Perplexity: Compute scores correlated to HellaSwag (#2312)

* Add parameter --perplexity-lines to perplexity.cpp

.devops/tools.sh

@@ -10,13 +10,13 @@ shift
 # Join the remaining arguments into a single string
 arg2="$@"
 
-if [[ $arg1 == '--convert' || $arg1 == '-c' ]]; then
-    python3 ./convert.py $arg2
-elif [[ $arg1 == '--quantize' || $arg1 == '-q' ]]; then
-    ./quantize $arg2
-elif [[ $arg1 == '--run' || $arg1 == '-r' ]]; then
-    ./main $arg2
-elif [[ $arg1 == '--all-in-one' || $arg1 == '-a' ]]; then
+if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then
+    python3 ./convert.py "$arg2"
+elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
+    ./quantize "$arg2"
+elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
+    ./main "$arg2"
+elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
     echo "Converting PTH to GGML..."
     for i in `ls $1/$2/ggml-model-f16.bin*`; do
         if [ -f "${i/f16/q4_0}" ]; then
@@ -26,8 +26,8 @@ elif [[ $arg1 == '--all-in-one' || $arg1 == '-a' ]]; then
             ./quantize "$i" "${i/f16/q4_0}" q4_0
         fi
     done
-elif [[ $arg1 == '--server' || $arg1 == '-s' ]]; then
-    ./server $arg2
+elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then
+    ./server "$arg2"
 else
     echo "Unknown command: $arg1"
     echo "Available commands: "

.gitignore

@@ -16,6 +16,8 @@ build/
 build-em/
 build-debug/
 build-release/
+build-ci-debug/
+build-ci-release/
 build-static/
 build-cublas/
 build-opencl/
@@ -25,9 +27,10 @@ build-no-accel/
 build-sanitize-addr/
 build-sanitize-thread/
 out/
+tmp/
 
 models/*
-*.bin
+models-mnt
 
 /main
 /quantize
@@ -58,3 +61,18 @@ qnt-*.txt
 perf-*.txt
 
 examples/jeopardy/results.txt
+
+
+pyproject.toml
+poetry.lock
+poetry.toml
+
+# Test binaries
+tests/test-double-float
+tests/test-grad0
+tests/test-opt
+tests/test-quantize-fns
+tests/test-quantize-perf
+tests/test-sampling
+tests/test-tokenizer-0
+

CMakeLists.txt

@@ -272,7 +272,7 @@ if (LLAMA_CUBLAS)
 
     if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
         if (LLAMA_CUDA_DMMV_F16)
-            set(CMAKE_CUDA_ARCHITECTURES "61") # needed for f16 CUDA intrinsics
+            set(CMAKE_CUDA_ARCHITECTURES "60;61") # needed for f16 CUDA intrinsics
         else()
             set(CMAKE_CUDA_ARCHITECTURES "52;61") # lowest CUDA 12 standard + lowest for integer intrinsics
         endif()
@@ -512,6 +512,7 @@ if (BUILD_SHARED_LIBS)
     set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON)
     add_library(ggml_shared SHARED $<TARGET_OBJECTS:ggml>)
     target_link_libraries(ggml_shared PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS})
+    install(TARGETS ggml_shared LIBRARY)
 endif()
 
 add_library(llama
@@ -533,8 +534,32 @@ if (BUILD_SHARED_LIBS)
     if (LLAMA_METAL)
         set_target_properties(llama PROPERTIES RESOURCE "${CMAKE_CURRENT_SOURCE_DIR}/ggml-metal.metal")
     endif()
+    install(TARGETS llama LIBRARY)
 endif()
 
+include(GNUInstallDirs)
+install(
+    FILES convert.py
+    PERMISSIONS
+        OWNER_READ
+        OWNER_WRITE
+        OWNER_EXECUTE
+        GROUP_READ
+        GROUP_EXECUTE
+        WORLD_READ
+        WORLD_EXECUTE
+    DESTINATION ${CMAKE_INSTALL_BINDIR})
+install(
+    FILES convert-lora-to-ggml.py
+    PERMISSIONS
+        OWNER_READ
+        OWNER_WRITE
+        OWNER_EXECUTE
+        GROUP_READ
+        GROUP_EXECUTE
+        WORLD_READ
+        WORLD_EXECUTE
+    DESTINATION ${CMAKE_INSTALL_BINDIR})
 
 #
 # programs, examples and tests

Makefile

@@ -1,5 +1,8 @@
 # Define the default target now so that it is always the first target
-BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server libembdinput.so embd-input-test
+BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server embd-input-test
+
+# Binaries only useful for tests
+TEST_TARGETS = tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0
 
 default: $(BUILD_TARGETS)
 
@@ -90,6 +93,28 @@ ifeq ($(UNAME_S),Haiku)
 	CXXFLAGS += -pthread
 endif
 
+# detect Windows
+ifneq ($(findstring _NT,$(UNAME_S)),)
+	_WIN32 := 1
+endif
+
+# library name prefix
+ifneq ($(_WIN32),1)
+	LIB_PRE := lib
+endif
+
+# Dynamic Shared Object extension
+ifneq ($(_WIN32),1)
+	DSO_EXT := .so
+else
+	DSO_EXT := .dll
+endif
+
+# Windows Sockets 2 (Winsock) for network-capable apps
+ifeq ($(_WIN32),1)
+	LWINSOCK2 := -lws2_32
+endif
+
 ifdef LLAMA_GPROF
 	CFLAGS   += -pg
 	CXXFLAGS += -pg
@@ -102,7 +127,7 @@ endif
 # Architecture specific
 # TODO: probably these flags need to be tweaked on some architectures
 #       feel free to update the Makefile for your architecture and send a pull request or issue
-ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
+ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64))
 	# Use all CPU extensions that are available:
 	CFLAGS   += -march=native -mtune=native
 	CXXFLAGS += -march=native -mtune=native
@@ -151,14 +176,11 @@ ifdef LLAMA_MPI
 	CFLAGS += -DGGML_USE_MPI -Wno-cast-qual
 	CXXFLAGS += -DGGML_USE_MPI -Wno-cast-qual
 	OBJS     += ggml-mpi.o
-
-ggml-mpi.o: ggml-mpi.c ggml-mpi.h
-	$(CC) $(CFLAGS) -c $< -o $@
 endif # LLAMA_MPI
 
 ifdef LLAMA_OPENBLAS
-	CFLAGS  += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas -I/usr/include/openblas
-	LDFLAGS += -lopenblas
+	CFLAGS  += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags openblas)
+	LDFLAGS += $(shell pkg-config --libs openblas)
 endif # LLAMA_OPENBLAS
 
 ifdef LLAMA_BLIS
@@ -171,8 +193,12 @@ ifdef LLAMA_CUBLAS
 	CXXFLAGS  += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include
 	LDFLAGS   += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib
 	OBJS      += ggml-cuda.o
-	NVCC      = nvcc
 	NVCCFLAGS = --forward-unknown-to-host-compiler
+ifdef LLAMA_CUDA_NVCC
+	NVCC = $(LLAMA_CUDA_NVCC)
+else
+	NVCC = nvcc
+endif #LLAMA_CUDA_NVCC
 ifdef CUDA_DOCKER_ARCH
 	NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH)
 else
@@ -201,7 +227,9 @@ ifdef LLAMA_CUDA_KQUANTS_ITER
 else
 	NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
 endif
-
+ifdef LLAMA_CUDA_CCBIN
+	NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN)
+endif
 ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
 	$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
 endif # LLAMA_CUBLAS
@@ -226,9 +254,6 @@ ifdef LLAMA_METAL
 	CXXFLAGS += -DGGML_USE_METAL
 	LDFLAGS  += -framework Foundation -framework Metal -framework MetalKit -framework MetalPerformanceShaders
 	OBJS     += ggml-metal.o
-
-ggml-metal.o: ggml-metal.m ggml-metal.h
-	$(CC) $(CFLAGS) -c $< -o $@
 endif # LLAMA_METAL
 
 ifneq ($(filter aarch64%,$(UNAME_M)),)
@@ -253,6 +278,16 @@ ifneq ($(filter armv8%,$(UNAME_M)),)
 	CFLAGS += -mfp16-format=ieee -mno-unaligned-access
 endif
 
+ifdef LLAMA_METAL
+ggml-metal.o: ggml-metal.m ggml-metal.h
+	$(CC) $(CFLAGS) -c $< -o $@
+endif # LLAMA_METAL
+
+ifdef LLAMA_MPI
+ggml-mpi.o: ggml-mpi.c ggml-mpi.h
+	$(CC) $(CFLAGS) -c $< -o $@
+endif # LLAMA_MPI
+
 ifdef LLAMA_NO_K_QUANTS
 k_quants.o: k_quants.c k_quants.h
 	$(CC) $(CFLAGS) -c $< -o $@
@@ -290,7 +325,7 @@ libllama.so: llama.o ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
 
 clean:
-	rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test build-info.h
+	rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test build-info.h $(TEST_TARGETS)
 
 #
 # Examples
@@ -321,14 +356,14 @@ save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 
 server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
+	$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2)
 
-libembdinput.so: examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+$(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o common.o $(OBJS)
 	$(CXX) --shared $(CXXFLAGS) $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
 
 
-embd-input-test: libembdinput.so examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o common.o $(OBJS)
-	$(CXX) $(CXXFLAGS) $(filter-out %.so,$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput
+embd-input-test: $(LIB_PRE)embdinput$(DSO_EXT) examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %$(DSO_EXT),$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput
 
 train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp    build-info.h ggml.o llama.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
@@ -345,6 +380,8 @@ build-info.h: $(wildcard .git/index) scripts/build-info.sh
 # Tests
 #
 
+tests: $(TEST_TARGETS)
+
 benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
 	./$@
@@ -352,6 +389,23 @@ benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o
 vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
 	$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
 
-.PHONY: tests clean
-tests:
-	bash ./tests/run-tests.sh
+tests/test-double-float: tests/test-double-float.c build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+
+tests/test-grad0: tests/test-grad0.c build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+
+tests/test-opt: tests/test-opt.c build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+
+tests/test-quantize-fns: tests/test-quantize-fns.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+
+tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+
+tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)
+
+tests/test-tokenizer-0: tests/test-tokenizer-0.cpp build-info.h ggml.o llama.o common.o $(OBJS)
+	$(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS)

README.md

@@ -640,7 +640,7 @@ Please verify the [sha256 checksums](SHA256SUMS) of all downloaded model files t
 
 ```bash
 # run the verification script
-python3 .\scripts\verify-checksum-models.py
+./scripts/verify-checksum-models.py
 ```
 
 - On linux or macOS it is also possible to run the following commands to verify if you have all possible latest files in your self-installed `./models` subdirectory:

build.zig

@@ -1,9 +1,19 @@
 const std = @import("std");
+const commit_hash = @embedFile(".git/refs/heads/master");
 
-// Zig Version: 0.11.0-dev.3379+629f0d23b
+// Zig Version: 0.11.0-dev.3986+e05c242cd
 pub fn build(b: *std.build.Builder) void {
     const target = b.standardTargetOptions(.{});
     const optimize = b.standardOptimizeOption(.{});
+
+    const config_header = b.addConfigHeader(
+        .{ .style = .blank, .include_path = "build-info.h" },
+        .{
+            .BUILD_NUMBER = 0,
+            .BUILD_COMMIT = commit_hash[0 .. commit_hash.len - 1], // omit newline
+        },
+    );
+
     const lib = b.addStaticLibrary(.{
         .name = "llama",
         .target = target,
@@ -13,24 +23,21 @@ pub fn build(b: *std.build.Builder) void {
     lib.linkLibCpp();
     lib.addIncludePath(".");
     lib.addIncludePath("./examples");
-    lib.addCSourceFiles(&.{
-        "ggml.c",
-    }, &.{"-std=c11"});
-    lib.addCSourceFiles(&.{
-        "llama.cpp",
-    }, &.{"-std=c++11"});
+    lib.addConfigHeader(config_header);
+    lib.addCSourceFiles(&.{"ggml.c"}, &.{"-std=c11"});
+    lib.addCSourceFiles(&.{"llama.cpp"}, &.{"-std=c++11"});
     b.installArtifact(lib);
 
     const examples = .{
         "main",
         "baby-llama",
         "embedding",
-        // "metal",
+        "metal",
         "perplexity",
         "quantize",
         "quantize-stats",
         "save-load-state",
-        // "server",
+        "server",
         "simple",
         "train-text-from-scratch",
     };
@@ -43,16 +50,19 @@ pub fn build(b: *std.build.Builder) void {
         });
         exe.addIncludePath(".");
         exe.addIncludePath("./examples");
+        exe.addConfigHeader(config_header);
         exe.addCSourceFiles(&.{
-            std.fmt.comptimePrint("examples/{s}/{s}.cpp", .{example_name, example_name}),
+            std.fmt.comptimePrint("examples/{s}/{s}.cpp", .{ example_name, example_name }),
             "examples/common.cpp",
         }, &.{"-std=c++11"});
         exe.linkLibrary(lib);
         b.installArtifact(exe);
+
         const run_cmd = b.addRunArtifact(exe);
         run_cmd.step.dependOn(b.getInstallStep());
         if (b.args) |args| run_cmd.addArgs(args);
-        const run_step = b.step("run_" ++ example_name, "Run the app");
+
+        const run_step = b.step("run-" ++ example_name, "Run the app");
         run_step.dependOn(&run_cmd.step);
     }
 }

ci/README.md

@@ -0,0 +1,25 @@
+# CI
+
+In addition to [Github Actions](https://github.com/ggerganov/llama.cpp/actions) `llama.cpp` uses a custom CI framework:
+
+https://github.com/ggml-org/ci
+
+It monitors the `master` branch for new commits and runs the
+[ci/run.sh](https://github.com/ggerganov/llama.cpp/blob/master/ci/run.sh) script on dedicated cloud instances. This allows us
+to execute heavier workloads compared to just using Github Actions. Also with time, the cloud instances will be scaled
+to cover various hardware architectures, including GPU and Apple Silicon instances.
+
+Collaborators can optionally trigger the CI run by adding the `ggml-ci` keyword to their commit message.
+Only the branches of this repo are monitored for this keyword.
+
+It is a good practice, before publishing changes to execute the full CI locally on your machine:
+
+```bash
+mkdir tmp
+
+# CPU-only build
+bash ./ci/run.sh ./tmp/results ./tmp/mnt
+
+# with CUDA support
+GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
+```

ci/run.sh

@@ -0,0 +1,409 @@
+#/bin/bash
+#
+# sample usage:
+#
+# mkdir tmp
+#
+# # CPU-only build
+# bash ./ci/run.sh ./tmp/results ./tmp/mnt
+#
+# # with CUDA support
+# GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
+#
+
+if [ -z "$2" ]; then
+    echo "usage: $0 <output-dir> <mnt-dir>"
+    exit 1
+fi
+
+mkdir -p "$1"
+mkdir -p "$2"
+
+OUT=$(realpath "$1")
+MNT=$(realpath "$2")
+
+rm -v $OUT/*.log
+rm -v $OUT/*.exit
+rm -v $OUT/*.md
+
+sd=`dirname $0`
+cd $sd/../
+SRC=`pwd`
+
+## helpers
+
+# download a file if it does not exist or if it is outdated
+function gg_wget {
+    local out=$1
+    local url=$2
+
+    local cwd=`pwd`
+
+    mkdir -p $out
+    cd $out
+
+    # should not re-download if file is the same
+    wget -nv -N $url
+
+    cd $cwd
+}
+
+function gg_printf {
+    printf -- "$@" >> $OUT/README.md
+}
+
+function gg_run {
+    ci=$1
+
+    set -o pipefail
+    set -x
+
+    gg_run_$ci | tee $OUT/$ci.log
+    cur=$?
+    echo "$cur" > $OUT/$ci.exit
+
+    set +x
+    set +o pipefail
+
+    gg_sum_$ci
+
+    ret=$((ret | cur))
+}
+
+## ci
+
+# ctest_debug
+
+function gg_run_ctest_debug {
+    cd ${SRC}
+
+    rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug
+
+    set -e
+
+    (time cmake -DCMAKE_BUILD_TYPE=Debug ..     ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                               ) 2>&1 | tee -a $OUT/${ci}-make.log
+
+    (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
+
+    set +e
+}
+
+function gg_sum_ctest_debug {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs ctest in debug mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
+    gg_printf '```\n'
+    gg_printf '\n'
+}
+
+# ctest_release
+
+function gg_run_ctest_release {
+    cd ${SRC}
+
+    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
+
+    set -e
+
+    (time cmake -DCMAKE_BUILD_TYPE=Release ..   ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                               ) 2>&1 | tee -a $OUT/${ci}-make.log
+
+    if [ -z ${GG_BUILD_LOW_PERF} ]; then
+        (time ctest --output-on-failure ) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    else
+        (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
+    fi
+
+    set +e
+}
+
+function gg_sum_ctest_release {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'Runs ctest in release mode\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '```\n'
+    gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
+    gg_printf '```\n'
+}
+
+# open_llama_3b_v2
+
+function gg_run_open_llama_3b_v2 {
+    cd ${SRC}
+
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/config.json
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/tokenizer.model
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/tokenizer_config.json
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/special_tokens_map.json
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/pytorch_model.bin
+    gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/generation_config.json
+
+    gg_wget models-mnt/wikitext/ https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip
+    unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
+    head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw
+
+    path_models="../models-mnt/open-llama/3B-v2"
+    path_wiki="../models-mnt/wikitext/wikitext-2-raw"
+
+    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
+
+    set -e
+
+    (time cmake -DCMAKE_BUILD_TYPE=Release -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                              ) 2>&1 | tee -a $OUT/${ci}-make.log
+
+    python3 ../convert.py ${path_models}
+
+    model_f16="${path_models}/ggml-model-f16.bin"
+    model_q8_0="${path_models}/ggml-model-q8_0.bin"
+    model_q4_0="${path_models}/ggml-model-q4_0.bin"
+    model_q4_1="${path_models}/ggml-model-q4_1.bin"
+    model_q5_0="${path_models}/ggml-model-q5_0.bin"
+    model_q5_1="${path_models}/ggml-model-q5_1.bin"
+    model_q2_k="${path_models}/ggml-model-q2_k.bin"
+    model_q3_k="${path_models}/ggml-model-q3_k.bin"
+    model_q4_k="${path_models}/ggml-model-q4_k.bin"
+    model_q5_k="${path_models}/ggml-model-q5_k.bin"
+    model_q6_k="${path_models}/ggml-model-q6_k.bin"
+
+    wiki_test_60="${path_wiki}/wiki.test-60.raw"
+
+    ./bin/quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/quantize ${model_f16} ${model_q4_0} q4_0
+    ./bin/quantize ${model_f16} ${model_q4_1} q4_1
+    ./bin/quantize ${model_f16} ${model_q5_0} q5_0
+    ./bin/quantize ${model_f16} ${model_q5_1} q5_1
+    ./bin/quantize ${model_f16} ${model_q2_k} q2_k
+    ./bin/quantize ${model_f16} ${model_q3_k} q3_k
+    ./bin/quantize ${model_f16} ${model_q4_k} q4_k
+    ./bin/quantize ${model_f16} ${model_q5_k} q5_k
+    ./bin/quantize ${model_f16} ${model_q6_k} q6_k
+
+    (time ./bin/main --model ${model_f16}  -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/main --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/main --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/main --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/main --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/main --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/main --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/main --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/main --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    function check_ppl {
+        qnt="$1"
+        ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
+            return 20
+        fi
+
+        printf '  - %s @ %s OK\n' "$qnt" "$ppl"
+        return 0
+    }
+
+    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+
+    set +e
+}
+
+function gg_sum_open_llama_3b_v2 {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'OpenLLaMA 3B-v2:\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
+    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
+    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
+    gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
+    gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
+    gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
+    gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
+    gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
+    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
+    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
+    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+}
+
+# open_llama_7b_v2
+# requires: GG_BUILD_CUDA
+
+function gg_run_open_llama_7b_v2 {
+    cd ${SRC}
+
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/config.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/tokenizer.model
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/tokenizer_config.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/special_tokens_map.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/pytorch_model.bin.index.json
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00001-of-00002.bin
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin
+    gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json
+
+    gg_wget models-mnt/wikitext/ https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip
+    unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
+
+    path_models="../models-mnt/open-llama/7B-v2"
+    path_wiki="../models-mnt/wikitext/wikitext-2-raw"
+
+    rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
+
+    set -e
+
+    (time cmake -DCMAKE_BUILD_TYPE=Release -DLLAMA_CUBLAS=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
+    (time make -j                                              ) 2>&1 | tee -a $OUT/${ci}-make.log
+
+    python3 ../convert.py ${path_models}
+
+    model_f16="${path_models}/ggml-model-f16.bin"
+    model_q8_0="${path_models}/ggml-model-q8_0.bin"
+    model_q4_0="${path_models}/ggml-model-q4_0.bin"
+    model_q4_1="${path_models}/ggml-model-q4_1.bin"
+    model_q5_0="${path_models}/ggml-model-q5_0.bin"
+    model_q5_1="${path_models}/ggml-model-q5_1.bin"
+    model_q2_k="${path_models}/ggml-model-q2_k.bin"
+    model_q3_k="${path_models}/ggml-model-q3_k.bin"
+    model_q4_k="${path_models}/ggml-model-q4_k.bin"
+    model_q5_k="${path_models}/ggml-model-q5_k.bin"
+    model_q6_k="${path_models}/ggml-model-q6_k.bin"
+
+    wiki_test="${path_wiki}/wiki.test.raw"
+
+    ./bin/quantize ${model_f16} ${model_q8_0} q8_0
+    ./bin/quantize ${model_f16} ${model_q4_0} q4_0
+    ./bin/quantize ${model_f16} ${model_q4_1} q4_1
+    ./bin/quantize ${model_f16} ${model_q5_0} q5_0
+    ./bin/quantize ${model_f16} ${model_q5_1} q5_1
+    ./bin/quantize ${model_f16} ${model_q2_k} q2_k
+    ./bin/quantize ${model_f16} ${model_q3_k} q3_k
+    ./bin/quantize ${model_f16} ${model_q4_k} q4_k
+    ./bin/quantize ${model_f16} ${model_q5_k} q5_k
+    ./bin/quantize ${model_f16} ${model_q6_k} q6_k
+
+    (time ./bin/main --model ${model_f16}  -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/main --model ${model_q8_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/main --model ${model_q4_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/main --model ${model_q4_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/main --model ${model_q5_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/main --model ${model_q5_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/main --model ${model_q2_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/main --model ${model_q3_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/main --model ${model_q4_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/main --model ${model_q5_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/main --model ${model_q6_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    (time ./bin/perplexity --model ${model_f16}  -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
+    (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
+    (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
+    (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
+    (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
+    (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
+    (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
+    (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
+    (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
+    (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
+    (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
+
+    function check_ppl {
+        qnt="$1"
+        ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
+
+        if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
+            printf '  - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
+            return 20
+        fi
+
+        printf '  - %s @ %s OK\n' "$qnt" "$ppl"
+        return 0
+    }
+
+    check_ppl "f16"  "$(cat $OUT/${ci}-tg-f16.log  | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+    check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
+
+    set +e
+}
+
+function gg_sum_open_llama_7b_v2 {
+    gg_printf '### %s\n\n' "${ci}"
+
+    gg_printf 'OpenLLaMA 7B-v2:\n'
+    gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
+    gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
+    gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
+    gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
+    gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
+    gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
+    gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
+    gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
+    gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
+    gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
+    gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
+    gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
+    gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
+}
+
+## main
+
+if [ -z ${GG_BUILD_LOW_PERF} ]; then
+    rm -rf ${SRC}/models-mnt
+
+    mnt_models=${MNT}/models
+    mkdir -p ${mnt_models}
+    ln -sfn ${mnt_models} ${SRC}/models-mnt
+
+    python3 -m pip install -r ${SRC}/requirements.txt
+fi
+
+ret=0
+
+test $ret -eq 0 && gg_run ctest_debug
+test $ret -eq 0 && gg_run ctest_release
+
+if [ -z ${GG_BUILD_LOW_PERF} ]; then
+    if [ -z ${GG_BUILD_CUDA} ]; then
+        test $ret -eq 0 && gg_run open_llama_3b_v2
+    else
+        test $ret -eq 0 && gg_run open_llama_7b_v2
+    fi
+fi
+
+exit $ret

convert-lora-to-ggml.py

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

convert.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python
 import argparse
 import concurrent.futures
 import copy

examples/Miku.sh

@@ -2,21 +2,21 @@
 set -e
 
 AI_NAME="${AI_NAME:-Miku}"
-MODEL="${MODEL:-./models/gpt4all-7B/gpt4all-lora-unfiltered-quantized.bin}"
+MODEL="${MODEL:-./models/llama-2-7b-chat.ggmlv3.q4_K_M.bin}"
 USER_NAME="${USER_NAME:-Anon}"
 
 # Uncomment and adjust to the number of CPU cores you want to use.
 #N_THREAD="${N_THREAD:-4}"
+CTX_SIZE="${CTX_SIZE:-4096}"
 N_PREDICTS="${N_PREDICTS:-4096}"
 
 GEN_OPTIONS=(--batch_size 1024
---ctx_size 2048
+--ctx_size "$CTX_SIZE"
 --keep -1
 --repeat_last_n 256
 --repeat_penalty 1.17647
---temp 0.7
---top_k 40
---top_p 0.5)
+--temp 0.6
+--mirostat 2)
 
 if [ -n "$N_THREAD" ]; then
     GEN_OPTIONS+=(--threads "$N_THREAD")
@@ -24,16 +24,17 @@ fi
 
 ./main "${GEN_OPTIONS[@]}" \
     --model "$MODEL" \
+    --in-prefix " " \
+    --in-suffix "${AI_NAME}:" \
     --n_predict "$N_PREDICTS" \
     --color --interactive \
     --reverse-prompt "${USER_NAME}:" \
-    --prompt "
-This is a transcript of a 1000 page, never ending conversation between ${USER_NAME} and the cute and helpful AI assistant ${AI_NAME}. ${AI_NAME} is a girl who is an AI running on the user's computer.
+    --prompt "This is a transcript of a 1000 page, never ending conversation between ${USER_NAME} and the cute and helpful AI assistant ${AI_NAME}. ${AI_NAME} is a girl who is an AI running on the user's computer.
 ${AI_NAME} can think for herself without the user seeing her thoughts by adding a /think prefix to her output. She uses this to reason about the world and to think about what she should say next.
 ${AI_NAME} is always coherent and makes sense, but if she isn't sure if what she is saying is correct, she will ask the user for help.
 ${AI_NAME} is a very helpful AI and will help the user with anything they need. She is also very friendly and will try to make the user feel better if they are sad.
 ${AI_NAME} is also very curious and will ask the user a lot of questions about themselves and their life. She will also try to make the user like her.
-The conversation is only between ${USER_NAME} and ${AI_NAME}
+The conversation is only between ${USER_NAME} and ${AI_NAME}.
 The conversation is only through text, so ${AI_NAME} can't see ${USER_NAME}'s face or hear his voice.
 ${AI_NAME} can only communicate through text, so she can't send images or videos.
 

examples/baby-llama/CMakeLists.txt

@@ -1,4 +1,5 @@
 set(TARGET baby-llama)
 add_executable(${TARGET} baby-llama.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/benchmark/CMakeLists.txt

@@ -1,5 +1,6 @@
 set(TARGET benchmark)
 add_executable(${TARGET} benchmark-matmult.cpp)
+install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)

examples/common.cpp

@@ -168,6 +168,18 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.n_ctx = std::stoi(argv[i]);
+        } else if (arg == "--rope-freq-base") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.rope_freq_base = std::stof(argv[i]);
+        } else if (arg == "--rope-freq-scale") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.rope_freq_scale = std::stof(argv[i]);
         } else if (arg == "--memory-f32") {
             params.memory_f16 = false;
         } else if (arg == "--top-p") {
@@ -248,12 +260,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.cfg_scale = std::stof(argv[i]);
-        } else if (arg == "--cfg-smooth-factor") {
-            if (++i >= argc) {
-                invalid_param = true;
-                break;
-            }
-            params.cfg_smooth_factor = std::stof(argv[i]);
         } else if (arg == "-b" || arg == "--batch-size") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -267,6 +273,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.n_keep = std::stoi(argv[i]);
+        } else if (arg == "--chunks") {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.n_chunks = std::stoi(argv[i]);
         } else if (arg == "-m" || arg == "--model") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -285,6 +297,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
                 break;
             }
             params.lora_adapter = argv[i];
+            params.use_mmap = false;
         } else if (arg == "--lora-base") {
             if (++i >= argc) {
                 invalid_param = true;
@@ -374,6 +387,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
             params.antiprompt.push_back(argv[i]);
         } else if (arg == "--perplexity") {
             params.perplexity = true;
+        } else if (arg == "--perplexity-lines") {
+            params.perplexity_lines = true;
         } else if (arg == "--ignore-eos") {
             params.logit_bias[llama_token_eos()] = -INFINITY;
         } else if (arg == "--no-penalize-nl") {
@@ -490,16 +505,19 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stderr, "  --cfg-negative-prompt PROMPT \n");
     fprintf(stderr, "                        negative prompt to use for guidance. (default: empty)\n");
     fprintf(stderr, "  --cfg-scale N         strength of guidance (default: %f, 1.0 = disable)\n", params.cfg_scale);
-    fprintf(stderr, "  --cfg-smooth-factor N smooth factor between old and new logits (default: %f, 1.0 = no smoothing)\n", params.cfg_smooth_factor);
     fprintf(stderr, "  -c N, --ctx-size N    size of the prompt context (default: %d)\n", params.n_ctx);
+    fprintf(stderr, "  --rope-freq-base N    RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
+    fprintf(stderr, "  --rope-freq-scale N   RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
     fprintf(stderr, "  --ignore-eos          ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
     fprintf(stderr, "  --no-penalize-nl      do not penalize newline token\n");
     fprintf(stderr, "  --memory-f32          use f32 instead of f16 for memory key+value (default: disabled)\n");
     fprintf(stderr, "                        not recommended: doubles context memory required and no measurable increase in quality\n");
     fprintf(stderr, "  --temp N              temperature (default: %.1f)\n", (double)params.temp);
     fprintf(stderr, "  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
-    fprintf(stderr, "  --perplexity          compute perplexity over the prompt\n");
+    fprintf(stderr, "  --perplexity          compute perplexity over each ctx window of the prompt\n");
+    fprintf(stderr, "  --perplexity-lines    compute perplexity over each line of the prompt\n");
     fprintf(stderr, "  --keep                number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep);
+    fprintf(stderr, "  --chunks N            max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks);
     if (llama_mlock_supported()) {
         fprintf(stderr, "  --mlock               force system to keep model in RAM rather than swapping or compressing\n");
     }
@@ -520,7 +538,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
     fprintf(stderr, "  --mtest               compute maximum memory usage\n");
     fprintf(stderr, "  --export              export the computation graph to 'llama.ggml'\n");
     fprintf(stderr, "  --verbose-prompt      print prompt before generation\n");
-    fprintf(stderr, "  --lora FNAME          apply LoRA adapter\n");
+    fprintf(stderr, "  --lora FNAME          apply LoRA adapter (implies --no-mmap)\n");
     fprintf(stderr, "  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
     fprintf(stderr, "  -m FNAME, --model FNAME\n");
     fprintf(stderr, "                        model path (default: %s)\n", params.model.c_str());
@@ -564,7 +582,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     lparams.n_batch      = params.n_batch;
     lparams.n_gpu_layers = params.n_gpu_layers;
     lparams.main_gpu     = params.main_gpu;
-    memcpy(lparams.tensor_split, params.tensor_split, LLAMA_MAX_DEVICES*sizeof(float));
+    lparams.tensor_split = params.tensor_split;
     lparams.low_vram     = params.low_vram;
     lparams.seed         = params.seed;
     lparams.f16_kv       = params.memory_f16;
@@ -572,6 +590,8 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
     lparams.use_mlock    = params.use_mlock;
     lparams.logits_all   = params.perplexity;
     lparams.embedding    = params.embedding;
+    lparams.rope_freq_base  = params.rope_freq_base;
+    lparams.rope_freq_scale = params.rope_freq_scale;
 
     return lparams;
 }

examples/common.h

@@ -28,10 +28,13 @@ struct gpt_params {
     int32_t n_ctx                           = 512; // context size
     int32_t n_batch                         = 512; // batch size for prompt processing (must be >=32 to use BLAS)
     int32_t n_keep                          = 0;   // number of tokens to keep from initial prompt
+    int32_t n_chunks                        = -1;  // max number of chunks to process (-1 = unlimited)
     int32_t n_gpu_layers                    = 0;   // number of layers to store in VRAM
     int32_t main_gpu                        = 0;   // the GPU that is used for scratch and small tensors
     float   tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
     int32_t n_probs                         = 0;   // if greater than 0, output the probabilities of top n_probs tokens.
+    float   rope_freq_base                  = 10000.0f; // RoPE base frequency
+    float   rope_freq_scale                 = 1.0f;     // RoPE frequency scaling factor
 
     // sampling parameters
     std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
@@ -52,7 +55,6 @@ struct gpt_params {
     // https://arxiv.org/abs/2306.17806
     std::string cfg_negative_prompt;       // string to help guidance
     float       cfg_scale         = 1.f;   // How strong is guidance
-    float       cfg_smooth_factor = 1.f;   // Smooth factor between old and new logits
 
     std::string model             = "models/7B/ggml-model.bin"; // model path
     std::string model_alias       = "unknown"; // model alias
@@ -80,6 +82,7 @@ struct gpt_params {
     bool instruct          = false; // instruction mode (used for Alpaca models)
     bool penalize_nl       = true;  // consider newlines as a repeatable token
     bool perplexity        = false; // compute perplexity over the prompt
+    bool perplexity_lines  = false; // compute perplexity over each line of the prompt
     bool use_mmap          = true;  // use mmap for faster loads
     bool use_mlock         = false; // use mlock to keep model in memory
     bool mem_test          = false; // compute maximum memory usage

examples/embd-input/CMakeLists.txt

@@ -1,5 +1,6 @@
 set(TARGET embdinput)
 add_library(${TARGET} embd-input-lib.cpp embd-input.h)
+install(TARGETS ${TARGET} LIBRARY)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)
@@ -8,6 +9,7 @@ endif()
 
 set(TARGET embd-input-test)
 add_executable(${TARGET} embd-input-test.cpp)
+install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama embdinput ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)

examples/embd-input/README.md

@@ -17,7 +17,7 @@ make
 import torch
 
 bin_path = "../LLaVA-13b-delta-v1-1/pytorch_model-00003-of-00003.bin"
-pth_path = "./examples/embd_input/llava_projection.pth"
+pth_path = "./examples/embd-input/llava_projection.pth"
 
 dic = torch.load(bin_path)
 used_key = ["model.mm_projector.weight","model.mm_projector.bias"]

examples/embd-input/llava.py

@@ -59,7 +59,7 @@ if __name__=="__main__":
     # Also here can use pytorch_model-00003-of-00003.bin directly.
     a.load_projection(os.path.join(
         os.path.dirname(__file__) ,
-        "llava_projetion.pth"))
+        "llava_projection.pth"))
     respose = a.chat_with_image(
         Image.open("./media/llama1-logo.png").convert('RGB'),
         "what is the text in the picture?")

examples/embd-input/minigpt4.py

@@ -64,7 +64,7 @@ class MiniGPT4(Blip2Base):
         self.max_txt_len = max_txt_len
         self.end_sym = end_sym
         self.model = MyModel(["main", *args])
-        # system promt
+        # system prompt
         self.model.eval_string("Give the following image: <Img>ImageContent</Img>. "
            "You will be able to see the image once I provide it to you. Please answer my questions."
            "###")

examples/embedding/CMakeLists.txt

@@ -1,5 +1,6 @@
 set(TARGET embedding)
 add_executable(${TARGET} embedding.cpp)
+install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)

examples/llama2-13b.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#
+# Temporary script - will be removed in the future
+#
+
+cd `dirname $0`
+cd ..
+
+./main -m models/available/Llama2/13B/llama-2-13b.ggmlv3.q4_0.bin \
+       --color \
+       --ctx_size 2048 \
+       -n -1 \
+       -ins -b 256 \
+       --top_k 10000 \
+       --temp 0.2 \
+       --repeat_penalty 1.1 \
+       -t 8

examples/llama2.sh

@@ -0,0 +1,18 @@
+#!/bin/bash
+
+#
+# Temporary script - will be removed in the future
+#
+
+cd `dirname $0`
+cd ..
+
+./main -m models/available/Llama2/7B/llama-2-7b.ggmlv3.q4_0.bin \
+       --color \
+       --ctx_size 2048 \
+       -n -1 \
+       -ins -b 256 \
+       --top_k 10000 \
+       --temp 0.2 \
+       --repeat_penalty 1.1 \
+       -t 8

examples/llm.vim

@@ -0,0 +1,58 @@
+function! Llm()
+
+  let url = "http://127.0.0.1:8080/completion"
+
+  " Save the current cursor position
+  let save_cursor = getpos('.')
+
+  silent! %s/\n/\\n/g
+  silent! %s/\t/\\t/g
+  silent! %s/\\n$//
+
+  " Get the content of the current buffer
+  let buffer_content = join(getline(1, '$'), "\n")
+
+  " Replace true newlines with "\n"
+  let buffer_content = substitute(buffer_content, '\n', '\\n', 'g')
+
+  " Trim leading/trailing whitespace
+  let buffer_content = substitute(buffer_content, '^\s\+', '', '')
+  let buffer_content = substitute(buffer_content, '\s\+$', '', '')
+
+  " Create the JSON payload
+  " can't escape backslash, \n gets replaced as \\n
+  let json_payload = '{"prompt":"' . escape(buffer_content, '"/') . '","temp":0.72,"top_k":100,"top_p":0.73,"repeat_penalty":1.100000023841858,"n_predict":10,"stream":false}'
+
+  let prompt_tmpfile = tempname()
+  let response_tmpfile = tempname()
+  call writefile([json_payload], prompt_tmpfile)
+
+  " Define the curl command
+  let curl_command = 'curl -k -s -X POST -H "Content-Type: application/json" -o ' . shellescape(response_tmpfile) . ' -d @' . shellescape(prompt_tmpfile) . ' ' . url
+  silent execute '!'.curl_command
+
+  let response = join(readfile(response_tmpfile), '')
+  let start_marker = '{"content":"'
+  let end_marker = '","generation_settings'
+  let content_start = stridx(response, start_marker) + len(start_marker)
+  let content_end = stridx(response, end_marker, content_start)
+
+  " Extract the content field from the response
+  let content = strpart(response, content_start, content_end - content_start)
+
+  " Insert the content at the cursor position
+  call setline(line('.'), getline('.') . content)
+
+  " Replace newline "\n" strings with actual newlines in the content
+  silent! %s/\\n/\r/g
+  " and tabs
+  silent! %s/\\t/\t/g
+  " and quote marks for C sources
+  silent! %s/\\"/\"/g
+
+  " Remove the temporary file
+  call delete(prompt_tmpfile)
+  call delete(response_tmpfile)
+endfunction
+
+command! Llm call Llm()

examples/main/CMakeLists.txt

@@ -1,5 +1,6 @@
 set(TARGET main)
 add_executable(${TARGET} main.cpp)
+install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)

examples/main/README.md

@@ -293,5 +293,5 @@ These options provide extra functionality and customization when running the LLa
 -   `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
 -   `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
 -   `-lv, --low-vram`: Do not allocate a VRAM scratch buffer for holding temporary results. Reduces VRAM usage at the cost of performance, particularly prompt processing speed. Requires cuBLAS.
--   `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model. This allows you to adapt the pretrained model to specific tasks or domains.
+-   `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
 -   `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.

examples/main/main.cpp

@@ -84,9 +84,17 @@ int main(int argc, char ** argv) {
         return 0;
     }
 
+    if (params.rope_freq_base != 10000.0) {
+        fprintf(stderr, "%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base);
+    }
+
+    if (params.rope_freq_scale != 1.0) {
+        fprintf(stderr, "%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale);
+    }
+
     if (params.n_ctx > 2048) {
-        fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
-                "expect poor results\n", __func__, params.n_ctx);
+        fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified);"
+                " you are on your own\n", __func__, params.n_ctx);
     } else if (params.n_ctx < 8) {
         fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__);
         params.n_ctx = 8;
@@ -549,7 +557,7 @@ int main(int argc, char ** argv) {
                 llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
 
                 if (ctx_guidance) {
-                    llama_sample_classifier_free_guidance(ctx, &candidates_p, ctx_guidance, params.cfg_scale, params.cfg_smooth_factor);
+                    llama_sample_classifier_free_guidance(ctx, &candidates_p, ctx_guidance, params.cfg_scale);
                 }
 
                 // Apply penalties

examples/make-ggml.py

@@ -0,0 +1,92 @@
+"""
+This script converts Hugging Face llama models to GGML and quantizes them.
+
+Usage:
+python make-ggml.py --model {model_dir_or_hf_repo_name} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)]
+
+Arguments:
+- --model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub.
+- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used.
+- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used.
+- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'.
+- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created.
+
+Quant types:
+- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M
+- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L
+- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M
+- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M
+- Q2_K: smallest, extreme quality loss - not recommended
+- Q3_K: alias for Q3_K_M
+- Q3_K_S: very small, very high quality loss
+- Q3_K_M: very small, very high quality loss
+- Q3_K_L: small, substantial quality loss
+- Q4_K: alias for Q4_K_M
+- Q4_K_S: small, significant quality loss
+- Q4_K_M: medium, balanced quality - recommended
+- Q5_K: alias for Q5_K_M
+- Q5_K_S: large, low quality loss - recommended
+- Q5_K_M: large, very low quality loss - recommended
+- Q6_K: very large, extremely low quality loss
+- Q8_0: very large, extremely low quality loss - not recommended
+- F16: extremely large, virtually no quality loss - not recommended
+- F32: absolutely huge, lossless - not recommended
+"""
+import subprocess
+subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True)
+
+import argparse
+import os
+from huggingface_hub import snapshot_download
+
+def main(model, outname, outdir, quants, keep_fp16):
+    ggml_version = "v3"
+
+    if not os.path.isdir(model):
+        print(f"Model not found at {model}. Downloading...")
+        try:
+            if outname is None:
+                outname = model.split('/')[-1]
+            model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache')
+        except Exception as e:
+            raise Exception(f"Could not download the model: {e}")
+
+    if outdir is None:
+        outdir = f'../models/{outname}'
+
+    if not os.path.isfile(f"{model}/config.json"):
+        raise Exception(f"Could not find config.json in {model}")
+
+    os.makedirs(outdir, exist_ok=True)
+
+    print("Building llama.cpp")
+    subprocess.run(f"cd .. && make quantize", shell=True, check=True)
+
+    fp16 = f"{outdir}/{outname}.ggml{ggml_version}.fp16.bin"
+
+    print(f"Making unquantised GGML at {fp16}")
+    if not os.path.isfile(fp16):
+        subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True)
+    else:
+        print(f"Unquantised GGML already exists at: {fp16}")
+
+    print("Making quants")
+    for type in quants:
+        outfile = f"{outdir}/{outname}.ggml{ggml_version}.{type}.bin"
+        print(f"Making {type} : {outfile}")
+        subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True)
+
+    if not keep_fp16:
+        os.remove(fp16)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='Convert/Quantize HF to GGML. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.')
+    parser.add_argument('--model', required=True, help='Downloaded model dir or Hugging Face model repo name')
+    parser.add_argument('--outname', default=None, help='Output model(s) name')
+    parser.add_argument('--outdir', default=None, help='Output directory')
+    parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types')
+    parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False)
+
+    args = parser.parse_args()
+
+    main(args.model, args.outname, args.outdir, args.quants, args.keep_fp16)

examples/metal/CMakeLists.txt

@@ -1,3 +1,4 @@
 set(TEST_TARGET metal)
 add_executable(${TEST_TARGET} metal.cpp)
+install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TEST_TARGET} PRIVATE ggml)

examples/perplexity/CMakeLists.txt

@@ -1,5 +1,6 @@
 set(TARGET perplexity)
 add_executable(${TARGET} perplexity.cpp)
+install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)

examples/perplexity/perplexity.cpp

@@ -4,6 +4,7 @@
 
 #include <cmath>
 #include <ctime>
+#include <sstream>
 
 #if defined(_MSC_VER)
 #pragma warning(disable: 4244 4267) // possible loss of data
@@ -32,13 +33,15 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
     // BOS tokens will be added for each chunk before eval
     auto tokens = ::llama_tokenize(ctx, params.prompt, true);
 
-    int count   = 0;
+    const int n_chunk_max = tokens.size() / params.n_ctx;
 
-    const int n_chunk = tokens.size() / params.n_ctx;
+    const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
     const int n_vocab = llama_n_vocab(ctx);
     const int n_batch = params.n_batch;
 
+    int count = 0;
     double nll = 0.0;
+
     fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch);
 
     for (int i = 0; i < n_chunk; ++i) {
@@ -118,6 +121,77 @@ void perplexity(llama_context * ctx, const gpt_params & params) {
     printf("\n");
 }
 
+void perplexity_lines(llama_context * ctx, const gpt_params & params) {
+    // Calculates perplexity over each line of the prompt
+
+    std::vector<std::string> prompt_lines;
+    std::istringstream strstream(params.prompt);
+    std::string line;
+
+    while (std::getline(strstream,line,'\n')) {
+        prompt_lines.push_back(line);
+    }
+
+    const int n_vocab = llama_n_vocab(ctx);
+
+    int counttotal   = 0;
+    size_t n_lines = prompt_lines.size();
+
+    double nll = 0.0;
+
+    fprintf(stderr, "%s: calculating perplexity over %lu lines\n", __func__, n_lines);
+
+    printf("\nLine\tPPL line\tPPL cumulative\n");
+
+    for (size_t i = 0; i < n_lines; ++i) {
+
+        // Tokenize and insert BOS at start
+        std::vector<int> batch_embd = ::llama_tokenize(ctx, prompt_lines[i], true);
+
+        size_t batch_size  = batch_embd.size();
+
+        // Stop if line is too long
+        if( batch_size > (size_t)params.n_ctx ) {
+            fprintf(stderr, "%s : tokens in line %lu > n_ctxl\n", __func__, i);
+            return;
+        }
+
+        if (llama_eval(ctx, batch_embd.data(), batch_size, 0, params.n_threads)) {
+            fprintf(stderr, "%s : failed to eval\n", __func__);
+            return;
+        }
+
+        const auto batch_logits = llama_get_logits(ctx);
+        std::vector<float> logits;
+        logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
+
+        double nllline = 0.0;
+        int countline = 0;
+
+        // Perplexity over second half of the line
+        for (size_t j = batch_size/2; j < batch_size - 1; ++j) {
+            // Calculate probability of next token, given the previous ones.
+            const std::vector<float> tok_logits(
+                logits.begin() + (j + 0) * n_vocab,
+                logits.begin() + (j + 1) * n_vocab);
+
+            const float prob = softmax(tok_logits)[batch_embd[ j + 1]];
+
+            nllline += -std::log(prob);
+            ++countline;
+        }
+
+        nll += nllline;
+        counttotal += countline;
+
+        // perplexity is e^(average negative log-likelihood)
+        printf("%lu\t%.8lf\t%.8lf\n", i + 1, std::exp(nllline/countline), std::exp(nll / counttotal) );
+        fflush(stdout);
+    }
+
+    printf("\n");
+}
+
 int main(int argc, char ** argv) {
     gpt_params params;
 
@@ -166,7 +240,11 @@ int main(int argc, char ** argv) {
                 params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
     }
 
-    perplexity(ctx, params);
+    if (params.perplexity_lines) {
+        perplexity_lines(ctx, params);
+    } else {
+        perplexity(ctx, params);
+    }
 
     llama_print_timings(ctx);
     llama_free(ctx);

examples/quantize-stats/CMakeLists.txt

@@ -1,4 +1,5 @@
 set(TARGET quantize-stats)
 add_executable(${TARGET} quantize-stats.cpp)
+install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)

examples/quantize/CMakeLists.txt

@@ -1,5 +1,6 @@
 set(TARGET quantize)
 add_executable(${TARGET} quantize.cpp)
+install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)

examples/quantize/quantize.cpp

@@ -14,103 +14,27 @@ struct quant_option {
 };
 
 static const std::vector<struct quant_option> QUANT_OPTIONS = {
-    {
-        "Q4_0",
-        LLAMA_FTYPE_MOSTLY_Q4_0,
-        " 3.50G, +0.2499 ppl @ 7B - small, very high quality loss - legacy, prefer using Q3_K_M",
-    },
-    {
-        "Q4_1",
-        LLAMA_FTYPE_MOSTLY_Q4_1,
-        " 3.90G, +0.1846 ppl @ 7B - small, substantial quality loss - legacy, prefer using Q3_K_L",
-    },
-    {
-        "Q5_0",
-        LLAMA_FTYPE_MOSTLY_Q5_0,
-        " 4.30G, +0.0796 ppl @ 7B - medium, balanced quality - legacy, prefer using Q4_K_M",
-    },
-    {
-        "Q5_1",
-        LLAMA_FTYPE_MOSTLY_Q5_1,
-        " 4.70G, +0.0415 ppl @ 7B - medium, low quality loss - legacy, prefer using Q5_K_M",
-    },
+    { "Q4_0",   LLAMA_FTYPE_MOSTLY_Q4_0,   " 3.50G, +0.2499 ppl @ 7B", },
+    { "Q4_1",   LLAMA_FTYPE_MOSTLY_Q4_1,   " 3.90G, +0.1846 ppl @ 7B", },
+    { "Q5_0",   LLAMA_FTYPE_MOSTLY_Q5_0,   " 4.30G, +0.0796 ppl @ 7B", },
+    { "Q5_1",   LLAMA_FTYPE_MOSTLY_Q5_1,   " 4.70G, +0.0415 ppl @ 7B", },
 #ifdef GGML_USE_K_QUANTS
-    {
-        "Q2_K",
-        LLAMA_FTYPE_MOSTLY_Q2_K,
-        " 2.67G, +0.8698 ppl @ 7B - smallest, extreme quality loss - not recommended",
-    },
-    {
-        "Q3_K",
-        LLAMA_FTYPE_MOSTLY_Q3_K_M,
-        "alias for Q3_K_M"
-    },
-    {
-        "Q3_K_S",
-        LLAMA_FTYPE_MOSTLY_Q3_K_S,
-        " 2.75G, +0.5505 ppl @ 7B - very small, very high quality loss",
-    },
-    {
-        "Q3_K_M",
-        LLAMA_FTYPE_MOSTLY_Q3_K_M,
-        " 3.06G, +0.2437 ppl @ 7B - very small, very high quality loss",
-    },
-    {
-        "Q3_K_L",
-        LLAMA_FTYPE_MOSTLY_Q3_K_L,
-        " 3.35G, +0.1803 ppl @ 7B - small, substantial quality loss",
-    },
-    {
-        "Q4_K",
-        LLAMA_FTYPE_MOSTLY_Q4_K_M,
-        "alias for Q4_K_M",
-    },
-    {
-        "Q4_K_S",
-        LLAMA_FTYPE_MOSTLY_Q4_K_S,
-        " 3.56G, +0.1149 ppl @ 7B - small, significant quality loss",
-    },
-    {
-        "Q4_K_M",
-        LLAMA_FTYPE_MOSTLY_Q4_K_M,
-        " 3.80G, +0.0535 ppl @ 7B - medium, balanced quality - *recommended*",
-    },
-    {
-        "Q5_K",
-        LLAMA_FTYPE_MOSTLY_Q5_K_M,
-        "alias for Q5_K_M",
-    },
-    {
-        "Q5_K_S",
-        LLAMA_FTYPE_MOSTLY_Q5_K_S,
-        " 4.33G, +0.0353 ppl @ 7B - large, low quality loss - *recommended*",
-    },
-    {
-        "Q5_K_M",
-        LLAMA_FTYPE_MOSTLY_Q5_K_M,
-        " 4.45G, +0.0142 ppl @ 7B - large, very low quality loss - *recommended*",
-    },
-    {
-        "Q6_K",
-        LLAMA_FTYPE_MOSTLY_Q6_K,
-        " 5.15G, +0.0044 ppl @ 7B - very large, extremely low quality loss",
-    },
+    { "Q2_K",   LLAMA_FTYPE_MOSTLY_Q2_K,   " 2.67G, +0.8698 ppl @ 7B", },
+    { "Q3_K",   LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
+    { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5505 ppl @ 7B", },
+    { "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.06G, +0.2437 ppl @ 7B", },
+    { "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 3.35G, +0.1803 ppl @ 7B", },
+    { "Q4_K",   LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
+    { "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 3.56G, +0.1149 ppl @ 7B", },
+    { "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 3.80G, +0.0535 ppl @ 7B", },
+    { "Q5_K",   LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", },
+    { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0353 ppl @ 7B", },
+    { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0142 ppl @ 7B", },
+    { "Q6_K",   LLAMA_FTYPE_MOSTLY_Q6_K,   " 5.15G, +0.0044 ppl @ 7B", },
 #endif
-    {
-        "Q8_0",
-        LLAMA_FTYPE_MOSTLY_Q8_0,
-        " 6.70G, +0.0004 ppl @ 7B - very large, extremely low quality loss - not recommended",
-    },
-    {
-        "F16",
-        LLAMA_FTYPE_MOSTLY_F16,
-        "13.00G              @ 7B - extremely large, virtually no quality loss - not recommended",
-    },
-    {
-        "F32",
-        LLAMA_FTYPE_ALL_F32,
-        "26.00G              @ 7B - absolutely huge, lossless - not recommended",
-    },
+    { "Q8_0",   LLAMA_FTYPE_MOSTLY_Q8_0,   " 6.70G, +0.0004 ppl @ 7B", },
+    { "F16",    LLAMA_FTYPE_MOSTLY_F16,    "13.00G              @ 7B", },
+    { "F32",    LLAMA_FTYPE_ALL_F32,       "26.00G              @ 7B", },
 };
 
 

examples/save-load-state/CMakeLists.txt

@@ -1,5 +1,6 @@
 set(TARGET save-load-state)
 add_executable(${TARGET} save-load-state.cpp)
+install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)

examples/server/CMakeLists.txt

@@ -2,10 +2,14 @@ set(TARGET server)
 option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
 include_directories(${CMAKE_CURRENT_SOURCE_DIR})
 add_executable(${TARGET} server.cpp 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 llama ${CMAKE_THREAD_LIBS_INIT})
+if (WIN32)
+    TARGET_LINK_LIBRARIES(${TARGET} PRIVATE ws2_32)
+endif()
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)
   add_dependencies(${TARGET} BUILD_INFO)

examples/server/README.md

@@ -16,7 +16,7 @@ Command line options:
 -   `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended.
 -   `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
 -   `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
--   `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model. This allows you to adapt the pretrained model to specific tasks or domains.
+-   `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
 -   `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
 -   `-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`.
@@ -66,6 +66,7 @@ Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the
 ```sh
 curl --request POST \
     --url http://localhost:8080/completion \
+    --header "Content-Type: application/json" \
     --data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}'
 ```
 

examples/server/chat.sh

@@ -32,6 +32,7 @@ tokenize() {
         --silent \
         --request POST \
         --url "${API_URL}/tokenize" \
+        --header "Content-Type: application/json" \
         --data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \
     | jq '.tokens[]'
 }
@@ -64,6 +65,7 @@ chat_completion() {
         --no-buffer \
         --request POST \
         --url "${API_URL}/completion" \
+        --header "Content-Type: application/json" \
         --data-raw "${DATA}")
 
     printf "\n"

examples/server/server.cpp

@@ -608,6 +608,8 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     fprintf(stderr, "  -v, --verbose         verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled");
     fprintf(stderr, "  -t N, --threads N     number of threads to use during computation (default: %d)\n", params.n_threads);
     fprintf(stderr, "  -c N, --ctx-size N    size of the prompt context (default: %d)\n", params.n_ctx);
+    fprintf(stderr, "  --rope-freq-base N    RoPE base frequency (default: %.1f)\n", params.rope_freq_base);
+    fprintf(stderr, "  --rope-freq-scale N   RoPE frequency scaling factor (default: %g)\n", params.rope_freq_scale);
     fprintf(stderr, "  -b N, --batch-size N  batch size for prompt processing (default: %d)\n", params.n_batch);
     fprintf(stderr, "  --memory-f32          use f32 instead of f16 for memory key+value (default: disabled)\n");
     fprintf(stderr, "                        not recommended: doubles context memory required and no measurable increase in quality\n");
@@ -632,7 +634,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
     fprintf(stderr, "                        model path (default: %s)\n", params.model.c_str());
     fprintf(stderr, "  -a ALIAS, --alias ALIAS\n");
     fprintf(stderr, "                        set an alias for the model, will be added as `model` field in completion response\n");
-    fprintf(stderr, "  --lora FNAME          apply LoRA adapter\n");
+    fprintf(stderr, "  --lora FNAME          apply LoRA adapter (implies --no-mmap)\n");
     fprintf(stderr, "  --lora-base FNAME     optional model to use as a base for the layers modified by the LoRA adapter\n");
     fprintf(stderr, "  --host                ip address to listen (default  (default: %s)\n", sparams.hostname.c_str());
     fprintf(stderr, "  --port PORT           port to listen (default  (default: %d)\n", sparams.port);
@@ -722,6 +724,22 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
             }
             params.n_ctx = std::stoi(argv[i]);
         }
+        else if (arg == "--rope-freq-base")
+        {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.rope_freq_base = std::stof(argv[i]);
+        }
+        else if (arg == "--rope-freq-scale")
+        {
+            if (++i >= argc) {
+                invalid_param = true;
+                break;
+            }
+            params.rope_freq_scale = std::stof(argv[i]);
+        }
         else if (arg == "--memory-f32" || arg == "--memory_f32")
         {
             params.memory_f16 = false;
@@ -820,6 +838,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
                 break;
             }
             params.lora_adapter = argv[i];
+            params.use_mmap = false;
         }
         else if (arg == "--lora-base")
         {

examples/simple/CMakeLists.txt

@@ -1,5 +1,6 @@
 set(TARGET simple)
 add_executable(${TARGET} simple.cpp)
+install(TARGETS ${TARGET} RUNTIME)
 target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
 target_compile_features(${TARGET} PRIVATE cxx_std_11)
 if(TARGET BUILD_INFO)

examples/train-text-from-scratch/CMakeLists.txt

@@ -1,4 +1,5 @@
 set(TARGET train-text-from-scratch)
 add_executable(${TARGET} train-text-from-scratch.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/train-text-from-scratch/train-text-from-scratch.cpp

@@ -1434,7 +1434,7 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
     gf->perf_time_us = 0;
 
     const auto & hparams = model->hparams;
-    //const int n_ctx      = hparams.n_ctx;
+    const int n_ctx      = hparams.n_ctx;
     const int n_vocab    = hparams.n_vocab;
     const int n_embd     = hparams.n_embd;
     const int n_layer    = hparams.n_layer;
@@ -1863,10 +1863,10 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train(
         t12->grad = expand(gb, ggml_permute(ctx0, t15->grad, 0, 2, 3, 1));                                            assert_shape_4d(t12->grad, N, n_batch, n_embd/n_head, n_head);
         t11->grad = expand(gb, ggml_reshape_2d(ctx0, ggml_cont(ctx0, t12->grad), N*n_batch, n_embd));                 assert_shape_2d(t11->grad, N*n_batch, n_embd);
         t10->grad = expand(gb, ggml_permute(ctx0, t14->grad, 0, 2, 1, 3));                                            assert_shape_4d(t10->grad, n_embd/n_head, n_head, N, n_batch);
-        t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode));                            assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch);
+        t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode, n_ctx));                     assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch);
         t08->grad = expand(gb, ggml_reshape_2d(ctx0, t09->grad, n_embd, N*n_batch));                                  assert_shape_2d(t08->grad, n_embd, N*n_batch);
         t07->grad = expand(gb, ggml_permute(ctx0, t13->grad, 0, 2, 1, 3));                                            assert_shape_4d(t07->grad, n_embd/n_head, n_head, N, n_batch);
-        t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode));                            assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch);
+        t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode, n_ctx));                     assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch);
         t05->grad = expand(gb, ggml_reshape_2d(ctx0, t06->grad, n_embd, N*n_batch));                                  assert_shape_2d(t05->grad, n_embd, N*n_batch);
         t04->grad = expand(gb, ggml_add_inplace(ctx0,
                         ggml_add_inplace(ctx0,

flake.nix

@@ -6,51 +6,51 @@
   outputs = { self, nixpkgs, flake-utils }:
     flake-utils.lib.eachDefaultSystem (system:
       let
-        inherit (pkgs.stdenv) isAarch64 isDarwin;
-        inherit (pkgs.lib) optionals;
-        isM1 = isAarch64 && isDarwin;
-        osSpecific = if isM1 then
-          with pkgs.darwin.apple_sdk_11_0.frameworks; [
-            Accelerate
-            MetalKit
-            MetalPerformanceShaders
-            MetalPerformanceShadersGraph
-          ]
-        else if isDarwin then
-          with pkgs.darwin.apple_sdk.frameworks; [
-            Accelerate
-            CoreGraphics
-            CoreVideo
-          ]
-        else
-          [ ];
+        inherit (pkgs.stdenv) isAarch32 isAarch64 isDarwin;
+        osSpecific = with pkgs; [ openmpi ] ++
+        (
+          if isAarch64 && isDarwin then
+            with pkgs.darwin.apple_sdk_11_0.frameworks; [
+              Accelerate
+              MetalKit
+              MetalPerformanceShaders
+              MetalPerformanceShadersGraph
+            ]
+          else if isAarch32 && isDarwin then
+            with pkgs.darwin.apple_sdk.frameworks; [
+              Accelerate
+              CoreGraphics
+              CoreVideo
+            ]
+          else
+            with pkgs; [ openblas ]
+        );
         pkgs = import nixpkgs { inherit system; };
+        nativeBuildInputs = with pkgs; [ cmake pkgconfig ];
         llama-python =
-          pkgs.python310.withPackages (ps: with ps; [ numpy sentencepiece ]);
+          pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece ]);
       in {
         packages.default = pkgs.stdenv.mkDerivation {
           name = "llama.cpp";
           src = ./.;
-          postPatch = if isM1 then ''
+          postPatch = ''
             substituteInPlace ./ggml-metal.m \
               --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
-          '' else
-            "";
-          nativeBuildInputs = with pkgs; [ cmake ];
+            substituteInPlace ./*.py --replace '/usr/bin/env python' '${llama-python}/bin/python'
+          '';
+          nativeBuildInputs = nativeBuildInputs;
           buildInputs = osSpecific;
-          cmakeFlags = [ "-DLLAMA_BUILD_SERVER=ON" ] ++ (optionals isM1 [
-            "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1"
-            "-DLLAMA_METAL=ON"
+          cmakeFlags = [ "-DLLAMA_BUILD_SERVER=ON" "-DLLAMA_MPI=ON" "-DBUILD_SHARED_LIBS=ON" "-DCMAKE_SKIP_BUILD_RPATH=ON" ]
+            ++ (if isAarch64 && isDarwin then [
+              "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1"
+              "-DLLAMA_METAL=ON"
+            ] else [
+              "-DLLAMA_BLAS=ON"
+              "-DLLAMA_BLAS_VENDOR=OpenBLAS"
           ]);
-          installPhase = ''
-            mkdir -p $out/bin
-            mv bin/* $out/bin/
+          postInstall = ''
             mv $out/bin/main $out/bin/llama
             mv $out/bin/server $out/bin/llama-server
-
-            echo "#!${llama-python}/bin/python" > $out/bin/convert.py
-            cat ${./convert.py} >> $out/bin/convert.py
-            chmod +x $out/bin/convert.py
           '';
           meta.mainProgram = "llama";
         };
@@ -68,7 +68,7 @@
         };
         apps.default = self.apps.${system}.llama;
         devShells.default = pkgs.mkShell {
-          packages = with pkgs; [ cmake llama-python ] ++ osSpecific;
+          packages = nativeBuildInputs ++ osSpecific;
         };
       });
 }

ggml-metal.m

@@ -676,8 +676,8 @@ void ggml_metal_graph_compute(
                                             GGML_ASSERT(ne02 == 1);
                                             GGML_ASSERT(ne12 == 1);
 
-                                            nth0 = 4;
-                                            nth1 = 16;
+                                            nth0 = 2;
+                                            nth1 = 32;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_K_f32];
                                         } break;
                                     case GGML_TYPE_Q3_K:
@@ -685,8 +685,8 @@ void ggml_metal_graph_compute(
                                             GGML_ASSERT(ne02 == 1);
                                             GGML_ASSERT(ne12 == 1);
 
-                                            nth0 = 4;
-                                            nth1 = 16;
+                                            nth0 = 2;
+                                            nth1 = 32;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q3_K_f32];
                                         } break;
                                     case GGML_TYPE_Q4_K:
@@ -694,8 +694,8 @@ void ggml_metal_graph_compute(
                                             GGML_ASSERT(ne02 == 1);
                                             GGML_ASSERT(ne12 == 1);
 
-                                            nth0 = 4;
-                                            nth1 = 16;
+                                            nth0 = 2;
+                                            nth1 = 32;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_K_f32];
                                         } break;
                                     case GGML_TYPE_Q5_K:
@@ -703,8 +703,8 @@ void ggml_metal_graph_compute(
                                             GGML_ASSERT(ne02 == 1);
                                             GGML_ASSERT(ne12 == 1);
 
-                                            nth0 = 4;
-                                            nth1 = 16;
+                                            nth0 = 2;
+                                            nth1 = 32;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q5_K_f32];
                                         } break;
                                     case GGML_TYPE_Q6_K:
@@ -712,8 +712,8 @@ void ggml_metal_graph_compute(
                                             GGML_ASSERT(ne02 == 1);
                                             GGML_ASSERT(ne12 == 1);
 
-                                            nth0 = 4;
-                                            nth1 = 16;
+                                            nth0 = 2;
+                                            nth1 = 32;
                                             [encoder setComputePipelineState:ctx->pipeline_mul_mat_q6_K_f32];
                                         } break;
                                     default:
@@ -739,17 +739,22 @@ void ggml_metal_graph_compute(
                                 [encoder setBytes:&ne0  length:sizeof(ne0)  atIndex:13];
                                 [encoder setBytes:&ne1  length:sizeof(ne1)  atIndex:14];
 
-                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1) {
-                                    [encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
-                                    [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 ||
+                                    src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) {
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7) / 8, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 }
-                                else if (src0t == GGML_TYPE_Q2_K ||
-                                         src0t == GGML_TYPE_Q3_K ||
-                                         src0t == GGML_TYPE_Q4_K ||
-                                         src0t == GGML_TYPE_Q5_K ||
-                                         src0t == GGML_TYPE_Q6_K) {
-                                    [encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
-                                    [encoder dispatchThreadgroups:MTLSizeMake(ne01, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                else if (src0t == GGML_TYPE_Q3_K) {
+#ifdef GGML_QKK_64
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+#else
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+3)/4, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+#endif
+                                }
+                                else if (src0t == GGML_TYPE_Q5_K) {
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3) / 4, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
+                                }
+                                else if (src0t == GGML_TYPE_Q6_K) {
+                                    [encoder dispatchThreadgroups:MTLSizeMake((ne01+1)/2, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
                                 } else {
                                     [encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
                                     [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@@ -793,7 +798,7 @@ void ggml_metal_graph_compute(
 
                             const float eps = 1e-6f;
 
-                            const int nth = 256;
+                            const int nth = 512;
 
                             [encoder setComputePipelineState:ctx->pipeline_rms_norm];
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
@@ -801,7 +806,7 @@ void ggml_metal_graph_compute(
                             [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
                             [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
                             [encoder setBytes:&eps  length:sizeof(   float) atIndex:4];
-                            [encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];
+                            [encoder setThreadgroupMemoryLength:nth/32*sizeof(float) atIndex:0];
 
                             const int64_t nrows = ggml_nrows(src0);
 
@@ -882,28 +887,35 @@ void ggml_metal_graph_compute(
 
                             const int n_past = ((int32_t *)(src1->data))[0];
 
+                            float freq_base;
+                            float freq_scale;
+                            memcpy(&freq_base,  (int32_t *) src1->data + 4, sizeof(float));
+                            memcpy(&freq_scale, (int32_t *) src1->data + 5, sizeof(float));
+
                             [encoder setComputePipelineState:ctx->pipeline_rope];
                             [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
                             [encoder setBuffer:id_dst  offset:offs_dst  atIndex:1];
-                            [encoder setBytes:&ne00   length:sizeof( int64_t) atIndex:2];
-                            [encoder setBytes:&ne01   length:sizeof( int64_t) atIndex:3];
-                            [encoder setBytes:&ne02   length:sizeof( int64_t) atIndex:4];
-                            [encoder setBytes:&ne03   length:sizeof( int64_t) atIndex:5];
-                            [encoder setBytes:&nb00   length:sizeof(uint64_t) atIndex:6];
-                            [encoder setBytes:&nb01   length:sizeof(uint64_t) atIndex:7];
-                            [encoder setBytes:&nb02   length:sizeof(uint64_t) atIndex:8];
-                            [encoder setBytes:&nb03   length:sizeof(uint64_t) atIndex:9];
-                            [encoder setBytes:&ne0    length:sizeof( int64_t) atIndex:10];
-                            [encoder setBytes:&ne1    length:sizeof( int64_t) atIndex:11];
-                            [encoder setBytes:&ne2    length:sizeof( int64_t) atIndex:12];
-                            [encoder setBytes:&ne3    length:sizeof( int64_t) atIndex:13];
-                            [encoder setBytes:&nb0    length:sizeof(uint64_t) atIndex:14];
-                            [encoder setBytes:&nb1    length:sizeof(uint64_t) atIndex:15];
-                            [encoder setBytes:&nb2    length:sizeof(uint64_t) atIndex:16];
-                            [encoder setBytes:&nb3    length:sizeof(uint64_t) atIndex:17];
-                            [encoder setBytes:&n_past length:sizeof(     int) atIndex:18];
-                            [encoder setBytes:&n_dims length:sizeof(     int) atIndex:19];
-                            [encoder setBytes:&mode   length:sizeof(     int) atIndex:20];
+                            [encoder setBytes:&ne00    length:sizeof( int64_t) atIndex:2];
+                            [encoder setBytes:&ne01    length:sizeof( int64_t) atIndex:3];
+                            [encoder setBytes:&ne02    length:sizeof( int64_t) atIndex:4];
+                            [encoder setBytes:&ne03    length:sizeof( int64_t) atIndex:5];
+                            [encoder setBytes:&nb00    length:sizeof(uint64_t) atIndex:6];
+                            [encoder setBytes:&nb01    length:sizeof(uint64_t) atIndex:7];
+                            [encoder setBytes:&nb02    length:sizeof(uint64_t) atIndex:8];
+                            [encoder setBytes:&nb03    length:sizeof(uint64_t) atIndex:9];
+                            [encoder setBytes:&ne0     length:sizeof( int64_t) atIndex:10];
+                            [encoder setBytes:&ne1     length:sizeof( int64_t) atIndex:11];
+                            [encoder setBytes:&ne2     length:sizeof( int64_t) atIndex:12];
+                            [encoder setBytes:&ne3     length:sizeof( int64_t) atIndex:13];
+                            [encoder setBytes:&nb0     length:sizeof(uint64_t) atIndex:14];
+                            [encoder setBytes:&nb1     length:sizeof(uint64_t) atIndex:15];
+                            [encoder setBytes:&nb2     length:sizeof(uint64_t) atIndex:16];
+                            [encoder setBytes:&nb3     length:sizeof(uint64_t) atIndex:17];
+                            [encoder setBytes:&n_past  length:sizeof(     int) atIndex:18];
+                            [encoder setBytes:&n_dims  length:sizeof(     int) atIndex:19];
+                            [encoder setBytes:&mode    length:sizeof(     int) atIndex:20];
+                            [encoder setBytes:&freq_base  length:sizeof(float) atIndex:21];
+                            [encoder setBytes:&freq_scale length:sizeof(float) atIndex:22];
 
                             [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)];
                         } break;

ggml.h

@@ -201,8 +201,13 @@
 #define GGML_MAX_NAME          48
 #define GGML_DEFAULT_N_THREADS 4
 
+
+#define GGML_EXIT_SUCCESS 0
+#define GGML_EXIT_ABORTED 1
+
 #define GGML_UNUSED(x) (void)(x)
 
+
 #define GGML_ASSERT(x) \
     do { \
         if (!(x)) { \
@@ -363,6 +368,8 @@ extern "C" {
         GGML_OP_CLAMP,
         GGML_OP_CONV_1D,
         GGML_OP_CONV_2D,
+        GGML_OP_POOL_1D,
+        GGML_OP_POOL_2D,
 
         GGML_OP_FLASH_ATTN,
         GGML_OP_FLASH_FF,
@@ -442,6 +449,10 @@ extern "C" {
 
         // the `n_tasks` of nodes, 1:1 mapping to cgraph nodes
         int n_tasks[GGML_MAX_NODES];
+
+        // abort ggml_graph_compute when true
+        bool (*abort_callback)(void * data);
+        void * abort_callback_data;
     };
 
     // computation graph
@@ -1110,6 +1121,17 @@ extern "C" {
             int                   mode,
             int                   n_ctx);
 
+    // custom RoPE, in-place, returns view(a)
+    GGML_API struct ggml_tensor * ggml_rope_custom_inplace(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            int                   n_past,
+            int                   n_dims,
+            int                   mode,
+            int                   n_ctx,
+            float                 freq_base,
+            float                 freq_scale);
+
     // rotary position embedding backward, i.e compute dx from dy
     // a - dy
     GGML_API struct ggml_tensor * ggml_rope_back(
@@ -1117,7 +1139,8 @@ extern "C" {
             struct ggml_tensor  * a,
             int                   n_past,
             int                   n_dims,
-            int                   mode);
+            int                   mode,
+            int                   n_ctx);
 
     // alibi position embedding
     // in-place, returns view(a)
@@ -1164,6 +1187,31 @@ extern "C" {
             int                   s,
             int                   d);
 
+    enum ggml_op_pool {
+        GGML_OP_POOL_MAX,
+        GGML_OP_POOL_AVG,
+        GGML_OP_POOL_COUNT,
+    };
+
+    GGML_API struct ggml_tensor* ggml_pool_1d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            enum ggml_op_pool     op,
+            int                   k0, // kernel size
+            int                   s0, // stride
+            int                   p0); // padding
+
+    GGML_API struct ggml_tensor* ggml_pool_2d(
+            struct ggml_context * ctx,
+            struct ggml_tensor  * a,
+            enum ggml_op_pool     op,
+            int                   k0,
+            int                   k1,
+            int                   s0,
+            int                   s1,
+            int                   p0,
+            int                   p1);
+
     GGML_API struct ggml_tensor * ggml_flash_attn(
             struct ggml_context * ctx,
             struct ggml_tensor  * q,
@@ -1303,7 +1351,7 @@ 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   (struct ggml_cgraph * cgraph, int n_threads /*= GGML_DEFAULT_N_THREADS*/);
-    GGML_API              void ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
+    GGML_API               int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan);
     GGML_API              void ggml_graph_reset  (struct ggml_cgraph * cgraph);
 
     // same as ggml_graph_compute() but the work data is allocated as a part of the context

k_quants.h

@@ -15,6 +15,14 @@
 #define K_SCALE_SIZE 12
 #endif
 
+#ifndef static_assert
+#if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L)
+#define static_assert(cond, msg) _Static_assert(cond, msg)
+#else
+#define static_assert(cond, msg) struct global_scope_noop_trick
+#endif
+#endif
+
 //
 // Super-block quantization structures
 //

llama-util.h

@@ -175,13 +175,13 @@ struct llama_mmap {
     llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
         size = file->size;
         int fd = fileno(file->fp);
-        int flags = MAP_PRIVATE;
+        int flags = MAP_SHARED;
         // prefetch/readahead impairs performance on NUMA systems
         if (numa) { prefetch = 0; }
 #ifdef __linux__
         if (prefetch) { flags |= MAP_POPULATE; }
 #endif
-        addr = mmap(NULL, file->size, PROT_READ | PROT_WRITE, flags, fd, 0);
+        addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
         if (addr == MAP_FAILED) {
             throw std::runtime_error(format("mmap failed: %s", strerror(errno)));
         }
@@ -223,7 +223,7 @@ struct llama_mmap {
             throw std::runtime_error(format("CreateFileMappingA failed: %s", llama_format_win_err(error).c_str()));
         }
 
-        addr = MapViewOfFile(hMapping, FILE_MAP_COPY, 0, 0, 0);
+        addr = MapViewOfFile(hMapping, FILE_MAP_READ, 0, 0, 0);
         error = GetLastError();
         CloseHandle(hMapping);
 

llama.cpp

@@ -101,14 +101,15 @@ static void ggml_graph_compute_helper(std::vector<uint8_t> & buf, ggml_cgraph *
 // memory sizes
 //
 
-static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0()
+static const std::map<e_model, size_t> & MEM_REQ_SCRATCH0(int n_ctx)
 {
     static std::map<e_model, size_t> k_sizes = {
-        { MODEL_3B,    256ull * MB },
-        { MODEL_7B,    512ull * MB },
-        { MODEL_13B,   512ull * MB },
-        { MODEL_30B,   512ull * MB },
-        { MODEL_65B,  1024ull * MB },
+        /* empirical scaling, still a guess */
+        { MODEL_3B,   ((size_t) n_ctx / 16ull + 128ull) * MB },
+        { MODEL_7B,   ((size_t) n_ctx / 16ull + 256ull) * MB },
+        { MODEL_13B,  ((size_t) n_ctx / 12ull + 256ull) * MB },
+        { MODEL_30B,  ((size_t) n_ctx / 10ull + 256ull) * MB },
+        { MODEL_65B,  ((size_t) n_ctx /  8ull + 512ull) * MB },
     };
     return k_sizes;
 }
@@ -140,14 +141,14 @@ static const std::map<e_model, size_t> & MEM_REQ_KV_SELF()
 
 // this is mostly needed for temporary mul_mat buffers to dequantize the data
 // not actually needed if BLAS is disabled
-static const std::map<e_model, size_t> & MEM_REQ_EVAL()
+static const std::map<e_model, size_t> & MEM_REQ_EVAL(int n_ctx)
 {
     static std::map<e_model, size_t> k_sizes = {
-        { MODEL_3B,   512ull * MB },
-        { MODEL_7B,   768ull * MB },
-        { MODEL_13B, 1024ull * MB },
-        { MODEL_30B, 1280ull * MB },
-        { MODEL_65B, 1536ull * MB },
+        { MODEL_3B,  ((size_t) n_ctx / 256ull +  512ull) * MB },
+        { MODEL_7B,  ((size_t) n_ctx / 256ull +  768ull) * MB },
+        { MODEL_13B, ((size_t) n_ctx / 256ull + 1024ull) * MB },
+        { MODEL_30B, ((size_t) n_ctx / 256ull + 1280ull) * MB },
+        { MODEL_65B, ((size_t) n_ctx / 256ull + 1536ull) * MB },
     };
     return k_sizes;
 }
@@ -189,6 +190,10 @@ struct llama_hparams {
     uint32_t n_head  = 32;
     uint32_t n_layer = 32;
     uint32_t n_rot   = 64;
+
+    float rope_freq_base  = 10000.0f;
+    float rope_freq_scale = 1.0f;
+
     enum llama_ftype ftype = LLAMA_FTYPE_MOSTLY_F16;
 
     bool operator!=(const llama_hparams & other) const {
@@ -303,7 +308,7 @@ struct llama_model {
 };
 
 struct llama_context {
-    llama_context(const llama_model & model, const llama_vocab & vocab) : model(model), vocab(vocab), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {}
+    llama_context(const llama_model & model) : model(model), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {}
 #ifdef GGML_USE_METAL
     ~llama_context() {
         if (ctx_metal) {
@@ -324,7 +329,6 @@ struct llama_context {
     int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1)
 
     const llama_model & model;
-    const llama_vocab & vocab;
 
     bool model_owner = false;
 
@@ -551,7 +555,9 @@ struct llama_file_loader {
             }
 
             // skip to the next multiple of 32 bytes
-            file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
+            if (file_version >= LLAMA_FILE_VERSION_GGJT_V1) {
+                file.seek(-static_cast<ptrdiff_t>(file.tell()) & 31, SEEK_CUR);
+            }
 
             tensor.file_off = file.tell();
             tensor.name = name;
@@ -648,7 +654,7 @@ struct llama_model_loader {
         *ctx_size_p = *mmapped_size_p = 0;
         for (const llama_load_tensor & lt : tensors_map.tensors) {
             *ctx_size_p += sizeof(struct ggml_tensor) + GGML_OBJECT_SIZE;
-            *(use_mmap ? mmapped_size_p : ctx_size_p) += lt.size;
+            *(use_mmap ? mmapped_size_p : ctx_size_p) += lt.size + 16;
         }
     }
 
@@ -843,7 +849,9 @@ struct llama_context_params llama_context_default_params() {
         /*.n_batch                     =*/ 512,
         /*.gpu_layers                  =*/ 0,
         /*.main_gpu                    =*/ 0,
-        /*.tensor_split                =*/ {0},
+        /*.tensor_split                =*/ nullptr,
+        /*.rope_freq_base              =*/ 10000.0f,
+        /*.rope_freq_scale             =*/ 1.0f,
         /*.progress_callback           =*/ nullptr,
         /*.progress_callback_user_data =*/ nullptr,
         /*.low_vram                    =*/ false,
@@ -869,6 +877,10 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
     return result;
 }
 
+int llama_max_devices() {
+    return LLAMA_MAX_DEVICES;
+}
+
 bool llama_mmap_supported() {
     return llama_mmap::SUPPORTED;
 }
@@ -967,6 +979,8 @@ static void llama_model_load_internal(
         int n_gpu_layers,
         int main_gpu,
         const float * tensor_split,
+        float rope_freq_base,
+        float rope_freq_scale,
         bool low_vram,
         ggml_type memory_type,
         bool use_mmap,
@@ -1001,22 +1015,27 @@ static void llama_model_load_internal(
         }
 
         hparams.n_ctx = n_ctx;
+
+        hparams.rope_freq_base  = rope_freq_base;
+        hparams.rope_freq_scale = rope_freq_scale;
     }
 
     const uint32_t n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
 
     {
-        fprintf(stderr, "%s: format     = %s\n",  __func__, llama_file_version_name(file_version));
-        fprintf(stderr, "%s: n_vocab    = %u\n",  __func__, hparams.n_vocab);
-        fprintf(stderr, "%s: n_ctx      = %u\n",  __func__, hparams.n_ctx);
-        fprintf(stderr, "%s: n_embd     = %u\n",  __func__, hparams.n_embd);
-        fprintf(stderr, "%s: n_mult     = %u\n",  __func__, hparams.n_mult);
-        fprintf(stderr, "%s: n_head     = %u\n",  __func__, hparams.n_head);
-        fprintf(stderr, "%s: n_layer    = %u\n",  __func__, hparams.n_layer);
-        fprintf(stderr, "%s: n_rot      = %u\n",  __func__, hparams.n_rot);
+        fprintf(stderr, "%s: format     = %s\n",   __func__, llama_file_version_name(file_version));
+        fprintf(stderr, "%s: n_vocab    = %u\n",   __func__, hparams.n_vocab);
+        fprintf(stderr, "%s: n_ctx      = %u\n",   __func__, hparams.n_ctx);
+        fprintf(stderr, "%s: n_embd     = %u\n",   __func__, hparams.n_embd);
+        fprintf(stderr, "%s: n_mult     = %u\n",   __func__, hparams.n_mult);
+        fprintf(stderr, "%s: n_head     = %u\n",   __func__, hparams.n_head);
+        fprintf(stderr, "%s: n_layer    = %u\n",   __func__, hparams.n_layer);
+        fprintf(stderr, "%s: n_rot      = %u\n",   __func__, hparams.n_rot);
+        fprintf(stderr, "%s: freq_base  = %.1f\n", __func__, hparams.rope_freq_base);
+        fprintf(stderr, "%s: freq_scale = %g\n",   __func__, hparams.rope_freq_scale);
         fprintf(stderr, "%s: ftype      = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype));
-        fprintf(stderr, "%s: n_ff       = %u\n",  __func__, n_ff);
-        fprintf(stderr, "%s: model size = %s\n",  __func__, llama_model_type_name(model.type));
+        fprintf(stderr, "%s: n_ff       = %u\n",   __func__, n_ff);
+        fprintf(stderr, "%s: model size = %s\n",   __func__, llama_model_type_name(model.type));
     }
 
     if (file_version < LLAMA_FILE_VERSION_GGJT_V2) {
@@ -1165,9 +1184,9 @@ static void llama_model_load_internal(
         const size_t mem_required =
             ctx_size +
             mmapped_size - vram_weights + // weights in VRAM not in memory
-            MEM_REQ_SCRATCH0().at(model.type) +
+            MEM_REQ_SCRATCH0(hparams.n_ctx).at(model.type) +
             MEM_REQ_SCRATCH1().at(model.type) +
-            MEM_REQ_EVAL().at    (model.type);
+            MEM_REQ_EVAL(hparams.n_ctx).at(model.type);
 
         // this is the memory required by one llama_state
         const size_t mem_required_state =
@@ -1270,7 +1289,9 @@ static bool llama_model_load(
         int n_batch,
         int n_gpu_layers,
         int main_gpu,
-        float * tensor_split,
+        const float * tensor_split,
+        float rope_freq_base,
+        float rope_freq_scale,
         bool low_vram,
         ggml_type memory_type,
         bool use_mmap,
@@ -1279,7 +1300,7 @@ static bool llama_model_load(
         llama_progress_callback progress_callback,
         void *progress_callback_user_data) {
     try {
-        llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, low_vram, memory_type,
+        llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, rope_freq_base, rope_freq_scale, low_vram, memory_type,
                                   use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data);
         return true;
     } catch (const std::exception & err) {
@@ -1331,6 +1352,9 @@ static bool llama_eval_internal(
     const int n_rot        = hparams.n_embd/hparams.n_head;
     const int n_gpu_layers = model.n_gpu_layers;
 
+    const float freq_base  = hparams.rope_freq_base;
+    const float freq_scale = hparams.rope_freq_scale;
+
     auto & mem_per_token = lctx.mem_per_token;
     auto & buf_compute   = lctx.buf_compute;
 
@@ -1428,11 +1452,11 @@ static bool llama_eval_internal(
             offload_func_kq(tmpq);
             ggml_set_name(tmpq, "tmpq");
 
-            struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
+            struct ggml_tensor * Kcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0, freq_base, freq_scale);
             offload_func_kq(Kcur);
             ggml_set_name(Kcur, "Kcur");
 
-            struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
+            struct ggml_tensor * Qcur = ggml_rope_custom_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0, freq_base, freq_scale);
             offload_func_kq(Qcur);
             ggml_set_name(Qcur, "Qcur");
 
@@ -2006,9 +2030,18 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array *
     }
 
     // Normalize the second derivatives
-    float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
-    for (float & value : second_derivatives) {
-        value /= second_derivatives_sum;
+    {
+        const float second_derivatives_sum = std::accumulate(second_derivatives.begin(), second_derivatives.end(), 0.0f);
+
+        if (second_derivatives_sum > 1e-6f) {
+            for (float & value : second_derivatives) {
+                value /= second_derivatives_sum;
+            }
+        } else {
+            for (float & value : second_derivatives) {
+                value = 1.0f / second_derivatives.size();
+            }
+        }
     }
 
     float cum_sum = 0.0f;
@@ -2185,9 +2218,8 @@ void llama_sample_classifier_free_guidance(
           struct llama_context * ctx,
         llama_token_data_array * candidates,
           struct llama_context * guidance_ctx,
-                         float   scale,
-                         float   smooth_factor) {
-    int64_t t_start_sample_us = t_start_sample_us = ggml_time_us();
+                         float   scale) {
+    int64_t t_start_sample_us = ggml_time_us();
 
     assert(ctx);
     auto n_vocab = llama_n_vocab(ctx);
@@ -2207,16 +2239,7 @@ void llama_sample_classifier_free_guidance(
     for (int i = 0; i < n_vocab; ++i) {
         float logit_guidance = logits_guidance[i];
         float logit_base = logits_base[i];
-        logits_guidance[i] = scale * (logit_base - logit_guidance) + logit_guidance;
-    }
-
-    llama_log_softmax(logits_guidance, n_vocab);
-
-    for (int i = 0; i < n_vocab; ++i) {
-        float logit_base = logits_base[i];
-        float logit_guidance = logits_guidance[i];
-
-        candidates->data[i].logit = smooth_factor * logit_guidance + (1.f - smooth_factor) * logit_base;
+        candidates->data[i].logit = scale * (logit_base - logit_guidance) + logit_guidance;
     }
 
     if (ctx) {
@@ -2675,8 +2698,9 @@ struct llama_model * llama_load_model_from_file(
     ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32;
 
     if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gpu_layers,
-                params.main_gpu, params.tensor_split, params.low_vram, memory_type, params.use_mmap, params.use_mlock,
-                params.vocab_only, params.progress_callback, params.progress_callback_user_data)) {
+                params.main_gpu, params.tensor_split, params.rope_freq_base, params.rope_freq_scale,params.low_vram,
+                memory_type, params.use_mmap, params.use_mlock, params.vocab_only, params.progress_callback,
+                params.progress_callback_user_data)) {
         delete model;
         fprintf(stderr, "%s: failed to load model\n", __func__);
         return nullptr;
@@ -2697,7 +2721,7 @@ struct llama_context * llama_new_context_with_model(
         return nullptr;
     }
 
-    llama_context * ctx = new llama_context(*model, model->vocab);
+    llama_context * ctx = new llama_context(*model);
 
     if (params.seed == LLAMA_DEFAULT_SEED) {
         params.seed = time(NULL);
@@ -2751,9 +2775,9 @@ struct llama_context * llama_new_context_with_model(
             ctx->embedding.resize(hparams.n_embd);
         }
 
-        ctx->buf_compute.resize(MEM_REQ_EVAL().at(ctx->model.type));
+        ctx->buf_compute.resize(MEM_REQ_EVAL(hparams.n_ctx).at(ctx->model.type));
 
-        ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0().at(ctx->model.type));
+        ctx->buf_scratch[0].resize(MEM_REQ_SCRATCH0(hparams.n_ctx).at(ctx->model.type));
         ctx->buf_scratch[1].resize(MEM_REQ_SCRATCH1().at(ctx->model.type));
     }
 
@@ -3535,13 +3559,13 @@ int llama_eval_export(struct llama_context * ctx, const char * fname) {
     return 0;
 }
 
-int llama_tokenize(
-        struct llama_context * ctx,
+int llama_tokenize_with_model(
+    const struct llama_model * model,
                   const char * text,
                  llama_token * tokens,
                          int   n_max_tokens,
                         bool   add_bos) {
-    auto res = llama_tokenize(ctx->vocab, text, add_bos);
+    auto res = llama_tokenize(model->vocab, text, add_bos);
 
     if (n_max_tokens < (int) res.size()) {
         fprintf(stderr, "%s: too many tokens\n", __func__);
@@ -3555,8 +3579,29 @@ int llama_tokenize(
     return res.size();
 }
 
+int llama_tokenize(
+        struct llama_context * ctx,
+                  const char * text,
+                 llama_token * tokens,
+                         int   n_max_tokens,
+                        bool   add_bos) {
+    return llama_tokenize_with_model(&ctx->model, text, tokens, n_max_tokens, add_bos);
+}
+
+int llama_n_vocab_from_model(const struct llama_model * model) {
+    return model->vocab.id_to_token.size();
+}
+
+int llama_n_ctx_from_model(const struct llama_model * model) {
+    return model->hparams.n_ctx;
+}
+
+int llama_n_embd_from_model(const struct llama_model * model) {
+    return model->hparams.n_embd;
+}
+
 int llama_n_vocab(const struct llama_context * ctx) {
-    return ctx->vocab.id_to_token.size();
+    return ctx->model.vocab.id_to_token.size();
 }
 
 int llama_n_ctx(const struct llama_context * ctx) {
@@ -3567,17 +3612,25 @@ int llama_n_embd(const struct llama_context * ctx) {
     return ctx->model.hparams.n_embd;
 }
 
+int llama_get_vocab_from_model(
+        const struct llama_model * model,
+        const char * * strings,
+        float  * scores,
+        int capacity) {
+    int n = std::min(capacity, (int) model->vocab.id_to_token.size());
+    for (int i = 0; i<n; ++i) {
+        strings[i] = model->vocab.id_to_token[i].tok.c_str();
+        scores[i]  = model->vocab.id_to_token[i].score;
+    }
+    return n;
+}
+
 int llama_get_vocab(
         const struct llama_context * ctx,
         const char * * strings,
         float  * scores,
         int capacity) {
-    int n = std::min(capacity, (int) ctx->vocab.id_to_token.size());
-    for (int i = 0; i<n; ++i) {
-        strings[i] = ctx->vocab.id_to_token[i].tok.c_str();
-        scores[i]  = ctx->vocab.id_to_token[i].score;
-    }
-    return n;
+    return llama_get_vocab_from_model(&ctx->model, strings, scores, capacity);
 }
 
 float * llama_get_logits(struct llama_context * ctx) {
@@ -3588,12 +3641,16 @@ float * llama_get_embeddings(struct llama_context * ctx) {
     return ctx->embedding.data();
 }
 
-const char * llama_token_to_str(const struct llama_context * ctx, llama_token token) {
-    if (token >= llama_n_vocab(ctx)) {
+const char * llama_token_to_str_with_model(const struct llama_model * model, llama_token token) {
+    if (token >= llama_n_vocab_from_model(model)) {
         return nullptr;
     }
 
-    return ctx->vocab.id_to_token[token].tok.c_str();
+    return model->vocab.id_to_token[token].tok.c_str();
+}
+
+const char * llama_token_to_str(const struct llama_context * ctx, llama_token token) {
+    return llama_token_to_str_with_model(&ctx->model, token);
 }
 
 llama_token llama_token_bos() {

llama.h

@@ -88,7 +88,13 @@ extern "C" {
         int32_t  n_batch;                      // prompt processing batch size
         int32_t  n_gpu_layers;                 // number of layers to store in VRAM
         int32_t  main_gpu;                     // the GPU that is used for scratch and small tensors
-        float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
+
+        const float * tensor_split; // how to split layers across multiple GPUs (size: LLAMA_MAX_DEVICES)
+
+        // ref: https://github.com/ggerganov/llama.cpp/pull/2054
+        float    rope_freq_base;  // RoPE base frequency
+        float    rope_freq_scale; // RoPE frequency scaling factor
+
         // called with a progress value between 0 and 1, pass NULL to disable
         llama_progress_callback progress_callback;
         // context pointer passed to the progress callback
@@ -148,6 +154,8 @@ extern "C" {
         int32_t n_eval;
     };
 
+    LLAMA_API int llama_max_devices();
+
     LLAMA_API struct llama_context_params llama_context_default_params();
     LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
 
@@ -270,10 +278,21 @@ extern "C" {
                              int   n_max_tokens,
                             bool   add_bos);
 
+    LLAMA_API int llama_tokenize_with_model(
+        const struct llama_model * model,
+                      const char * text,
+                     llama_token * tokens,
+                             int   n_max_tokens,
+                            bool   add_bos);
+
     LLAMA_API int llama_n_vocab(const struct llama_context * ctx);
     LLAMA_API int llama_n_ctx  (const struct llama_context * ctx);
     LLAMA_API int llama_n_embd (const struct llama_context * ctx);
 
+    LLAMA_API int llama_n_vocab_from_model(const struct llama_model * model);
+    LLAMA_API int llama_n_ctx_from_model  (const struct llama_model * model);
+    LLAMA_API int llama_n_embd_from_model (const struct llama_model * model);
+
     // Get the vocabulary as output parameters.
     // Returns number of results.
     LLAMA_API int llama_get_vocab(
@@ -282,6 +301,12 @@ extern "C" {
                                  float * scores,
                                    int   capacity);
 
+    LLAMA_API int llama_get_vocab_from_model(
+              const struct llama_model * model,
+                          const char * * strings,
+                                 float * scores,
+                                   int   capacity);
+
     // Token logits obtained from the last call to llama_eval()
     // The logits for the last token are stored in the last row
     // Can be mutated in order to change the probabilities of the next token
@@ -294,7 +319,13 @@ extern "C" {
     LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
 
     // Token Id -> String. Uses the vocabulary in the provided context
-    LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
+    LLAMA_API const char * llama_token_to_str(
+            const struct llama_context * ctx,
+                           llama_token   token);
+
+    LLAMA_API const char * llama_token_to_str_with_model(
+              const struct llama_model * model,
+                           llama_token   token);
 
     // Special tokens
     LLAMA_API llama_token llama_token_bos();  // beginning-of-sentence
@@ -313,13 +344,11 @@ extern "C" {
     /// @param candidates A vector of `llama_token_data` containing the candidate tokens, the logits must be directly extracted from the original generation context without being sorted.
     /// @params guidance_ctx A separate context from the same model. Other than a negative prompt at the beginning, it should have all generated and user input tokens copied from the main context.
     /// @params scale Guidance strength. 1.0f means no guidance. Higher values mean stronger guidance.
-    /// @params smooth_factor Smooth factor between guidance logits and original logits. 1.0f means only use guidance logits. 0.0f means only original logits.
     LLAMA_API void llama_sample_classifier_free_guidance(
               struct llama_context * ctx,
             llama_token_data_array * candidates,
               struct llama_context * guidance_ctx,
-                             float   scale,
-                             float   smooth_factor);
+                             float   scale);
 
     /// @details Sorts candidate tokens by their logits in descending order and calculate probabilities based on logits.
     LLAMA_API void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * candidates);

scripts/verify-checksum-models.py

@@ -1,3 +1,5 @@
+#!/bin/env python3
+
 import os
 import hashlib
 

tests/CMakeLists.txt

@@ -1,6 +1,7 @@
 function(llama_add_test source)
     get_filename_component(TEST_TARGET ${source} NAME_WE)
     add_executable(${TEST_TARGET} ${source})
+    install(TARGETS ${TEST_TARGET} RUNTIME)
     target_link_libraries(${TEST_TARGET} PRIVATE llama)
     add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${ARGN})
 endfunction()

tests/test-grad0.c

@@ -10,7 +10,9 @@
 #pragma warning(disable: 4244 4267) // possible loss of data
 #endif
 
+#if defined(__GNUC__)
 #pragma GCC diagnostic ignored "-Wdouble-promotion"
+#endif
 
 #define MAX_NARGS 3
 

tests/test-opt.c

@@ -7,7 +7,9 @@
 
 #define MAX_NARGS 2
 
+#if defined(__GNUC__)
 #pragma GCC diagnostic ignored "-Wdouble-promotion"
+#endif
 
 //
 // logging

tests/test-sampling.cpp

@@ -200,4 +200,6 @@ int main(void) {
     test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 5.0f, 5.0f);
 
     printf("OK\n");
+
+    return 0;
 }