip_ctx * ctx_clip, int n_threads, const clip_image_u8 * img, float ** image_embd_out, int * n_img_pos_out);
 
 /** build an image embed from image file bytes */
 LLAVA_API struct llava_image_embed * llava_image_embed_make_with_bytes(struct clip_ctx * ctx_clip, int n_threads, const unsigned char * image_bytes, int image_bytes_length);

examples/main/main.cpp

@@ -511,14 +511,6 @@ int main(int argc, char ** argv) {
     std::vector<llama_token> embd;
     std::vector<llama_token> embd_guidance;
 
-    // tokenized antiprompts
-    std::vector<std::vector<llama_token>> antiprompt_ids;
-
-    antiprompt_ids.reserve(params.antiprompt.size());
-    for (const std::string & antiprompt : params.antiprompt) {
-        antiprompt_ids.emplace_back(::llama_tokenize(ctx, antiprompt, false, true));
-    }
-
     struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams);
 
     while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
@@ -777,18 +769,6 @@ int main(int argc, char ** argv) {
                     }
                 }
 
-                // check for reverse prompt using special tokens
-                llama_token last_token = llama_sampling_last(ctx_sampling);
-                for (std::vector<llama_token> ids : antiprompt_ids) {
-                    if (ids.size() == 1 && last_token == ids[0]) {
-                        if (params.interactive) {
-                            is_interacting = true;
-                        }
-                        is_antiprompt = true;
-                        break;
-                    }
-                }
-
                 if (is_antiprompt) {
                     LOG("found antiprompt: %s\n", last_output.c_str());
                 }
@@ -878,7 +858,6 @@ int main(int argc, char ** argv) {
                     const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true);
                     const auto line_inp = ::llama_tokenize(ctx, buffer,              false, false);
                     const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true);
-
                     LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str());
 
                     embd_inp.insert(embd_inp.end(), line_pfx.begin(), line_pfx.end());

examples/parallel/parallel.cpp

@@ -107,9 +107,6 @@ int main(int argc, char ** argv) {
     // number of simultaneous "clients" to simulate
     const int32_t n_clients = params.n_parallel;
 
-    // dedicate one sequence to the system prompt
-    params.n_parallel += 1;
-
     // requests to simulate
     const int32_t n_seq = params.n_sequences;
 
@@ -199,8 +196,8 @@ int main(int argc, char ** argv) {
         }
 
         // assign the system KV cache to all parallel sequences
-        for (int32_t i = 1; i <= n_clients; ++i) {
-            llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
+        for (int32_t i = 1; i < n_clients; ++i) {
+            llama_kv_cache_seq_cp(ctx, 0, i, 0, n_tokens_system);
         }
 
         LOG_TEE("\n");
@@ -224,17 +221,15 @@ int main(int argc, char ** argv) {
 
             client.i_batch = batch.n_tokens;
 
-            llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id + 1 }, true);
+            llama_batch_add(batch, client.sampled, n_tokens_system + client.n_prompt + client.n_decoded, { client.id }, true);
 
             client.n_decoded += 1;
         }
 
         if (batch.n_tokens == 0) {
             // all sequences have ended - clear the entire KV cache
-            for (int i = 1; i <= n_clients; ++i) {
-                llama_kv_cache_seq_rm(ctx, i, -1, -1);
-                // but keep the system prompt
-                llama_kv_cache_seq_cp(ctx, 0, i, -1, -1);
+            for (int i = 0; i < n_clients; ++i) {
+                llama_kv_cache_seq_rm(ctx, i, n_tokens_system, -1);
             }
 
             LOG_TEE("%s: clearing the KV cache\n", __func__);
@@ -260,7 +255,7 @@ int main(int argc, char ** argv) {
                     tokens_prompt = ::llama_tokenize(ctx, client.prompt, false);
 
                     for (size_t i = 0; i < tokens_prompt.size(); ++i) {
-                        llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id + 1 }, false);
+                        llama_batch_add(batch, tokens_prompt[i], i + n_tokens_system, { client.id }, false);
                     }
 
                     // extract the logits only for the last token
@@ -371,8 +366,7 @@ int main(int argc, char ** argv) {
                     }
 
                     // delete only the generated part of the sequence, i.e. keep the system prompt in the cache
-                    llama_kv_cache_seq_rm(ctx, client.id + 1, -1, -1);
-                    llama_kv_cache_seq_cp(ctx, 0, client.id + 1, -1, -1);
+                    llama_kv_cache_seq_rm(ctx, client.id, n_tokens_system, -1);
 
                     const auto t_main_end = ggml_time_us();
 

examples/perplexity/perplexity.cpp

@@ -442,7 +442,7 @@ static results_perplexity perplexity_v2(llama_context * ctx, const gpt_params &
     return {tokens, std::exp(nll / count), logit_history, prob_history};
 }
 
-static results_perplexity perplexity(llama_context * ctx, const gpt_params & params, const int32_t n_ctx) {
+static results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {
     if (params.ppl_stride > 0) {
         return perplexity_v2(ctx, params);
     }
@@ -453,6 +453,7 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
     // BOS tokens will be added for each chunk before eval
 
     const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
+    const int n_ctx = llama_n_ctx(ctx);
 
     std::ofstream logits_stream;
     if (!params.logits_file.empty()) {
@@ -498,19 +499,13 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
     double nll2 = 0.0;
 
     const int num_batches = (n_ctx + n_batch - 1) / n_batch;
-    const int n_seq = std::max(1, n_batch / n_ctx);
-
-    GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0);
-    GGML_ASSERT(params.n_ctx == n_seq * n_ctx);
-
-    llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1);
 
     std::vector<float> logits;
     if (num_batches > 1) {
         logits.reserve((size_t)n_ctx * n_vocab);
     }
 
-    fprintf(stderr, "%s: calculating perplexity over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
+    fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
 
     std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
 
@@ -523,26 +518,10 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
         log_probs.resize(n_ctx * nv);
     }
 
-    // We get the logits for all the tokens in the context window (params.n_ctx)
-    // from llama_eval above.  Now, based on https://huggingface.co/docs/transformers/perplexity,
-    // calculate the perplexity over the last half of the window (so the model always has
-    // some context to predict the token).
-    //
-    // We rely on the fact that attention in the forward pass only looks at previous
-    // tokens here, so the logits returned for each token are an accurate representation
-    // of what the model would have predicted at that point.
-    //
-    // Example, we have a context window of 512, we will compute perplexity for each of the
-    // last 256 tokens.  Then, we split the input up into context window size chunks to
-    // process the entire prompt.
-    const int first = n_ctx/2;
-
-    for (int i = 0; i < n_chunk; i += n_seq) {
+    for (int i = 0; i < n_chunk; ++i) {
         const int start =     i * n_ctx;
         const int end   = start + n_ctx;
 
-        const int n_seq_batch = std::min(n_seq, n_chunk - i);
-
         const auto t_start = std::chrono::high_resolution_clock::now();
 
         // clear the KV cache
@@ -552,50 +531,34 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
             const int batch_start = start + j * n_batch;
             const int batch_size  = std::min(end - batch_start, n_batch);
 
-            batch.n_tokens = 0;
-            for (int seq = 0; seq < n_seq_batch; seq++) {
-                int seq_start = batch_start + seq*n_ctx;
+            // save original token and restore it after eval
+            const auto token_org = tokens[batch_start];
 
-                // save original token and restore it after eval
-                const auto token_org = tokens[seq_start];
-
-                // add BOS token for the first batch of each chunk
-                if (add_bos && j == 0) {
-                    tokens[seq_start] = llama_token_bos(llama_get_model(ctx));
-                }
-
-                for (int k = 0; k < batch_size; ++k) {
-                    const int idx = seq*n_ctx + k;
-                    batch.token[idx] = tokens[seq_start + k];
-                    batch.pos[idx] = j*n_batch + k;
-                    batch.n_seq_id[idx] = 1;
-                    batch.seq_id[idx][0] = seq;
-                    batch.logits[idx] = batch.pos[idx] >= first ? 1 : 0;
-                }
-                batch.n_tokens += batch_size;
-
-                // restore the original token in case it was set to BOS
-                tokens[seq_start] = token_org;
+            // add BOS token for the first batch of each chunk
+            if (add_bos && j == 0) {
+                tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
             }
 
-            if (llama_decode(ctx, batch)) {
+            if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
                 fprintf(stderr, "%s : failed to eval\n", __func__);
                 return {tokens, -1, logit_history, prob_history};
             }
 
+            // restore the original token in case it was set to BOS
+            tokens[batch_start] = token_org;
+
             if (num_batches > 1) {
                 const auto * batch_logits = llama_get_logits(ctx);
                 logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
             }
         }
 
+        const auto t_end = std::chrono::high_resolution_clock::now();
 
         if (i == 0) {
-            llama_synchronize(ctx);
-            const auto t_end = std::chrono::high_resolution_clock::now();
             const float t_total = std::chrono::duration<float>(t_end - t_start).count();
             fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
-            int total_seconds = (int)(t_total*n_chunk/n_seq);
+            int total_seconds = (int)(t_total * n_chunk);
             if (total_seconds >= 60*60) {
                 fprintf(stderr, "%d hours ", total_seconds / (60*60));
                 total_seconds = total_seconds % (60*60);
@@ -603,31 +566,37 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
             fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
         }
 
-        for (int seq = 0; seq < n_seq_batch; seq++) {
-            const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx);
-            llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
-            if (!params.logits_file.empty()) {
-                process_logits(logits_stream, n_vocab, all_logits + first*n_vocab,
-                        tokens_data, n_ctx - 1 - first,
-                        workers, log_probs, nll, nll2);
-            } else {
-                process_logits(n_vocab, all_logits + first*n_vocab,
-                        tokens_data, n_ctx - 1 - first,
-                        workers, nll, nll2,
-                        logit_history.data() + start + seq*n_ctx + first,
-                        prob_history.data()  + start + seq*n_ctx + first);
-            }
-            count += n_ctx - first - 1;
+        // We get the logits for all the tokens in the context window (params.n_ctx)
+        // from llama_eval above.  Now, based on https://huggingface.co/docs/transformers/perplexity,
+        // calculate the perplexity over the last half of the window (so the model always has
+        // some context to predict the token).
+        //
+        // We rely on the fact that attention in the forward pass only looks at previous
+        // tokens here, so the logits returned for each token are an accurate representation
+        // of what the model would have predicted at that point.
+        //
+        // Example, we have a context window of 512, we will compute perplexity for each of the
+        // last 256 tokens.  Then, we split the input up into context window size chunks to
+        // process the entire prompt.
+        const int first = n_ctx/2;
+        const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
+        if (!params.logits_file.empty()) {
+            process_logits(logits_stream, n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
+                    workers, log_probs, nll, nll2);
+        } else {
+            process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
+                    workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
+        }
+        count += n_ctx - first - 1;
 
-            // perplexity is e^(average negative log-likelihood)
-            if (params.ppl_output_type == 0) {
-                printf("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
-            } else {
-                double av = nll/count;
-                double av2 = nll2/count - av*av;
-                if (av2 > 0) av2 = sqrt(av2/(count-1));
-                printf("%8d  %.4lf  %4lf  %4lf\n", i*n_ctx, std::exp(nll / count), av, av2);
-            }
+        // perplexity is e^(average negative log-likelihood)
+        if (params.ppl_output_type == 0) {
+            printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
+        } else {
+            double av = nll/count;
+            double av2 = nll2/count - av*av;
+            if (av2 > 0) av2 = sqrt(av2/(count-1));
+            printf("%8d  %.4lf  %4lf  %4lf\n", i*n_ctx, std::exp(nll / count), av, av2);
         }
         fflush(stdout);
 
@@ -646,8 +615,6 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par
         printf("Unexpected negative standard deviation of log(prob)\n");
     }
 
-    llama_batch_free(batch);
-
     return {tokens, ppl, logit_history, prob_history};
 }
 
@@ -842,7 +809,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) {
     const int n_batch = params.n_batch;
 
     const int max_tasks_per_batch = 32;
-    const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
+    const int max_seq = 4*max_tasks_per_batch;
 
     llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
 
@@ -1119,7 +1086,7 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) {
     const int n_batch = params.n_batch;
 
     const int max_tasks_per_batch = 128;
-    const int max_seq = std::min(2*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
+    const int max_seq = 2*max_tasks_per_batch;
 
     llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
 
@@ -1471,7 +1438,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params
     const int n_batch = params.n_batch;
 
     const int max_tasks_per_batch = 32;
-    const int max_seq = std::min(4*max_tasks_per_batch, (int) llama_n_seq_max(ctx));
+    const int max_seq = 4*max_tasks_per_batch;
 
     llama_batch batch = llama_batch_init(n_ctx, 0, max_seq);
 
@@ -1815,24 +1782,13 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) {
 int main(int argc, char ** argv) {
     gpt_params params;
 
+    params.n_batch = 512;
     if (!gpt_params_parse(argc, argv, params)) {
         return 1;
     }
 
     params.logits_all = true;
-
-    const int32_t n_ctx = params.n_ctx;
-
-    const bool ppl = !params.hellaswag && !params.winogrande && !params.multiple_choice && !params.kl_divergence;
-    if (ppl) {
-        int n_seq = std::max(1, params.n_batch / n_ctx);
-        int32_t n_kv = n_seq * n_ctx;
-        params.n_parallel = n_seq;
-        params.n_ctx = n_kv;
-        params.n_batch = std::min(params.n_batch, n_kv);
-    } else {
-        params.n_batch = std::min(params.n_batch, params.n_ctx);
-    }
+    params.n_batch = std::min(params.n_batch, params.n_ctx);
 
     if (params.ppl_stride > 0) {
         fprintf(stderr, "Will perform strided perplexity calculation -> adjusting context size from %d to %d\n",
@@ -1859,9 +1815,6 @@ int main(int argc, char ** argv) {
     llama_model * model;
     llama_context * ctx;
 
-    // ensure there's at least enough seq_ids for HellaSwag
-    params.n_parallel = std::max(4, params.n_parallel);
-
     // load the model and apply lora adapter, if any
     std::tie(model, ctx) = llama_init_from_gpt_params(params);
     if (model == NULL) {
@@ -1891,7 +1844,7 @@ int main(int argc, char ** argv) {
     } else if (params.kl_divergence) {
         kl_divergence(ctx, params);
     } else {
-        results = perplexity(ctx, params, n_ctx);
+        results = perplexity(ctx, params);
     }
 
     llama_print_timings(ctx);

examples/server-embd.py

@@ -1,34 +0,0 @@
-import asyncio
-import requests
-import numpy as np
-
-n = 8
-
-result = []
-
-async def requests_post_async(*args, **kwargs):
-    return await asyncio.to_thread(requests.post, *args, **kwargs)
-
-async def main():
-    model_url = "http://127.0.0.1:6900"
-    responses: list[requests.Response] = await asyncio.gather(*[requests_post_async(
-        url= f"{model_url}/embedding",
-        json= {"content": str(0)*1024}
-    ) for i in range(n)])
-
-    for response in responses:
-        embedding = response.json()["embedding"]
-        print(embedding[-8:])
-        result.append(embedding)
-
-asyncio.run(main())
-
-# compute cosine similarity
-
-for i in range(n-1):
-    for j in range(i+1, n):
-        embedding1 = np.array(result[i])
-        embedding2 = np.array(result[j])
-        similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
-        print(f"Similarity between {i} and {j}: {similarity:.2f}")
-

examples/server/CMakeLists.txt

@@ -1,18 +1,12 @@
 set(TARGET server)
 option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
-option(LLAMA_SERVER_SSL "Build SSL support for the server" OFF)
 include_directories(${CMAKE_CURRENT_SOURCE_DIR})
-add_executable(${TARGET} server.cpp utils.hpp json.hpp httplib.h)
+add_executable(${TARGET} server.cpp oai.hpp utils.hpp json.hpp httplib.h)
 install(TARGETS ${TARGET} RUNTIME)
 target_compile_definitions(${TARGET} PRIVATE
     SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
 )
-target_link_libraries(${TARGET} PRIVATE common ${CMAKE_THREAD_LIBS_INIT})
-if (LLAMA_SERVER_SSL)
-    find_package(OpenSSL REQUIRED)
-    target_link_libraries(${TARGET} PRIVATE OpenSSL::SSL OpenSSL::Crypto)
-    target_compile_definitions(${TARGET} PRIVATE CPPHTTPLIB_OPENSSL_SUPPORT)
-endif()
+target_link_libraries(${TARGET} PRIVATE common llava ${CMAKE_THREAD_LIBS_INIT})
 if (WIN32)
     TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
 endif()

examples/server/README.md

@@ -18,7 +18,6 @@ The project is under active development, and we are [looking for feedback and co
 
 - `--threads N`, `-t N`: Set the number of threads to use during generation.
 - `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation.
-- `--threads-http N`: number of threads in the http server pool to process requests (default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)`)
 - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`).
 - `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
 - `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096.
@@ -42,7 +41,7 @@ see https://github.com/ggerganov/llama.cpp/issues/1437
 - `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
 - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
 - `--port`: Set the port to listen. Default: `8080`.
-- `--path`: path from which to serve static files (default: disabled)
+- `--path`: path from which to serve static files (default examples/server/public)
 - `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys.
 - `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s.
 - `--embedding`: Enable embedding extraction, Default: disabled.
@@ -59,10 +58,6 @@ see https://github.com/ggerganov/llama.cpp/issues/1437
 - `--log-disable`: Output logs to stdout only, default: enabled.
 - `--log-format FORMAT`: Define the log output to FORMAT: json or text (default: json)
 
-**If compiled with `LLAMA_SERVER_SSL=ON`**
-- `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key
-- `--ssl-cert-file FNAME`: path to file a PEM-encoded SSL certificate
-
 ## Build
 
 server is build alongside everything else from the root of the project
@@ -79,28 +74,6 @@ server is build alongside everything else from the root of the project
   cmake --build . --config Release
   ```
 
-## Build with SSL
-
-server can also be built with SSL support using OpenSSL 3
-
-- Using `make`:
-
-  ```bash
-  # NOTE: For non-system openssl, use the following:
-  #   CXXFLAGS="-I /path/to/openssl/include"
-  #   LDFLAGS="-L /path/to/openssl/lib"
-  make LLAMA_SERVER_SSL=true server
-  ```
-
-- Using `CMake`:
-
-  ```bash
-  mkdir build
-  cd build
-  cmake .. -DLLAMA_SERVER_SSL=ON
-  make server
-  ```
-
 ## Quick Start
 
 To get started right away, run the following command, making sure to use the correct path for the model you have:
@@ -123,10 +96,10 @@ You can consume the endpoints with Postman or NodeJS with axios library. You can
 ### Docker
 
 ```bash
-docker run -p 8080:8080 -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
+docker run -p 8080:8080 -v /path/to/models:/models ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080
 
 # or, with CUDA:
-docker run -p 8080:8080 -v /path/to/models:/models --gpus all ghcr.io/ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
+docker run -p 8080:8080 -v /path/to/models:/models --gpus all ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99
 ```
 
 ## Testing with CURL
@@ -195,11 +168,7 @@ node index.js
 
     *Options:*
 
-    `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, if `cache_prompt` is `true`, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. A `BOS` token is inserted at the start, if all of the following conditions are true:
-
-      - The prompt is a string or an array with the first element given as a string
-      - The model's `tokenizer.ggml.add_bos_token` metadata is `true`
-      - The system prompt is empty
+    `prompt`: Provide the prompt for this completion as a string or as an array of strings or numbers representing tokens. Internally, the prompt is compared to the previous completion and only the "unseen" suffix is evaluated. If the prompt is a string or an array with the first element given as a string, a `bos` token is inserted in the front like `main` does.
 
     `temperature`: Adjust the randomness of the generated text (default: 0.8).
 
@@ -312,7 +281,7 @@ Notice that each `probs` is an array of length `n_probs`.
 
     `content`: Set the text to tokenize.
 
-    Note that a special `BOS` token is never inserted.
+    Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`.
 
 - **POST** `/detokenize`: Convert tokens to text.
 
@@ -356,7 +325,7 @@ Notice that each `probs` is an array of length `n_probs`.
 - `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint.
 - `total_slots` - the total number of slots for process requests (defined by `--parallel` option)
 
-- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint.
+- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.
 
     *Options:*
 
@@ -466,7 +435,7 @@ Notice that each `probs` is an array of length `n_probs`.
         "next_token": {
             "has_next_token": true,
             "n_remain": -1,
-            "n_decoded": 0,
+            "num_tokens_predicted": 0,
             "stopped_eos": false,
             "stopped_limit": false,
             "stopped_word": false,
@@ -556,55 +525,26 @@ Run with bash:
 bash chat.sh
 ```
 
-### OAI-like API
+### API like OAI
 
-The HTTP server supports OAI-like API: https://github.com/openai/openai-openapi
+API example using Python Flask: [api_like_OAI.py](api_like_OAI.py)
+This example must be used with server.cpp
 
-### API errors
-
-Server returns error in the same format as OAI: https://github.com/openai/openai-openapi
-
-Example of an error:
-
-```json
-{
-    "error": {
-        "code": 401,
-        "message": "Invalid API Key",
-        "type": "authentication_error"
-    }
-}
+```sh
+python api_like_OAI.py
 ```
 
-Apart from error types supported by OAI, we also have custom types that are specific to functionalities of llama.cpp:
+After running the API server, you can use it in Python by setting the API base URL.
 
-**When /metrics or /slots endpoint is disabled**
-
-```json
-{
-    "error": {
-        "code": 501,
-        "message": "This server does not support metrics endpoint.",
-        "type": "not_supported_error"
-    }
-}
+```python
+openai.api_base = "http://<Your api-server IP>:port"
 ```
 
-**When the server receives invalid grammar via */completions endpoint**
-
-```json
-{
-    "error": {
-        "code": 400,
-        "message": "Failed to parse grammar",
-        "type": "invalid_request_error"
-    }
-}
-```
+Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API
 
 ### Extending or building alternative Web Front End
 
-You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
+The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
 
 Read the documentation in `/completion.js` to see convenient ways to access llama.
 

examples/server/api_like_OAI.py

@@ -0,0 +1,228 @@
+#!/usr/bin/env python3
+import argparse
+from flask import Flask, jsonify, request, Response
+import urllib.parse
+import requests
+import time
+import json
+
+
+app = Flask(__name__)
+slot_id = -1
+
+parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
+parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')
+parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ")
+parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ")
+parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ")
+parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
+parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
+parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
+parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1')
+parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081)
+
+args = parser.parse_args()
+
+def is_present(json, key):
+    try:
+        buf = json[key]
+    except KeyError:
+        return False
+    if json[key] == None:
+        return False
+    return True
+
+#convert chat to prompt
+def convert_chat(messages):
+
+    system_n = args.system_name
+    user_n = args.user_name
+    ai_n = args.ai_name
+    stop = args.stop
+
+    prompt = "" + args.chat_prompt + stop
+
+    for line in messages:
+        if (line["role"] == "system"):
+            prompt += f"{system_n}{line['content']}{stop}"
+        if (line["role"] == "user"):
+            prompt += f"{user_n}{line['content']}{stop}"
+        if (line["role"] == "assistant"):
+            prompt += f"{ai_n}{line['content']}{stop}"
+    prompt += ai_n.rstrip()
+
+    return prompt
+
+def make_postData(body, chat=False, stream=False):
+    postData = {}
+    if (chat):
+        postData["prompt"] = convert_chat(body["messages"])
+    else:
+        postData["prompt"] = body["prompt"]
+    if(is_present(body, "temperature")): postData["temperature"] = body["temperature"]
+    if(is_present(body, "top_k")): postData["top_k"] = body["top_k"]
+    if(is_present(body, "top_p")): postData["top_p"] = body["top_p"]
+    if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"]
+    if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"]
+    if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"]
+    if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"]
+    if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"]
+    if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
+    if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
+    if(is_present(body, "seed")): postData["seed"] = body["seed"]
+    if(is_present(body, "grammar")): postData["grammar"] = body["grammar"]
+    if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
+    if (args.stop != ""):
+        postData["stop"] = [args.stop]
+    else:
+        postData["stop"] = []
+    if(is_present(body, "stop")): postData["stop"] += body["stop"]
+    postData["n_keep"] = -1
+    postData["stream"] = stream
+    postData["cache_prompt"] = True
+    postData["slot_id"] = slot_id
+    return postData
+
+def make_resData(data, chat=False, promptToken=[]):
+    resData = {
+        "id": "chatcmpl" if (chat) else "cmpl",
+        "object": "chat.completion" if (chat) else "text_completion",
+        "created": int(time.time()),
+        "truncated": data["truncated"],
+        "model": "LLaMA_CPP",
+        "usage": {
+            "prompt_tokens": data["tokens_evaluated"],
+            "completion_tokens": data["tokens_predicted"],
+            "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"]
+        }
+    }
+    if (len(promptToken) != 0):
+        resData["promptToken"] = promptToken
+    if (chat):
+        #only one choice is supported
+        resData["choices"] = [{
+            "index": 0,
+            "message": {
+                "role": "assistant",
+                "content": data["content"],
+            },
+            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+        }]
+    else:
+        #only one choice is supported
+        resData["choices"] = [{
+            "text": data["content"],
+            "index": 0,
+            "logprobs": None,
+            "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+        }]
+    return resData
+
+def make_resData_stream(data, chat=False, time_now = 0, start=False):
+    resData = {
+        "id": "chatcmpl" if (chat) else "cmpl",
+        "object": "chat.completion.chunk" if (chat) else "text_completion.chunk",
+        "created": time_now,
+        "model": "LLaMA_CPP",
+        "choices": [
+            {
+                "finish_reason": None,
+                "index": 0
+            }
+        ]
+    }
+    slot_id = data.get("slot_id")
+    if (chat):
+        if (start):
+            resData["choices"][0]["delta"] =  {
+                "role": "assistant"
+            }
+        else:
+            resData["choices"][0]["delta"] =  {
+                "content": data["content"]
+            }
+            if (data["stop"]):
+                resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+    else:
+        resData["choices"][0]["text"] = data["content"]
+        if (data["stop"]):
+            resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
+
+    return resData
+
+
+@app.route('/chat/completions', methods=['POST', 'OPTIONS'])
+@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS'])
+def chat_completions():
+    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
+        return Response(status=403)
+    if request.method == 'OPTIONS':
+        return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
+    body = request.get_json()
+    stream = False
+    tokenize = False
+    if(is_present(body, "stream")): stream = body["stream"]
+    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
+    postData = make_postData(body, chat=True, stream=stream)
+
+    promptToken = []
+    if (tokenize):
+        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
+        promptToken = tokenData["tokens"]
+
+    if (not stream):
+        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
+        print(data.json())
+        resData = make_resData(data.json(), chat=True, promptToken=promptToken)
+        return jsonify(resData)
+    else:
+        def generate():
+            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
+            time_now = int(time.time())
+            resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
+            yield 'data: {}\n\n'.format(json.dumps(resData))
+            for line in data.iter_lines():
+                if line:
+                    decoded_line = line.decode('utf-8')
+                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
+                    yield 'data: {}\n\n'.format(json.dumps(resData))
+        return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
+
+
+@app.route('/completions', methods=['POST', 'OPTIONS'])
+@app.route('/v1/completions', methods=['POST', 'OPTIONS'])
+def completion():
+    if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
+        return Response(status=403)
+    if request.method == 'OPTIONS':
+        return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
+    body = request.get_json()
+    stream = False
+    tokenize = False
+    if(is_present(body, "stream")): stream = body["stream"]
+    if(is_present(body, "tokenize")): tokenize = body["tokenize"]
+    postData = make_postData(body, chat=False, stream=stream)
+
+    promptToken = []
+    if (tokenize):
+        tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
+        promptToken = tokenData["tokens"]
+
+    if (not stream):
+        data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
+        print(data.json())
+        resData = make_resData(data.json(), chat=False, promptToken=promptToken)
+        return jsonify(resData)
+    else:
+        def generate():
+            data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
+            time_now = int(time.time())
+            for line in data.iter_lines():
+                if line:
+                    decoded_line = line.decode('utf-8')
+                    resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
+                    yield 'data: {}\n\n'.format(json.dumps(resData))
+        return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
+
+if __name__ == '__main__':
+    app.run(args.host, port=args.port)

examples/server/bench/README.md

@@ -1,88 +0,0 @@
-### Server benchmark tools
-
-Benchmark is using [k6](https://k6.io/).
-
-##### Install k6
-
-Follow instruction from: https://k6.io/docs/get-started/installation/
-
-Example for ubuntu:
-```shell
-snap install k6
-```
-
-#### Download a dataset
-
-This dataset was originally proposed in [vLLM benchmarks](https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md).
-
-```shell
-wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
-```
-
-#### Download a model
-Example for PHI-2
-
-```shell
-../../../scripts/hf.sh --repo ggml-org/models --file phi-2/ggml-model-q4_0.gguf
-```
-
-#### Start the server
-The server must answer OAI Chat completion requests on `http://localhost:8080/v1` or according to the environment variable `SERVER_BENCH_URL`.
-
-Example:
-```shell
-server --host localhost --port 8080 \
-  --model ggml-model-q4_0.gguf \
-  --cont-batching \
-  --metrics \
-  --parallel 8 \
-  --batch-size 512 \
-  --ctx-size 4096 \
-  --log-format text \
-  -ngl 33
-```
-
-#### Run the benchmark
-
-For 500 chat completions request with 8 concurrent users during maximum 10 minutes, run:
-```shell
-k6 run script.js --duration 10m --iterations 500 --vus 8
-```
-
-The benchmark values can be overridden with:
-- `SERVER_BENCH_URL` server url prefix for chat completions, default `http://localhost:8080/v1`
-- `SERVER_BENCH_N_PROMPTS` total prompts to randomly select in the benchmark, default `480`
-- `SERVER_BENCH_MODEL_ALIAS` model alias to pass in the completion request, default `my-model`
-- `SERVER_BENCH_MAX_TOKENS` max tokens to predict, default: `512`
-- `SERVER_BENCH_DATASET` path to the benchmark dataset file
-- `SERVER_BENCH_MAX_PROMPT_TOKENS` maximum prompt tokens to filter out in the dataset: default `1024`
-- `SERVER_BENCH_MAX_CONTEXT` maximum context size of the completions request to filter out in the dataset: prompt + predicted tokens, default `2048`
-
-Note: the local tokenizer is just a string space split, real number of tokens will differ.
-
-Or with [k6 options](https://k6.io/docs/using-k6/k6-options/reference/):
-
-```shell
-SERVER_BENCH_N_PROMPTS=500 k6 run script.js --duration 10m --iterations 500 --vus 8
-```
-
-To [debug http request](https://k6.io/docs/using-k6/http-debugging/) use `--http-debug="full"`.
-
-#### Metrics
-
-Following metrics are available computed from the OAI chat completions response `usage`:
-- `llamacpp_tokens_second` Trend of `usage.total_tokens / request duration`
-- `llamacpp_prompt_tokens` Trend of `usage.prompt_tokens`
-- `llamacpp_prompt_tokens_total_counter` Counter of `usage.prompt_tokens`
-- `llamacpp_completion_tokens` Trend of `usage.completion_tokens`
-- `llamacpp_completion_tokens_total_counter` Counter of `usage.completion_tokens`
-- `llamacpp_completions_truncated_rate` Rate of completions truncated, i.e. if `finish_reason === 'length'`
-- `llamacpp_completions_stop_rate` Rate of completions stopped by the model, i.e. if `finish_reason === 'stop'`
-
-The script will fail if too many completions are truncated, see `llamacpp_completions_truncated_rate`.
-
-K6 metrics might be compared against [server metrics](../README.md), with:
-
-```shell
-curl http://localhost:8080/metrics
-```

examples/server/bench/script.js

@@ -1,120 +0,0 @@
-import http from 'k6/http'
-import {check, sleep} from 'k6'
-import {SharedArray} from 'k6/data'
-import {Counter, Rate, Trend} from 'k6/metrics'
-import exec from 'k6/execution';
-
-// Server chat completions prefix
-const server_url = __ENV.SERVER_BENCH_URL ? __ENV.SERVER_BENCH_URL : 'http://localhost:8080/v1'
-
-// Number of total prompts in the dataset - default 10m / 10 seconds/request * number of users
-const n_prompt = __ENV.SERVER_BENCH_N_PROMPTS ? parseInt(__ENV.SERVER_BENCH_N_PROMPTS) : 600 / 10 * 8
-
-// Model name to request
-const model = __ENV.SERVER_BENCH_MODEL_ALIAS ? __ENV.SERVER_BENCH_MODEL_ALIAS : 'my-model'
-
-// Dataset path
-const dataset_path = __ENV.SERVER_BENCH_DATASET ? __ENV.SERVER_BENCH_DATASET : './ShareGPT_V3_unfiltered_cleaned_split.json'
-
-// Max tokens to predict
-const max_tokens = __ENV.SERVER_BENCH_MAX_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_TOKENS) : 512
-
-// Max prompt tokens
-const n_prompt_tokens = __ENV.SERVER_BENCH_MAX_PROMPT_TOKENS ? parseInt(__ENV.SERVER_BENCH_MAX_PROMPT_TOKENS) : 1024
-
-// Max slot context
-const n_ctx_slot = __ENV.SERVER_BENCH_MAX_CONTEXT ? parseInt(__ENV.SERVER_BENCH_MAX_CONTEXT) : 2048
-
-export function setup() {
-    console.info(`Benchmark config: server_url=${server_url} n_prompt=${n_prompt} model=${model} dataset_path=${dataset_path} max_tokens=${max_tokens}`)
-}
-
-const data = new SharedArray('conversations', function () {
-    const tokenizer = (message) => message.split(/[\s,'".?]/)
-
-    return JSON.parse(open(dataset_path))
-        // Filter out the conversations with less than 2 turns.
-        .filter(data => data["conversations"].length >= 2)
-        .filter(data => data["conversations"][0]["from"] === "human")
-        .map(data => {
-            return {
-                prompt: data["conversations"][0]["value"],
-                n_prompt_tokens: tokenizer(data["conversations"][0]["value"]).length,
-                n_completion_tokens: tokenizer(data["conversations"][1]["value"]).length,
-            }
-        })
-        // Filter out too short sequences
-        .filter(conv => conv.n_prompt_tokens >= 4 && conv.n_completion_tokens >= 4)
-        // Filter out too long sequences.
-        .filter(conv => conv.n_prompt_tokens <= n_prompt_tokens && conv.n_prompt_tokens + conv.n_completion_tokens <= n_ctx_slot)
-        // Keep only first n prompts
-        .slice(0, n_prompt)
-})
-
-const llamacpp_prompt_tokens = new Trend('llamacpp_prompt_tokens')
-const llamacpp_completion_tokens = new Trend('llamacpp_completion_tokens')
-const llamacpp_tokens_second = new Trend('llamacpp_tokens_second')
-
-const llamacpp_prompt_tokens_total_counter = new Counter('llamacpp_prompt_tokens_total_counter')
-const llamacpp_completion_tokens_total_counter = new Counter('llamacpp_completion_tokens_total_counter')
-
-const llamacpp_completions_truncated_rate = new Rate('llamacpp_completions_truncated_rate')
-const llamacpp_completions_stop_rate = new Rate('llamacpp_completions_stop_rate')
-
-export const options = {
-    thresholds: {
-        llamacpp_completions_truncated_rate: [
-            // more than 80% of truncated input will abort the test
-            {threshold: 'rate < 0.8', abortOnFail: true, delayAbortEval: '1m'},
-        ],
-    },
-    duration: '10m',
-    vus: 8,
-}
-
-export default function () {
-    const conversation = data[exec.scenario.iterationInInstance % data.length]
-    const payload = {
-        "messages": [
-            {
-                "role": "system",
-                "content": "You are ChatGPT, an AI assistant.",
-            },
-            {
-                "role": "user",
-                "content": conversation.prompt,
-            }
-        ],
-        "model": model,
-        "stream": false,
-        "max_tokens": max_tokens
-    }
-
-    const body = JSON.stringify(payload)
-
-    let res = http.post(`${server_url}/chat/completions`, body, {
-        headers: {'Content-Type': 'application/json'},
-        timeout: '300s'
-    })
-
-    check(res, {'success completion': (r) => r.status === 200})
-
-    if (res.status === 200) {
-        const completions = res.json()
-
-        llamacpp_prompt_tokens.add(completions.usage.prompt_tokens)
-        llamacpp_prompt_tokens_total_counter.add(completions.usage.prompt_tokens)
-
-        llamacpp_completion_tokens.add(completions.usage.completion_tokens)
-        llamacpp_completion_tokens_total_counter.add(completions.usage.completion_tokens)
-
-        llamacpp_completions_truncated_rate.add(completions.choices[0].finish_reason === 'length')
-        llamacpp_completions_stop_rate.add(completions.choices[0].finish_reason === 'stop')
-
-        llamacpp_tokens_second.add(completions.usage.total_tokens / res.timings.duration * 1.e3)
-    } else {
-        console.error(`response: ${res.body} request=${payload}`)
-    }
-
-    sleep(0.3)
-}

examples/server/completion.js.hpp

@@ -231,256 +231,255 @@ unsigned char completion_js[] = {
   0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20,
   0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74,
   0x2e, 0x65, 0x72, 0x72, 0x6f, 0x72, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20,
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+  0x0a, 0x20, 0x20, 0x7d, 0x29, 0x3b, 0x0a, 0x7d, 0x3b, 0x0a, 0x0a, 0x2f,
+  0x2a, 0x2a, 0x0a, 0x20, 0x2a, 0x20, 0x28, 0x64, 0x65, 0x70, 0x72, 0x65,
+  0x63, 0x61, 0x74, 0x65, 0x64, 0x29, 0x0a, 0x20, 0x2a, 0x2f, 0x0a, 0x65,
+  0x78, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20,
+  0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x43, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74,
+  0x65, 0x20, 0x3d, 0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, 0x70,
+  0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72,
+  0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2c, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62,
+  0x61, 0x63, 0x6b, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20,
+  0x66, 0x6f, 0x72, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, 0x28, 0x63,
+  0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x20, 0x6f,
+  0x66, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x28, 0x70, 0x61, 0x72, 0x61,
+  0x6d, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x2c, 0x20, 0x70,
+  0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x7b, 0x20, 0x63, 0x6f, 0x6e,
+  0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x7d, 0x29, 0x29, 0x20,
+  0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61,
+  0x63, 0x6b, 0x28, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x29, 0x3b, 0x0a, 0x20,
+  0x20, 0x7d, 0x0a, 0x7d, 0x0a, 0x0a, 0x2f, 0x2f, 0x20, 0x47, 0x65, 0x74,
+  0x20, 0x74, 0x68, 0x65, 0x20, 0x6d, 0x6f, 0x64, 0x65, 0x6c, 0x20, 0x69,
+  0x6e, 0x66, 0x6f, 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x74, 0x68, 0x65,
+  0x20, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, 0x2e, 0x20, 0x54, 0x68, 0x69,
+  0x73, 0x20, 0x69, 0x73, 0x20, 0x75, 0x73, 0x65, 0x66, 0x75, 0x6c, 0x20,
+  0x66, 0x6f, 0x72, 0x20, 0x67, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x20,
+  0x74, 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x20,
+  0x77, 0x69, 0x6e, 0x64, 0x6f, 0x77, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x73,
+  0x6f, 0x20, 0x6f, 0x6e, 0x2e, 0x0a, 0x65, 0x78, 0x70, 0x6f, 0x72, 0x74,
+  0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61,
+  0x4d, 0x6f, 0x64, 0x65, 0x6c, 0x49, 0x6e, 0x66, 0x6f, 0x20, 0x3d, 0x20,
+  0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20,
+  0x7b, 0x0a, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x67, 0x65, 0x6e,
+  0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74,
+  0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20,
+  0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x20,
+  0x3d, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, 0x66, 0x65, 0x74, 0x63,
+  0x68, 0x28, 0x22, 0x2f, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x22, 0x29, 0x2e,
+  0x74, 0x68, 0x65, 0x6e, 0x28, 0x72, 0x20, 0x3d, 0x3e, 0x20, 0x72, 0x2e,
+  0x6a, 0x73, 0x6f, 0x6e, 0x28, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20,
   0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f,
-  0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a
+  0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x70,
+  0x72, 0x6f, 0x70, 0x73, 0x2e, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74,
+  0x5f, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f,
+  0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20,
+  0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67,
+  0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65,
+  0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a
 };
-unsigned int completion_js_len = 5796;
+unsigned int completion_js_len = 5782;

examples/server/oai.hpp

@@ -0,0 +1,225 @@
+#pragma once
+
+#include <string>
+#include <vector>
+#include <set>
+#include <mutex>
+#include <condition_variable>
+#include <unordered_map>
+
+#include "json.hpp"
+#include "utils.hpp"
+
+#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
+
+using json = nlohmann::json;
+
+inline static json oaicompat_completion_params_parse(
+    const struct llama_model * model,
+    const json &body, /* openai api json semantics */
+    const std::string &chat_template)
+{
+    json llama_params;
+
+    llama_params["__oaicompat"] = true;
+
+    // Map OpenAI parameters to llama.cpp parameters
+    //
+    // For parameters that are defined by the OpenAI documentation (e.g.
+    // temperature), we explicitly specify OpenAI's intended default; we
+    // need to do that because sometimes OpenAI disagrees with llama.cpp
+    //
+    // https://platform.openai.com/docs/api-reference/chat/create
+    llama_sampling_params default_sparams;
+    llama_params["model"]             = json_value(body, "model", std::string("unknown"));
+    llama_params["prompt"]            = format_chat(model, chat_template, body["messages"]);
+    llama_params["cache_prompt"]      = json_value(body, "cache_prompt", false);
+    llama_params["temperature"]       = json_value(body, "temperature", 0.0);
+    llama_params["top_k"]             = json_value(body, "top_k", default_sparams.top_k);
+    llama_params["top_p"]             = json_value(body, "top_p", 1.0);
+    llama_params["n_predict"]         = json_value(body, "max_tokens", -1);
+    llama_params["logit_bias"]        = json_value(body, "logit_bias",json::object());
+    llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
+    llama_params["presence_penalty"]  = json_value(body, "presence_penalty", 0.0);
+    llama_params["seed"]              = json_value(body, "seed", LLAMA_DEFAULT_SEED);
+    llama_params["stream"]            = json_value(body, "stream", false);
+    llama_params["mirostat"]          = json_value(body, "mirostat", default_sparams.mirostat);
+    llama_params["mirostat_tau"]      = json_value(body, "mirostat_tau", default_sparams.mirostat_tau);
+    llama_params["mirostat_eta"]      = json_value(body, "mirostat_eta", default_sparams.mirostat_eta);
+    llama_params["penalize_nl"]       = json_value(body, "penalize_nl", default_sparams.penalize_nl);
+    llama_params["typical_p"]         = json_value(body, "typical_p", default_sparams.typical_p);
+    llama_params["repeat_last_n"]     = json_value(body, "repeat_last_n", default_sparams.penalty_last_n);
+    llama_params["ignore_eos"]        = json_value(body, "ignore_eos", false);
+    llama_params["tfs_z"]             = json_value(body, "tfs_z", default_sparams.tfs_z);
+
+    if (body.count("grammar") != 0) {
+        llama_params["grammar"] = json_value(body, "grammar", json::object());
+    }
+
+    // Handle 'stop' field
+    if (body.contains("stop") && body["stop"].is_string()) {
+        llama_params["stop"] = json::array({body["stop"].get<std::string>()});
+    } else {
+        llama_params["stop"] = json_value(body, "stop", json::array());
+    }
+
+    // Ensure there is ChatML-specific end sequence among stop words
+    llama_params["stop"].push_back("<|im_end|>");
+
+    return llama_params;
+}
+
+inline static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false)
+{
+    json result = response.result_json;
+
+    bool stopped_word        = result.count("stopped_word") != 0;
+    bool stopped_eos         = json_value(result, "stopped_eos", false);
+    int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
+    int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
+    std::string content      = json_value(result, "content", std::string(""));
+
+    std::string finish_reason = "length";
+    if (stopped_word || stopped_eos) {
+        finish_reason = "stop";
+    }
+
+    json choices =
+        streaming ? json::array({json{{"finish_reason", finish_reason},
+                                        {"index", 0},
+                                        {"delta", json::object()}}})
+                  : json::array({json{{"finish_reason", finish_reason},
+                                        {"index", 0},
+                                        {"message", json{{"content", content},
+                                                         {"role", "assistant"}}}}});
+
+    std::time_t t = std::time(0);
+
+    json res =
+        json{{"choices", choices},
+            {"created", t},
+            {"model",
+                json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
+            {"object", streaming ? "chat.completion.chunk" : "chat.completion"},
+            {"usage",
+                json{{"completion_tokens", num_tokens_predicted},
+                     {"prompt_tokens",     num_prompt_tokens},
+                     {"total_tokens",      num_tokens_predicted + num_prompt_tokens}}},
+            {"id", gen_chatcmplid()}};
+
+    if (server_verbose) {
+        res["__verbose"] = result;
+    }
+
+    if (result.contains("completion_probabilities")) {
+        res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
+    }
+
+    return res;
+}
+
+// return value is vector as there is one case where we might need to generate two responses
+inline static std::vector<json> format_partial_response_oaicompat(const task_result &response) {
+    json result = response.result_json;
+
+    if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
+        return std::vector<json>({response.result_json});
+    }
+
+    bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
+    std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
+
+    bool stopped_word   = json_value(result, "stopped_word", false);
+    bool stopped_eos    = json_value(result, "stopped_eos", false);
+    bool stopped_limit  = json_value(result, "stopped_limit", false);
+    std::string content = json_value(result, "content", std::string(""));
+
+    std::string finish_reason;
+    if (stopped_word || stopped_eos) {
+        finish_reason = "stop";
+    }
+    if (stopped_limit) {
+        finish_reason = "length";
+    }
+
+    std::time_t t = std::time(0);
+
+    json choices;
+
+    if (!finish_reason.empty()) {
+        choices = json::array({json{{"finish_reason", finish_reason},
+                                    {"index", 0},
+                                    {"delta", json::object()}}});
+    } else {
+        if (first) {
+            if (content.empty()) {
+                choices = json::array({json{{"finish_reason", nullptr},
+                                            {"index", 0},
+                                            {"delta", json{{"role", "assistant"}}}}});
+            } else {
+                // We have to send this as two updates to conform to openai behavior
+                json initial_ret = json{{"choices", json::array({json{
+                                        {"finish_reason", nullptr},
+                                        {"index", 0},
+                                        {"delta", json{
+                                            {"role", "assistant"}
+                                        }}}})},
+                            {"created", t},
+                            {"id", gen_chatcmplid()},
+                            {"model", modelname},
+                            {"object", "chat.completion.chunk"}};
+
+                json second_ret = json{
+                            {"choices", json::array({json{{"finish_reason", nullptr},
+                                                            {"index", 0},
+                                                            {"delta", json{
+                                                            {"content", content}}}
+                                                            }})},
+                            {"created", t},
+                            {"id", gen_chatcmplid()},
+                            {"model", modelname},
+                            {"object", "chat.completion.chunk"}};
+
+                return std::vector<json>({initial_ret, second_ret});
+            }
+        } else {
+            // Some idiosyncrasy in task processing logic makes several trailing calls
+            // with empty content, we ignore these at the calee site.
+            if (content.empty()) {
+                return std::vector<json>({json::object()});
+            }
+
+            choices = json::array({json{
+                {"finish_reason", nullptr},
+                {"index", 0},
+                {"delta",
+                json{
+                    {"content", content},
+                }},
+            }});
+        }
+    }
+
+    json ret = json{{"choices", choices},
+                    {"created", t},
+                    {"id", gen_chatcmplid()},
+                    {"model", modelname},
+                    {"object", "chat.completion.chunk"}};
+
+    return std::vector<json>({ret});
+}
+
+inline static json format_embeddings_response_oaicompat(const json &request, const json &embeddings)
+{
+    json res =
+        json{
+            {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
+            {"object", "list"},
+            {"usage",
+                json{{"prompt_tokens", 0},
+                     {"total_tokens", 0}}},
+            {"data", embeddings}
+        };
+    return res;
+}
+

examples/server/public/completion.js

@@ -96,18 +96,18 @@ export async function* llama(prompt, params = {}, config = {}) {
             }
           }
           if (result.error) {
-            try {
-              result.error = JSON.parse(result.error);
-              if (result.error.message.includes('slot unavailable')) {
-                // Throw an error to be caught by upstream callers
-                throw new Error('slot unavailable');
-              } else {
-                console.error(`llama.cpp error [${result.error.code} - ${result.error.type}]: ${result.error.message}`);
-              }
-            } catch(e) {
-              console.error(`llama.cpp error ${result.error}`)
+            result.error = JSON.parse(result.error);
+            if (result.error.content.includes('slot unavailable')) {
+              // Throw an error to be caught by upstream callers
+              throw new Error('slot unavailable');
+            } else {
+              console.error(`llama.cpp error: ${result.error.content}`);
             }
           }
+          if (result.error) {
+            result.error = JSON.parse(result.error);
+            console.error(`llama.cpp error: ${result.error.content}`);
+          }
         }
       }
     }

examples/server/tests/README.md

@@ -1,30 +1,22 @@
 # Server tests
 
-Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development)
-and [behave](https://behave.readthedocs.io/en/latest/):
-
-* [issues.feature](./features/issues.feature) Pending issues scenario
-* [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests
-* [security.feature](./features/security.feature) Security, CORS and API Key
-* [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc...
+Python based server tests scenario using [BDD](https://en.wikipedia.org/wiki/Behavior-driven_development) and [behave](https://behave.readthedocs.io/en/latest/):
+ * [issues.feature](./features/issues.feature) Pending issues scenario
+ * [parallel.feature](./features/parallel.feature) Scenario involving multi slots and concurrent requests
+ * [security.feature](./features/security.feature) Security, CORS and API Key
+ * [server.feature](./features/server.feature) Server base scenario: completion, embedding, tokenization, etc...
 
 Tests target GitHub workflows job runners with 4 vCPU.
 
-Requests are
-using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html)
-based http client.
+Requests are using [aiohttp](https://docs.aiohttp.org/en/stable/client_reference.html), [asyncio](https://docs.python.org/fr/3/library/asyncio.html) based http client.
 
-Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail.
-To mitigate it, you can increase values in `n_predict`, `kv_size`.
+Note: If the host architecture inference speed is faster than GitHub runners one, parallel scenario may randomly fail. To mitigate it, you can increase values in `n_predict`, `kv_size`.
 
 ### Install dependencies
-
 `pip install -r requirements.txt`
 
 ### Run tests
-
 1. Build the server
-
 ```shell
 cd ../../..
 mkdir build
@@ -32,36 +24,24 @@ cd build
 cmake ../
 cmake --build . --target server
 ```
-
-2. Start the test: `./tests.sh`
+2. download required models:
+   1. `../../../scripts/hf.sh --repo ggml-org/models --file tinyllamas/stories260K.gguf`
+3. Start the test: `./tests.sh`
 
 It's possible to override some scenario steps values with environment variables:
-
-| variable                 | description                                                                                    |
-|--------------------------|------------------------------------------------------------------------------------------------|
-| `PORT`                   | `context.server_port` to set the listening port of the server during scenario, default: `8080` |
-| `LLAMA_SERVER_BIN_PATH`  | to change the server binary path, default: `../../../build/bin/server`                         |
-| `DEBUG`                  | "ON" to enable steps and server verbose mode `--verbose`                                       |
-| `SERVER_LOG_FORMAT_JSON` | if set switch server logs to json format                                                       |
-| `N_GPU_LAYERS`           | number of model layers to offload to VRAM `-ngl --n-gpu-layers`                                |
+ - `PORT` -> `context.server_port` to set the listening port of the server during scenario, default: `8080`
+ - `LLAMA_SERVER_BIN_PATH` -> to change the server binary path, default: `../../../build/bin/server`
+ - `DEBUG` -> "ON" to enable steps and server verbose mode `--verbose`
+ - `SERVER_LOG_FORMAT_JSON` -> if set switch server logs to json format
 
 ### Run @bug, @wip or @wrong_usage annotated scenario
 
 Feature or Scenario must be annotated with `@llama.cpp` to be included in the default scope.
-
 - `@bug` annotation aims to link a scenario with a GitHub issue.
 - `@wrong_usage` are meant to show user issue that are actually an expected behavior
 - `@wip` to focus on a scenario working in progress
-- `@slow` heavy test, disabled by default
 
 To run a scenario annotated with `@bug`, start:
-
-```shell
-DEBUG=ON ./tests.sh --no-skipped --tags bug
-```
+`DEBUG=ON ./tests.sh --no-skipped --tags bug`
 
 After changing logic in `steps.py`, ensure that `@bug` and `@wrong_usage` scenario are updated.
-
-```shell
-./tests.sh --no-skipped --tags bug,wrong_usage || echo "should failed but compile"
-```

examples/server/tests/features/embeddings.feature

@@ -1,95 +0,0 @@
-@llama.cpp
-@embeddings
-Feature: llama.cpp server
-
-  Background: Server startup
-    Given a server listening on localhost:8080
-    And   a model file bert-bge-small/ggml-model-f16.gguf from HF repo ggml-org/models
-    And   a model alias bert-bge-small
-    And   42 as server seed
-    And   2 slots
-    And   1024 as batch size
-    And   1024 as ubatch size
-    And   2048 KV cache size
-    And   embeddings extraction
-    Then  the server is starting
-    Then  the server is healthy
-
-  Scenario: Embedding
-    When embeddings are computed for:
-    """
-    What is the capital of Bulgaria ?
-    """
-    Then embeddings are generated
-
-  Scenario: OAI Embeddings compatibility
-    Given a model bert-bge-small
-    When an OAI compatible embeddings computation request for:
-    """
-    What is the capital of Spain ?
-    """
-    Then embeddings are generated
-
-  Scenario: OAI Embeddings compatibility with multiple inputs
-    Given a model bert-bge-small
-    Given a prompt:
-      """
-      In which country Paris is located ?
-      """
-    And a prompt:
-      """
-      Is Madrid the capital of Spain ?
-      """
-    When an OAI compatible embeddings computation request for multiple inputs
-    Then embeddings are generated
-
-  Scenario: Multi users embeddings
-    Given a prompt:
-      """
-      Write a very long story about AI.
-      """
-    And a prompt:
-      """
-      Write another very long music lyrics.
-      """
-    And a prompt:
-      """
-      Write a very long poem.
-      """
-    And a prompt:
-      """
-      Write a very long joke.
-      """
-    Given concurrent embedding requests
-    Then the server is busy
-    Then the server is idle
-    Then all embeddings are generated
-
-  Scenario: Multi users OAI compatibility embeddings
-    Given a prompt:
-      """
-      In which country Paris is located ?
-      """
-    And a prompt:
-      """
-      Is Madrid the capital of Spain ?
-      """
-    And a prompt:
-      """
-      What is the biggest US city ?
-      """
-    And a prompt:
-      """
-      What is the capital of Bulgaria ?
-      """
-    And   a model bert-bge-small
-    Given concurrent OAI embedding requests
-    Then the server is busy
-    Then the server is idle
-    Then all embeddings are generated
-
-  Scenario: All embeddings should be the same
-    Given 10 fixed prompts
-    And   a model bert-bge-small
-    Given concurrent OAI embedding requests
-    Then all embeddings are the same

examples/server/tests/features/environment.py

@@ -1,17 +1,13 @@
-import errno
 import os
 import socket
 import subprocess
 import time
 from contextlib import closing
-import signal
+from signal import SIGKILL
 
 
 def before_scenario(context, scenario):
-    context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON'
-    if context.debug:
-        print("DEBUG=ON\n")
-    print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m\n")
+    print(f"\x1b[33;42mStarting new scenario: {scenario.name}!\x1b[0m")
     port = 8080
     if 'PORT' in os.environ:
         port = int(os.environ['PORT'])
@@ -30,71 +26,44 @@ def after_scenario(context, scenario):
                     for line in f:
                         print(line)
         if not is_server_listening(context.server_fqdn, context.server_port):
-            print("\x1b[33;101mERROR: Server stopped listening\x1b[0m\n")
+            print("\x1b[33;101mERROR: Server stopped listening\x1b[0m")
 
     if not pid_exists(context.server_process.pid):
         assert False, f"Server not running pid={context.server_process.pid} ..."
 
-    server_graceful_shutdown(context)
-
+    print(f"stopping server pid={context.server_process.pid} ...")
+    context.server_process.kill()
     # Wait few for socket to free up
     time.sleep(0.05)
 
     attempts = 0
-    while pid_exists(context.server_process.pid) or is_server_listening(context.server_fqdn, context.server_port):
-        server_kill(context)
+    while is_server_listening(context.server_fqdn, context.server_port):
+        print(f"stopping server pid={context.server_process.pid} ...")
+        os.kill(context.server_process.pid, SIGKILL)
         time.sleep(0.1)
         attempts += 1
         if attempts > 5:
-            server_kill_hard(context)
-
-
-def server_graceful_shutdown(context):
-    print(f"shutting down server pid={context.server_process.pid} ...\n")
-    if os.name == 'nt':
-        os.kill(context.server_process.pid, signal.CTRL_C_EVENT)
-    else:
-        os.kill(context.server_process.pid, signal.SIGINT)
-
-
-def server_kill(context):
-    print(f"killing server pid={context.server_process.pid} ...\n")
-    context.server_process.kill()
-
-
-def server_kill_hard(context):
-    pid = context.server_process.pid
-    path = context.server_path
-
-    print(f"Server dangling exits, hard killing force {pid}={path}...\n")
-    if os.name == 'nt':
-        process = subprocess.check_output(['taskkill', '/F', '/pid', str(pid)]).decode()
-        print(process)
-    else:
-        os.kill(-pid, signal.SIGKILL)
+            print(f"Server dangling exits, killing all {context.server_path} ...")
+            process = subprocess.run(['killall', '-9', context.server_path],
+                                     stderr=subprocess.PIPE,
+                                     universal_newlines=True)
+            print(process)
 
 
 def is_server_listening(server_fqdn, server_port):
     with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
         result = sock.connect_ex((server_fqdn, server_port))
-        _is_server_listening = result == 0
-        if _is_server_listening:
-            print(f"server is listening on {server_fqdn}:{server_port}...\n")
-        return _is_server_listening
+        return result == 0
 
 
 def pid_exists(pid):
     """Check whether pid exists in the current process table."""
+    import errno
     if pid < 0:
         return False
-    if os.name == 'nt':
-        output = subprocess.check_output(['TASKLIST', '/FI', f'pid eq {pid}']).decode()
-        print(output)
-        return "No tasks are running" not in output
+    try:
+        os.kill(pid, 0)
+    except OSError as e:
+        return e.errno == errno.EPERM
     else:
-        try:
-            os.kill(pid, 0)
-        except OSError as e:
-            return e.errno == errno.EPERM
-        else:
-            return True
+        return True

examples/server/tests/features/issues.feature

@@ -1,5 +1,4 @@
 # List of ongoing issues
-# run with: DEBUG=ON ./tests.sh --no-skipped --tags bug
 @bug
 Feature: Issues
   # No confirmed issue at the moment

examples/server/tests/features/parallel.feature

@@ -1,14 +1,14 @@
 @llama.cpp
-@parallel
 Feature: Parallel
 
   Background: Server startup
     Given a server listening on localhost:8080
-    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    And   a model file stories260K.gguf
+    And   a model alias tinyllama-2
     And   42 as server seed
-    And   128 as batch size
-    And   256 KV cache size
+    And   64 KV cache size
     And   2 slots
+    And   embeddings extraction
     And   continuous batching
     Then  the server is starting
     Then  the server is healthy
@@ -76,7 +76,6 @@ Feature: Parallel
       | disabled  | 128       |
       | enabled   | 64        |
 
-
   Scenario:  Multi users with total number of tokens to predict exceeds the KV Cache size #3969
     Given a prompt:
       """
@@ -99,3 +98,48 @@ Feature: Parallel
     Then the server is busy
     Then the server is idle
     Then all prompts are predicted
+
+  Scenario: Multi users embeddings
+    Given a prompt:
+      """
+      Write a very long story about AI.
+      """
+    And a prompt:
+      """
+      Write another very long music lyrics.
+      """
+    And a prompt:
+      """
+      Write a very long poem.
+      """
+    And a prompt:
+      """
+      Write a very long joke.
+      """
+    Given concurrent embedding requests
+    Then the server is busy
+    Then the server is idle
+    Then all embeddings are generated
+
+  Scenario: Multi users OAI compatibility embeddings
+    Given a prompt:
+      """
+      In which country Paris is located ?
+      """
+    And a prompt:
+      """
+      Is Madrid the capital of Spain ?
+      """
+    And a prompt:
+      """
+      What is the biggest US city ?
+      """
+    And a prompt:
+      """
+      What is the capital of Bulgaria ?
+      """
+    And   a model tinyllama-2
+    Given concurrent OAI embedding requests
+    Then the server is busy
+    Then the server is idle
+    Then all embeddings are generated

examples/server/tests/features/passkey.feature

@@ -1,55 +0,0 @@
-# run with: ./tests.sh --no-skipped --tags passkey
-@passkey
-@slow
-Feature: Passkey / Self-extend with context shift
-
-  Background: Server startup
-    Given a server listening on localhost:8080
-
-  # Generates a long text of junk and inserts a secret passkey number inside it.
-  # Then we query the LLM for the secret passkey.
-  # see #3856 and #4810
-  Scenario Outline: Passkey
-    Given a model file <hf_file> from HF repo <hf_repo>
-    And   <n_batch> as batch size
-    And   <n_junk> as number of junk
-    And   <n_predicted> server max tokens to predict
-    And   42 as seed
-    And   <n_ctx> KV cache size
-    And   1 slots
-    And   <n_ga> group attention factor to extend context size through self-extend
-    And   <n_ga_w> group attention width to extend context size through self-extend
-    # Can be override with N_GPU_LAYERS
-    And   <ngl> GPU offloaded layers
-    Then  the server is starting
-    Then  the server is healthy
-    Given available models
-    Then  model 0 is trained on <n_ctx_train> tokens context
-    Given a prefix prompt:
-    """
-    here is an important info hidden inside a lot of irrelevant text. Find it and memorize them. I will quiz you about the important information there.
-    """
-    And a passkey prompt template:
-    """
-    The pass key is <passkey> Remember it. <passkey> is the pass key.
-    """
-    And a junk suffix prompt:
-    """
-    The grass is green. The sky is blue. The sun is yellow. Here we go. There and back again.
-    """
-    And a suffix prompt:
-    """
-    What is the pass key? The pass key is
-    """
-    Given a "<passkey>" passkey challenge prompt with the passkey inserted every <i_pos> junk
-    And  a completion request with no api error
-    Then <n_predicted> tokens are predicted matching <re_content>
-
-    Examples:
-      | hf_repo                         | hf_file                     | n_ctx_train | ngl | n_ctx | n_batch | n_ga | n_ga_w | n_junk | i_pos | passkey | n_predicted | re_content     |
-      | TheBloke/phi-2-GGUF             | phi-2.Q4_K_M.gguf           | 2048        | 5   | 8192  | 512     | 4    | 512    | 250    | 50    | 42      | 1           | 42             |
-      | TheBloke/phi-2-GGUF             | phi-2.Q4_K_M.gguf           | 2048        | 5   | 8192  | 512     | 2    | 512    | 250    | 50    | 42      | 1           | \b((?!42)\w)+\b  |
-      #| TheBloke/Llama-2-7B-GGUF        | llama-2-7b.Q2_K.gguf        | 4096        | 3   | 16384 | 512     | 4    | 512    | 500    | 300   | 1234    | 5           | 1234           |
-      #| TheBloke/Mixtral-8x7B-v0.1-GGUF | mixtral-8x7b-v0.1.Q2_K.gguf | 32768       | 2   | 16384 | 512     | 4    | 512    | 500    | 100   | 0987    | 5           | 0
-      # 987           |
-

examples/server/tests/features/security.feature

@@ -1,10 +1,9 @@
 @llama.cpp
-@security
 Feature: Security
 
   Background: Server startup with an api key defined
     Given a server listening on localhost:8080
-    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    And   a model file stories260K.gguf
     And   a server api key llama.cpp
     Then  the server is starting
     Then  the server is healthy
@@ -39,9 +38,8 @@ Feature: Security
 
 
   Scenario Outline: CORS Options
-    Given a user api key llama.cpp
-    When  an OPTIONS request is sent from <origin>
-    Then  CORS header <cors_header> is set to <cors_header_value>
+    When an OPTIONS request is sent from <origin>
+    Then CORS header <cors_header> is set to <cors_header_value>
 
     Examples: Headers
       | origin          | cors_header                      | cors_header_value |

examples/server/tests/features/server.feature

@@ -1,19 +1,18 @@
 @llama.cpp
-@server
 Feature: llama.cpp server
 
   Background: Server startup
     Given a server listening on localhost:8080
-    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    And   a model file stories260K.gguf
     And   a model alias tinyllama-2
     And   42 as server seed
       # KV Cache corresponds to the total amount of tokens
       # that can be stored across all independent sequences: #4130
       # see --ctx-size and #5568
-    And   256 KV cache size
-    And   32 as batch size
-    And   2 slots
-    And   64 server max tokens to predict
+    And   32 KV cache size
+    And   1 slots
+    And   embeddings extraction
+    And   32 server max tokens to predict
     And   prometheus compatible metrics exposed
     Then  the server is starting
     Then  the server is healthy
@@ -22,35 +21,17 @@ Feature: llama.cpp server
     Then the server is ready
     And  all slots are idle
 
-
   Scenario Outline: Completion
     Given a prompt <prompt>
     And   <n_predict> max tokens to predict
     And   a completion request with no api error
     Then  <n_predicted> tokens are predicted matching <re_content>
-    And   the completion is <truncated> truncated
-    And   <n_prompt> prompt tokens are processed
     And   prometheus metrics are exposed
-    And   metric llamacpp:tokens_predicted is <n_predicted>
 
     Examples: Prompts
-      | prompt                                                                    | n_predict | re_content                    | n_prompt | n_predicted | truncated |
-      | I believe the meaning of life is                                          | 8         | (read\|going)+                | 18       | 8           | not       |
-      | Write a joke about AI from a very long prompt which will not be truncated | 256       | (princesses\|everyone\|kids)+ | 46       | 64          | not       |
-
-  Scenario: Completion prompt truncated
-    Given a prompt:
-    """
-    Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
-    Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat.
-    Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur.
-    Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
-    """
-    And   a completion request with no api error
-    Then  64 tokens are predicted matching fun|Annaks|popcorns|pictry
-    And   the completion is  truncated
-    And   109 prompt tokens are processed
-
+      | prompt                           | n_predict | re_content                             | n_predicted |
+      | I believe the meaning of life is | 8         | (read<or>going)+                       | 8           |
+      | Write a joke about AI            | 64        | (park<or>friends<or>scared<or>always)+ | 32          |
 
   Scenario Outline: OAI Compatibility
     Given a model <model>
@@ -60,13 +41,39 @@ Feature: llama.cpp server
     And   streaming is <enable_streaming>
     Given an OAI compatible chat completions request with no api error
     Then  <n_predicted> tokens are predicted matching <re_content>
-    And   <n_prompt> prompt tokens are processed
-    And   the completion is <truncated> truncated
 
     Examples: Prompts
-      | model        | system_prompt               | user_prompt                          | max_tokens | re_content             | n_prompt | n_predicted | enable_streaming | truncated |
-      | llama-2      | Book                        | What is the best book                | 8          | (Here\|what)+          | 77       | 8           | disabled         | not       |
-      | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128        | (thanks\|happy\|bird)+ | -1       | 64          | enabled          |           |
+      | model        | system_prompt               | user_prompt                          | max_tokens | re_content                 | n_predicted | enable_streaming |
+      | llama-2      | Book                        | What is the best book                | 8          | (Mom<or>what)+             | 8           | disabled         |
+      | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 64         | (thanks<or>happy<or>bird)+ | 32          | enabled          |
+
+  Scenario: Embedding
+    When embeddings are computed for:
+    """
+    What is the capital of Bulgaria ?
+    """
+    Then embeddings are generated
+
+  Scenario: OAI Embeddings compatibility
+    Given a model tinyllama-2
+    When an OAI compatible embeddings computation request for:
+    """
+    What is the capital of Spain ?
+    """
+    Then embeddings are generated
+
+  Scenario: OAI Embeddings compatibility with multiple inputs
+    Given a model tinyllama-2
+    Given a prompt:
+      """
+      In which country Paris is located ?
+      """
+    And a prompt:
+      """
+      Is Madrid the capital of Spain ?
+      """
+    When an OAI compatible embeddings computation request for multiple inputs
+    Then embeddings are generated
 
 
   Scenario: Tokenize / Detokenize
@@ -75,9 +82,3 @@ Feature: llama.cpp server
     What is the capital of France ?
     """
     Then tokens can be detokenize
-
-  Scenario: Models available
-    Given available models
-    Then  1 models are supported
-    Then  model 0 is identified by tinyllama-2
-    Then  model 0 is trained on 128 tokens context

examples/server/tests/features/steps/steps.py

@@ -10,46 +10,33 @@ from contextlib import closing
 from re import RegexFlag
 
 import aiohttp
-import numpy as np
 import openai
 from behave import step
 from behave.api.async_step import async_run_until_complete
-from huggingface_hub import hf_hub_download
 from prometheus_client import parser
 
 
-@step("a server listening on {server_fqdn}:{server_port}")
+@step(u"a server listening on {server_fqdn}:{server_port}")
 def step_server_config(context, server_fqdn, server_port):
     context.server_fqdn = server_fqdn
     context.server_port = int(server_port)
     if 'PORT' in os.environ:
         context.server_port = int(os.environ['PORT'])
         print(f"$PORT set, overriding server port with to {context.server_port}")
-    if 'FQDN' in os.environ:
-        context.server_fqdn = os.environ['FQDN']
-        print(f"$FQDN set, overriding server fqdn with to {context.server_fqdn}")
 
     context.base_url = f'http://{context.server_fqdn}:{context.server_port}'
 
+    context.debug = 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON'
     context.model_alias = None
-    context.n_batch = None
-    context.n_ubatch = None
     context.n_ctx = None
-    context.n_ga = None
-    context.n_ga_w = None
-    context.n_gpu_layer = None
     context.n_predict = None
-    context.n_prompts = 0
     context.n_server_predict = None
     context.n_slots = None
-    context.prompt_prefix = None
-    context.prompt_suffix = None
     context.server_api_key = None
     context.server_continuous_batching = False
     context.server_embeddings = False
     context.server_metrics = False
     context.server_process = None
-    context.seed = None
     context.server_seed = None
     context.user_api_key = None
 
@@ -58,71 +45,55 @@ def step_server_config(context, server_fqdn, server_port):
     context.prompts = []
 
 
-@step('a model file {hf_file} from HF repo {hf_repo}')
-def step_download_hf_model(context, hf_file, hf_repo):
-    context.model_file = hf_hub_download(repo_id=hf_repo, filename=hf_file)
-    if context.debug:
-        print(f"model file: {context.model_file}\n")
+@step(u'a model file {model_file}')
+def step_model_file(context, model_file):
+    context.model_file = model_file
 
 
-@step('a model alias {model_alias}')
+@step(u'a model alias {model_alias}')
 def step_model_alias(context, model_alias):
     context.model_alias = model_alias
 
 
-@step('{seed:d} as server seed')
+@step(u'{seed} as server seed')
 def step_seed(context, seed):
-    context.server_seed = seed
+    context.server_seed = int(seed)
 
 
-@step('{ngl:d} GPU offloaded layers')
-def step_n_gpu_layer(context, ngl):
-    if 'N_GPU_LAYERS' in os.environ:
-        new_ngl = int(os.environ['N_GPU_LAYERS'])
-        if context.debug:
-            print(f"-ngl upgraded from {ngl} to {new_ngl}")
-        ngl = new_ngl
-    context.n_gpu_layer = ngl
-
-
-@step('{n_ctx:d} KV cache size')
+@step(u'{n_ctx} KV cache size')
 def step_n_ctx(context, n_ctx):
-    context.n_ctx = n_ctx
+    context.n_ctx = int(n_ctx)
 
 
-@step('{n_slots:d} slots')
+@step(u'{n_slots} slots')
 def step_n_slots(context, n_slots):
-    context.n_slots = n_slots
+    context.n_slots = int(n_slots)
 
 
-@step('{n_predict:d} server max tokens to predict')
+@step(u'{n_predict} server max tokens to predict')
 def step_server_n_predict(context, n_predict):
-    context.n_server_predict = n_predict
+    context.n_server_predict = int(n_predict)
 
 
-@step('continuous batching')
+@step(u'continuous batching')
 def step_server_continuous_batching(context):
     context.server_continuous_batching = True
 
 
-@step('embeddings extraction')
+@step(u'embeddings extraction')
 def step_server_embeddings(context):
     context.server_embeddings = True
 
 
-@step('prometheus compatible metrics exposed')
+@step(u'prometheus compatible metrics exposed')
 def step_server_metrics(context):
     context.server_metrics = True
 
 
-@step("the server is starting")
+@step(u"the server is starting")
 def step_start_server(context):
     start_server_background(context)
     attempts = 0
-    max_attempts = 20
-    if 'GITHUB_ACTIONS' in os.environ:
-        max_attempts *= 2
-
     while True:
         with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock:
             result = sock.connect_ex((context.server_fqdn, context.server_port))
@@ -130,13 +101,13 @@ def step_start_server(context):
                 print("\x1b[33;46mserver started!\x1b[0m")
                 return
             attempts += 1
-            if attempts > max_attempts:
+            if attempts > 20:
                 assert False, "server not started"
             print(f"waiting for server to start, connect error code = {result}...")
             time.sleep(0.1)
 
 
-@step("the server is {expecting_status}")
+@step(u"the server is {expecting_status}")
 @async_run_until_complete
 async def step_wait_for_the_server_to_be_started(context, expecting_status):
     match expecting_status:
@@ -145,13 +116,11 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status):
 
         case 'ready' | 'idle':
             await wait_for_health_status(context, context.base_url, 200, 'ok',
-                                         timeout=10,
                                          params={'fail_on_no_slot': 0, 'include_slots': 0},
                                          slots_idle=context.n_slots,
                                          slots_processing=0,
                                          expected_slots=[{'id': slot_id, 'state': 0}
-                                                         for slot_id in
-                                                         range(context.n_slots if context.n_slots else 1)])
+                                                         for slot_id in range(context.n_slots)])
         case 'busy':
             await wait_for_health_status(context, context.base_url, 503,
                                          'no slot available',
@@ -159,13 +128,12 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status):
                                          slots_idle=0,
                                          slots_processing=context.n_slots,
                                          expected_slots=[{'id': slot_id, 'state': 1}
-                                                         for slot_id in
-                                                         range(context.n_slots if context.n_slots else 1)])
+                                                         for slot_id in range(context.n_slots)])
         case _:
             assert False, "unknown status"
 
 
-@step('all slots are {expected_slot_status_string}')
+@step(u'all slots are {expected_slot_status_string}')
 @async_run_until_complete
 async def step_all_slots_status(context, expected_slot_status_string):
     match expected_slot_status_string:
@@ -181,7 +149,7 @@ async def step_all_slots_status(context, expected_slot_status_string):
     await request_slots_status(context, expected_slots)
 
 
-@step('a completion request with {api_error} api error')
+@step(u'a completion request with {api_error} api error')
 @async_run_until_complete
 async def step_request_completion(context, api_error):
     expect_api_error = api_error == 'raised'
@@ -189,166 +157,76 @@ async def step_request_completion(context, api_error):
                                           context.base_url,
                                           debug=context.debug,
                                           n_predict=context.n_predict,
-                                          seed=await completions_seed(context),
+                                          server_seed=context.server_seed,
                                           expect_api_error=expect_api_error,
                                           user_api_key=context.user_api_key)
     context.tasks_result.append(completion)
     if context.debug:
-        print(f"Completion response: {completion}\n")
+        print(f"Completion response: {completion}")
     if expect_api_error:
         assert completion == 401, f"completion must be an 401 status code: {completion}"
 
 
-@step('{predicted_n:d} tokens are predicted matching {re_content}')
+@step(u'{predicted_n} tokens are predicted matching {re_content}')
 def step_n_tokens_predicted_with_content(context, predicted_n, re_content):
-    context.completion = context.tasks_result.pop()
-    assert_n_tokens_predicted(context.completion, predicted_n, re_content)
+    assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n), re_content)
 
 
-@step('{predicted_n:d} tokens are predicted')
+@step(u'{predicted_n} tokens are predicted')
 def step_n_tokens_predicted(context, predicted_n):
-    context.completion = context.tasks_result.pop()
-    assert_n_tokens_predicted(context.completion, predicted_n)
+    assert_n_tokens_predicted(context.tasks_result.pop(), int(predicted_n))
 
 
-@step('the completion is  truncated')
-def step_assert_completion_truncated(context):
-    step_assert_completion_truncated(context, '')
-
-
-@step('the completion is {truncated} truncated')
-def step_assert_completion_truncated(context, truncated):
-    truncated = truncated != "not"
-    assert context.completion['truncated'] == truncated, f'{context.completion}'
-
-
-@step('{n_prompt:d} prompt tokens are processed')
-def step_impl(context, n_prompt):
-    assert n_prompt < 0 or n_prompt == context.completion['timings']['prompt_n'], f"n_prompt={context.completion['timings']['prompt_n']}"
-
-
-@step('a user prompt {user_prompt}')
+@step(u'a user prompt {user_prompt}')
 def step_user_prompt(context, user_prompt):
     context.prompts.append(user_prompt)
-    context.n_prompts = len(context.prompts)
 
 
-@step('a system prompt {system_prompt}')
+@step(u'a system prompt {system_prompt}')
 def step_system_prompt(context, system_prompt):
     context.system_prompt = system_prompt
 
 
-@step('a model {model}')
+@step(u'a model {model}')
 def step_model(context, model):
     context.model = model
 
 
-@step('{max_tokens:d} max tokens to predict')
+@step(u'{max_tokens} max tokens to predict')
 def step_max_tokens(context, max_tokens):
-    context.n_predict = max_tokens
+    context.n_predict = int(max_tokens)
 
 
-@step('streaming is {enable_streaming}')
+@step(u'streaming is {enable_streaming}')
 def step_streaming(context, enable_streaming):
     context.enable_streaming = enable_streaming == 'enabled'
 
 
-@step('a user api key {user_api_key}')
+@step(u'a user api key {user_api_key}')
 def step_user_api_key(context, user_api_key):
     context.user_api_key = user_api_key
 
 
-@step('no user api key')
+@step(u'no user api key')
 def step_no_user_api_key(context):
     context.user_api_key = None
 
 
-@step('a user api key ')
+@step(u'a user api key ')
 def step_no_user_api_key_space(context):
     context.user_api_key = None
 
 
-@step('a server api key {server_api_key}')
+@step(u'a server api key {server_api_key}')
 def step_server_api_key(context, server_api_key):
     context.server_api_key = server_api_key
 
 
-@step('{n_junk:d} as number of junk')
-def step_n_junk(context, n_junk):
-    context.n_junk = n_junk
-
-
-@step('{n_batch:d} as batch size')
-def step_n_batch(context, n_batch):
-    context.n_batch = n_batch
-
-
-@step('{n_ubatch:d} as ubatch size')
-def step_n_ubatch(context, n_ubatch):
-    context.n_ubatch = n_ubatch
-
-
-@step('{seed:d} as seed')
-def step_seed(context, seed):
-    context.seed = seed
-
-
-@step('a prefix prompt')
-def step_prompt_prefix(context):
-    context.prompt_prefix = context_text(context)
-
-
-@step('a junk suffix prompt')
-def step_prompt_junk_suffix(context):
-    context.prompt_junk_suffix = context_text(context)
-
-
-@step('a suffix prompt')
-def step_prompt_suffix(context):
-    context.prompt_suffix = context_text(context)
-
-
-@step('{n_ga:d} group attention factor'
-      ' to extend context size through self-extend')
-def step_impl(context, n_ga):
-    context.n_ga = n_ga
-
-
-@step('{n_ga_w:d} group attention width to extend context size through self-extend')
-def step_impl(context, n_ga_w):
-    context.n_ga_w = n_ga_w
-
-
-@step('a passkey prompt template')
-def step_prompt_passkey(context):
-    context.prompt_passkey = context_text(context)
-
-
-@step('{n_prompts:d} fixed prompts')
-def step_fixed_prompts(context, n_prompts):
-    context.prompts.extend([str(0)*(context.n_batch if context.n_batch is not None else 512) for i in range(n_prompts)])
-    context.n_prompts = n_prompts
-
-
-@step('a "{passkey}" passkey challenge prompt with the passkey inserted every {i_pos:d} junk')
-def step_prompt_passkey(context, passkey, i_pos):
-    prompt = ""
-    for i in range(context.n_junk):
-        if i % context.n_junk == i_pos:
-            prompt += context.prompt_passkey # the passkey is already substituted
-        prompt += context.prompt_junk_suffix
-    if context.debug:
-        passkey_highlight = "\x1b[33m" + passkey + "\x1b[0m"
-        print(f"Passkey challenge:\n```{prompt.replace(passkey, passkey_highlight)}```\n")
-    context.prompts.append(context.prompt_prefix + prompt + context.prompt_suffix)
-    context.n_prompts = len(context.prompts)
-
-
-@step('an OAI compatible chat completions request with {api_error} api error')
+@step(u'an OAI compatible chat completions request with {api_error} api error')
 @async_run_until_complete
 async def step_oai_chat_completions(context, api_error):
     if context.debug:
-        print(f"Submitting OAI compatible completions request...\n")
+        print(f"Submitting OAI compatible completions request...")
     expect_api_error = api_error == 'raised'
     completion = await oai_chat_completions(context.prompts.pop(),
                                             context.system_prompt,
@@ -363,7 +241,8 @@ async def step_oai_chat_completions(context, api_error):
                                             enable_streaming=context.enable_streaming
                                             if hasattr(context, 'enable_streaming') else None,
 
-                                            seed=await completions_seed(context),
+                                            server_seed=context.server_seed
+                                            if hasattr(context, 'server_seed') else None,
 
                                             user_api_key=context.user_api_key
                                             if hasattr(context, 'user_api_key') else None,
@@ -379,19 +258,17 @@ async def step_oai_chat_completions(context, api_error):
         print(f"Completion response: {completion}")
 
 
-@step('a prompt')
+@step(u'a prompt')
 def step_a_prompt(context):
-    context.prompts.append(context_text(context))
-    context.n_prompts = len(context.prompts)
+    context.prompts.append(context.text)
 
 
-@step('a prompt {prompt}')
+@step(u'a prompt {prompt}')
 def step_a_prompt_prompt(context, prompt):
     context.prompts.append(prompt)
-    context.n_prompts = len(context.prompts)
 
 
-@step('concurrent completion requests')
+@step(u'concurrent completion requests')
 @async_run_until_complete()
 async def step_concurrent_completion_requests(context):
     await concurrent_requests(context,
@@ -399,15 +276,13 @@ async def step_concurrent_completion_requests(context):
                               # prompt is inserted automatically
                               context.base_url,
                               debug=context.debug,
-                              prompt_prefix=context.prompt_prefix,
-                              prompt_suffix=context.prompt_suffix,
                               n_predict=context.n_predict if hasattr(context, 'n_predict') else None,
-                              seed=await completions_seed(context),
+                              server_seed=context.server_seed if hasattr(context, 'server_seed') else None,
                               user_api_key=context.user_api_key if hasattr(context,
                                                                            'user_api_key') else None)
 
 
-@step('concurrent OAI completions requests')
+@step(u'concurrent OAI completions requests')
 @async_run_until_complete
 async def step_oai_chat_completions(context):
     await concurrent_requests(context, oai_chat_completions,
@@ -422,12 +297,13 @@ async def step_oai_chat_completions(context):
                               if hasattr(context, 'n_predict') else None,
                               enable_streaming=context.enable_streaming
                               if hasattr(context, 'enable_streaming') else None,
-                              seed=await completions_seed(context),
+                              server_seed=context.server_seed
+                              if hasattr(context, 'server_seed') else None,
                               user_api_key=context.user_api_key
                               if hasattr(context, 'user_api_key') else None)
 
 
-@step('concurrent OAI completions requests no v1')
+@step(u'concurrent OAI completions requests no v1')
 @async_run_until_complete
 async def step_oai_chat_completions(context):
     await concurrent_requests(context, oai_chat_completions,
@@ -442,24 +318,23 @@ async def step_oai_chat_completions(context):
                               if hasattr(context, 'n_predict') else None,
                               enable_streaming=context.enable_streaming
                               if hasattr(context, 'enable_streaming') else None,
-                              seed=context.seed
-                              if hasattr(context, 'seed') else
-                              context.server_seed
+                              server_seed=context.server_seed
                               if hasattr(context, 'server_seed') else None,
                               user_api_key=context.user_api_key
                               if hasattr(context, 'user_api_key') else None)
 
 
-@step('all prompts are predicted')
+@step(u'all prompts are predicted')
 @async_run_until_complete
 async def step_all_prompts_are_predicted(context):
     await all_prompts_are_predicted(context)
 
 
-@step('all prompts are predicted with {n_expected_predicted:d} tokens')
+@step(u'all prompts are predicted with {n_predict} tokens')
 @async_run_until_complete
-async def step_all_prompts_are_predicted_with_n_tokens(context, n_expected_predicted):
-    await all_prompts_are_predicted(context, n_expected_predicted)
+async def step_all_prompts_are_predicted_with_n_tokens(context, n_predict):
+    expected_predicted_n = int(n_predict)
+    await all_prompts_are_predicted(context, expected_predicted_n)
 
 
 async def all_prompts_are_predicted(context, expected_predicted_n=None):
@@ -470,68 +345,44 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None):
     assert len(context.concurrent_tasks) == 0, f"{len(context.concurrent_tasks)} pending requests"
 
 
-@step('embeddings are computed for')
+@step(u'embeddings are computed for')
 @async_run_until_complete
 async def step_compute_embedding(context):
-    context.n_prompts = 1
-    context.embeddings = await request_embedding(context_text(context), base_url=context.base_url)
+    context.embeddings = await request_embedding(context.text, base_url=context.base_url)
 
 
-@step('all embeddings are the same')
-@async_run_until_complete
-async def step_all_embeddings_are_the_same(context):
-    n_embedding_requests = await gather_tasks_results(context)
-    assert n_embedding_requests > 0
-    embeddings = []
-    for i in range(n_embedding_requests):
-        embedding = context.tasks_result.pop().pop()
-        embeddings.append(embedding)
-        assert_embeddings(embedding)
-    n = len(embeddings)
-    for i in range(n-1):
-        for j in range(i+1, n):
-            embedding1 = np.array(embeddings[i])
-            embedding2 = np.array(embeddings[j])
-            if context.debug:
-                print(f"embedding1: {embedding1[-8:]}\n")
-                print(f"embedding2: {embedding2[-8:]}\n")
-            similarity = np.dot(embedding1, embedding2) / (np.linalg.norm(embedding1) * np.linalg.norm(embedding2))
-            msg = f"Similarity between {i} and {j}: {similarity:.10f}"
-            if context.debug:
-                print(f"{msg}\n")
-            assert np.isclose(similarity, 1.0, rtol=1e-05, atol=1e-08, equal_nan=False), msg
-
-
-@step('embeddings are generated')
+@step(u'embeddings are generated')
 def step_assert_embeddings(context):
-    assert context.n_prompts == len(context.embeddings), (f"unexpected response:\n"
-                                                             f"context.n_prompts={context.n_prompts}\n"
-                                                             f"context.embeddings={context.embeddings}")
-    for embedding in context.embeddings:
-        assert_embeddings(embedding)
+    if len(context.prompts) == 0:
+        assert_embeddings(context.embeddings)
+    else:
+        assert len(context.embeddings) == len(context.prompts), (f"unexpected response:\n"
+                                                                 f"context.prompts={context.prompts}\n"
+                                                                 f"context.embeddings={context.embeddings}")
+        for embedding in context.embeddings:
+            context.prompts.pop()
+            assert_embeddings(embedding)
 
 
-@step('an OAI compatible embeddings computation request for')
+@step(u'an OAI compatible embeddings computation request for')
 @async_run_until_complete
 async def step_oai_compute_embeddings(context):
-    context.n_prompts = 1
-    context.embeddings = await request_oai_embeddings(context_text(context),
+    context.embeddings = await request_oai_embeddings(context.text,
                                                       base_url=context.base_url,
                                                       user_api_key=context.user_api_key,
                                                       model=context.model)
 
 
-@step('an OAI compatible embeddings computation request for multiple inputs')
+@step(u'an OAI compatible embeddings computation request for multiple inputs')
 @async_run_until_complete
 async def step_oai_compute_embeddings_multiple_inputs(context):
     context.embeddings = await request_oai_embeddings(context.prompts,
                                                       base_url=context.base_url,
                                                       user_api_key=context.user_api_key,
                                                       model=context.model)
-    context.prompts.clear()
 
 
-@step('concurrent embedding requests')
+@step(u'concurrent embedding requests')
 @async_run_until_complete()
 async def step_concurrent_embedding_requests(context):
     await concurrent_requests(context,
@@ -540,7 +391,7 @@ async def step_concurrent_embedding_requests(context):
                               base_url=context.base_url)
 
 
-@step('concurrent OAI embedding requests')
+@step(u'concurrent OAI embedding requests')
 @async_run_until_complete()
 async def step_concurrent_oai_embedding_requests(context):
     await concurrent_requests(context,
@@ -551,19 +402,19 @@ async def step_concurrent_oai_embedding_requests(context):
                               model=context.model)
 
 
-@step('all embeddings are generated')
+@step(u'all embeddings are generated')
 @async_run_until_complete()
 async def all_embeddings_are_generated(context):
     n_embedding_requests = await gather_tasks_results(context)
-    assert n_embedding_requests == context.n_prompts
+    assert n_embedding_requests > 0
     for i in range(n_embedding_requests):
-        assert_embeddings(context.tasks_result.pop().pop())
+        assert_embeddings(context.tasks_result.pop())
 
 
-@step('tokenizing')
+@step(u'tokenizing')
 @async_run_until_complete
 async def step_tokenize(context):
-    context.tokenized_text = context_text(context)
+    context.tokenized_text = context.text
     async with aiohttp.ClientSession() as session:
         async with session.post(f'{context.base_url}/tokenize',
                                 json={
@@ -574,7 +425,7 @@ async def step_tokenize(context):
             context.tokens = tokenize_json['tokens']
 
 
-@step('tokens can be detokenize')
+@step(u'tokens can be detokenize')
 @async_run_until_complete
 async def step_detokenize(context):
     assert len(context.tokens) > 0
@@ -589,23 +440,22 @@ async def step_detokenize(context):
             assert context.tokenized_text == detokenize_json['content'].strip()
 
 
-@step('an OPTIONS request is sent from {origin}')
+@step(u'an OPTIONS request is sent from {origin}')
 @async_run_until_complete
 async def step_options_request(context, origin):
     async with aiohttp.ClientSession() as session:
-        headers = {'Authorization': f'Bearer {context.user_api_key}', 'Origin': origin}
         async with session.options(f'{context.base_url}/v1/chat/completions',
-                                    headers=headers) as response:
+                                   headers={"Origin": origin}) as response:
             assert response.status == 200
             context.options_response = response
 
 
-@step('CORS header {cors_header} is set to {cors_header_value}')
+@step(u'CORS header {cors_header} is set to {cors_header_value}')
 def step_check_options_header_value(context, cors_header, cors_header_value):
     assert context.options_response.headers[cors_header] == cors_header_value
 
 
-@step('prometheus metrics are exposed')
+@step(u'prometheus metrics are exposed')
 @async_run_until_complete
 async def step_prometheus_metrics_exported(context):
     async with aiohttp.ClientSession() as session:
@@ -614,63 +464,20 @@ async def step_prometheus_metrics_exported(context):
             assert metrics_response.headers['Content-Type'] == "text/plain; version=0.0.4"
             metrics_raw = await metrics_response.text()
             metric_exported = False
-            if context.debug:
-                print(f"/metrics answer:\n{metrics_raw}\n")
-            context.metrics = {}
             for metric in parser.text_string_to_metric_families(metrics_raw):
                 match metric.name:
                     case "llamacpp:kv_cache_usage_ratio":
                         assert len(metric.samples) > 0
                         metric_exported = True
-                context.metrics[metric.name] = metric
-            assert int(metrics_response.headers["Process-Start-Time-Unix"]) > 0, "no header process start time"
             assert metric_exported, "No metrics exported"
 
 
-@step('metric {metric_name} is {metric_value:d}')
-def step_assert_metric_value(context, metric_name, metric_value):
-    if metric_name not in context.metrics:
-        assert False, f"no metric {metric_name} in {context.metrics.keys()}"
-    assert context.metrics[metric_name].samples[0].value == metric_value, f"metric: {context.metrics[metric_name]}"
-
-
-@step('available models')
-def step_available_models(context):
-    # openai client always expects an api_key
-    openai.api_key = context.user_api_key if context.user_api_key is not None else 'nope'
-    openai.api_base = f'{context.base_url}/v1'
-    context.models = openai.Model.list().data
-
-
-@step('{n_model:d} models are supported')
-def step_supported_models(context, n_model):
-    if context.debug:
-        print("server models available:", context.models)
-    assert len(context.models) == n_model
-
-
-@step('model {i_model:d} is {param} {preposition} {param_value}')
-def step_supported_models(context, i_model, param, preposition, param_value):
-    assert i_model < len(context.models)
-    model = context.models[i_model]
-
-    param_value = param_value.split(' ', 1)[0]
-    match param:
-        case 'identified':
-            value = model.id
-        case 'trained':
-            value = str(model.meta.n_ctx_train)
-        case _:
-            assert False, "param {param} not supported"
-    assert param_value == value, f"model param {param} {value} != {param_value}"
-
-
 async def concurrent_requests(context, f_completion, *args, **kwargs):
-    context.n_prompts = len(context.prompts)
+    n_prompts = len(context.prompts)
     if context.debug:
-        print(f"starting {context.n_prompts} concurrent completion requests...")
-    assert context.n_prompts > 0
-    for prompt_no in range(context.n_prompts):
+        print(f"starting {n_prompts} concurrent completion requests...")
+    assert n_prompts > 0
+    for prompt_no in range(n_prompts):
         shifted_args = [context.prompts.pop(), *args]
         context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs)))
     await asyncio.sleep(0.1)
@@ -679,10 +486,8 @@ async def concurrent_requests(context, f_completion, *args, **kwargs):
 async def request_completion(prompt,
                              base_url,
                              debug=False,
-                             prompt_prefix=None,
-                             prompt_suffix=None,
                              n_predict=None,
-                             seed=None,
+                             server_seed=None,
                              expect_api_error=None,
                              user_api_key=None):
     if debug:
@@ -699,14 +504,11 @@ async def request_completion(prompt,
     async with aiohttp.ClientSession() as session:
         async with session.post(f'{base_url}/completion',
                                 json={
-                                    "input_prefix": prompt_prefix,
                                     "prompt": prompt,
-                                    "input_suffix": prompt_suffix,
-                                    "n_predict": n_predict if n_predict is not None else -1,
-                                    "seed": seed if seed is not None else 42
+                                    "n_predict": int(n_predict) if n_predict is not None else -1,
+                                    "seed": server_seed if server_seed is not None else 42
                                 },
-                                headers=headers,
-                                timeout=3600) as response:
+                                headers=headers) as response:
             if expect_api_error is None or not expect_api_error:
                 assert response.status == 200
                 assert response.headers['Access-Control-Allow-Origin'] == origin
@@ -724,14 +526,14 @@ async def oai_chat_completions(user_prompt,
                                model=None,
                                n_predict=None,
                                enable_streaming=None,
-                               seed=None,
+                               server_seed=None,
                                user_api_key=None,
                                expect_api_error=None):
     if debug:
         print(f"Sending OAI Chat completions request: {user_prompt}")
     # openai client always expects an api key
     user_api_key = user_api_key if user_api_key is not None else 'nope'
-    seed = seed if seed is not None else 42
+    seed = server_seed if server_seed is not None else 42
     enable_streaming = enable_streaming if enable_streaming is not None else False
     payload = {
         "messages": [
@@ -752,8 +554,7 @@ async def oai_chat_completions(user_prompt,
     completion_response = {
         'content': '',
         'timings': {
-            'predicted_n': 0,
-            'prompt_n': 0
+            'predicted_n': 0
         }
     }
     if async_client:
@@ -794,8 +595,7 @@ async def oai_chat_completions(user_prompt,
                         completion_response = {
                             'content': chat_completion_raw['choices'][0]['message'],
                             'timings': {
-                                'predicted_n': chat_completion_raw['usage']['completion_tokens'],
-                                'prompt_n': chat_completion_raw['usage']['prompt_tokens']
+                                'predicted_n': chat_completion_raw['usage']['completion_tokens']
                             }
                         }
                     else:
@@ -811,7 +611,7 @@ async def oai_chat_completions(user_prompt,
                 stream=enable_streaming,
                 seed=seed
             )
-        except openai.error.AuthenticationError as e:
+        except openai.error.APIError as e:
             if expect_api_error is not None and expect_api_error:
                 return 401
             else:
@@ -824,16 +624,13 @@ async def oai_chat_completions(user_prompt,
                 if 'content' in delta:
                     completion_response['content'] += delta['content']
                     completion_response['timings']['predicted_n'] += 1
-                completion_response['truncated'] = chunk.choices[0].finish_reason != 'stop'
         else:
             assert len(chat_completion.choices) == 1
             completion_response = {
                 'content': chat_completion.choices[0].message.content,
                 'timings': {
-                    'predicted_n': chat_completion.usage.completion_tokens,
-                    'prompt_n': chat_completion.usage.prompt_tokens
-                    },
-                'truncated': chat_completion.choices[0].finish_reason != 'stop'
+                    'predicted_n': chat_completion.usage.completion_tokens
+                }
             }
     if debug:
         print("OAI response formatted to llama.cpp:", completion_response)
@@ -848,7 +645,7 @@ async def request_embedding(content, base_url=None):
                                 }) as response:
             assert response.status == 200
             response_json = await response.json()
-            return [response_json['embedding']]
+            return response_json['embedding']
 
 
 async def request_oai_embeddings(input,
@@ -858,7 +655,6 @@ async def request_oai_embeddings(input,
     user_api_key = user_api_key if user_api_key is not None else 'nope'
     if async_client:
         origin = 'llama.cpp'
-        headers=[]
         if user_api_key is not None:
             headers = {'Authorization': f'Bearer {user_api_key}', 'Origin': origin}
         async with aiohttp.ClientSession() as session:
@@ -867,21 +663,14 @@ async def request_oai_embeddings(input,
                                         "input": input,
                                         "model": model,
                                     },
-                                    headers=headers,
-                                    timeout=3600) as response:
+                                    headers=headers) as response:
                 assert response.status == 200, f"received status code not expected: {response.status}"
                 assert response.headers['Access-Control-Allow-Origin'] == origin
                 assert response.headers['Content-Type'] == "application/json; charset=utf-8"
                 response_json = await response.json()
                 assert response_json['model'] == model, f"invalid model received: {response_json['model']}"
                 assert response_json['object'] == 'list'
-                if isinstance(input, collections.abc.Sequence):
-                    embeddings = []
-                    for an_oai_embeddings in response_json['data']:
-                        embeddings.append(an_oai_embeddings['embedding'])
-                else:
-                    embeddings = [response_json['data']['embedding']]
-                return embeddings
+                return response_json['data']
     else:
         openai.api_key = user_api_key
         openai.api_base = f'{base_url}/v1'
@@ -895,7 +684,7 @@ async def request_oai_embeddings(input,
             for an_oai_embeddings in oai_embeddings.data:
                 embeddings.append(an_oai_embeddings.embedding)
         else:
-            embeddings = [oai_embeddings.data.embedding]
+            embeddings = oai_embeddings.data.embedding
         return embeddings
 
 
@@ -903,31 +692,20 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re
     content = completion_response['content']
     n_predicted = completion_response['timings']['predicted_n']
     assert len(content) > 0, "no token predicted"
-    if re_content is not None:
-        p = re.compile(re_content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL)
-        matches = p.finditer(content)
-        last_match = 0
-        highlighted = ''
-        for match in matches:
-            start, end = match.span()
-            highlighted += content[last_match: start]
-            highlighted += '\x1b[33m'
-            highlighted += content[start: end]
-            highlighted += '\x1b[0m'
-            last_match = end
-        highlighted += content[last_match:]
-        if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON':
-          print(f"Checking completion response: {highlighted}\n")
-        assert last_match > 0, f'/{re_content}/ must match ```{highlighted}```'
-    if expected_predicted_n and expected_predicted_n > 0:
+    if expected_predicted_n is not None:
         assert n_predicted == expected_predicted_n, (f'invalid number of tokens predicted:'
                                                      f' {n_predicted} <> {expected_predicted_n}')
+    if re_content is not None:
+        re_content = '^.*' + re_content.replace('<or>', '|') + '.*$'
+        assert re.match(re_content, content, flags=RegexFlag.IGNORECASE | RegexFlag.MULTILINE | RegexFlag.DOTALL), (
+            f'invalid tokens predicted:'
+            f' ```\n{content}\n``` do not match /{re_content}/')
 
 
 async def gather_tasks_results(context):
     n_tasks = len(context.concurrent_tasks)
     if context.debug:
-        print(f"Waiting for all {n_tasks} tasks results...\n")
+        print(f"Waiting for all {n_tasks} tasks results...")
     for task_no in range(n_tasks):
         context.tasks_result.append(await context.concurrent_tasks.pop())
     n_completions = len(context.tasks_result)
@@ -938,18 +716,17 @@ async def wait_for_health_status(context,
                                  base_url,
                                  expected_http_status_code,
                                  expected_health_status,
-                                 timeout=3,
                                  params=None,
                                  slots_idle=None,
                                  slots_processing=None,
                                  expected_slots=None):
     if context.debug:
-        print(f"Starting checking for health for expected_health_status={expected_health_status}\n")
+        print(f"Starting checking for health for expected_health_status={expected_health_status}")
+    timeout = 3  # seconds
+    if expected_health_status == 'ok':
+        timeout = 10 # CI slow inference
     interval = 0.5
     counter = 0
-    if 'GITHUB_ACTIONS' in os.environ:
-        timeout *= 2
-
     async with aiohttp.ClientSession() as session:
         while True:
             async with await session.get(f'{base_url}/health', params=params) as health_response:
@@ -957,7 +734,7 @@ async def wait_for_health_status(context,
                 health = await health_response.json()
                 if context.debug:
                     print(f"HEALTH - response for expected health status='{expected_health_status}' on "
-                          f"'{base_url}/health'?{params} is {health}\n")
+                          f"'{base_url}/health'?{params} is {health}")
                 if (status_code == expected_http_status_code
                         and health['status'] == expected_health_status
                         and (slots_idle is None or health['slots_idle'] == slots_idle)
@@ -980,7 +757,7 @@ async def wait_for_health_status(context,
                 if expected_http_status_code == 503:
                     if len(context.tasks_result) == 0:
                         print("\x1b[5;37;43mWARNING: forcing concurrent tasks,"
-                              " busy health check missed, probably too fast inference\x1b[0m\n")
+                              " busy health check missed, probably too fast inference\x1b[0m")
                         n_completions = await gather_tasks_results(context)
                         if n_completions > 0:
                             return
@@ -992,8 +769,6 @@ def assert_embeddings(embeddings):
     assert len(embeddings) > 0
     embeddings_computed = False
     for emb in embeddings:
-        if not isinstance(emb, float):
-            assert False, f"Bad embeddings: {embeddings}"
         if emb != 0:
             embeddings_computed = True
     assert embeddings_computed, f"Embeddings: {embeddings}"
@@ -1016,71 +791,37 @@ def assert_slots_status(slots, expected_slots):
                                                 f" = {expected[key]} != {slot[key]}")
 
 
-async def completions_seed(context):
-    return context.seed if hasattr(context, 'seed') and context.seed is not None \
-        else context.server_seed if hasattr(context, 'server_seed') else None
-
-
-def context_text(context):
-    return context.text.replace('\r', '')
-
-
 def start_server_background(context):
-    if os.name == 'nt':
-        context.server_path = '../../../build/bin/Release/server.exe'
-    else:
-        context.server_path = '../../../build/bin/server'
+    context.server_path = '../../../build/bin/server'
     if 'LLAMA_SERVER_BIN_PATH' in os.environ:
         context.server_path = os.environ['LLAMA_SERVER_BIN_PATH']
-    server_listen_addr = context.server_fqdn
-    if os.name == 'nt':
-        server_listen_addr = '0.0.0.0'
     server_args = [
-        '--host', server_listen_addr,
+        '--host', context.server_fqdn,
         '--port', context.server_port,
         '--model', context.model_file
     ]
-    if context.n_batch:
-        server_args.extend(['--batch-size', context.n_batch])
-    if context.n_ubatch:
-        server_args.extend(['--ubatch-size', context.n_ubatch])
-    if context.n_gpu_layer:
-        server_args.extend(['--n-gpu-layers', context.n_gpu_layer])
     if context.server_continuous_batching:
         server_args.append('--cont-batching')
     if context.server_embeddings:
         server_args.append('--embedding')
     if context.server_metrics:
         server_args.append('--metrics')
-    if context.model_alias:
+    if context.model_alias is not None:
         server_args.extend(['--alias', context.model_alias])
-    if context.n_ctx:
+    if context.n_ctx is not None:
         server_args.extend(['--ctx-size', context.n_ctx])
-    if context.n_slots:
+    if context.n_slots is not None:
         server_args.extend(['--parallel', context.n_slots])
-    if context.n_server_predict:
+    if context.n_server_predict is not None:
         server_args.extend(['--n-predict', context.n_server_predict])
-    if context.server_api_key:
+    if context.server_api_key is not None:
         server_args.extend(['--api-key', context.server_api_key])
-    if context.n_ga:
-        server_args.extend(['--grp-attn-n', context.n_ga])
-    if context.n_ga_w:
-        server_args.extend(['--grp-attn-w', context.n_ga_w])
     if context.debug:
         server_args.append('--verbose')
     if 'SERVER_LOG_FORMAT_JSON' not in os.environ:
         server_args.extend(['--log-format', "text"])
-    print(f"starting server with: {context.server_path} {server_args}\n")
-    flags = 0
-    if 'nt' == os.name:
-        flags |= subprocess.DETACHED_PROCESS
-        flags |= subprocess.CREATE_NEW_PROCESS_GROUP
-        flags |= subprocess.CREATE_NO_WINDOW
-
-    pkwargs = {
-        'creationflags': flags,
-    }
+    print(f"starting server with: {context.server_path}", *server_args)
     context.server_process = subprocess.Popen(
         [str(arg) for arg in [context.server_path, *server_args]],
-        **pkwargs)
-    print(f"server pid={context.server_process.pid}, behave pid={os.getpid()}")
+        close_fds=True)
+    print(f"server pid={context.server_process.pid}")

examples/server/tests/features/wrong_usages.feature

@@ -1,4 +1,4 @@
-# run with: ./tests.sh --no-skipped --tags wrong_usage
+# run with ./test.sh --tags wrong_usage
 @wrong_usage
 Feature: Wrong usage of llama.cpp server
 
@@ -7,7 +7,7 @@ Feature: Wrong usage of llama.cpp server
   # or pass n_predict/max_tokens in the request.
   Scenario: Infinite loop
     Given a server listening on localhost:8080
-    And   a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models
+    And   a model file stories260K.gguf
     # Uncomment below to fix the issue
     #And   64 server max tokens to predict
     Then  the server is starting
@@ -18,5 +18,4 @@ Feature: Wrong usage of llama.cpp server
     # Uncomment below to fix the issue
     #And   128 max tokens to predict
     Given concurrent completion requests
-    Then the server is idle
     Then all prompts are predicted

examples/server/tests/requirements.txt

@@ -1,6 +1,4 @@
 aiohttp~=3.9.3
 behave~=1.2.6
-huggingface_hub~=0.20.3
-numpy~=1.24.4
 openai~=0.25.0
 prometheus-client~=0.20.0

examples/server/tests/tests.sh

@@ -5,7 +5,7 @@ set -eu
 if [ $# -lt 1 ]
 then
   # Start @llama.cpp scenario
-  behave --summary --stop --no-capture --exclude 'issues|wrong_usages|passkey' --tags llama.cpp
+  behave --summary --stop --no-capture --exclude 'issues|wrong_usages' --tags llama.cpp
 else
   behave "$@"
 fi

examples/server/utils.hpp

@@ -1,30 +1,18 @@
 #pragma once
 
-#include "llama.h"
-#include "common.h"
-
-#include "json.hpp"
-
 #include <string>
 #include <vector>
-#include <sstream>
-#include <random>
+#include <set>
+#include <mutex>
+#include <condition_variable>
+#include <unordered_map>
 
-#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
+#include "json.hpp"
+
+#include "../llava/clip.h"
 
 using json = nlohmann::json;
 
-// https://community.openai.com/t/openai-chat-list-of-error-codes-and-types/357791/11
-enum error_type {
-    ERROR_TYPE_INVALID_REQUEST,
-    ERROR_TYPE_AUTHENTICATION,
-    ERROR_TYPE_SERVER,
-    ERROR_TYPE_NOT_FOUND,
-    ERROR_TYPE_PERMISSION,
-    ERROR_TYPE_UNAVAILABLE, // custom error
-    ERROR_TYPE_NOT_SUPPORTED, // custom error
-};
-
 extern bool server_verbose;
 extern bool server_log_json;
 
@@ -49,35 +37,125 @@ extern bool server_log_json;
 #define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__)
 #define LOG_INFO(   MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
 
-template <typename T>
-static T json_value(const json &body, const std::string &key, const T &default_value) {
-    // Fallback null to default value
-    return body.contains(key) && !body.at(key).is_null()
-        ? body.value(key, default_value)
-        : default_value;
-}
+//
+// parallel
+//
 
-static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) {
+enum server_state {
+    SERVER_STATE_LOADING_MODEL,  // Server is starting up, model not fully loaded yet
+    SERVER_STATE_READY,          // Server is ready and model is loaded
+    SERVER_STATE_ERROR           // An error occurred, load_model failed
+};
+
+enum task_type {
+    TASK_TYPE_COMPLETION,
+    TASK_TYPE_CANCEL,
+    TASK_TYPE_NEXT_RESPONSE,
+    TASK_TYPE_METRICS
+};
+
+struct task_server {
+    int id = -1; // to be filled by llama_server_queue
+    int target_id;
+    task_type type;
+    json data;
+    bool infill_mode = false;
+    bool embedding_mode = false;
+    int multitask_id = -1;
+};
+
+struct task_result {
+    int id;
+    int multitask_id = -1;
+    bool stop;
+    bool error;
+    json result_json;
+};
+
+struct task_multi {
+    int id;
+    std::set<int> subtasks_remaining{};
+    std::vector<task_result> results{};
+};
+
+// TODO: can become bool if we can't find use of more states
+enum slot_state
+{
+    IDLE,
+    PROCESSING,
+};
+
+enum slot_command
+{
+    NONE,
+    LOAD_PROMPT,
+    RELEASE,
+};
+
+struct slot_params
+{
+    bool stream       = true;
+    bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt
+
+    uint32_t seed      = -1; // RNG seed
+    int32_t  n_keep    =  0; // number of tokens to keep from initial prompt
+    int32_t  n_predict = -1; // new tokens to predict
+
+    std::vector<std::string> antiprompt;
+
+    json input_prefix;
+    json input_suffix;
+};
+
+struct slot_image
+{
+    int32_t id;
+
+    bool request_encode_image = false;
+    float * image_embedding = nullptr;
+    int32_t image_tokens = 0;
+
+    clip_image_u8 * img_data;
+
+    std::string prefix_prompt; // before of this image
+};
+
+// completion token output with probabilities
+struct completion_token_output
+{
+    struct token_prob
+    {
+        llama_token tok;
+        float prob;
+    };
+
+    std::vector<token_prob> probs;
+    llama_token tok;
+    std::string text_to_send;
+};
+
+static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra)
+{
     std::stringstream ss_tid;
     ss_tid << std::this_thread::get_id();
     json log = nlohmann::ordered_json{
-        {"tid",       ss_tid.str()},
+        {"tid", ss_tid.str()},
         {"timestamp", time(nullptr)},
     };
 
     if (server_log_json) {
-        log.merge_patch( {
-            {"level",    level},
-            {"function", function},
-            {"line",     line},
-            {"msg",      message},
-        });
-
+        log.merge_patch(
+                {
+                        {"level",     level},
+                        {"function",  function},
+                        {"line",      line},
+                        {"msg",       message},
+                });
         if (!extra.empty()) {
             log.merge_patch(extra);
         }
 
-        printf("%s\n", log.dump(-1, ' ', false, json::error_handler_t::replace).c_str());
+        std::cout << log.dump(-1, ' ', false, json::error_handler_t::replace) << "\n" << std::flush;
     } else {
         char buf[1024];
         snprintf(buf, 1024, "%4s [%24s] %s", level, function, message);
@@ -90,7 +168,8 @@ static inline void server_log(const char *level, const char *function, int line,
         for (const auto& el : log.items())
         {
             const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace);
-            ss << " " << el.key() << "=" << value;
+            snprintf(buf, 1024, " %s=%s", el.key().c_str(), value.c_str());
+            ss << buf;
         }
 
         const std::string str = ss.str();
@@ -100,25 +179,36 @@ static inline void server_log(const char *level, const char *function, int line,
 }
 
 //
-// chat template utils
+// server utils
 //
 
+template <typename T>
+static T json_value(const json &body, const std::string &key, const T &default_value)
+{
+    // Fallback null to default value
+    return body.contains(key) && !body.at(key).is_null()
+        ? body.value(key, default_value)
+        : default_value;
+}
+
 // Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
 inline bool verify_custom_template(const std::string & tmpl) {
     llama_chat_message chat[] = {{"user", "test"}};
-    int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0);
+    std::vector<char> buf(1);
+    int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, buf.data(), buf.size());
     return res >= 0;
 }
 
 // Format given chat. If tmpl is empty, we take the template from model metadata
-inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages) {
+inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector<json> & messages)
+{
     size_t alloc_size = 0;
     // vector holding all allocated string to be passed to llama_chat_apply_template
     std::vector<std::string> str(messages.size() * 2);
     std::vector<llama_chat_message> chat(messages.size());
 
     for (size_t i = 0; i < messages.size(); ++i) {
-        const auto & curr_msg = messages[i];
+        auto &curr_msg = messages[i];
         str[i*2 + 0]    = json_value(curr_msg, "role",    std::string(""));
         str[i*2 + 1]    = json_value(curr_msg, "content", std::string(""));
         alloc_size     += str[i*2 + 1].length();
@@ -138,13 +228,252 @@ inline std::string format_chat(const struct llama_model * model, const std::stri
         res = llama_chat_apply_template(model, ptr_tmpl, chat.data(), chat.size(), true, buf.data(), buf.size());
     }
 
-    const std::string formatted_chat(buf.data(), res);
-
+    std::string formatted_chat(buf.data(), res);
     LOG_VERBOSE("formatted_chat", {{"text", formatted_chat.c_str()}});
 
     return formatted_chat;
 }
 
+//
+// work queue utils
+//
+
+struct llama_server_queue {
+    int id = 0;
+    std::mutex mutex_tasks;
+    bool running;
+    // queues
+    std::vector<task_server> queue_tasks;
+    std::vector<task_server> queue_tasks_deferred;
+    std::vector<task_multi> queue_multitasks;
+    std::condition_variable condition_tasks;
+    // callback functions
+    std::function<void(task_server&)> callback_new_task;
+    std::function<void(task_multi&)> callback_finish_multitask;
+    std::function<void(void)> callback_all_task_finished;
+
+    // Add a new task to the end of the queue
+    int post(task_server task) {
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        if (task.id == -1) {
+            task.id = id++;
+            LOG_VERBOSE("new task id", {{"new_id", task.id}});
+        }
+        queue_tasks.push_back(std::move(task));
+        condition_tasks.notify_one();
+        return task.id;
+    }
+
+    // Add a new task, but defer until one slot is available
+    void defer(task_server task) {
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        queue_tasks_deferred.push_back(std::move(task));
+    }
+
+    // Get the next id for creating anew task
+    int get_new_id() {
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        int new_id = id++;
+        LOG_VERBOSE("new task id", {{"new_id", new_id}});
+        return new_id;
+    }
+
+    // Register function to process a new task
+    void on_new_task(std::function<void(task_server&)> callback) {
+        callback_new_task = callback;
+    }
+
+    // Register function to process a multitask
+    void on_finish_multitask(std::function<void(task_multi&)> callback) {
+        callback_finish_multitask = callback;
+    }
+
+    // Register the function to be called when the batch of tasks is finished
+    void on_all_tasks_finished(std::function<void(void)> callback) {
+        callback_all_task_finished = callback;
+    }
+
+    // Call when the state of one slot is changed
+    void notify_slot_changed() {
+        // move deferred tasks back to main loop
+        std::unique_lock<std::mutex> lock(mutex_tasks);
+        for (auto & task : queue_tasks_deferred) {
+            queue_tasks.push_back(std::move(task));
+        }
+        queue_tasks_deferred.clear();
+    }
+
+    // end the start_loop routine
+    void terminate() {
+        {
+            std::unique_lock<std::mutex> lock(mutex_tasks);
+            running = false;
+        }
+        condition_tasks.notify_all();
+    }
+
+    // Start the main loop.
+    void start_loop() {
+        running = true;
+        while (true) {
+            LOG_VERBOSE("new task may arrive", {});
+            {
+                while (true)
+                {
+                    std::unique_lock<std::mutex> lock(mutex_tasks);
+                    if (queue_tasks.empty()) {
+                        lock.unlock();
+                        break;
+                    }
+                    task_server task = queue_tasks.front();
+                    queue_tasks.erase(queue_tasks.begin());
+                    lock.unlock();
+                    LOG_VERBOSE("callback_new_task", {{"task_id", task.id}});
+                    callback_new_task(task);
+                }
+                LOG_VERBOSE("callback_all_task_finished", {});
+                // process and update all the multitasks
+                auto queue_iterator = queue_multitasks.begin();
+                while (queue_iterator != queue_multitasks.end())
+                {
+                    if (queue_iterator->subtasks_remaining.empty())
+                    {
+                        // all subtasks done == multitask is done
+                        task_multi current_multitask = *queue_iterator;
+                        callback_finish_multitask(current_multitask);
+                        // remove this multitask
+                        queue_iterator = queue_multitasks.erase(queue_iterator);
+                    }
+                    else
+                    {
+                        ++queue_iterator;
+                    }
+                }
+                // all tasks in the current loop is finished
+                callback_all_task_finished();
+            }
+            LOG_VERBOSE("wait for new task", {});
+            // wait for new task
+            {
+                std::unique_lock<std::mutex> lock(mutex_tasks);
+                if (queue_tasks.empty()) {
+                    if (!running) {
+                        LOG_VERBOSE("ending start_loop", {});
+                        return;
+                    }
+                    condition_tasks.wait(lock, [&]{
+                        return (!queue_tasks.empty() || !running);
+                    });
+                }
+            }
+        }
+    }
+
+    //
+    // functions to manage multitasks
+    //
+
+    // add a multitask by specifying the id of all subtask (subtask is a task_server)
+    void add_multitask(int multitask_id, std::vector<int>& sub_ids)
+    {
+        std::lock_guard<std::mutex> lock(mutex_tasks);
+        task_multi multi;
+        multi.id = multitask_id;
+        std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
+        queue_multitasks.push_back(multi);
+    }
+
+    // updatethe remaining subtasks, while appending results to multitask
+    void update_multitask(int multitask_id, int subtask_id, task_result& result)
+    {
+        std::lock_guard<std::mutex> lock(mutex_tasks);
+        for (auto& multitask : queue_multitasks)
+        {
+            if (multitask.id == multitask_id)
+            {
+                multitask.subtasks_remaining.erase(subtask_id);
+                multitask.results.push_back(result);
+            }
+        }
+    }
+};
+
+struct llama_server_response {
+    typedef std::function<void(int, int, task_result&)> callback_multitask_t;
+    callback_multitask_t callback_update_multitask;
+    // for keeping track of all tasks waiting for the result
+    std::set<int> waiting_task_ids;
+    // the main result queue
+    std::vector<task_result> queue_results;
+    std::mutex mutex_results;
+    std::condition_variable condition_results;
+
+    void add_waiting_task_id(int task_id) {
+        LOG_VERBOSE("waiting for task id", {{"task_id", task_id}});
+        std::unique_lock<std::mutex> lock(mutex_results);
+        waiting_task_ids.insert(task_id);
+    }
+
+    void remove_waiting_task_id(int task_id) {
+        LOG_VERBOSE("remove waiting for task id", {{"task_id", task_id}});
+        std::unique_lock<std::mutex> lock(mutex_results);
+        waiting_task_ids.erase(task_id);
+    }
+
+    // This function blocks the thread until there is a response for this task_id
+    task_result recv(int task_id) {
+        while (true)
+        {
+            std::unique_lock<std::mutex> lock(mutex_results);
+            condition_results.wait(lock, [&]{
+                return !queue_results.empty();
+            });
+
+            for (int i = 0; i < (int) queue_results.size(); i++)
+            {
+                if (queue_results[i].id == task_id)
+                {
+                    assert(queue_results[i].multitask_id == -1);
+                    task_result res = queue_results[i];
+                    queue_results.erase(queue_results.begin() + i);
+                    return res;
+                }
+            }
+        }
+
+        // should never reach here
+    }
+
+    // Register the function to update multitask
+    void on_multitask_update(callback_multitask_t callback) {
+        callback_update_multitask = callback;
+    }
+
+    // Send a new result to a waiting task_id
+    void send(task_result result) {
+        std::unique_lock<std::mutex> lock(mutex_results);
+        LOG_VERBOSE("send new result", {{"task_id", result.id}});
+        for (auto& task_id : waiting_task_ids) {
+            // LOG_TEE("waiting task id %i \n", task_id);
+            // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
+            if (result.multitask_id == task_id)
+            {
+                LOG_VERBOSE("callback_update_multitask", {{"task_id", task_id}});
+                callback_update_multitask(task_id, result.id, result);
+                continue;
+            }
+
+            if (result.id == task_id)
+            {
+                LOG_VERBOSE("queue_results.push_back", {{"task_id", task_id}});
+                queue_results.push_back(result);
+                condition_results.notify_all();
+                return;
+            }
+        }
+    }
+};
+
 //
 // base64 utils (TODO: move to common in the future)
 //
@@ -154,11 +483,13 @@ static const std::string base64_chars =
              "abcdefghijklmnopqrstuvwxyz"
              "0123456789+/";
 
-static inline bool is_base64(uint8_t c) {
+static inline bool is_base64(uint8_t c)
+{
     return (isalnum(c) || (c == '+') || (c == '/'));
 }
 
-static inline std::vector<uint8_t> base64_decode(const std::string & encoded_string) {
+static inline std::vector<uint8_t> base64_decode(const std::string & encoded_string)
+{
     int i = 0;
     int j = 0;
     int in_ = 0;
@@ -170,10 +501,13 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
 
     std::vector<uint8_t> ret;
 
-    while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) {
+    while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_]))
+    {
         char_array_4[i++] = encoded_string[in_]; in_++;
-        if (i == 4) {
-            for (i = 0; i < 4; i++) {
+        if (i == 4)
+        {
+            for (i = 0; i <4; i++)
+            {
                 char_array_4[i] = base64_chars.find(char_array_4[i]);
             }
 
@@ -181,20 +515,23 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
             char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
             char_array_3[2] = ((char_array_4[2] & 0x3) << 6) +   char_array_4[3];
 
-            for (i = 0; (i < 3); i++) {
+            for (i = 0; (i < 3); i++)
+            {
                 ret.push_back(char_array_3[i]);
             }
-
             i = 0;
         }
     }
 
-    if (i) {
-        for (j = i; j < 4; j++) {
+    if (i)
+    {
+        for (j = i; j <4; j++)
+        {
             char_array_4[j] = 0;
         }
 
-        for (j = 0; j < 4; j++) {
+        for (j = 0; j <4; j++)
+        {
             char_array_4[j] = base64_chars.find(char_array_4[j]);
         }
 
@@ -202,7 +539,8 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
         char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2);
         char_array_3[2] = ((char_array_4[2] & 0x3) << 6) +   char_array_4[3];
 
-        for (j = 0; j < i - 1; j++) {
+        for (j = 0; (j < i - 1); j++)
+        {
             ret.push_back(char_array_3[j]);
         }
     }
@@ -214,7 +552,8 @@ static inline std::vector<uint8_t> base64_decode(const std::string & encoded_str
 // random string / id
 //
 
-static std::string random_string() {
+static std::string random_string()
+{
     static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
 
     std::random_device rd;
@@ -229,377 +568,9 @@ static std::string random_string() {
     return result;
 }
 
-static std::string gen_chatcmplid() {
+static std::string gen_chatcmplid()
+{
     std::stringstream chatcmplid;
     chatcmplid << "chatcmpl-" << random_string();
-
     return chatcmplid.str();
 }
-
-//
-// other common utils
-//
-
-static size_t common_part(const std::vector<llama_token> & a, const std::vector<llama_token> & b) {
-    size_t i;
-    for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {}
-
-    return i;
-}
-
-static bool ends_with(const std::string & str, const std::string & suffix) {
-    return str.size() >= suffix.size() && 0 == str.compare(str.size() - suffix.size(), suffix.size(), suffix);
-}
-
-static size_t find_partial_stop_string(const std::string &stop, const std::string &text) {
-    if (!text.empty() && !stop.empty()) {
-        const char text_last_char = text.back();
-        for (int64_t char_index = stop.size() - 1; char_index >= 0; char_index--) {
-            if (stop[char_index] == text_last_char) {
-                const std::string current_partial = stop.substr(0, char_index + 1);
-                if (ends_with(text, current_partial)) {
-                    return text.size() - char_index - 1;
-                }
-            }
-        }
-    }
-
-    return std::string::npos;
-}
-
-// TODO: reuse llama_detokenize
-template <class Iter>
-static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
-    std::string ret;
-    for (; begin != end; ++begin) {
-        ret += llama_token_to_piece(ctx, *begin);
-    }
-
-    return ret;
-}
-
-// format incomplete utf-8 multibyte character for output
-static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) {
-    std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token);
-
-    // if the size is 1 and first bit is 1, meaning it's a partial character
-    //   (size > 1 meaning it's already a known token)
-    if (out.size() == 1 && (out[0] & 0x80) == 0x80) {
-        std::stringstream ss;
-        ss << std::hex << (out[0] & 0xff);
-        std::string res(ss.str());
-        out = "byte: \\x" + res;
-    }
-
-    return out;
-}
-
-struct completion_token_output {
-    llama_token tok;
-    std::string text_to_send;
-
-    struct token_prob {
-        llama_token tok;
-        float prob;
-    };
-
-    std::vector<token_prob> probs;
-};
-
-// convert a vector of completion_token_output to json
-static json probs_vector_to_json(const llama_context * ctx, const std::vector<completion_token_output> & probs) {
-    json out = json::array();
-
-    for (const auto & prob : probs) {
-        json probs_for_token = json::array();
-
-        for (const auto & p : prob.probs) {
-            const std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok);
-            probs_for_token.push_back(json {
-                {"tok_str", tok_str},
-                {"prob",    p.prob},
-            });
-        }
-
-        const std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok);
-        out.push_back(json {
-            {"content", tok_str},
-            {"probs",   probs_for_token},
-        });
-    }
-
-    return out;
-}
-
-//
-// OAI utils
-//
-
-static json oaicompat_completion_params_parse(
-    const struct llama_model * model,
-    const json & body, /* openai api json semantics */
-    const std::string & chat_template) {
-    json llama_params;
-
-    llama_params["__oaicompat"] = true;
-
-    // Map OpenAI parameters to llama.cpp parameters
-    //
-    // For parameters that are defined by the OpenAI documentation (e.g.
-    // temperature), we explicitly specify OpenAI's intended default; we
-    // need to do that because sometimes OpenAI disagrees with llama.cpp
-    //
-    // https://platform.openai.com/docs/api-reference/chat/create
-    llama_sampling_params default_sparams;
-    llama_params["model"]             = json_value(body,   "model",             std::string("unknown"));
-    llama_params["prompt"]            = format_chat(model, chat_template,       body["messages"]);
-    llama_params["cache_prompt"]      = json_value(body,   "cache_prompt",      false);
-    llama_params["temperature"]       = json_value(body,   "temperature",       0.0);
-    llama_params["top_k"]             = json_value(body,   "top_k",             default_sparams.top_k);
-    llama_params["top_p"]             = json_value(body,   "top_p",             1.0);
-    llama_params["n_predict"]         = json_value(body,   "max_tokens",        -1);
-    llama_params["logit_bias"]        = json_value(body,   "logit_bias",        json::object());
-    llama_params["frequency_penalty"] = json_value(body,   "frequency_penalty", 0.0);
-    llama_params["presence_penalty"]  = json_value(body,   "presence_penalty",  0.0);
-    llama_params["seed"]              = json_value(body,   "seed",              LLAMA_DEFAULT_SEED);
-    llama_params["stream"]            = json_value(body,   "stream",            false);
-    llama_params["mirostat"]          = json_value(body,   "mirostat",          default_sparams.mirostat);
-    llama_params["mirostat_tau"]      = json_value(body,   "mirostat_tau",      default_sparams.mirostat_tau);
-    llama_params["mirostat_eta"]      = json_value(body,   "mirostat_eta",      default_sparams.mirostat_eta);
-    llama_params["penalize_nl"]       = json_value(body,   "penalize_nl",       default_sparams.penalize_nl);
-    llama_params["typical_p"]         = json_value(body,   "typical_p",         default_sparams.typical_p);
-    llama_params["repeat_last_n"]     = json_value(body,   "repeat_last_n",     default_sparams.penalty_last_n);
-    llama_params["ignore_eos"]        = json_value(body,   "ignore_eos",        false);
-    llama_params["tfs_z"]             = json_value(body,   "tfs_z",             default_sparams.tfs_z);
-
-    if (body.count("grammar") != 0) {
-        llama_params["grammar"] = json_value(body, "grammar", json::object());
-    }
-
-    // Handle 'stop' field
-    if (body.contains("stop") && body["stop"].is_string()) {
-        llama_params["stop"] = json::array({body["stop"].get<std::string>()});
-    } else {
-        llama_params["stop"] = json_value(body, "stop", json::array());
-    }
-
-    // Ensure there is ChatML-specific end sequence among stop words
-    llama_params["stop"].push_back("<|im_end|>");
-
-    return llama_params;
-}
-
-static json format_final_response_oaicompat(const json & request, json result, const std::string & completion_id, bool streaming = false) {
-    bool stopped_word        = result.count("stopped_word") != 0;
-    bool stopped_eos         = json_value(result, "stopped_eos", false);
-    int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
-    int num_prompt_tokens    = json_value(result, "tokens_evaluated", 0);
-    std::string content      = json_value(result, "content", std::string(""));
-
-    std::string finish_reason = "length";
-    if (stopped_word || stopped_eos) {
-        finish_reason = "stop";
-    }
-
-    json choices =
-        streaming ? json::array({json{{"finish_reason", finish_reason},
-                                        {"index", 0},
-                                        {"delta", json::object()}}})
-                  : json::array({json{{"finish_reason", finish_reason},
-                                        {"index", 0},
-                                        {"message", json{{"content", content},
-                                                         {"role", "assistant"}}}}});
-
-    std::time_t t = std::time(0);
-
-    json res = json {
-        {"choices", choices},
-        {"created", t},
-        {"model",
-            json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
-        {"object", streaming ? "chat.completion.chunk" : "chat.completion"},
-        {"usage", json {
-            {"completion_tokens", num_tokens_predicted},
-            {"prompt_tokens",     num_prompt_tokens},
-            {"total_tokens",      num_tokens_predicted + num_prompt_tokens}
-        }},
-        {"id", completion_id}
-    };
-
-    if (server_verbose) {
-        res["__verbose"] = result;
-    }
-
-    if (result.contains("completion_probabilities")) {
-        res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
-    }
-
-    return res;
-}
-
-// return value is vector as there is one case where we might need to generate two responses
-static std::vector<json> format_partial_response_oaicompat(json result, const std::string & completion_id) {
-    if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
-        return std::vector<json>({result});
-    }
-
-    bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
-    std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
-
-    bool stopped_word   = json_value(result, "stopped_word",  false);
-    bool stopped_eos    = json_value(result, "stopped_eos",   false);
-    bool stopped_limit  = json_value(result, "stopped_limit", false);
-    std::string content = json_value(result, "content",       std::string(""));
-
-    std::string finish_reason;
-    if (stopped_word || stopped_eos) {
-        finish_reason = "stop";
-    }
-    if (stopped_limit) {
-        finish_reason = "length";
-    }
-
-    std::time_t t = std::time(0);
-
-    json choices;
-
-    if (!finish_reason.empty()) {
-        choices = json::array({json{{"finish_reason", finish_reason},
-                                    {"index", 0},
-                                    {"delta", json::object()}}});
-    } else {
-        if (first) {
-            if (content.empty()) {
-                choices = json::array({json{{"finish_reason", nullptr},
-                                            {"index", 0},
-                                            {"delta", json{{"role", "assistant"}}}}});
-            } else {
-                // We have to send this as two updates to conform to openai behavior
-                json initial_ret = json{{"choices", json::array({json{
-                                        {"finish_reason", nullptr},
-                                        {"index", 0},
-                                        {"delta", json{
-                                            {"role", "assistant"}
-                                        }}}})},
-                            {"created", t},
-                            {"id", completion_id},
-                            {"model", modelname},
-                            {"object", "chat.completion.chunk"}};
-
-                json second_ret = json{
-                            {"choices", json::array({json{{"finish_reason", nullptr},
-                                                            {"index", 0},
-                                                            {"delta", json{
-                                                            {"content", content}}}
-                                                            }})},
-                            {"created", t},
-                            {"id", completion_id},
-                            {"model", modelname},
-                            {"object", "chat.completion.chunk"}};
-
-                return std::vector<json>({initial_ret, second_ret});
-            }
-        } else {
-            // Some idiosyncrasy in task processing logic makes several trailing calls
-            // with empty content, we ignore these at the calee site.
-            if (content.empty()) {
-                return std::vector<json>({json::object()});
-            }
-
-            choices = json::array({json{
-                {"finish_reason", nullptr},
-                {"index", 0},
-                {"delta",
-                json{
-                    {"content", content},
-                }},
-            }});
-        }
-    }
-
-    json ret = json {
-        {"choices", choices},
-        {"created", t},
-        {"id",      completion_id},
-        {"model",   modelname},
-        {"object",  "chat.completion.chunk"}
-    };
-
-    return std::vector<json>({ret});
-}
-
-static json format_embeddings_response_oaicompat(const json & request, const json & embeddings) {
-    json data = json::array();
-    int i = 0;
-    for (auto & elem : embeddings) {
-        data.push_back(json{
-            {"embedding", json_value(elem, "embedding", json::array())},
-            {"index",     i++},
-            {"object",    "embedding"}
-        });
-    }
-
-    json res = json {
-        {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
-        {"object", "list"},
-        {"usage", json {
-            {"prompt_tokens", 0},
-            {"total_tokens", 0}
-        }},
-        {"data", data}
-    };
-
-    return res;
-}
-
-static json format_tokenizer_response(const std::vector<llama_token> & tokens) {
-    return json {
-        {"tokens", tokens}
-    };
-}
-
-static json format_detokenized_response(const std::string & content) {
-    return json {
-        {"content", content}
-    };
-}
-
-static json format_error_response(const std::string & message, const enum error_type type) {
-    std::string type_str;
-    int code = 500;
-    switch (type) {
-        case ERROR_TYPE_INVALID_REQUEST:
-            type_str = "invalid_request_error";
-            code = 400;
-            break;
-        case ERROR_TYPE_AUTHENTICATION:
-            type_str = "authentication_error";
-            code = 401;
-            break;
-        case ERROR_TYPE_NOT_FOUND:
-            type_str = "not_found_error";
-            code = 404;
-            break;
-        case ERROR_TYPE_SERVER:
-            type_str = "server_error";
-            code = 500;
-            break;
-        case ERROR_TYPE_PERMISSION:
-            type_str = "permission_error";
-            code = 403;
-            break;
-        case ERROR_TYPE_NOT_SUPPORTED:
-            type_str = "not_supported_error";
-            code = 501;
-            break;
-        case ERROR_TYPE_UNAVAILABLE:
-            type_str = "unavailable_error";
-            code = 503;
-            break;
-    }
-    return json {
-        {"code", code},
-        {"message", message},
-        {"type", type_str},
-    };
-}

examples/speculative/README.md

@@ -6,4 +6,3 @@ More info:
 
 - https://github.com/ggerganov/llama.cpp/pull/2926
 - https://github.com/ggerganov/llama.cpp/pull/3624
-- https://github.com/ggerganov/llama.cpp/pull/5625

examples/speculative/speculative.cpp

@@ -5,7 +5,6 @@
 #include <cstdio>
 #include <string>
 #include <vector>
-#include <set>
 
 #define SPEC_VOCAB_MAX_SIZE_DIFFERENCE  100
 #define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
@@ -19,7 +18,6 @@ struct seq_draft {
     std::vector<int> i_batch_tgt;
 
     std::vector<llama_token> tokens;
-    std::vector<std::vector<llama_token_data>> dists;
 
     struct llama_sampling_context * ctx_sampling;
 };
@@ -39,15 +37,12 @@ int main(int argc, char ** argv) {
     // max number of parallel drafting sequences (i.e. tree branches)
     const int n_seq_dft = params.n_parallel;
 
+    // probability threshold for accepting a token from the draft model
+    const float p_accept = params.p_accept;
+
     // probability threshold for splitting a draft branch (only for n_seq_dft > 1)
     const float p_split  = params.p_split;
 
-    if (params.seed == LLAMA_DEFAULT_SEED) {
-        params.seed = time(NULL);
-    }
-    std::default_random_engine rng(params.seed);
-    std::uniform_real_distribution<> u_dist;
-
 #ifndef LOG_DISABLE_LOGS
     log_set_target(log_filename_generator("speculative", "log"));
     LOG_TEE("Log start\n");
@@ -171,9 +166,7 @@ int main(int argc, char ** argv) {
     std::vector<seq_draft> drafts(n_seq_dft);
 
     params.sparams.grammar.clear(); // the draft samplers will copy the target sampler's grammar
-    if (params.sparams.temp == 0) {
-        params.sparams.temp = -1.0f; // force greedy sampling with probs for the draft model
-    }
+    params.sparams.temp = -1.0f;    // force greedy sampling with probs for the draft model
 
     for (int s = 0; s < n_seq_dft; ++s) {
         drafts[s].ctx_sampling = llama_sampling_init(params.sparams);
@@ -189,15 +182,12 @@ int main(int argc, char ** argv) {
     drafts[0].i_batch_tgt[0] = 0;
 
     while (true) {
-        std::set<int> active_seqs = {};
-
         // print current draft sequences
         for (int s = 0; s < n_seq_dft; ++s) {
             if (!drafts[s].active) {
                 continue;
             }
 
-            active_seqs.insert(s);
             const auto & tokens = drafts[s].tokens;
 
             LOG("draft %d: %s\n", s, LOG_TOKENS_TOSTR_PRETTY(ctx_dft, tokens).c_str());
@@ -206,156 +196,48 @@ int main(int argc, char ** argv) {
         int i_dft  = 0;
         int s_keep = 0;
 
-        llama_token token_id;
-        std::string token_str;
-
-        // loop until we fail to accept a drafted token or we run out of drafted tokens
         while (true) {
+            LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
+
+            // sample from the target model
+            llama_token id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
+
+            llama_sampling_accept(ctx_sampling, ctx_tgt, id, true);
+
+            //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str());
+
+            const std::string token_str = llama_token_to_piece(ctx_tgt, id);
+
+            if (!params.use_color) {
+                printf("%s", token_str.c_str());
+            }
+
+            if (id == llama_token_eos(model_tgt)) {
+                has_eos = true;
+            }
+
+            ++n_predict;
 
             // check if the target token matches any of the drafts
-            // for stochastic sampling, attempt to match the token with the drafted tokens
             {
-                bool accept = false;
-                if (params.sparams.temp > 0) {
-                    // stochastic verification
+                bool matches = false;
 
-                    llama_token_data_array dist_tgt = llama_sampling_probability_distribution(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
-                    float p_tgt = 0, p_dft = 0;
-
-                    // GGML_ASSERT(dist_tgt.size() == dist_dft.size());
-
-                    while (active_seqs.size() > 0) {
-                        // randomly select a sequence to verify from active sequences
-                        std::uniform_int_distribution<unsigned int> u_int_dist(0, active_seqs.size() - 1);
-                        int s = *std::next(active_seqs.begin(), u_int_dist(rng));
-                        if (i_dft >= (int) drafts[s].tokens.size()) {
-                            drafts[s].active = false;
-                            active_seqs.erase(s);
-                            continue;
-                        }
-                        if (accept) {
-                            // if we already accepted a token, we can skip the rest
-                            if (drafts[s].tokens[i_dft] != drafts[s_keep].tokens[i_dft]) {
-                                drafts[s].active = false;
-                                active_seqs.erase(s);
-                            }
-                            continue;
-                        }
-                        LOG("verifying sequence #%d at pos #%d from %d active sequence(s)\n", s, i_dft, (int) active_seqs.size());
-                        float r = u_dist(rng);
-                        llama_token_data_array dist_dft = { drafts[s].dists[i_dft].data() , drafts[s].dists[i_dft].size(), true };
-                        // acquire the token probabilities assigned by the draft and target models
-                        for (size_t i = 0; i < dist_tgt.size; i++) {
-                            if (dist_tgt.data[i].id == drafts[s].tokens[i_dft]) {
-                                p_tgt = dist_tgt.data[i].p;
-                            }
-                            if (dist_dft.data[i].id == drafts[s].tokens[i_dft]) {
-                                p_dft = dist_dft.data[i].p;
-                            }
-                            if (p_tgt && p_dft) {
-                                break;
-                            }
-                        }
-                        LOG("r = %f, p_dft = %f, p_tgt = %f\n", r, p_dft, p_tgt);
-                        if (r <= p_tgt / p_dft) {
-                            s_keep = s;
-                            accept = true;
-                            token_id = drafts[s].tokens[i_dft];
-                            token_str = llama_token_to_piece(ctx_tgt, token_id);
-                            llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
-
-                            LOG("draft token %d of sequence %d (%d, '%s') accepted\n", i_dft, s, token_id, token_str.c_str());
-                            break;
-                        } else {
-                            LOG("draft token %d of sequence %d (%d, '%s') rejected\n", i_dft, s, drafts[s].tokens[i_dft], llama_token_to_piece(ctx_tgt, drafts[s].tokens[i_dft]).c_str());
-                            drafts[s].active = false;
-
-                            // calculate residual probability
-                            GGML_ASSERT(dist_tgt.sorted);
-                            GGML_ASSERT(dist_dft.sorted);
-                            float sum_probs = 0.0f;
-
-                            // sort dist by id
-                            std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) {
-                                return a.id < b.id;
-                            });
-                            std::sort(dist_dft.data, dist_dft.data + dist_dft.size, [](const llama_token_data &a, const llama_token_data &b) {
-                                return a.id < b.id;
-                            });
-
-                            for (size_t i = 0; i < dist_tgt.size; i++) {
-                                dist_tgt.data[i].p = std::max(0.0f, dist_tgt.data[i].p - dist_dft.data[i].p);
-                                sum_probs += dist_tgt.data[i].p;
-                            }
-                            for (size_t i = 0; i < dist_tgt.size; i++) {
-                                dist_tgt.data[i].p /= sum_probs;
-                            }
-
-                            // sort dist_tgt by p desc
-                            std::sort(dist_tgt.data, dist_tgt.data + dist_tgt.size, [](const llama_token_data &a, const llama_token_data &b) {
-                                return a.p > b.p;
-                            });
-                        }
-
-                        active_seqs.erase(s);
-                        for(int i = 0; i < n_seq_dft; i++) {
-                            if (i == s) {
-                                continue;
-                            }
-                            if (drafts[i].tokens[i_dft] == drafts[s].tokens[i_dft]) {
-                                // synchronize active status for sequences with the same drafted token
-                                drafts[i].active = drafts[i].active && accept;
-                                if (!drafts[i].active) {
-                                    active_seqs.erase(s);
-                                }
-                            }
-                        }
+                for (int s = 0; s < n_seq_dft; ++s) {
+                    if (!drafts[s].active) {
+                        continue;
                     }
 
-                    if (!accept) {
-                        // all drafted tokens were rejected
-                        // sample from the target model
-                        LOG("all drafted tokens were rejected, sampling from residual distribution\n");
-                        token_id = llama_sample_token(ctx_tgt, &dist_tgt);
-                        llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
-                        token_str = llama_token_to_piece(ctx_tgt, token_id);
-                    }
+                    if (i_dft < (int) drafts[s].tokens.size() && id == drafts[s].tokens[i_dft]) {
+                        LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, id, token_str.c_str());
 
-                } else {
-                    // greedy verification
-
-                    // sample from the target model
-                    LOG("sampling target: s_keep = %3d, i_dft = %3d, i_batch_tgt = %3d\n", s_keep, i_dft, drafts[s_keep].i_batch_tgt[i_dft]);
-                    token_id = llama_sampling_sample(ctx_sampling, ctx_tgt, NULL, drafts[s_keep].i_batch_tgt[i_dft]);
-
-                    llama_sampling_accept(ctx_sampling, ctx_tgt, token_id, true);
-
-                    //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, ctx_sampling->prev).c_str());
-
-                    token_str = llama_token_to_piece(ctx_tgt, token_id);
-
-                    for (int s = 0; s < n_seq_dft; ++s) {
-                        if (!drafts[s].active) {
-                            continue;
-                        }
-
-                        if (i_dft < (int) drafts[s].tokens.size() && token_id == drafts[s].tokens[i_dft]) {
-                            LOG("the sampled target token matches the %dth drafted token of sequence %d (%d, '%s') - accepted\n", i_dft, s, token_id, token_str.c_str());
-
-                            s_keep = s;
-                            accept = true;
-                        } else {
-                            drafts[s].active = false;
-                        }
+                        s_keep = s;
+                        matches = true;
+                    } else {
+                        drafts[s].active = false;
                     }
                 }
 
-                if (token_id == llama_token_eos(model_tgt)) {
-                    has_eos = true;
-                }
-                ++n_predict;
-
-                if (accept) {
+                if (matches) {
                     ++n_accept;
                     ++n_past_tgt;
                     ++n_past_dft;
@@ -363,21 +245,17 @@ int main(int argc, char ** argv) {
                     if (params.use_color) {
                         // Color token according to its origin sequence
                         printf("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str());
-                    } else {
-                        printf("%s", token_str.c_str());
+                        fflush(stdout);
                     }
-                    fflush(stdout);
                     continue;
-                } else {
-                    printf("%s", token_str.c_str());
-                    fflush(stdout);
-                    break;
                 }
             }
-        }
+            if (params.use_color) {
+                printf("%s", token_str.c_str());
+            }
+            fflush(stdout);
 
-        {
-            LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", token_id, token_str.c_str());
+            LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());
 
             // TODO: simplify
             {
@@ -397,21 +275,21 @@ int main(int argc, char ** argv) {
                 drafts[s].active = false;
                 drafts[s].tokens.clear();
                 drafts[s].i_batch_tgt.clear();
-                drafts[s].dists.clear();
             }
             // note: will be erased after the speculation phase
-            drafts[0].tokens.push_back(token_id);
-            drafts[0].dists.push_back(std::vector<llama_token_data>());
+            drafts[0].tokens.push_back(id);
             drafts[0].i_batch_tgt.push_back(0);
 
             llama_batch_clear(batch_dft);
-            llama_batch_add  (batch_dft, token_id, n_past_dft, { 0 }, true);
+            llama_batch_add  (batch_dft, id, n_past_dft, { 0 }, true);
 
             llama_kv_cache_seq_rm(ctx_dft, 0, n_past_dft, -1);
             // LOG("dft batch: %s\n", LOG_BATCH_TOSTR_PRETTY(ctx_dft, batch_dft).c_str());
-            llama_decode(ctx_dft, batch_dft);
+            llama_decode         (ctx_dft, batch_dft);
 
             ++n_past_dft;
+
+            break;
         }
 
         if (n_predict > params.n_predict || has_eos) {
@@ -456,6 +334,12 @@ int main(int argc, char ** argv) {
                             k, s, i, cur_p[k].id, cur_p[k].p, llama_token_to_piece(ctx_dft, cur_p[k].id).c_str());
                 }
 
+                if (cur_p[0].p < p_accept) {
+                    LOG("stopping drafting for seq %3d, probability too low: %.3f < %.3f\n", s, cur_p[0].p, p_accept);
+                    drafts[s].drafting = false;
+                    continue;
+                }
+
                 std::vector<int> sa(1, s);
 
                 // attempt to split the branch if the probability is high enough
@@ -483,7 +367,6 @@ int main(int argc, char ** argv) {
                         drafts[n_seq_cur].skip     = true;
 
                         drafts[n_seq_cur].tokens      = drafts[s].tokens;
-                        drafts[n_seq_cur].dists       = drafts[s].dists;
                         drafts[n_seq_cur].i_batch_dft = drafts[s].i_batch_dft;
                         drafts[n_seq_cur].i_batch_tgt = drafts[s].i_batch_tgt;
 
@@ -506,8 +389,6 @@ int main(int argc, char ** argv) {
                     llama_sampling_accept(drafts[s].ctx_sampling, ctx_dft, id, true);
 
                     drafts[s].tokens.push_back(id);
-                    // save cur_p.data into drafts[s].dists
-                    drafts[s].dists.push_back(cur_p);
 
                     // add unique drafted tokens to the target batch
                     drafts[s].i_batch_tgt.push_back(batch_tgt.n_tokens);
@@ -559,7 +440,6 @@ int main(int argc, char ** argv) {
             }
 
             drafts[s].tokens.erase(drafts[s].tokens.begin());
-            drafts[s].dists.erase(drafts[s].dists.begin());
         }
     }
 

examples/sycl/build.sh

@@ -13,11 +13,8 @@ source /opt/intel/oneapi/setvars.sh
 #for FP32
 cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
 
-#build example/main
+#build example/main only
 #cmake --build . --config Release --target main
 
-#build example/llama-bench
-#cmake --build . --config Release --target llama-bench
-
 #build all binary
 cmake --build . --config Release -v

examples/sycl/ls-sycl-device.cpp

@@ -7,7 +7,7 @@
 
 #include "ggml-sycl.h"
 
-int main() {
+int main(int argc, char ** argv) {
     ggml_backend_sycl_print_sycl_devices();
     return 0;
 }

examples/sycl/run-llama2.sh

@@ -8,29 +8,12 @@ INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
 source /opt/intel/oneapi/setvars.sh
 
 if [ $# -gt 0 ]; then
-    GGML_SYCL_DEVICE=$1
-    GGML_SYCL_SINGLE_GPU=1
+    export GGML_SYCL_DEVICE=$1
 else
-    GGML_SYCL_DEVICE=0
+    export GGML_SYCL_DEVICE=0
 fi
-
+echo GGML_SYCL_DEVICE=$GGML_SYCL_DEVICE
 #export GGML_SYCL_DEBUG=1
-
-
-#ZES_ENABLE_SYSMAN=1, Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory. Recommended to use when --split-mode = layer.
-
-if [ $GGML_SYCL_SINGLE_GPU -eq 1 ]; then
-    echo "use $GGML_SYCL_DEVICE as main GPU"
-    #use signle GPU only
-    ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
-else
-    #use multiple GPUs with same max compute units
-    ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
-fi
-
-#use main GPU only
-#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 -mg $GGML_SYCL_DEVICE -sm none
-
-#use multiple GPUs with same max compute units
-#ZES_ENABLE_SYSMAN=1 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
+./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0
+#./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 5 -e -ngl 33 -t 1 -s 0
 

examples/train-text-from-scratch/train-text-from-scratch.cpp

@@ -711,7 +711,6 @@ static bool load_checkpoint_file(const char * filename, struct my_llama_model *
 
     load_checkpoint_gguf(fctx, f_ggml_ctx, model, train);
 
-    gguf_free(fctx);
     return true;
 }
 

flake.lock

@@ -5,11 +5,11 @@
         "nixpkgs-lib": "nixpkgs-lib"
       },
       "locked": {
-        "lastModified": 1709336216,
-        "narHash": "sha256-Dt/wOWeW6Sqm11Yh+2+t0dfEWxoMxGBvv3JpIocFl9E=",
+        "lastModified": 1706830856,
+        "narHash": "sha256-a0NYyp+h9hlb7ddVz4LUn1vT/PLwqfrWYcHMvFB1xYg=",
         "owner": "hercules-ci",
         "repo": "flake-parts",
-        "rev": "f7b3c975cf067e56e7cda6cb098ebe3fb4d74ca2",
+        "rev": "b253292d9c0a5ead9bc98c4e9a26c6312e27d69f",
         "type": "github"
       },
       "original": {
@@ -20,11 +20,11 @@
     },
     "nixpkgs": {
       "locked": {
-        "lastModified": 1709703039,
-        "narHash": "sha256-6hqgQ8OK6gsMu1VtcGKBxKQInRLHtzulDo9Z5jxHEFY=",
+        "lastModified": 1708655239,
+        "narHash": "sha256-ZrP/yACUvDB+zbqYJsln4iwotbH6CTZiTkANJ0AgDv4=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "9df3e30ce24fd28c7b3e2de0d986769db5d6225d",
+        "rev": "cbc4211f0afffe6dfd2478a62615dd5175a13f9a",
         "type": "github"
       },
       "original": {
@@ -37,11 +37,11 @@
     "nixpkgs-lib": {
       "locked": {
         "dir": "lib",
-        "lastModified": 1709237383,
-        "narHash": "sha256-cy6ArO4k5qTx+l5o+0mL9f5fa86tYUX3ozE1S+Txlds=",
+        "lastModified": 1706550542,
+        "narHash": "sha256-UcsnCG6wx++23yeER4Hg18CXWbgNpqNXcHIo5/1Y+hc=",
         "owner": "NixOS",
         "repo": "nixpkgs",
-        "rev": "1536926ef5621b09bba54035ae2bb6d806d72ac8",
+        "rev": "97b17f32362e475016f942bbdfda4a4a72a8a652",
         "type": "github"
       },
       "original": {

flake.nix

@@ -107,12 +107,11 @@
         # ```
         #
         # Cf. https://nixos.org/manual/nix/unstable/command-ref/new-cli/nix3-flake.html?highlight=flake#flake-format
-        flake.overlays.default = (
-          final: prev: {
+        flake.overlays.default =
+          (final: prev: {
             llamaPackages = final.callPackage .devops/nix/scope.nix { inherit llamaVersion; };
             inherit (final.llamaPackages) llama-cpp;
-          }
-        );
+          });
 
         systems = [
           "aarch64-darwin"
@@ -132,9 +131,6 @@
             ...
           }:
           {
-            # For standardised reproducible formatting with `nix fmt`
-            formatter = pkgs.nixfmt-rfc-style;
-
             # Unlike `.#packages`, legacyPackages may contain values of
             # arbitrary types (including nested attrsets) and may even throw
             # exceptions. This attribute isn't recursed into by `nix flake

ggml-alloc.c

@@ -61,6 +61,7 @@ static bool ggml_op_can_inplace(enum ggml_op op) {
     }
 }
 
+// TODO: GGML_PAD ?
 static size_t aligned_offset(const void * buffer, size_t offset, size_t alignment) {
     assert(alignment && !(alignment & (alignment - 1))); // power of 2
     size_t align = (alignment - (((uintptr_t)buffer + offset) % alignment)) % alignment;
@@ -68,14 +69,25 @@ static size_t aligned_offset(const void * buffer, size_t offset, size_t alignmen
 }
 
 // tallocr
+struct ggml_tallocr {
+    ggml_backend_buffer_t buffer;
+    void * base;
+    size_t alignment;
+    size_t offset;
+};
+
+ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer) {
+    ggml_tallocr_t talloc = malloc(sizeof(struct ggml_tallocr));
+    if (talloc == NULL) {
+        return NULL;
+    }
 
-struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer) {
     void * base = ggml_backend_buffer_get_base(buffer);
     size_t align = ggml_backend_buffer_get_alignment(buffer);
 
     assert(align && !(align & (align - 1))); // power of 2
 
-    struct ggml_tallocr talloc = (struct ggml_tallocr) {
+    *talloc = (struct ggml_tallocr) {
         /*.buffer    = */ buffer,
         /*.base      = */ base,
         /*.alignment = */ align,
@@ -84,7 +96,11 @@ struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer) {
     return talloc;
 }
 
-void ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor) {
+void ggml_tallocr_free(ggml_tallocr_t talloc) {
+    free(talloc);
+}
+
+void ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor) {
     size_t size = ggml_backend_buffer_get_alloc_size(talloc->buffer, tensor);
     size = GGML_PAD(size, talloc->alignment);
 
@@ -338,16 +354,12 @@ struct hash_node {
     bool allocated;
 };
 
+//
 struct tensor_alloc {
     size_t offset;
     size_t size_max; // 0 = pre-allocated, unused, or view
 };
 
-struct leaf_alloc {
-    int buffer_id;
-    struct tensor_alloc leaf;
-};
-
 struct node_alloc {
     int buffer_id;
     struct tensor_alloc dst;
@@ -366,7 +378,7 @@ struct ggml_gallocr {
     struct node_alloc * node_allocs; // [n_nodes]
     int n_nodes;
 
-    struct leaf_alloc * leaf_allocs; // [n_leafs]
+    struct tensor_alloc * leaf_allocs; // [n_leafs]
     int n_leafs;
 };
 
@@ -531,20 +543,13 @@ static int get_node_buffer_id(const int * node_buffer_ids, int i) {
     return node_buffer_ids ? node_buffer_ids[i] : 0;
 }
 
-static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
+static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) {
     // clear hash tables
     memset(galloc->hash_set.keys, 0, galloc->hash_set.size * sizeof(struct ggml_tensor *));
     memset(galloc->hash_values,   0, galloc->hash_set.size * sizeof(struct hash_node));
 
-    // allocate leafs
-    // these may be tensors that the application is not using in the graph, but may still want to allocate for other purposes
-    for (int i = 0; i < graph->n_leafs; i++) {
-        struct ggml_tensor * leaf = graph->leafs[i];
-        ggml_gallocr_allocate_node(galloc, leaf, get_node_buffer_id(leaf_buffer_ids, i));
-    }
-
     // count number of children and views
-    // allocate other graph inputs and leafs first to avoid overwriting them
+    // allocate all graph inputs and leafs first to avoid overwriting them
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
 
@@ -572,6 +577,19 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
         }
     }
 
+    // allocate the remaining leafs that are unused on the graph
+    // these are effectively static tensors that the application is not using in the graph, but may still want to allocate for other purposes
+    for (int i = 0; i < graph->n_leafs; i++) {
+        struct ggml_tensor * leaf = graph->leafs[i];
+        struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
+
+        if (hn->n_children == 0) {
+            assert(!hn->allocated);
+            // since buffer ids are only given for nodes, these leafs are always allocated in the first buffer
+            ggml_gallocr_allocate_node(galloc, leaf, 0);
+        }
+    }
+
     // allocate tensors
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
@@ -634,7 +652,7 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr
     }
 }
 
-bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) {
+bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids) {
     size_t hash_size = graph->visited_hash_table.size;
 
     // initialize hash table
@@ -658,7 +676,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
     }
 
     // allocate in hash table
-    ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids);
+    ggml_gallocr_alloc_graph_impl(galloc, graph, node_buffer_ids);
 
     // set the node_allocs from the hash table
     if (galloc->n_nodes < graph->n_nodes) {
@@ -693,16 +711,15 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
     }
     if (galloc->n_leafs < graph->n_leafs) {
         free(galloc->leaf_allocs);
-        galloc->leaf_allocs = calloc(sizeof(galloc->leaf_allocs[0]), graph->n_leafs);
+        galloc->leaf_allocs = calloc(sizeof(struct tensor_alloc), graph->n_leafs);
         GGML_ASSERT(galloc->leaf_allocs != NULL);
     }
     galloc->n_leafs = graph->n_leafs;
     for (int i = 0; i < graph->n_leafs; i++) {
         struct ggml_tensor * leaf = graph->leafs[i];
         struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
-        galloc->leaf_allocs[i].buffer_id = hn->buffer_id;
-        galloc->leaf_allocs[i].leaf.offset = hn->offset;
-        galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
+        galloc->leaf_allocs[i].offset = hn->offset;
+        galloc->leaf_allocs[i].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
     }
 
     // reallocate buffers if needed
@@ -710,8 +727,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
         size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0;
         size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]);
 
-        // even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
-        if (new_size > cur_size || galloc->buffers[i] == NULL) {
+        if (new_size > cur_size) {
 #ifndef NDEBUG
             fprintf(stderr, "%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
 #endif
@@ -728,30 +744,30 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
 }
 
 bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
-    return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL);
+    return ggml_gallocr_reserve_n(galloc, graph, NULL);
 }
 
-static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, int buffer_id, struct tensor_alloc * tensor_alloc) {
-    assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
+static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * node, int buffer_id, struct tensor_alloc * tensor_alloc) {
+    assert(node->data || node->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max);
 
-    if (tensor->view_src != NULL) {
-        if (tensor->buffer == NULL) {
+    if (node->view_src != NULL) {
+        if (node->buffer == NULL) {
             assert(tensor_alloc->offset == SIZE_MAX);
-            if (tensor->view_src->buffer == NULL) {
+            if (node->view_src->buffer == NULL) {
                 // this tensor was allocated without ggml-backend
                 return;
             }
-            ggml_backend_view_init(galloc->buffers[buffer_id], tensor);
+            ggml_backend_view_init(galloc->buffers[buffer_id], node);
         }
     } else {
-        if (tensor->data == NULL) {
+        if (node->data == NULL) {
             assert(tensor_alloc->offset != SIZE_MAX);
-            assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
+            assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], node) <= tensor_alloc->size_max);
             void * base = ggml_backend_buffer_get_base(galloc->buffers[buffer_id]);
             void * addr = (char *)base + tensor_alloc->offset;
-            ggml_backend_tensor_alloc(galloc->buffers[buffer_id], tensor, addr);
+            ggml_backend_tensor_alloc(galloc->buffers[buffer_id], node, addr);
         } else {
-            if (tensor->buffer == NULL) {
+            if (node->buffer == NULL) {
                 // this tensor was allocated without ggml-backend
                 return;
             }
@@ -827,18 +843,13 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
 
     // reset buffers
     for (int i = 0; i < galloc->n_buffers; i++) {
+        // zero size buffers are not allocated
         if (galloc->buffers[i] != NULL) {
             ggml_backend_buffer_reset(galloc->buffers[i]);
         }
     }
 
     // allocate the graph tensors from the previous assignments
-    // leafs
-    for (int i = 0; i < graph->n_leafs; i++) {
-        struct ggml_tensor * leaf = graph->leafs[i];
-        struct leaf_alloc * leaf_alloc = &galloc->leaf_allocs[i];
-        ggml_gallocr_init_tensor(galloc, leaf, leaf_alloc->buffer_id, &leaf_alloc->leaf);
-    }
     // nodes
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
@@ -852,6 +863,12 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
         }
         ggml_gallocr_init_tensor(galloc, node, node_alloc->buffer_id, &node_alloc->dst);
     }
+    // leafs
+    for (int i = 0; i < graph->n_leafs; i++) {
+        struct ggml_tensor * leaf = graph->leafs[i];
+        struct tensor_alloc * leaf_alloc = &galloc->leaf_allocs[i];
+        ggml_gallocr_init_tensor(galloc, leaf, 0, leaf_alloc);
+    }
 
     return true;
 }
@@ -883,12 +900,12 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
         return false;
     }
 
-    struct ggml_tallocr tallocr = ggml_tallocr_new(buffer);
+    struct ggml_tallocr * tallocr = ggml_tallocr_new(buffer);
 
     for (struct ggml_tensor * t = first; t != last; t = ggml_get_next_tensor(ctx, t)) {
         if (t->data == NULL) {
             if (t->view_src == NULL) {
-                ggml_tallocr_alloc(&tallocr, t);
+                ggml_tallocr_alloc(tallocr, t);
             } else if (t->buffer == NULL) {
                 ggml_backend_view_init(buffer, t);
             }
@@ -900,6 +917,8 @@ static bool alloc_tensor_range(struct ggml_context * ctx,
         }
     }
 
+    ggml_tallocr_free(tallocr);
+
     *buffers = realloc(*buffers, sizeof(ggml_backend_buffer_t) * (*n_buffers + 1));
     (*buffers)[(*n_buffers)++] = buffer;
 

ggml-alloc.h

@@ -11,15 +11,11 @@ typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
 typedef struct ggml_backend * ggml_backend_t;
 
 // Tensor allocator
-struct ggml_tallocr {
-    ggml_backend_buffer_t buffer;
-    void * base;
-    size_t alignment;
-    size_t offset;
-};
+typedef struct ggml_tallocr * ggml_tallocr_t;
 
-GGML_API struct ggml_tallocr ggml_tallocr_new(ggml_backend_buffer_t buffer);
-GGML_API void                ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_tensor * tensor);
+GGML_API ggml_tallocr_t ggml_tallocr_new(ggml_backend_buffer_t buffer);
+GGML_API void           ggml_tallocr_free(ggml_tallocr_t talloc);
+GGML_API void           ggml_tallocr_alloc(ggml_tallocr_t talloc, struct ggml_tensor * tensor);
 
 // Graph allocator
 /*
@@ -54,11 +50,7 @@ GGML_API void           ggml_gallocr_free(ggml_gallocr_t galloc);
 // not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed
 // returns false if the buffer allocation failed
 GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph);
-GGML_API bool ggml_gallocr_reserve_n(
-    ggml_gallocr_t galloc,
-    struct ggml_cgraph * graph,
-    const int * node_buffer_ids,
-    const int * leaf_buffer_ids);
+GGML_API bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids);
 
 // automatic reallocation if the topology changes when using a single buffer
 // returns false if using multiple buffers and a re-allocation is needed (call ggml_gallocr_reserve_n first to set the node buffers)

ggml-backend-impl.h

@@ -86,41 +86,29 @@ extern "C" {
         // (optional) asynchronous tensor data access
         void (*GGML_CALL set_tensor_async)(ggml_backend_t backend,       struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
         void (*GGML_CALL get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor,       void * data, size_t offset, size_t size);
-        bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend_src, ggml_backend_t backend_dst, const struct ggml_tensor * src, struct ggml_tensor * dst);
+        bool (*GGML_CALL cpy_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * src, struct ggml_tensor * dst);
 
         // (optional) complete all pending operations
         void (*GGML_CALL synchronize)(ggml_backend_t backend);
 
-        // compute graph with a plan (not used currently)
+        // compute graph with a plan
         ggml_backend_graph_plan_t (*GGML_CALL graph_plan_create) (ggml_backend_t backend, const struct ggml_cgraph * cgraph);
         void                      (*GGML_CALL graph_plan_free)   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+        void                      (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
 
-        // compute graph with a plan
-        enum ggml_status (*GGML_CALL graph_plan_compute)(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
         // compute graph without a plan (async)
-        enum ggml_status (*GGML_CALL graph_compute)     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+        bool (*GGML_CALL graph_compute)(ggml_backend_t backend, struct ggml_cgraph * cgraph);
 
         // check if the backend supports an operation
         bool (*GGML_CALL supports_op)(ggml_backend_t backend, const struct ggml_tensor * op);
-
-        // (optional) event synchronization
-        ggml_backend_event_t (*GGML_CALL event_new)         (ggml_backend_t backend);
-        void                 (*GGML_CALL event_free)        (ggml_backend_event_t event);
-        void                 (*GGML_CALL event_record)      (ggml_backend_event_t event);
-        void                 (*GGML_CALL event_wait)        (ggml_backend_t backend, ggml_backend_event_t event);
-        void                 (*GGML_CALL event_synchronize) (ggml_backend_event_t event);
     };
 
     struct ggml_backend {
         ggml_guid_t guid;
 
         struct ggml_backend_i iface;
-        ggml_backend_context_t context;
-    };
 
-    struct ggml_backend_event {
-        ggml_backend_t backend;
-        void * context;
+        ggml_backend_context_t context;
     };
 
     //

ggml-backend.c

@@ -221,29 +221,29 @@ void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_ten
 GGML_CALL void ggml_backend_tensor_set(struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) {
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
-    GGML_ASSERT(buf != NULL && "tensor buffer not set");
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(buf != NULL && "tensor buffer not set");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds");
 
     if (!size) {
         return;
     }
 
-    buf->iface.set_tensor(buf, tensor, data, offset, size);
+    tensor->buffer->iface.set_tensor(buf, tensor, data, offset, size);
 }
 
 GGML_CALL void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size) {
     ggml_backend_buffer_t buf = tensor->view_src ? tensor->view_src->buffer : tensor->buffer;
 
-    GGML_ASSERT(buf != NULL && "tensor buffer not set");
     GGML_ASSERT(tensor->data != NULL && "tensor not allocated");
+    GGML_ASSERT(tensor->buffer != NULL && "tensor buffer not set");
     GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor read out of bounds");
 
     if (!size) {
         return;
     }
 
-    buf->iface.get_tensor(buf, tensor, data, offset, size);
+    tensor->buffer->iface.get_tensor(buf, tensor, data, offset, size);
 }
 
 void ggml_backend_synchronize(ggml_backend_t backend) {
@@ -255,30 +255,18 @@ void ggml_backend_synchronize(ggml_backend_t backend) {
 }
 
 ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    GGML_ASSERT(backend->iface.graph_plan_create != NULL);
-
     return backend->iface.graph_plan_create(backend, cgraph);
 }
 
 void ggml_backend_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    GGML_ASSERT(backend->iface.graph_plan_free != NULL);
-
     backend->iface.graph_plan_free(backend, plan);
 }
 
-enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
-    GGML_ASSERT(backend->iface.graph_plan_compute != NULL);
-
-    return backend->iface.graph_plan_compute(backend, plan);
+void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+    backend->iface.graph_plan_compute(backend, plan);
 }
 
-enum ggml_status ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
-    enum ggml_status err = ggml_backend_graph_compute_async(backend, cgraph);
-    ggml_backend_synchronize(backend);
-    return err;
-}
-
-bool ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+bool ggml_backend_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     return backend->iface.graph_compute(backend, cgraph);
 }
 
@@ -326,68 +314,34 @@ void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst
     }
 }
 
-void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst) {
+void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst) {
     GGML_ASSERT(ggml_are_same_layout(src, dst) && "cannot copy tensors with different layouts");
 
     if (src == dst) {
         return;
     }
 
-    if (backend_dst->iface.cpy_tensor_async != NULL) {
-        if (backend_dst->iface.cpy_tensor_async(backend_src, backend_dst, src, dst)) {
-            return;
+    if (ggml_backend_buft_supports_backend(src->buffer->buft, backend) && ggml_backend_buft_supports_backend(dst->buffer->buft, backend)) {
+        if (backend->iface.cpy_tensor_async != NULL) {
+            if (backend->iface.cpy_tensor_async(backend, src, dst)) {
+                return;
+            }
         }
     }
 
-    // an async copy would normally happen after all the queued operations on both backends are completed
-    // sync src, set_async dst
+    size_t nbytes = ggml_nbytes(src);
     if (ggml_backend_buffer_is_host(src->buffer)) {
-        ggml_backend_synchronize(backend_src);
-        ggml_backend_tensor_set_async(backend_dst, dst, src->data, 0, ggml_nbytes(src));
-    } else {
-        ggml_backend_synchronize(backend_src);
+        ggml_backend_tensor_set_async(backend, dst, src->data, 0, nbytes);
+    }
+    else {
         ggml_backend_tensor_copy(src, dst);
-        ggml_backend_synchronize(backend_dst);
     }
 }
 
-// events
-
-ggml_backend_event_t ggml_backend_event_new(ggml_backend_t backend) {
-    if (backend->iface.event_new == NULL) {
-        return NULL;
-    }
-    return backend->iface.event_new(backend);
-}
-
-void ggml_backend_event_free(ggml_backend_event_t event) {
-    if (event == NULL) {
-        return;
-    }
-    event->backend->iface.event_free(event);
-}
-
-void ggml_backend_event_record(ggml_backend_event_t event) {
-    GGML_ASSERT(event->backend->iface.event_record != NULL);
-
-    event->backend->iface.event_record(event);
-}
-
-void ggml_backend_event_synchronize(ggml_backend_event_t event) {
-    GGML_ASSERT(event->backend->iface.event_synchronize != NULL);
-
-    event->backend->iface.event_synchronize(event);
-}
-
-void ggml_backend_event_wait(ggml_backend_t backend, ggml_backend_event_t event) {
-    GGML_ASSERT(backend->iface.event_wait != NULL);
-
-    backend->iface.event_wait(backend, event);
-}
 
 // backend registry
 
-#define GGML_REG_MAX_BACKENDS 16
+#define GGML_MAX_BACKENDS_REG 16
 
 struct ggml_backend_reg {
     char name[128];
@@ -396,7 +350,7 @@ struct ggml_backend_reg {
     void * user_data;
 };
 
-static struct ggml_backend_reg ggml_backend_registry[GGML_REG_MAX_BACKENDS];
+static struct ggml_backend_reg ggml_backend_registry[GGML_MAX_BACKENDS_REG];
 static size_t ggml_backend_registry_count = 0;
 
 GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, void * user_data);
@@ -441,7 +395,7 @@ GGML_CALL static void ggml_backend_registry_init(void) {
 }
 
 GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) {
-    GGML_ASSERT(ggml_backend_registry_count < GGML_REG_MAX_BACKENDS);
+    GGML_ASSERT(ggml_backend_registry_count < GGML_MAX_BACKENDS_REG);
 
     size_t id = ggml_backend_registry_count;
 
@@ -778,26 +732,22 @@ GGML_CALL static void ggml_backend_cpu_graph_plan_free(ggml_backend_t backend, g
     GGML_UNUSED(backend);
 }
 
-GGML_CALL static enum ggml_status ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
+GGML_CALL static void ggml_backend_cpu_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) {
     struct ggml_backend_plan_cpu * cpu_plan = (struct ggml_backend_plan_cpu *)plan;
 
-    return ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
+    ggml_graph_compute(&cpu_plan->cgraph, &cpu_plan->cplan);
 
     GGML_UNUSED(backend);
 }
 
-GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+GGML_CALL static bool ggml_backend_cpu_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     struct ggml_backend_cpu_context * cpu_ctx = (struct ggml_backend_cpu_context *)backend->context;
 
     struct ggml_cplan cplan = ggml_graph_plan(cgraph, cpu_ctx->n_threads);
 
     if (cpu_ctx->work_size < cplan.work_size) {
-        free(cpu_ctx->work_data);
-        cpu_ctx->work_data = malloc(cplan.work_size);
-        if (cpu_ctx->work_data == NULL) {
-            cpu_ctx->work_size = 0;
-            return GGML_STATUS_ALLOC_FAILED;
-        }
+        // TODO: may be faster to free and use malloc to avoid the copy
+        cpu_ctx->work_data = realloc(cpu_ctx->work_data, cplan.work_size);
         cpu_ctx->work_size = cplan.work_size;
     }
     cplan.work_data = cpu_ctx->work_data;
@@ -805,7 +755,8 @@ GGML_CALL static enum ggml_status ggml_backend_cpu_graph_compute(ggml_backend_t
     cplan.abort_callback      = cpu_ctx->abort_callback;
     cplan.abort_callback_data = cpu_ctx->abort_callback_data;
 
-    return ggml_graph_compute(cgraph, &cplan);
+    ggml_graph_compute(cgraph, &cplan);
+    return true;
 }
 
 GGML_CALL static bool ggml_backend_cpu_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
@@ -834,11 +785,6 @@ static struct ggml_backend_i cpu_backend_i = {
     /* .graph_plan_compute      = */ ggml_backend_cpu_graph_plan_compute,
     /* .graph_compute           = */ ggml_backend_cpu_graph_compute,
     /* .supports_op             = */ ggml_backend_cpu_supports_op,
-    /* .event_new               = */ NULL,
-    /* .event_free              = */ NULL,
-    /* .event_record            = */ NULL,
-    /* .event_wait              = */ NULL,
-    /* .event_synchronize       = */ NULL,
 };
 
 static ggml_guid_t ggml_backend_cpu_guid(void) {
@@ -994,27 +940,15 @@ static bool ggml_is_view_op(enum ggml_op op) {
 
 // scheduler
 
-#ifndef GGML_SCHED_MAX_BACKENDS
-#define GGML_SCHED_MAX_BACKENDS 16
-#endif
-
-#ifndef GGML_SCHED_MAX_SPLITS
-#define GGML_SCHED_MAX_SPLITS 256
-#endif
-
-#ifndef GGML_SCHED_MAX_SPLIT_INPUTS
-#define GGML_SCHED_MAX_SPLIT_INPUTS 16
-#endif
-
-#ifndef GGML_SCHED_MAX_COPIES
-#define GGML_SCHED_MAX_COPIES 4
-#endif
+#define GGML_MAX_BACKENDS 16
+#define GGML_MAX_SPLITS 256
+#define GGML_MAX_SPLIT_INPUTS 16
 
 struct ggml_backend_sched_split {
     int backend_id;
     int i_start;
     int i_end;
-    struct ggml_tensor * inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
+    struct ggml_tensor * inputs[GGML_MAX_SPLIT_INPUTS];
     int n_inputs;
     // graph view of this split
     struct ggml_cgraph graph;
@@ -1022,53 +956,45 @@ struct ggml_backend_sched_split {
 
 struct ggml_backend_sched {
     bool is_reset; // true if the scheduler has been reset since the last graph split
-    bool is_alloc;
 
     int n_backends;
+    ggml_backend_t backends[GGML_MAX_BACKENDS];
+    ggml_backend_buffer_type_t bufts[GGML_MAX_BACKENDS];
 
-    ggml_backend_t backends[GGML_SCHED_MAX_BACKENDS];
-    ggml_backend_buffer_type_t bufts[GGML_SCHED_MAX_BACKENDS];
     ggml_gallocr_t galloc;
 
     // hash keys of the nodes in the graph
     struct ggml_hash_set    hash_set;
     // hash values
     int * tensor_backend_id;
-    struct ggml_tensor * (* tensor_copies)[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
+    struct ggml_tensor * (* tensor_copies)[GGML_MAX_BACKENDS];
 
-    int * node_backend_ids; // [graph_size]
-    int * leaf_backend_ids; // [graph_size]
+    int * node_backend_ids; // [n_nodes]
+    int n_nodes;
 
     // copy of the graph with modified inputs
     struct ggml_cgraph * graph;
 
-    // graph splits
-    struct ggml_backend_sched_split splits[GGML_SCHED_MAX_SPLITS];
+    struct ggml_backend_sched_split splits[GGML_MAX_SPLITS];
     int n_splits;
 
-    // pipeline parallelism support
-    int n_copies;
-    int cur_copy;
-    ggml_backend_event_t events[GGML_SCHED_MAX_BACKENDS][GGML_SCHED_MAX_COPIES];
-    struct ggml_tensor * graph_inputs[GGML_SCHED_MAX_SPLIT_INPUTS];
-    int n_graph_inputs;
-
     struct ggml_context * ctx;
 
     ggml_backend_sched_eval_callback callback_eval;
     void * callback_eval_user_data;
 
     // align context_buffer to GGML_MEM_ALIGN
-#ifdef _MSC_VER
+    #ifdef _MSC_VER
     __declspec(align(GGML_MEM_ALIGN))
-#else
+    #else
     __attribute__((aligned(GGML_MEM_ALIGN)))
-#endif
-    char context_buffer[GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
+    #endif
+    char context_buffer[GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS*2*sizeof(struct ggml_tensor) + sizeof(struct ggml_cgraph)];
 };
 
-#define hash_id(tensor) ggml_hash_find_or_insert(sched->hash_set, tensor)
-#define tensor_backend_id(tensor) sched->tensor_backend_id[hash_id(tensor)]
+#define hash_id(node) ggml_hash_find_or_insert(sched->hash_set, node)
+#define tensor_backend_id(node) sched->tensor_backend_id[hash_id(node)]
+#define tensor_backend(node) (tensor_backend_id(node) == -1 ? NULL : sched->backends[tensor_backend_id(node)])
 
 // returns the priority of the backend, lower id is higher priority
 static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backend_t backend) {
@@ -1080,8 +1006,7 @@ static int ggml_backend_sched_backend_id(ggml_backend_sched_t sched, ggml_backen
     return -1;
 }
 
-static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, const struct ggml_tensor * tensor) {
-    ggml_backend_buffer_t buffer = tensor->buffer;
+static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, ggml_backend_buffer_t buffer) {
     if (buffer == NULL) {
         return -1;
     }
@@ -1092,16 +1017,12 @@ static int ggml_backend_sched_backend_from_buffer(ggml_backend_sched_t sched, co
             return i;
         }
     }
-
-    fprintf(stderr, "%s: error: no backend supports buffer type %s used in tensor %s\n",
-        __func__, ggml_backend_buffer_name(buffer), tensor->name);
-    GGML_ASSERT(false);
-
-    return -1;
+    GGML_ASSERT(false && "tensor buffer type not supported by any backend");
+    return -1; // silence warning
 }
 
 #if 0
-static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS][128]; // debug only
+static char causes[GGML_DEFAULT_GRAPH_SIZE*16 + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS][128]; // debug only
 #define SET_CAUSE(node, ...) sprintf(causes[hash_id(node)], __VA_ARGS__)
 #define GET_CAUSE(node) causes[hash_id(node)]
 #else
@@ -1115,28 +1036,19 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
 
     // assign pre-allocated nodes to their backend
     // dst
-    int cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor);
+    int cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->buffer);
     if (cur_backend != -1) {
-        SET_CAUSE(tensor, "1.dst");
+        SET_CAUSE(node, "1.dst");
         return cur_backend;
     }
-
     // view_src
     if (tensor->view_src != NULL) {
-        cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src);
+        cur_backend = ggml_backend_sched_backend_from_buffer(sched, tensor->view_src->buffer);
         if (cur_backend != -1) {
-            SET_CAUSE(tensor, "1.vsrc");
+            SET_CAUSE(node, "1.vsrc");
             return cur_backend;
         }
     }
-
-    // input
-    if (tensor->flags & GGML_TENSOR_FLAG_INPUT) {
-        cur_backend = sched->n_backends - 1; // last backend (assumed CPU)
-        SET_CAUSE(tensor, "1.inp");
-        return cur_backend;
-    }
-
     // assign nodes that use weights to the backend of the weights
     for (int i = 0; i < GGML_MAX_SRC; i++) {
         const struct ggml_tensor * src = tensor->src[i];
@@ -1144,9 +1056,9 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st
             continue;
         }
         if (src->buffer != NULL && src->buffer->usage == GGML_BACKEND_BUFFER_USAGE_WEIGHTS) {
-            int src_backend = ggml_backend_sched_backend_from_buffer(sched, src);
+            int src_backend = ggml_backend_sched_backend_from_buffer(sched, src->buffer);
             // operations with weights are always run on the same backend as the weights
-            SET_CAUSE(tensor, "1.wgt%d", i);
+            SET_CAUSE(node, "1.wgt%d", i);
             return src_backend;
         }
     }
@@ -1182,7 +1094,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
         if (ggml_is_view_op(node->op)) {
             continue;
         }
-        ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
+        ggml_backend_t tensor_backend = tensor_backend(node);
         fprintf(stderr, "node #%3d (%10.10s): %20.20s (%5.5s) [%5.5s %8.8s]:", i, ggml_op_name(node->op), node->name,
             fmt_size(ggml_nbytes(node)), tensor_backend ? ggml_backend_name(tensor_backend) : "NULL", GET_CAUSE(node));
         for (int j = 0; j < GGML_MAX_SRC; j++) {
@@ -1190,7 +1102,7 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
             if (src == NULL) {
                 continue;
             }
-            ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
+            ggml_backend_t src_backend = tensor_backend(src);
             fprintf(stderr, " %20.20s (%5.5s) [%5.5s %8.8s]", src->name,
                 fmt_size(ggml_nbytes(src)), src_backend ? ggml_backend_name(src_backend) : "NULL", GET_CAUSE(src));
         }
@@ -1207,7 +1119,6 @@ static void ggml_backend_sched_print_assignments(ggml_backend_sched_t sched, str
 static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
     // reset splits
     sched->n_splits = 0;
-    sched->n_graph_inputs = 0;
     sched->is_reset = false;
 
     struct ggml_init_params params = {
@@ -1253,7 +1164,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
         }
     }
 #ifdef DEBUG_PASS1
-    fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
+    fprintf(stderr, "PASS 1 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
 #endif
 
     // pass 2: expand current backend assignments
@@ -1261,30 +1172,6 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
     // expand gpu backends (i.e. non last prio) up and down, ignoring cpu (the lowest priority backend)
     // thus, cpu will never be used unless weights are on cpu, or there are no gpu ops between cpu ops
 
-
-    // pass 2.2 expand gpu down
-    {
-        int cur_backend_id = -1;
-        for (int i = 0; i < graph->n_nodes; i++) {
-            struct ggml_tensor * node = graph->nodes[i];
-            if (ggml_is_view_op(node->op)) {
-                continue;
-            }
-            int tensor_backend_id = tensor_backend_id(node);
-            if (tensor_backend_id != -1) {
-                if (tensor_backend_id == sched->n_backends - 1) {
-                    // skip cpu (lowest prio backend)
-                    cur_backend_id = -1;
-                } else {
-                    cur_backend_id = tensor_backend_id;
-                }
-            } else {
-                tensor_backend_id(node) = cur_backend_id;
-                SET_CAUSE(node, "2.2");
-            }
-        }
-    }
-
     // pass 2.1 expand gpu up
     {
         int cur_backend_id = -1;
@@ -1308,6 +1195,46 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
         }
     }
 
+    // pass 2.2 expand gpu down
+    {
+        int cur_backend_id = -1;
+        for (int i = 0; i < graph->n_nodes; i++) {
+            struct ggml_tensor * node = graph->nodes[i];
+            if (ggml_is_view_op(node->op)) {
+                continue;
+            }
+            int tensor_backend_id = tensor_backend_id(node);
+            if (tensor_backend_id != -1) {
+                if (tensor_backend_id == sched->n_backends - 1) {
+                    // skip cpu (lowest prio backend)
+                    cur_backend_id = -1;
+                } else {
+                    cur_backend_id = tensor_backend_id;
+                }
+            } else {
+                tensor_backend_id(node) = cur_backend_id;
+                SET_CAUSE(node, "2.2");
+            }
+        }
+    }
+
+    // pass 2.3 expand rest up
+    {
+        int cur_backend_id = -1;
+        for (int i = graph->n_nodes - 1; i >= 0; i--) {
+            struct ggml_tensor * node = graph->nodes[i];
+            if (ggml_is_view_op(node->op)) {
+                continue;
+            }
+            int tensor_backend_id = tensor_backend_id(node);
+            if (tensor_backend_id != -1) {
+                cur_backend_id = tensor_backend_id;
+            } else {
+                tensor_backend_id(node) = cur_backend_id;
+                SET_CAUSE(node, "2.3");
+            }
+        }
+    }
 
     // pass 2.4 expand rest down
     {
@@ -1326,26 +1253,8 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
             }
         }
     }
-        // pass 2.3 expand rest up
-    {
-        int cur_backend_id = -1;
-        for (int i = graph->n_nodes - 1; i >= 0; i--) {
-            struct ggml_tensor * node = graph->nodes[i];
-            if (ggml_is_view_op(node->op)) {
-                continue;
-            }
-            int tensor_backend_id = tensor_backend_id(node);
-            if (tensor_backend_id != -1) {
-                cur_backend_id = tensor_backend_id;
-            } else {
-                tensor_backend_id(node) = cur_backend_id;
-                SET_CAUSE(node, "2.3");
-            }
-        }
-    }
-
 #ifdef DEBUG_PASS2
-    fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
+    fprintf(stderr, "PASS 2 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
 #endif
 
     // pass 3: assign backends to remaining src from dst and view_src
@@ -1375,7 +1284,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
         }
     }
 #ifdef DEBUG_PASS3
-    fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
+    fprintf(stderr, "PASS 3 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
 #endif
 
     // pass 4: split graph, find tensors that need to be copied
@@ -1407,7 +1316,7 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
             if (tensor_backend_id != cur_backend_id) {
                 sched->splits[cur_split].i_end = i;
                 cur_split++;
-                GGML_ASSERT(cur_split < GGML_SCHED_MAX_SPLITS);
+                GGML_ASSERT(cur_split < GGML_MAX_SPLITS);
                 sched->splits[cur_split].backend_id = tensor_backend_id;
                 sched->splits[cur_split].i_start = i;
                 sched->splits[cur_split].n_inputs = 0;
@@ -1420,57 +1329,25 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
                 if (src == NULL) {
                     continue;
                 }
-
                 int src_backend_id = tensor_backend_id(src);
                 assert(src_backend_id != -1); // all inputs should be assigned by now
-
-                if (src->flags & GGML_TENSOR_FLAG_INPUT)  {
-                    size_t id = hash_id(src);
-                    if (sched->tensor_copies[id][src_backend_id][0] == NULL) {
-                        ggml_backend_t backend = sched->backends[src_backend_id];
-                        for (int c = 0; c < sched->n_copies; c++) {
-                            struct ggml_tensor * tensor_copy;
-                            if (c == sched->cur_copy) {
-                                tensor_copy = src; // use the original tensor as the current copy
-                            } else {
-                                tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
-                                ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
-                            }
-                            if (sched->n_copies > 1) {
-                                ggml_set_input(tensor_copy);
-                                ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
-                            }
-                            sched->tensor_copies[id][src_backend_id][c] = tensor_copy;
-                            tensor_backend_id(tensor_copy) = src_backend_id;
-                            SET_CAUSE(tensor_copy, "4.cpy");
-                        }
-                        int n_graph_inputs = sched->n_graph_inputs++;
-                        GGML_ASSERT(n_graph_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
-                        sched->graph_inputs[n_graph_inputs] = src;
-                    }
-                }
-
                 if (src_backend_id != tensor_backend_id) {
                     // create a copy of the input in the split's backend
                     size_t id = hash_id(src);
-                    if (sched->tensor_copies[id][cur_backend_id][0] == NULL) {
+                    if (sched->tensor_copies[id][cur_backend_id] == NULL) {
                         ggml_backend_t backend = sched->backends[cur_backend_id];
-                        for (int c = 0; c < sched->n_copies; c++) {
-                            struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
-                            ggml_format_name(tensor_copy, "%s#%s#%d", ggml_backend_name(backend), src->name, c);
-                            if (sched->n_copies > 1) {
-                                ggml_set_input(tensor_copy);
-                                ggml_set_output(tensor_copy); // prevent ggml-alloc from overwriting the tensor
-                            }
-                            sched->tensor_copies[id][cur_backend_id][c] = tensor_copy;
-                            tensor_backend_id(tensor_copy) = cur_backend_id;
-                            SET_CAUSE(tensor_copy, "4.cpy");
-                        }
+                        struct ggml_tensor * tensor_copy = ggml_dup_tensor_layout(sched->ctx, src);
+                        ggml_format_name(tensor_copy, "%s#%s", ggml_backend_name(backend), src->name);
+
+                        sched->tensor_copies[id][cur_backend_id] = tensor_copy;
+                        tensor_backend_id(tensor_copy) = cur_backend_id;
+                        SET_CAUSE(tensor_copy, "4.cpy");
+
                         int n_inputs = sched->splits[cur_split].n_inputs++;
-                        GGML_ASSERT(n_inputs < GGML_SCHED_MAX_SPLIT_INPUTS);
+                        GGML_ASSERT(n_inputs < GGML_MAX_SPLIT_INPUTS);
                         sched->splits[cur_split].inputs[n_inputs] = src;
                     }
-                    node->src[j] = sched->tensor_copies[id][cur_backend_id][sched->cur_copy];
+                    node->src[j] = sched->tensor_copies[id][cur_backend_id];
                 }
             }
         }
@@ -1478,39 +1355,37 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
         sched->n_splits = cur_split + 1;
     }
 #ifdef DEBUG_PASS4
-    fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); ggml_backend_sched_print_assignments(sched, graph);
+    fprintf(stderr, "PASS 4 ASSIGNMENTS\n"); sched_print_assignments(sched, graph);
 #endif
 
 #ifndef NDEBUG
     // sanity check: all sources should have the same backend as the node
     for (int i = 0; i < graph->n_nodes; i++) {
         struct ggml_tensor * node = graph->nodes[i];
-        ggml_backend_t tensor_backend = ggml_backend_sched_get_tensor_backend(sched, node);
+        ggml_backend_t tensor_backend = tensor_backend(node);
         if (tensor_backend == NULL) {
             fprintf(stderr, "!!!!!!! %s has no backend\n", node->name);
         }
-        if (node->view_src != NULL && tensor_backend != ggml_backend_sched_get_tensor_backend(sched, node->view_src)) {
+        if (node->view_src != NULL && tensor_backend != tensor_backend(node->view_src)) {
             fprintf(stderr, "!!!!!!! %s has backend %s, view_src %s has backend %s\n",
                 node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL",
-                node->view_src->name, ggml_backend_sched_get_tensor_backend(sched, node->view_src) ?
-                    ggml_backend_name(ggml_backend_sched_get_tensor_backend(sched, node->view_src)) : "NULL");
+                node->view_src->name, tensor_backend(node->view_src) ? ggml_backend_name(tensor_backend(node->view_src)) : "NULL");
         }
         for (int j = 0; j < GGML_MAX_SRC; j++) {
             struct ggml_tensor * src = node->src[j];
             if (src == NULL) {
                 continue;
             }
-            ggml_backend_t src_backend = ggml_backend_sched_get_tensor_backend(sched, src);
+            ggml_backend_t src_backend = tensor_backend(src);
             if (src_backend != tensor_backend /* && src_backend != NULL */) {
                 fprintf(stderr, "!!!! %s has backend %s, src %d (%s) has backend %s\n",
                     node->name, tensor_backend ? ggml_backend_name(tensor_backend) : "NULL",
                     j, src->name, src_backend ? ggml_backend_name(src_backend) : "NULL");
             }
-            if (src->view_src != NULL && src_backend != ggml_backend_sched_get_tensor_backend(sched, src->view_src)) {
+            if (src->view_src != NULL && src_backend != tensor_backend(src->view_src)) {
                 fprintf(stderr, "!!!!!!! [src] %s has backend %s, view_src %s has backend %s\n",
                     src->name, src_backend ? ggml_backend_name(src_backend) : "NULL",
-                    src->view_src->name, ggml_backend_sched_get_tensor_backend(sched, src->view_src) ?
-                        ggml_backend_name(ggml_backend_sched_get_tensor_backend(sched, src->view_src)) : "NULL");
+                    src->view_src->name, tensor_backend(src->view_src) ? ggml_backend_name(tensor_backend(src->view_src)) : "NULL");
             }
         }
     }
@@ -1518,20 +1393,18 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
 #endif
 
     // create copies of the graph for each split
-    // TODO: avoid this copy
-    struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_SCHED_MAX_SPLIT_INPUTS, false);
+    // FIXME: avoid this copy, pass split inputs to ggml_gallocr_alloc_graph_n in some other way
+    struct ggml_cgraph * graph_copy = ggml_new_graph_custom(sched->ctx, graph->n_nodes + sched->n_splits*GGML_MAX_SPLIT_INPUTS, false);
     for (int i = 0; i < sched->n_splits; i++) {
         struct ggml_backend_sched_split * split = &sched->splits[i];
         split->graph = ggml_graph_view(graph, split->i_start, split->i_end);
 
-        // add inputs to the graph copy so that they are allocated by ggml-alloc at the start of the split
         for (int j = 0; j < split->n_inputs; j++) {
             struct ggml_tensor * input = split->inputs[j];
-            struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split->backend_id][sched->cur_copy];
+            struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split->backend_id];
 
             // add a dependency to the input source so that it is not freed before the copy is done
             struct ggml_tensor * input_dep = ggml_view_tensor(sched->ctx, input);
-            input_dep->src[0] = input;
             sched->node_backend_ids[graph_copy->n_nodes] = tensor_backend_id(input);
             graph_copy->nodes[graph_copy->n_nodes++] = input_dep;
 
@@ -1545,56 +1418,18 @@ static void ggml_backend_sched_split_graph(ggml_backend_sched_t sched, struct gg
             graph_copy->nodes[graph_copy->n_nodes++] = graph->nodes[j];
         }
     }
-
-    if (sched->n_copies > 1) {
-        // add input copies as leafs so that they are allocated first
-        for (int i = 0; i < sched->n_graph_inputs; i++) {
-            struct ggml_tensor * input = sched->graph_inputs[i];
-            size_t id = hash_id(input);
-            int backend_id = tensor_backend_id(input);
-            for (int c = 0; c < sched->n_copies; c++) {
-                struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c];
-                sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
-                graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
-            }
-        }
-
-        for (int i = 0; i < sched->n_splits; i++) {
-            struct ggml_backend_sched_split * split = &sched->splits[i];
-            int backend_id = split->backend_id;
-            for (int j = 0; j < split->n_inputs; j++) {
-                struct ggml_tensor * input = split->inputs[j];
-                size_t id = hash_id(input);
-                for (int c = 0; c < sched->n_copies; c++) {
-                    struct ggml_tensor * input_cpy = sched->tensor_copies[id][backend_id][c];
-                    sched->leaf_backend_ids[graph_copy->n_leafs] = backend_id;
-                    graph_copy->leafs[graph_copy->n_leafs++] = input_cpy;
-                }
-            }
-        }
-    }
-
-    // add leafs from the original graph
-    for (int i = 0; i < graph->n_leafs; i++) {
-        struct ggml_tensor * leaf = graph->leafs[i];
-        sched->leaf_backend_ids[graph_copy->n_leafs] = tensor_backend_id(leaf);
-        graph_copy->leafs[graph_copy->n_leafs++] = leaf;
-    }
-
     sched->graph = graph_copy;
 }
 
 static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
-    // allocate graph
+    // ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids);
     if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) {
-        // the re-allocation may cause the split inputs to be moved to a different address
-        ggml_backend_sched_synchronize(sched);
 #ifndef NDEBUG
-        fprintf(stderr, "%s: failed to allocate graph, reserving\n", __func__);
+        fprintf(stderr, "ggml_backend_sched: failed to allocate graph, reserving\n");
 #endif
-        ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids);
+        ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids);
         if (!ggml_gallocr_alloc_graph(sched->galloc, sched->graph)) {
-            fprintf(stderr, "%s: failed to allocate graph\n", __func__);
+            fprintf(stderr, "ggml_backend_sched: failed to allocate graph\n");
             return false;
         }
     }
@@ -1602,7 +1437,10 @@ static bool ggml_backend_sched_alloc_splits(ggml_backend_sched_t sched) {
     return true;
 }
 
-static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
+static bool ggml_backend_sched_compute_splits(ggml_backend_sched_t sched) {
+    uint64_t copy_us[GGML_MAX_BACKENDS] = {0};
+    uint64_t compute_us[GGML_MAX_BACKENDS] = {0};
+
     struct ggml_backend_sched_split * splits = sched->splits;
 
     for (int i = 0; i < sched->n_splits; i++) {
@@ -1611,36 +1449,33 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
         ggml_backend_t split_backend = sched->backends[split_backend_id];
 
         // copy the input tensors to the split backend
+        uint64_t copy_start_us = ggml_time_us();
         for (int j = 0; j < split->n_inputs; j++) {
-            ggml_backend_t input_backend = ggml_backend_sched_get_tensor_backend(sched, split->inputs[j]);
             struct ggml_tensor * input = split->inputs[j];
-            struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id][sched->cur_copy];
+            struct ggml_tensor * input_cpy = sched->tensor_copies[hash_id(input)][split_backend_id];
 
-            if (input->flags & GGML_TENSOR_FLAG_INPUT) {
-                // inputs from the user must be copied immediately to prevent the user overwriting the data before the copy is done
-                if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
-                    ggml_backend_event_synchronize(sched->events[split_backend_id][sched->cur_copy]);
-                } else {
-                    ggml_backend_synchronize(split_backend);
-                }
-                ggml_backend_tensor_copy(input, input_cpy);
-            } else {
-                if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
-                    ggml_backend_event_wait(split_backend, sched->events[split_backend_id][sched->cur_copy]);
-                } else {
-                    ggml_backend_synchronize(split_backend);
-                    ggml_backend_synchronize(input_backend);
-                }
+            GGML_ASSERT(input->buffer != NULL);
+            GGML_ASSERT(input_cpy->buffer != NULL);
 
-                ggml_backend_tensor_copy_async(input_backend, split_backend, input, input_cpy);
-            }
+            ggml_backend_tensor_copy_async(split_backend, input, input_cpy);
         }
+        //ggml_backend_synchronize(split_backend); // necessary to measure copy time
+        int64_t copy_end_us = ggml_time_us();
+        copy_us[split_backend_id] += copy_end_us - copy_start_us;
 
+#if 0
+        char split_filename[GGML_MAX_NAME];
+        snprintf(split_filename, GGML_MAX_NAME, "split_%i_%s.dot", i, ggml_backend_name(split_backend));
+        ggml_graph_dump_dot(split->graph, NULL, split_filename);
+#endif
+
+
+        uint64_t compute_start_us = ggml_time_us();
         if (!sched->callback_eval) {
-            enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &split->graph);
-            if (ec != GGML_STATUS_SUCCESS) {
-                return ec;
+            if (!ggml_backend_graph_compute(split_backend, &split->graph)) {
+                return false;
             }
+            //ggml_backend_synchronize(split_backend); // necessary to measure compute time
         } else {
             // similar to ggml_backend_compare_graph_backend
             for (int j0 = 0; j0 < split->graph.n_nodes; j0++) {
@@ -1659,14 +1494,10 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
 
                 struct ggml_cgraph gv = ggml_graph_view(&split->graph, j0, j1 + 1);
 
-                enum ggml_status ec = ggml_backend_graph_compute_async(split_backend, &gv);
-                if (ec != GGML_STATUS_SUCCESS) {
-                    return ec;
+                if (!ggml_backend_graph_compute(split_backend, &gv)) {
+                    return false;
                 }
 
-                // TODO: pass backend to the callback, then the user can decide if they want to synchronize
-                ggml_backend_synchronize(split_backend);
-
                 if (need && !sched->callback_eval(t, false, sched->callback_eval_user_data)) {
                     break;
                 }
@@ -1674,54 +1505,39 @@ static enum ggml_status ggml_backend_sched_compute_splits(ggml_backend_sched_t s
                 j0 = j1;
             }
         }
-
-        // record the event of this copy
-        if (split->n_inputs > 0) {
-            if (sched->events[split_backend_id][sched->cur_copy] != NULL) {
-                ggml_backend_event_record(sched->events[split_backend_id][sched->cur_copy]);
-            }
-        }
+        uint64_t compute_end_us = ggml_time_us();
+        compute_us[split_backend_id] += compute_end_us - compute_start_us;
     }
 
-    sched->cur_copy = (sched->cur_copy + 1) % sched->n_copies;
+#if 0
+    // per-backend timings
+    fprintf(stderr, "sched_compute_splits times (%d splits):\n", sched->n_splits);
+    for (int i = 0; i < sched->n_backends; i++) {
+        if (copy_us[i] > 0 || compute_us[i] > 0) {
+            fprintf(stderr, "\t%5.5s: %lu us copy, %lu us compute\n", ggml_backend_name(sched->backends[i]), copy_us[i], compute_us[i]);
+        }
+    }
+#endif
 
-    return GGML_STATUS_SUCCESS;
+    return true;
 }
 
-ggml_backend_sched_t ggml_backend_sched_new(
-        ggml_backend_t * backends,
-        ggml_backend_buffer_type_t * bufts,
-        int n_backends,
-        size_t graph_size,
-        bool parallel) {
+ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size) {
     GGML_ASSERT(n_backends > 0);
-    GGML_ASSERT(n_backends <= GGML_SCHED_MAX_BACKENDS);
-    GGML_ASSERT(ggml_backend_is_cpu(backends[n_backends - 1])); // last backend must be CPU
+    GGML_ASSERT(n_backends <= GGML_MAX_BACKENDS);
 
     struct ggml_backend_sched * sched = calloc(sizeof(struct ggml_backend_sched), 1);
 
     // initialize hash table
-    sched->hash_set          = ggml_hash_set_new(graph_size + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS);
+    sched->hash_set          = ggml_hash_set_new(graph_size + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS);
     sched->tensor_backend_id = calloc(sizeof(sched->tensor_backend_id[0]), sched->hash_set.size);
     sched->tensor_copies     = calloc(sizeof(sched->tensor_copies[0]), sched->hash_set.size);
     sched->node_backend_ids  = calloc(sizeof(sched->node_backend_ids[0]), graph_size);
-    sched->leaf_backend_ids  = calloc(sizeof(sched->leaf_backend_ids[0]), graph_size);
 
     sched->n_backends = n_backends;
-
-    sched->n_copies = parallel ? GGML_SCHED_MAX_COPIES : 1;
-
-    GGML_ASSERT(sched->n_copies <= GGML_SCHED_MAX_COPIES);
-
-    for (int b = 0; b < n_backends; b++) {
-        sched->backends[b] = backends[b];
-        sched->bufts[b] = bufts ? bufts[b] : ggml_backend_get_default_buffer_type(backends[b]);
-        GGML_ASSERT(ggml_backend_buft_supports_backend(sched->bufts[b], backends[b]));
-        if (sched->n_copies > 1) {
-            for (int c = 0; c < sched->n_copies; c++) {
-                sched->events[b][c] = ggml_backend_event_new(backends[b]);
-            }
-        }
+    for (int i = 0; i < n_backends; i++) {
+        sched->backends[i] = backends[i];
+        sched->bufts[i] = bufts ? bufts[i] : ggml_backend_get_default_buffer_type(backends[i]);
     }
 
     sched->galloc = ggml_gallocr_new_n(sched->bufts, n_backends);
@@ -1735,18 +1551,12 @@ void ggml_backend_sched_free(ggml_backend_sched_t sched) {
     if (sched == NULL) {
         return;
     }
-    for (int b = 0; b < sched->n_backends; b++) {
-        for (int c = 0; c < sched->n_copies; c++) {
-            ggml_backend_event_free(sched->events[b][c]);
-        }
-    }
     ggml_gallocr_free(sched->galloc);
     ggml_free(sched->ctx);
     free(sched->hash_set.keys);
     free(sched->tensor_backend_id);
     free(sched->tensor_copies);
     free(sched->node_backend_ids);
-    free(sched->leaf_backend_ids);
     free(sched);
 }
 
@@ -1758,63 +1568,38 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) {
     memset(sched->tensor_copies,      0, sizeof(sched->tensor_copies[0])     * hash_size);
 
     sched->is_reset = true;
-    sched->is_alloc = false;
 }
 
 bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) {
     ggml_backend_sched_split_graph(sched, measure_graph);
 
-    // TODO: extract this to a separate function
-    if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids, sched->leaf_backend_ids)) {
+    if (!ggml_gallocr_reserve_n(sched->galloc, sched->graph, sched->node_backend_ids)) {
         return false;
     }
 
     ggml_backend_sched_reset(sched);
-    ggml_backend_sched_synchronize(sched);
-
     return true;
 }
 
-bool ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_SCHED_MAX_SPLITS*GGML_SCHED_MAX_SPLIT_INPUTS);
+bool ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
+    GGML_ASSERT((int)sched->hash_set.size >= graph->n_nodes + GGML_MAX_SPLITS*GGML_MAX_SPLIT_INPUTS);
+
+    if (!sched->is_reset) {
+        ggml_backend_sched_reset(sched);
+    }
 
     ggml_backend_sched_split_graph(sched, graph);
-
     if (!ggml_backend_sched_alloc_splits(sched)) {
         return false;
     }
 
-    sched->is_alloc = true;
+    if (!ggml_backend_sched_compute_splits(sched)) {
+        return false;
+    }
 
     return true;
 }
 
-enum ggml_status ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    enum ggml_status err = ggml_backend_sched_graph_compute_async(sched, graph);
-    ggml_backend_sched_synchronize(sched);
-    return err;
-}
-
-enum ggml_status ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph) {
-    if (!sched->is_reset && !sched->is_alloc) {
-        ggml_backend_sched_reset(sched);
-    }
-
-    if (!sched->is_alloc) {
-        if (!ggml_backend_sched_alloc_graph(sched, graph)) {
-            return GGML_STATUS_ALLOC_FAILED;
-        }
-    }
-
-    return ggml_backend_sched_compute_splits(sched);
-}
-
-void ggml_backend_sched_synchronize(ggml_backend_sched_t sched) {
-    for (int i = 0; i < sched->n_backends; i++) {
-        ggml_backend_synchronize(sched->backends[i]);
-    }
-}
-
 void ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data) {
     sched->callback_eval = callback;
     sched->callback_eval_user_data = user_data;
@@ -1824,24 +1609,19 @@ int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched) {
     return sched->n_splits;
 }
 
-int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched) {
-    return sched->n_copies;
-}
-
 size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
-
     return ggml_gallocr_get_buffer_size(sched->galloc, backend_index);
 }
 
-void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
+void ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend) {
     int backend_index = ggml_backend_sched_backend_id(sched, backend);
     GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
     tensor_backend_id(node) = backend_index;
 }
 
-ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
+ggml_backend_t ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node) {
     int backend_index = tensor_backend_id(node);
     if (backend_index == -1) {
         return NULL;

ggml-backend.h

@@ -9,7 +9,6 @@ extern "C" {
 
     typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
     typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
-    typedef struct ggml_backend_event * ggml_backend_event_t;
     typedef struct ggml_backend * ggml_backend_t;
     typedef void * ggml_backend_graph_plan_t;
 
@@ -67,30 +66,16 @@ extern "C" {
 
     GGML_API void ggml_backend_synchronize(ggml_backend_t backend);
 
-    GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create(ggml_backend_t backend, struct ggml_cgraph * cgraph);
-    GGML_API void                      ggml_backend_graph_plan_free  (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+    GGML_API ggml_backend_graph_plan_t ggml_backend_graph_plan_create (ggml_backend_t backend, struct ggml_cgraph * cgraph);
 
-    GGML_API enum ggml_status ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
-    GGML_API enum ggml_status ggml_backend_graph_compute     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
-
-    GGML_API bool ggml_backend_graph_compute_async(ggml_backend_t backend, struct ggml_cgraph * cgraph);
-    GGML_API bool ggml_backend_supports_op(ggml_backend_t backend, const struct ggml_tensor * op);
+    GGML_API void ggml_backend_graph_plan_free   (ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+    GGML_API void ggml_backend_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan);
+    GGML_API bool ggml_backend_graph_compute     (ggml_backend_t backend, struct ggml_cgraph * cgraph);
+    GGML_API bool ggml_backend_supports_op       (ggml_backend_t backend, const struct ggml_tensor * op);
 
     // tensor copy between different backends
     GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
-
-    // asynchronous copy
-    // the copy is performed after all the currently queued operations in backend_src
-    // backend_dst will wait for the copy to complete before performing other operations
-    // automatic fallback to sync copy if async is not supported
-    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend_src, ggml_backend_t backend_dst, struct ggml_tensor * src, struct ggml_tensor * dst);
-
-    // events
-    GGML_API ggml_backend_event_t   ggml_backend_event_new        (ggml_backend_t backend);
-    GGML_API void                   ggml_backend_event_free       (ggml_backend_event_t event);
-    GGML_API void                   ggml_backend_event_record     (ggml_backend_event_t event);
-    GGML_API void                   ggml_backend_event_synchronize(ggml_backend_event_t event);
-    GGML_API void                   ggml_backend_event_wait       (ggml_backend_t backend, ggml_backend_event_t event); // wait async on event
+    GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); // automatic fallback to sync copy
 
     //
     // CPU backend
@@ -137,31 +122,27 @@ extern "C" {
     /*
       Example usage:
 
-        // operations that use tensors allocated in a buffer with USAGE_WEIGHTS will be asigned
-        // preferrably to run on the same backend as the buffer
-        ggml_backend_buffer_set_usage(buf_weights, GGML_BACKEND_BUFFER_USAGE_WEIGHTS);
+        sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, num_backends);
+        // sched is initialized with measure allocators and cannot be used until allocated with a measure graph
 
-        sched = ggml_backend_sched_new({backend_gpu, backend_gpu2, backend_cpu}, NULL, num_backends, GGML_DEFAULT_GRAPH_SIZE, false);
+        // initialize buffers from a measure graph
+        measure_graph = build_graph(sched); // use the allocr to allocate inputs as needed
 
-        // initialize buffers from a max size graph (optional)
-        reserve_graph = build_graph(sched, max_batch_size);
+        // in build_graph:
+        build_graph(...) {
+            // manually assign nodes to a backend (optional, should not be needed in most cases)
+            struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
+            ggml_backend_sched_set_node_backend(sched, node, backend_gpu);
+        }
 
-        // manually assign nodes to a backend (optional, should not be needed in most cases)
-        struct ggml_tensor * node = ggml_mul_mat(ctx, ...);
-        ggml_backend_sched_set_tensor_backend(sched, node, backend_gpu);
+        // allocate backend buffers from measure graph
+        ggml_backend_sched_init_measure(sched, measure_graph);
 
-        ggml_backend_sched_reserve(sched, reserve_graph);
+        // the scheduler is now ready to compute graphs
 
         // compute
         graph = build_graph(sched);
         ggml_backend_sched_graph_compute(sched, graph);
-
-        // if there are graph inputs:
-        ggml_backend_sched_reset(sched);
-        ggml_backend_sched_alloc_graph(sched, graph);
-        ggml_backend_tensor_set(input_tensor, ...);
-        ggml_backend_sched_graph_compute(sched, graph);
-    }
     */
 
     struct ggml_backend_sched;
@@ -176,32 +157,26 @@ extern "C" {
     typedef bool (*ggml_backend_sched_eval_callback)(struct ggml_tensor * t, bool ask, void * user_data);
 
     // Initialize a backend scheduler
-    GGML_API ggml_backend_sched_t ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size, bool parallel);
-    GGML_API void                 ggml_backend_sched_free(ggml_backend_sched_t sched);
-
+    GGML_API ggml_backend_sched_t  ggml_backend_sched_new(ggml_backend_t * backends, ggml_backend_buffer_type_t * bufts, int n_backends, size_t graph_size);
+    GGML_API void                  ggml_backend_sched_free(ggml_backend_sched_t sched);
     // Initialize backend buffers from a measure graph
-    GGML_API bool                 ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
-
+    GGML_API bool                  ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph);
     // Get the number of splits of the last graph
-    GGML_API int                  ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
-    GGML_API int                  ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
+    GGML_API int                   ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
 
-    GGML_API size_t               ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
+    GGML_API size_t                ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
 
-    GGML_API void                 ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
-    GGML_API ggml_backend_t       ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
+    GGML_API void                  ggml_backend_sched_set_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
+    GGML_API ggml_backend_t        ggml_backend_sched_get_node_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);
 
     // Allocate and compute graph on the backend scheduler
-    GGML_API bool                 ggml_backend_sched_alloc_graph(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
-    GGML_API enum ggml_status     ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
-    GGML_API enum ggml_status     ggml_backend_sched_graph_compute_async(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
-    GGML_API void                 ggml_backend_sched_synchronize(ggml_backend_sched_t sched);
+    GGML_API bool                  ggml_backend_sched_graph_compute(ggml_backend_sched_t sched, struct ggml_cgraph * graph);
 
     // Reset all assignments and allocators - must be called before changing the node backends
-    GGML_API void                 ggml_backend_sched_reset(ggml_backend_sched_t sched);
+    GGML_API void                  ggml_backend_sched_reset(ggml_backend_sched_t sched);
 
     // Set a callback to be called for each resulting node during graph compute
-    GGML_API void                 ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
+    GGML_API void                  ggml_backend_sched_set_eval_callback(ggml_backend_sched_t sched, ggml_backend_sched_eval_callback callback, void * user_data);
 
     //
     // Utils

ggml-impl.h

@@ -53,30 +53,26 @@ extern "C" {
 //
 #include <arm_neon.h>
 
-typedef __fp16 ggml_fp16_internal_t;
-
 #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
 #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x)
 
 #define GGML_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x)
 
 static inline float ggml_compute_fp16_to_fp32(ggml_fp16_t h) {
-    ggml_fp16_internal_t tmp;
+    __fp16 tmp;
     memcpy(&tmp, &h, sizeof(ggml_fp16_t));
     return (float)tmp;
 }
 
 static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) {
     ggml_fp16_t res;
-    ggml_fp16_internal_t tmp = f;
+    __fp16 tmp = f;
     memcpy(&res, &tmp, sizeof(ggml_fp16_t));
     return res;
 }
 
 #else
 
-typedef uint16_t ggml_fp16_internal_t;
-
 #ifdef __wasm_simd128__
 #include <wasm_simd128.h>
 #else

ggml-kompute.cpp

@@ -1927,10 +1927,10 @@ static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(g
     return ggml_backend_kompute_buffer_type(ctx->device);
 }
 
-static ggml_status ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+static bool ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     auto * ctx = static_cast<ggml_kompute_context *>(backend->context);
     ggml_vk_graph_compute(ctx, cgraph);
-    return GGML_STATUS_SUCCESS;
+    return true;
 }
 
 static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) {
@@ -1951,11 +1951,6 @@ static struct ggml_backend_i kompute_backend_i = {
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_kompute_graph_compute,
     /* .supports_op             = */ ggml_backend_kompute_supports_op,
-    /* .event_new               = */ NULL,
-    /* .event_free              = */ NULL,
-    /* .event_record            = */ NULL,
-    /* .event_wait              = */ NULL,
-    /* .event_synchronize       = */ NULL,
 };
 
 static ggml_guid_t ggml_backend_kompute_guid() {

ggml-metal.m

@@ -163,8 +163,6 @@ enum ggml_metal_kernel_type {
     GGML_METAL_KERNEL_TYPE_IM2COL_F32,
     GGML_METAL_KERNEL_TYPE_UPSCALE_F32,
     GGML_METAL_KERNEL_TYPE_PAD_F32,
-    GGML_METAL_KERNEL_TYPE_ARANGE_F32,
-    GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,
     GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,
     GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,
     GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,
@@ -280,11 +278,6 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
     id<MTLLibrary> metal_library;
 
     // load library
-    //
-    // - first check if the library is embedded
-    // - then check if the library is in the bundle
-    // - if not found, load the source and compile it
-    // - if that fails, return NULL
     {
         NSBundle * bundle = nil;
 #ifdef SWIFT_PACKAGE
@@ -292,21 +285,12 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
 #else
         bundle = [NSBundle bundleForClass:[GGMLMetalClass class]];
 #endif
-
         NSError * error = nil;
-
-#if GGML_METAL_EMBED_LIBRARY
-        const bool try_metallib = false;
-#else
-        const bool try_metallib = true;
-#endif
-
-        NSString * path_lib = [bundle pathForResource:@"default" ofType:@"metallib"];
-        if (try_metallib && path_lib != nil) {
+        NSString * libPath = [bundle pathForResource:@"default" ofType:@"metallib"];
+        if (libPath != nil) {
             // pre-compiled library found
-            NSURL * libURL = [NSURL fileURLWithPath:path_lib];
-            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_lib UTF8String]);
-
+            NSURL * libURL = [NSURL fileURLWithPath:libPath];
+            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]);
             metal_library = [ctx->device newLibraryWithURL:libURL error:&error];
             if (error) {
                 GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
@@ -319,41 +303,38 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
             extern const char ggml_metallib_start[];
             extern const char ggml_metallib_end[];
 
-            NSString * src = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
+            NSString * src  = [[NSString alloc] initWithBytes:ggml_metallib_start length:(ggml_metallib_end-ggml_metallib_start) encoding:NSUTF8StringEncoding];
 #else
             GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__);
 
-            NSString * path_source;
-            NSString * path_resource = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
+            NSString * sourcePath;
+            NSString * ggmlMetalPathResources = [[NSProcessInfo processInfo].environment objectForKey:@"GGML_METAL_PATH_RESOURCES"];
 
-            GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, path_resource ? [path_resource UTF8String] : "nil");
+            GGML_METAL_LOG_INFO("%s: GGML_METAL_PATH_RESOURCES = %s\n", __func__, ggmlMetalPathResources ? [ggmlMetalPathResources UTF8String] : "nil");
 
-            if (path_resource) {
-                path_source = [path_resource stringByAppendingPathComponent:@"ggml-metal.metal"];
+            if (ggmlMetalPathResources) {
+                sourcePath = [ggmlMetalPathResources stringByAppendingPathComponent:@"ggml-metal.metal"];
             } else {
-                path_source = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
+                sourcePath = [bundle pathForResource:@"ggml-metal" ofType:@"metal"];
             }
-
-            if (path_source == nil) {
+            if (sourcePath == nil) {
                 GGML_METAL_LOG_WARN("%s: error: could not use bundle path to find ggml-metal.metal, falling back to trying cwd\n", __func__);
-                path_source = @"ggml-metal.metal";
+                sourcePath = @"ggml-metal.metal";
             }
-
-            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [path_source UTF8String]);
-
-            NSString * src = [NSString stringWithContentsOfFile:path_source encoding:NSUTF8StringEncoding error:&error];
+            GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [sourcePath UTF8String]);
+            NSString * src = [NSString stringWithContentsOfFile:sourcePath encoding:NSUTF8StringEncoding error:&error];
             if (error) {
                 GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]);
                 return NULL;
             }
-#endif // GGML_METAL_EMBED_LIBRARY
+#endif
 
             @autoreleasepool {
                 // dictionary of preprocessor macros
                 NSMutableDictionary * prep = [NSMutableDictionary dictionary];
 
 #ifdef GGML_QKK_64
-                prep[@"GGML_QKK_64"] = @(1);
+                prep[@"QK_K"] = @(64);
 #endif
 
                 MTLCompileOptions* options = [MTLCompileOptions new];
@@ -588,8 +569,6 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) {
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32,                im2col_f32,             true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32,               upscale_f32,            true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32,                   pad_f32,                true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32,    timestep_embedding_f32, true);
-        GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32,                arange_f32,             true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC,       argsort_f32_i32_asc,    true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC,      argsort_f32_i32_desc,   true);
         GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32,            leaky_relu_f32,         true);
@@ -718,8 +697,6 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
             return false;
         case GGML_OP_UPSCALE:
         case GGML_OP_PAD:
-        case GGML_OP_ARANGE:
-        case GGML_OP_TIMESTEP_EMBEDDING:
         case GGML_OP_ARGSORT:
         case GGML_OP_LEAKY_RELU:
             return true;
@@ -765,7 +742,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const
     }
 }
 
-static enum ggml_status ggml_metal_graph_compute(
+static bool ggml_metal_graph_compute(
         struct ggml_metal_context * ctx,
                struct ggml_cgraph * gf) {
 
@@ -1114,8 +1091,7 @@ static enum ggml_status ggml_metal_graph_compute(
                     {
                         GGML_ASSERT(ggml_is_contiguous(src0));
 
-                        float scale;
-                        memcpy(&scale, dst->op_params, sizeof(scale));
+                        const float scale = *(const float *) dst->op_params;
 
                         int64_t n = ggml_nelements(dst);
 
@@ -1274,15 +1250,11 @@ static enum ggml_status ggml_metal_graph_compute(
                             pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline;
                         }
 
-                        float scale;
-                        float max_bias;
-
-                        memcpy(&scale,    ((int32_t *) dst->op_params) + 0, sizeof(scale));
-                        memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias));
+                        const float scale    = ((float *) dst->op_params)[0];
+                        const float max_bias = ((float *) dst->op_params)[1];
 
                         const int64_t nrows_x = ggml_nrows(src0);
                         const int64_t nrows_y = src0->ne[1];
-
                         const uint32_t n_head_kv   = nrows_x/nrows_y;
                         const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv));
 
@@ -1659,8 +1631,8 @@ static enum ggml_status ggml_metal_graph_compute(
                         // TODO: make this more general
                         GGML_ASSERT(n_as <= 8);
 
-                        // max size of the src1ids array in the kernel shared buffer
-                        GGML_ASSERT(ne11 <= 4096);
+                        // max size of the src1ids array in the kernel stack
+                        GGML_ASSERT(ne11 <= 512);
 
                         const int64_t  ne20 = src2 ? src2->ne[0] : 0;
                         const int64_t  ne21 = src2 ? src2->ne[1] : 0;
@@ -1758,7 +1730,7 @@ static enum ggml_status ggml_metal_graph_compute(
                                 [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j];
                             }
 
-                            [encoder setThreadgroupMemoryLength:GGML_PAD(8192 + 2*ne11, 16) atIndex:0];
+                            [encoder setThreadgroupMemoryLength:8192 atIndex:0];
 
                             [encoder dispatchThreadgroups:MTLSizeMake((ne11 + 31)/32, (ne21 + 63)/64, n_as*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(128, 1, 1)];
                         } else {
@@ -2114,7 +2086,6 @@ static enum ggml_status ggml_metal_graph_compute(
 
                         //const int n_past = ((int32_t *) dst->op_params)[0];
                         const int n_head = ((int32_t *) dst->op_params)[1];
-
                         float max_bias;
                         memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float));
 
@@ -2329,50 +2300,6 @@ static enum ggml_status ggml_metal_graph_compute(
 
                         [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
                     } break;
-                case GGML_OP_ARANGE:
-                    {
-                        GGML_ASSERT(dst->type == GGML_TYPE_F32);
-
-                        float start;
-                        float step;
-
-                        memcpy(&start, ((int32_t *) dst->op_params) + 0, sizeof(float));
-                        memcpy(&step,  ((int32_t *) dst->op_params) + 2, sizeof(float));
-
-                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ARANGE_F32].pipeline;
-
-                        [encoder setComputePipelineState:pipeline];
-                        [encoder setBuffer:id_dst  offset:offs_dst    atIndex:0];
-                        [encoder setBytes:&ne0   length:sizeof(ne0)   atIndex:1];
-                        [encoder setBytes:&start length:sizeof(start) atIndex:2];
-                        [encoder setBytes:&step  length:sizeof(step)  atIndex:3];
-
-                        const int nth = MIN(1024, ne0);
-
-                        [encoder dispatchThreadgroups:MTLSizeMake(1, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                    } break;
-                case GGML_OP_TIMESTEP_EMBEDDING:
-                    {
-                        GGML_ASSERT(src0->type == GGML_TYPE_F32);
-
-                        const int dim        = dst->op_params[0];
-                        const int max_period = dst->op_params[1];
-
-                        const int half = dim / 2;
-
-                        id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32].pipeline;
-
-                        [encoder setComputePipelineState:pipeline];
-                        [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
-                        [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                        [encoder setBytes:&nb1   length:sizeof(nb1) atIndex:2];
-                        [encoder setBytes:&dim   length:sizeof(dim) atIndex:3];
-                        [encoder setBytes:&max_period length:sizeof(max_period) atIndex:4];
-
-                        const int nth = MIN(1024, half);
-
-                        [encoder dispatchThreadgroups:MTLSizeMake(ne00, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
-                    } break;
                 case GGML_OP_ARGSORT:
                     {
                         GGML_ASSERT(src0->type == GGML_TYPE_F32);
@@ -2501,7 +2428,7 @@ static enum ggml_status ggml_metal_graph_compute(
         MTLCommandBufferStatus status = [command_buffer status];
         if (status != MTLCommandBufferStatusCompleted) {
             GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status);
-            return GGML_STATUS_FAILED;
+            return false;
         }
     }
 
@@ -2510,7 +2437,7 @@ static enum ggml_status ggml_metal_graph_compute(
     }
 
     }
-    return GGML_STATUS_SUCCESS;
+    return true;
 }
 
 ////////////////////////////////////////////////////////////////////////////////
@@ -2812,7 +2739,7 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_metal_get_default_buffe
     UNUSED(backend);
 }
 
-GGML_CALL static enum ggml_status ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
+GGML_CALL static bool ggml_backend_metal_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) {
     struct ggml_metal_context * metal_ctx = (struct ggml_metal_context *)backend->context;
 
     return ggml_metal_graph_compute(metal_ctx, cgraph);
@@ -2837,11 +2764,6 @@ static struct ggml_backend_i ggml_backend_metal_i = {
     /* .graph_plan_compute      = */ NULL,
     /* .graph_compute           = */ ggml_backend_metal_graph_compute,
     /* .supports_op             = */ ggml_backend_metal_supports_op,
-    /* .event_new               = */ NULL,
-    /* .event_free              = */ NULL,
-    /* .event_record            = */ NULL,
-    /* .event_wait              = */ NULL,
-    /* .event_synchronize       = */ NULL,
 };
 
 void ggml_backend_metal_log_set_callback(ggml_log_callback log_callback, void * user_data) {

ggml-opencl.cpp

@@ -2231,7 +2231,7 @@ static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(gg
     GGML_UNUSED(backend);
 }
 
-static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
+static bool ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) {
     for (int i = 0; i < graph->n_nodes; ++i) {
         ggml_tensor * node = graph->nodes[i];
         switch (node->op) {
@@ -2246,7 +2246,7 @@ static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggm
         }
     }
 
-    return GGML_STATUS_SUCCESS;
+    return true;
 
     GGML_UNUSED(backend);
 }

ggml-quants.h

@@ -1,12 +1,248 @@
 #pragma once
 
-#define GGML_COMMON_DECL_C
-#include "ggml-common.h"
-
-#include "ggml.h"
+#include "ggml-impl.h"
 
 // GGML internal header
 
+#include <stdint.h>
+#include <stddef.h>
+
+#define QK4_0 32
+typedef struct {
+    ggml_fp16_t d;          // delta
+    uint8_t qs[QK4_0 / 2];  // nibbles / quants
+} block_q4_0;
+static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding");
+
+#define QK4_1 32
+typedef struct {
+    ggml_fp16_t d;          // delta
+    ggml_fp16_t m;          // min
+    uint8_t qs[QK4_1 / 2];  // nibbles / quants
+} block_q4_1;
+static_assert(sizeof(block_q4_1) == 2 * sizeof(ggml_fp16_t) + QK4_1 / 2, "wrong q4_1 block size/padding");
+
+#define QK5_0 32
+typedef struct {
+    ggml_fp16_t d;         // delta
+    uint8_t qh[4];         // 5-th bit of quants
+    uint8_t qs[QK5_0 / 2]; // nibbles / quants
+} block_q5_0;
+static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding");
+
+#define QK5_1 32
+typedef struct {
+    ggml_fp16_t d;         // delta
+    ggml_fp16_t m;         // min
+    uint8_t qh[4];         // 5-th bit of quants
+    uint8_t qs[QK5_1 / 2]; // nibbles / quants
+} block_q5_1;
+static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding");
+
+#define QK8_0 32
+typedef struct {
+    ggml_fp16_t d;         // delta
+    int8_t  qs[QK8_0];     // quants
+} block_q8_0;
+static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding");
+
+#define QK8_1 32
+typedef struct {
+    float d;               // delta
+    float s;               // d * sum(qs[i])
+    int8_t  qs[QK8_1];     // quants
+} block_q8_1;
+static_assert(sizeof(block_q8_1) == 2*sizeof(float) + QK8_1, "wrong q8_1 block size/padding");
+
+//
+// Super-block quantization structures
+//
+
+// Super-block size
+#ifdef GGML_QKK_64
+#define QK_K 64
+#define K_SCALE_SIZE 4
+#else
+#define QK_K 256
+#define K_SCALE_SIZE 12
+#endif
+
+// 2-bit quantization
+// weight is represented as x = a * q + b
+// 16 blocks of 16 elements each
+// Effectively 2.625 bits per weight
+typedef struct {
+    uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits
+    uint8_t qs[QK_K/4];      // quants
+    ggml_fp16_t d;           // super-block scale for quantized scales
+    ggml_fp16_t dmin;        // super-block scale for quantized mins
+} block_q2_K;
+static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding");
+
+// 3-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elements each
+// Effectively 3.4375 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    uint8_t hmask[QK_K/8];     // quants - high bit
+    uint8_t qs[QK_K/4];        // quants - low 2 bits
+    uint8_t scales[2];
+    ggml_fp16_t d;             // super-block scale
+} block_q3_K;
+static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
+#else
+typedef struct {
+    uint8_t hmask[QK_K/8];     // quants - high bit
+    uint8_t qs[QK_K/4];        // quants - low 2 bits
+    uint8_t scales[12];        // scales, quantized with 6 bits
+    ggml_fp16_t d;             // super-block scale
+} block_q3_K;
+static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
+#endif
+
+// 4-bit quantization
+// 8 blocks of 32 elements each
+// weight is represented as x = a * q + b
+// Effectively 4.5 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    ggml_fp16_t d[2];          // super-block scales/mins
+    uint8_t scales[2];         // 4-bit block scales/mins
+    uint8_t qs[QK_K/2];        // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
+#else
+typedef struct {
+    ggml_fp16_t d;             // super-block scale for quantized scales
+    ggml_fp16_t dmin;          // super-block scale for quantized mins
+    uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
+    uint8_t qs[QK_K/2];        // 4--bit quants
+} block_q4_K;
+static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
+#endif
+
+// 5-bit quantization
+// 8 blocks of 32 elements each
+// weight is represented as x = a * q + b
+// Effectively 5.5 bits per weight
+#ifdef GGML_QKK_64
+typedef struct {
+    ggml_fp16_t d;               // super-block scale
+    int8_t  scales[QK_K/16];     // 8-bit block scales
+    uint8_t qh[QK_K/8];          // quants, high bit
+    uint8_t qs[QK_K/2];          // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
+#else
+typedef struct {
+    ggml_fp16_t d;               // super-block scale for quantized scales
+    ggml_fp16_t dmin;            // super-block scale for quantized mins
+    uint8_t scales[K_SCALE_SIZE];   // scales and mins, quantized with 6 bits
+    uint8_t qh[QK_K/8];          // quants, high bit
+    uint8_t qs[QK_K/2];          // quants, low 4 bits
+} block_q5_K;
+static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
+#endif
+
+// 6-bit quantization
+// weight is represented as x = a * q
+// 16 blocks of 16 elements each
+// Effectively 6.5625 bits per weight
+typedef struct {
+    uint8_t ql[QK_K/2];      // quants, lower 4 bits
+    uint8_t qh[QK_K/4];      // quants, upper 2 bits
+    int8_t  scales[QK_K/16]; // scales, quantized with 8 bits
+    ggml_fp16_t d;           // super-block scale
+} block_q6_K;
+static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + QK_K / 16 + 3*QK_K/4, "wrong q6_K block size/padding");
+
+// This is only used for intermediate quantization and dot products
+typedef struct {
+    float   d;              // delta
+    int8_t  qs[QK_K];       // quants
+    int16_t bsums[QK_K/16]; // sum of quants in groups of 16
+} block_q8_K;
+static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding");
+
+// (Almost) "true" 2-bit quantization.
+// Due to the need to use blocks as per ggml design, it ends up using
+// 2.0625 bpw because of the 16-bit scale for each block of 256.
+typedef struct {
+    ggml_fp16_t d;
+    uint16_t qs[QK_K/8];
+} block_iq2_xxs;
+static_assert(sizeof(block_iq2_xxs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t), "wrong iq2_xxs block size/padding");
+
+// 2.3125 bpw quants
+typedef struct {
+    ggml_fp16_t d;
+    uint16_t qs[QK_K/8];
+    uint8_t  scales[QK_K/32];
+} block_iq2_xs;
+static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding");
+
+// 2.5625 bpw quants
+typedef struct {
+    ggml_fp16_t d;
+    uint8_t qs[QK_K/4];
+    uint8_t qh[QK_K/32];
+    uint8_t scales[QK_K/32];
+} block_iq2_s;
+static_assert(sizeof(block_iq2_s) == sizeof(ggml_fp16_t) + QK_K/4 + QK_K/16, "wrong iq2_s block size/padding");
+
+// (Almost) "true" 3-bit quantization.
+// Due to the need to use blocks as per ggml design, it ends up using
+// 3.0625 bpw because of the 16-bit scale for each block of 256.
+typedef struct {
+    ggml_fp16_t d;
+    uint8_t qs[3*QK_K/8];
+} block_iq3_xxs;
+static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding");
+
+// 3.4375 bpw
+#if QK_K == 64
+#define IQ3S_N_SCALE 2
+#else
+#define IQ3S_N_SCALE QK_K/64
+#endif
+typedef struct {
+    ggml_fp16_t d;
+    uint8_t qs[QK_K/4];
+    uint8_t qh[QK_K/32];
+    uint8_t signs[QK_K/8];
+    uint8_t scales[IQ3S_N_SCALE];
+} block_iq3_s;
+static_assert(sizeof(block_iq3_s) == sizeof(ggml_fp16_t) + 13*(QK_K/32) + IQ3S_N_SCALE, "wrong iq3_s block size/padding");
+
+typedef struct {
+    ggml_fp16_t d;
+    uint8_t qs[QK_K/8];
+    uint8_t scales[QK_K/16];
+} block_iq1_s;
+static_assert(sizeof(block_iq1_s) == sizeof(ggml_fp16_t) + QK_K/8 + QK_K/16, "wrong iq1_s block size/padding");
+
+// Non-linear quants
+#define QK4_NL 32
+typedef struct {
+    ggml_fp16_t d;
+    uint8_t qs[QK4_NL/2];
+} block_iq4_nl;
+static_assert(sizeof(block_iq4_nl) == sizeof(ggml_fp16_t) + QK4_NL/2, "wrong iq4_nl block size/padding");
+
+#if QK_K == 64
+#define block_iq4_xs block_iq4_nl
+//typedef struct block_iq4_nl block_iq4_xs;
+#else
+typedef struct {
+    ggml_fp16_t d;
+    uint16_t scales_h;
+    uint8_t  scales_l[QK_K/64];
+    uint8_t  qs[QK_K/2];
+} block_iq4_xs;
+static_assert(sizeof(block_iq4_xs) == sizeof(ggml_fp16_t) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding");
+#endif
+
 #ifdef __cplusplus
 extern "C" {
 #endif
@@ -25,7 +261,6 @@ void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGM
 void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k);
 void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k);
 void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k);
-
 void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k);
 void quantize_row_iq4_nl_reference (const float * GGML_RESTRICT x, block_iq4_nl  * GGML_RESTRICT y, int k);
 void quantize_row_iq4_xs_reference (const float * GGML_RESTRICT x, block_iq4_xs  * GGML_RESTRICT y, int k);
@@ -45,7 +280,6 @@ void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, in
 void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
 void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
 void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
-
 void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
 void quantize_row_iq4_nl (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
 void quantize_row_iq4_xs (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
@@ -66,7 +300,6 @@ void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRI
 void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
 void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
 void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
-
 void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
 void dequantize_row_iq2_xs (const block_iq2_xs  * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
 void dequantize_row_iq2_s  (const block_iq2_s   * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
@@ -88,7 +321,6 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const voi
 void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
-
 void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_iq2_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
@@ -98,26 +330,26 @@ void ggml_vec_dot_iq4_nl_q8_0 (int n, float * GGML_RESTRICT s, size_t bs, const
 void ggml_vec_dot_iq4_xs_q8_K (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 void ggml_vec_dot_iq3_s_q8_K  (int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc);
 
+//
 // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization")
-size_t quantize_iq2_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_iq2_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_iq2_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_iq3_xxs(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_iq1_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_iq4_nl (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_iq4_xs (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_iq3_s  (const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-
-size_t quantize_q2_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_q3_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_q4_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_q5_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_q6_K(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_q4_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_q4_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_q5_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_q5_1(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
-size_t quantize_q8_0(const float * GGML_RESTRICT src, void * GGML_RESTRICT dst, int nrows, int n_per_row, const float * imatrix);
+//
+size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_iq2_s  (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_iq1_s  (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_iq4_nl (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_iq4_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_iq3_s  (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q2_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q3_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q4_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q5_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q6_K   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q4_0   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q4_1   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q5_0   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
+size_t quantize_q5_1   (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
 
 void iq2xs_init_impl(enum ggml_type type);
 void iq2xs_free_impl(enum ggml_type type);

ggml-sycl.h

@@ -24,16 +24,6 @@ GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void);
 GGML_API void   ggml_backend_sycl_print_sycl_devices(void);
 GGML_API GGML_CALL void   ggml_sycl_get_gpu_list(int *id_list, int max_len);
 GGML_API GGML_CALL void   ggml_sycl_get_device_description(int device, char *description, size_t description_size);
-GGML_API GGML_CALL int   ggml_backend_sycl_get_device_count();
-GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_sycl_split_buffer_type(const float * tensor_split);
-GGML_API GGML_CALL void ggml_backend_sycl_get_device_memory(int device, size_t *free, size_t *total);
-GGML_API GGML_CALL int ggml_backend_sycl_get_device_index(int device_id);
-
-// TODO: these are temporary
-//       ref: https://github.com/ggerganov/llama.cpp/pull/6022#issuecomment-1992615670
-GGML_API GGML_CALL int ggml_backend_sycl_get_device_id(int device_index);
-GGML_API GGML_CALL void ggml_backend_sycl_set_single_device_mode(int main_gpu_id);
-GGML_API GGML_CALL void ggml_backend_sycl_set_mul_device_mode();
 #ifdef  __cplusplus
 }
 #endif

ggml-vulkan.h

@@ -10,7 +10,6 @@ extern "C" {
 #define GGML_VK_NAME "Vulkan"
 #define GGML_VK_MAX_DEVICES 16
 
-GGML_API void ggml_vk_instance_init(void);
 GGML_API void ggml_vk_init_cpu_assist(void);
 
 GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node);

ggml.h

@@ -315,16 +315,6 @@
 extern "C" {
 #endif
 
-    enum ggml_status {
-        GGML_STATUS_ALLOC_FAILED = -2,
-        GGML_STATUS_FAILED = -1,
-        GGML_STATUS_SUCCESS = 0,
-        GGML_STATUS_ABORTED = 1,
-    };
-
-    // get ggml_status name string
-    GGML_API GGML_CALL const char * ggml_status_to_string(enum ggml_status status);
-
     typedef uint16_t ggml_fp16_t;
 
     // convert FP16 <-> FP32
@@ -337,24 +327,24 @@ extern "C" {
     struct ggml_object;
     struct ggml_context;
 
-    // NOTE: always add types at the end of the enum to keep backward compatibility
     enum ggml_type {
-        GGML_TYPE_F32     = 0,
-        GGML_TYPE_F16     = 1,
-        GGML_TYPE_Q4_0    = 2,
-        GGML_TYPE_Q4_1    = 3,
+        GGML_TYPE_F32  = 0,
+        GGML_TYPE_F16  = 1,
+        GGML_TYPE_Q4_0 = 2,
+        GGML_TYPE_Q4_1 = 3,
         // GGML_TYPE_Q4_2 = 4, support has been removed
-        // GGML_TYPE_Q4_3 = 5, support has been removed
-        GGML_TYPE_Q5_0    = 6,
-        GGML_TYPE_Q5_1    = 7,
-        GGML_TYPE_Q8_0    = 8,
-        GGML_TYPE_Q8_1    = 9,
-        GGML_TYPE_Q2_K    = 10,
-        GGML_TYPE_Q3_K    = 11,
-        GGML_TYPE_Q4_K    = 12,
-        GGML_TYPE_Q5_K    = 13,
-        GGML_TYPE_Q6_K    = 14,
-        GGML_TYPE_Q8_K    = 15,
+        // GGML_TYPE_Q4_3 (5) support has been removed
+        GGML_TYPE_Q5_0 = 6,
+        GGML_TYPE_Q5_1 = 7,
+        GGML_TYPE_Q8_0 = 8,
+        GGML_TYPE_Q8_1 = 9,
+        // k-quantizations
+        GGML_TYPE_Q2_K = 10,
+        GGML_TYPE_Q3_K = 11,
+        GGML_TYPE_Q4_K = 12,
+        GGML_TYPE_Q5_K = 13,
+        GGML_TYPE_Q6_K = 14,
+        GGML_TYPE_Q8_K = 15,
         GGML_TYPE_IQ2_XXS = 16,
         GGML_TYPE_IQ2_XS  = 17,
         GGML_TYPE_IQ3_XXS = 18,
@@ -363,11 +353,9 @@ extern "C" {
         GGML_TYPE_IQ3_S   = 21,
         GGML_TYPE_IQ2_S   = 22,
         GGML_TYPE_IQ4_XS  = 23,
-        GGML_TYPE_I8      = 24,
-        GGML_TYPE_I16     = 25,
-        GGML_TYPE_I32     = 26,
-        GGML_TYPE_I64     = 27,
-        GGML_TYPE_F64     = 28,
+        GGML_TYPE_I8,
+        GGML_TYPE_I16,
+        GGML_TYPE_I32,
         GGML_TYPE_COUNT,
     };
 
@@ -385,20 +373,20 @@ extern "C" {
 
     // model file types
     enum ggml_ftype {
-        GGML_FTYPE_UNKNOWN        = -1,
-        GGML_FTYPE_ALL_F32        = 0,
-        GGML_FTYPE_MOSTLY_F16     = 1,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_0    = 2,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_1    = 3,  // except 1d tensors
+        GGML_FTYPE_UNKNOWN     = -1,
+        GGML_FTYPE_ALL_F32     = 0,
+        GGML_FTYPE_MOSTLY_F16  = 1,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_0 = 2,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_1 = 3,  // except 1d tensors
         GGML_FTYPE_MOSTLY_Q4_1_SOME_F16 = 4, // tok_embeddings.weight and output.weight are F16
-        GGML_FTYPE_MOSTLY_Q8_0    = 7,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_0    = 8,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_1    = 9,  // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q2_K    = 10, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q3_K    = 11, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q4_K    = 12, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q5_K    = 13, // except 1d tensors
-        GGML_FTYPE_MOSTLY_Q6_K    = 14, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q8_0 = 7,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_0 = 8,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_1 = 9,  // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q2_K = 10, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q3_K = 11, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q4_K = 12, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q5_K = 13, // except 1d tensors
+        GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ2_XS  = 16, // except 1d tensors
         GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors
@@ -466,16 +454,12 @@ extern "C" {
         GGML_OP_POOL_2D,
         GGML_OP_UPSCALE, // nearest interpolate
         GGML_OP_PAD,
-        GGML_OP_ARANGE,
-        GGML_OP_TIMESTEP_EMBEDDING,
         GGML_OP_ARGSORT,
         GGML_OP_LEAKY_RELU,
 
         GGML_OP_FLASH_ATTN,
         GGML_OP_FLASH_FF,
         GGML_OP_FLASH_ATTN_BACK,
-        GGML_OP_SSM_CONV,
-        GGML_OP_SSM_SCAN,
         GGML_OP_WIN_PART,
         GGML_OP_WIN_UNPART,
         GGML_OP_GET_REL_POS,
@@ -1677,15 +1661,6 @@ extern "C" {
             int                  p2,
             int                  p3);
 
-    // Ref: https://github.com/CompVis/stable-diffusion/blob/main/ldm/modules/diffusionmodules/util.py#L151
-    // timesteps: [N,]
-    // return: [N, dim]
-    GGML_API struct ggml_tensor * ggml_timestep_embedding(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * timesteps,
-            int                   dim,
-            int                   max_period);
-
     // sort rows
     enum ggml_sort_order {
         GGML_SORT_ORDER_ASC,
@@ -1697,12 +1672,6 @@ extern "C" {
             struct ggml_tensor  * a,
             enum ggml_sort_order  order);
 
-    GGML_API struct ggml_tensor * ggml_arange(
-            struct ggml_context * ctx,
-            float                 start,
-            float                 stop,
-            float                 step);
-
     // top k elements per row
     GGML_API struct ggml_tensor * ggml_top_k(
             struct ggml_context * ctx,
@@ -1732,23 +1701,6 @@ extern "C" {
             struct ggml_tensor  * c0,
             struct ggml_tensor  * c1);
 
-    GGML_API struct ggml_tensor * ggml_ssm_conv(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * s,
-            struct ggml_tensor  * x,
-            struct ggml_tensor  * c,
-            struct ggml_tensor  * sq);
-
-    GGML_API struct ggml_tensor * ggml_ssm_scan(
-            struct ggml_context * ctx,
-            struct ggml_tensor  * s,
-            struct ggml_tensor  * x,
-            struct ggml_tensor  * dt,
-            struct ggml_tensor  * A,
-            struct ggml_tensor  * B,
-            struct ggml_tensor  * C,
-            struct ggml_tensor  * sq);
-
     // partition into non-overlapping windows with padding if needed
     // example:
     // a:   768   64   64    1
@@ -1971,11 +1923,12 @@ extern "C" {
 
     // ggml_graph_plan() has to be called before ggml_graph_compute()
     // when plan.work_size > 0, caller must allocate memory for plan.work_data
-    GGML_API struct ggml_cplan ggml_graph_plan            (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
-    GGML_API enum ggml_status  ggml_graph_compute         (      struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
+    GGML_API struct ggml_cplan ggml_graph_plan   (const struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
+    GGML_API int               ggml_graph_compute(      struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
+
     // same as ggml_graph_compute() but the work data is allocated as a part of the context
     // note: the drawback of this API is that you must have ensured that the context has enough memory for the work data
-    GGML_API enum ggml_status  ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
+    GGML_API void ggml_graph_compute_with_ctx(struct ggml_context * ctx, struct ggml_cgraph * cgraph, int n_threads);
 
     GGML_API struct ggml_tensor * ggml_graph_get_tensor(struct ggml_cgraph * cgraph, const char * name);
 
@@ -2196,18 +2149,25 @@ extern "C" {
     GGML_API void ggml_quantize_init(enum ggml_type type);
     GGML_API void ggml_quantize_free(void);
 
+    // TODO: these would probably get removed in favor of the more general ggml_quantize_chunk
+    GGML_API size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q8_0(const float * src, void * dst, int n, int k, int64_t * hist);
+
+    GGML_API size_t ggml_quantize_q2_K(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q3_K(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q4_K(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q5_K(const float * src, void * dst, int n, int k, int64_t * hist);
+    GGML_API size_t ggml_quantize_q6_K(const float * src, void * dst, int n, int k, int64_t * hist);
+
     // some quantization type cannot be used without an importance matrix
     GGML_API bool ggml_quantize_requires_imatrix(enum ggml_type type);
 
     // calls ggml_quantize_init internally (i.e. can allocate memory)
-    GGML_API size_t ggml_quantize_chunk(
-            enum ggml_type   type,
-               const float * src,
-                      void * dst,
-                       int   start,
-                       int   nrows,
-                       int   n_per_row,
-               const float * imatrix);
+    GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst,
+            int start, int nrows, int n_per_row, int64_t * hist, const float * imatrix);
 
     //
     // gguf