Merge upstream changes, fix conflicts, adapt per-layer kv

This commit is contained in:
0cc4m 2023-12-10 18:16:57 +01:00
commit 2f5529e2dc
89 changed files with 9395 additions and 2417 deletions

View file

@ -13,6 +13,8 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then
./quantize "$@"
elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then
./main "$@"
elif [[ "$arg1" == '--finetune' || "$arg1" == '-f' ]]; then
./finetune "$@"
elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then
echo "Converting PTH to GGML..."
for i in `ls $1/$2/ggml-model-f16.bin*`; do
@ -34,6 +36,8 @@ else
echo " ex: --outtype f16 \"/models/7B/\" "
echo " --quantize (-q): Optimize with quantization process ggml"
echo " ex: \"/models/7B/ggml-model-f16.bin\" \"/models/7B/ggml-model-q4_0.bin\" 2"
echo " --finetune (-f): Run finetune command to create a lora finetune of the model"
echo " See documentation for finetune for command-line parameters"
echo " --all-in-one (-a): Execute --convert & --quantize"
echo " ex: \"/models/\" 7B"
echo " --server (-s): Run a model on the server"

View file

@ -143,6 +143,9 @@ jobs:
cd build
ctest --verbose
# TODO: build with LLAMA_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know
# how to debug it.
# ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124
macOS-latest-make:
runs-on: macos-latest
@ -160,14 +163,18 @@ jobs:
- name: Build
id: make_build
run: |
make -j $(sysctl -n hw.logicalcpu)
LLAMA_NO_METAL=1 make -j $(sysctl -n hw.logicalcpu)
- name: Test
id: make_test
run: |
make tests -j $(sysctl -n hw.logicalcpu)
make test -j $(sysctl -n hw.logicalcpu)
LLAMA_NO_METAL=1 make tests -j $(sysctl -n hw.logicalcpu)
LLAMA_NO_METAL=1 make test -j $(sysctl -n hw.logicalcpu)
# TODO: build with LLAMA_METAL=OFF because test-backend-ops fail on "Apple Paravirtual device" and I don't know
# how to debug it.
# ref: https://github.com/ggerganov/llama.cpp/actions/runs/7132125951/job/19422043567?pr=4359#step:5:6584
# would be great if we fix these
macOS-latest-cmake:
runs-on: macos-latest
@ -188,7 +195,7 @@ jobs:
sysctl -a
mkdir build
cd build
cmake ..
cmake -DLLAMA_METAL=OFF ..
cmake --build . --config Release -j $(sysctl -n hw.logicalcpu)
- name: Test
@ -498,6 +505,17 @@ jobs:
path: |
cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip
ios-xcode-build:
runs-on: macos-latest
steps:
- name: Checkout code
uses: actions/checkout@v3
- name: Build Xcode project
run: xcodebuild -project examples/llama.swiftui/llama.swiftui.xcodeproj -scheme llama.swiftui -sdk iphoneos CODE_SIGNING_REQUIRED=NO CODE_SIGN_IDENTITY= -destination 'generic/platform=iOS' build
# freeBSD-latest:
# runs-on: macos-12
# steps:

20
.github/workflows/python-lint.yml vendored Normal file
View file

@ -0,0 +1,20 @@
name: flake8 Lint
on: [push, pull_request]
jobs:
flake8-lint:
runs-on: ubuntu-latest
name: Lint
steps:
- name: Check out source repository
uses: actions/checkout@v3
- name: Set up Python environment
uses: actions/setup-python@v4
with:
python-version: "3.11"
- name: flake8 Lint
uses: py-actions/flake8@v2
with:
ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704"
exclude: "examples/*,examples/*/**,*/**/__init__.py"

28
.gitignore vendored
View file

@ -47,6 +47,7 @@ models-mnt
/libllama.so
/llama-bench
/llava-cli
/lookahead
/main
/metal
/perplexity
@ -64,6 +65,7 @@ models-mnt
/speculative
/parallel
/train-text-from-scratch
/tokenize
/vdot
/common/build-info.cpp
arm_neon.h
@ -86,15 +88,17 @@ poetry.lock
poetry.toml
# Test binaries
tests/test-grammar-parser
tests/test-llama-grammar
tests/test-double-float
tests/test-grad0
tests/test-opt
tests/test-quantize-fns
tests/test-quantize-perf
tests/test-sampling
tests/test-tokenizer-0-llama
tests/test-tokenizer-0-falcon
tests/test-tokenizer-1-llama
tests/test-tokenizer-1-bpe
/tests/test-grammar-parser
/tests/test-llama-grammar
/tests/test-double-float
/tests/test-grad0
/tests/test-opt
/tests/test-quantize-fns
/tests/test-quantize-perf
/tests/test-sampling
/tests/test-tokenizer-0-llama
/tests/test-tokenizer-0-falcon
/tests/test-tokenizer-1-llama
/tests/test-tokenizer-1-bpe
/tests/test-rope
/tests/test-backend-ops

View file

@ -43,6 +43,7 @@ else()
endif()
# general
option(BUILD_SHARED_LIBS "build shared libraries" OFF)
option(LLAMA_STATIC "llama: static link libraries" OFF)
option(LLAMA_NATIVE "llama: enable -march=native flag" ON)
option(LLAMA_LTO "llama: enable link time optimization" OFF)
@ -101,6 +102,9 @@ option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALO
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_SERVER "llama: build server example" ON)
# Required for relocatable CMake package
include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
#
# Compile flags
#
@ -113,6 +117,11 @@ set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads REQUIRED)
include(CheckCXXCompilerFlag)
# enable libstdc++ assertions for debug builds
if (CMAKE_SYSTEM_NAME MATCHES "Linux")
add_compile_definitions($<$<CONFIG:Debug>:_GLIBCXX_ASSERTIONS>)
endif()
if (NOT MSVC)
if (LLAMA_SANITIZE_THREAD)
add_compile_options(-fsanitize=thread)
@ -162,7 +171,7 @@ if (LLAMA_METAL)
#add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
# copy ggml-metal.metal to bin directory
configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
configure_file(ggml-metal.metal ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/ggml-metal.metal COPYONLY)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
${FOUNDATION_LIBRARY}

View file

@ -2,13 +2,14 @@
BUILD_TARGETS = \
main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \
speculative infill benchmark-matmult parallel finetune export-lora tests/test-c.o
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead tests/test-c.o
# Binaries only useful for tests
TEST_TARGETS = \
tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt \
tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama \
tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe
tests/test-tokenizer-0-falcon tests/test-tokenizer-1-llama tests/test-tokenizer-1-bpe tests/test-rope \
tests/test-backend-ops
# Code coverage output files
COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
@ -30,7 +31,7 @@ ifeq '' '$(findstring clang,$(shell $(CC) --version))'
CC_VER := $(shell $(CC) -dumpfullversion -dumpversion | awk -F. '{ printf("%02d%02d%02d", $$1, $$2, $$3) }')
else
CC_IS_CLANG=1
ifeq '' '$(findstring Apple LLVM,$(shell $(CC) --version))'
ifeq '' '$(findstring Apple,$(shell $(CC) --version))'
CC_IS_LLVM_CLANG=1
else
CC_IS_APPLE_CLANG=1
@ -174,6 +175,10 @@ ifdef LLAMA_DEBUG
MK_CFLAGS += -O0 -g
MK_CXXFLAGS += -O0 -g
MK_LDFLAGS += -g
ifeq ($(UNAME_S),Linux)
MK_CXXFLAGS += -Wp,-D_GLIBCXX_ASSERTIONS
endif
else
MK_CPPFLAGS += -DNDEBUG
endif
@ -609,6 +614,9 @@ infill: examples/infill/infill.cpp ggml.o llama.o $(C
simple: examples/simple/simple.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tokenize: examples/tokenize/tokenize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
batched: examples/batched/batched.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
@ -660,7 +668,7 @@ beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS)
finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
export-lora: examples/export-lora/export-lora.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
@ -669,6 +677,9 @@ speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS)
parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
ifdef LLAMA_METAL
metal: examples/metal/metal.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
@ -710,28 +721,28 @@ vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS)
q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS)
tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-double-float: tests/test-double-float.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-grad0: tests/test-grad0.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-opt: tests/test-opt.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
@ -746,5 +757,11 @@ tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMM
tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
tests/test-c.o: tests/test-c.c llama.h
$(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@
tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

View file

@ -2,33 +2,14 @@
import PackageDescription
#if arch(arm) || arch(arm64)
let platforms: [SupportedPlatform]? = [
let package = Package(
name: "llama",
platforms: [
.macOS(.v12),
.iOS(.v14),
.watchOS(.v4),
.tvOS(.v14)
]
let exclude: [String] = []
let resources: [Resource] = [
.process("ggml-metal.metal")
]
let additionalSources: [String] = ["ggml-metal.m"]
let additionalSettings: [CSetting] = [
.unsafeFlags(["-fno-objc-arc"]),
.define("GGML_USE_METAL")
]
#else
let platforms: [SupportedPlatform]? = nil
let exclude: [String] = ["ggml-metal.metal"]
let resources: [Resource] = []
let additionalSources: [String] = []
let additionalSettings: [CSetting] = []
#endif
let package = Package(
name: "llama",
platforms: platforms,
],
products: [
.library(name: "llama", targets: ["llama"]),
],
@ -36,25 +17,30 @@ let package = Package(
.target(
name: "llama",
path: ".",
exclude: exclude,
exclude: [],
sources: [
"ggml.c",
"llama.cpp",
"ggml-alloc.c",
"ggml-backend.c",
"ggml-quants.c",
] + additionalSources,
resources: resources,
"ggml-metal.m",
],
resources: [
.process("ggml-metal.metal")
],
publicHeadersPath: "spm-headers",
cSettings: [
.unsafeFlags(["-Wno-shorten-64-to-32", "-O3", "-DNDEBUG"]),
.define("GGML_USE_ACCELERATE")
.define("GGML_USE_ACCELERATE"),
.unsafeFlags(["-fno-objc-arc"]),
.define("GGML_USE_METAL"),
// NOTE: NEW_LAPACK will required iOS version 16.4+
// We should consider add this in the future when we drop support for iOS 14
// (ref: ref: https://developer.apple.com/documentation/accelerate/1513264-cblas_sgemm?language=objc)
// .define("ACCELERATE_NEW_LAPACK"),
// .define("ACCELERATE_LAPACK_ILP64")
] + additionalSettings,
],
linkerSettings: [
.linkedFramework("Accelerate")
]

View file

@ -10,7 +10,10 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
### Hot topics
- *No hot topics atm. Open to suggestions about what is hot today*
- **llama.h API change for handling KV cache offloading and data type: https://github.com/ggerganov/llama.cpp/pull/4309**
- Using `llama.cpp` with AWS instances: https://github.com/ggerganov/llama.cpp/discussions/4225
- Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216
- Collecting Apple Silicon performance stats: https://github.com/ggerganov/llama.cpp/discussions/4167
----
@ -114,6 +117,8 @@ as the main playground for developing new features for the [ggml](https://github
- [nat/openplayground](https://github.com/nat/openplayground)
- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [withcatai/catai](https://github.com/withcatai/catai)
- [semperai/amica](https://github.com/semperai/amica)
- [psugihara/FreeChat](https://github.com/psugihara/FreeChat)
---
@ -320,7 +325,7 @@ mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128
### BLAS Build
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). BLAS doesn't affect the normal generation performance. There are currently three different implementations of it:
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use:
- #### Accelerate Framework:
@ -410,19 +415,28 @@ Building the program with BLAS support may lead to some performance improvements
This provides BLAS acceleration on HIP-supported AMD GPUs.
Make sure to have ROCm installed.
You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/en/latest/deploy/linux/quick_start.html).
Windows support is coming soon...
- Using `make`:
```bash
make LLAMA_HIPBLAS=1
```
- Using `CMake`:
- Using `CMake` for Linux:
```bash
mkdir build
cd build
CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ cmake .. -DLLAMA_HIPBLAS=ON
cmake --build .
```
- Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS):
```bash
set PATH=%HIP_PATH%\bin;%PATH%
mkdir build
cd build
cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ ..
cmake --build .
```
Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 or 11.0.0 on RDNA3.
@ -883,7 +897,7 @@ Additionally, there the following images, similar to the above:
- `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
- `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`)
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the Gitlab Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now).
#### Usage

View file

@ -11,8 +11,13 @@ if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/../.git")
if(NOT IS_DIRECTORY "${GIT_DIR}")
file(READ ${GIT_DIR} REAL_GIT_DIR_LINK)
string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK})
string(FIND "${REAL_GIT_DIR}" "/" SLASH_POS)
if (SLASH_POS EQUAL 0)
set(GIT_DIR "${REAL_GIT_DIR}")
else()
set(GIT_DIR "${CMAKE_CURRENT_SOURCE_DIR}/../${REAL_GIT_DIR}")
endif()
endif()
set(GIT_INDEX "${GIT_DIR}/index")
else()
@ -26,7 +31,7 @@ add_custom_command(
COMMENT "Generating build details from Git"
COMMAND ${CMAKE_COMMAND} -DMSVC=${MSVC} -DCMAKE_C_COMPILER_VERSION=${CMAKE_C_COMPILER_VERSION}
-DCMAKE_C_COMPILER_ID=${CMAKE_C_COMPILER_ID} -DCMAKE_VS_PLATFORM_NAME=${CMAKE_VS_PLATFORM_NAME}
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/build-info.cmake"
-DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -P "${CMAKE_CURRENT_SOURCE_DIR}/../scripts/gen-build-info-cpp.cmake"
WORKING_DIRECTORY "${CMAKE_CURRENT_SOURCE_DIR}/.."
DEPENDS "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in" ${GIT_INDEX}
VERBATIM

View file

@ -12,6 +12,7 @@
#include <regex>
#include <sstream>
#include <string>
#include <unordered_map>
#include <unordered_set>
#include <vector>
#include <cinttypes>
@ -277,8 +278,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
break;
}
params.yarn_beta_slow = std::stof(argv[i]);
} else if (arg == "--memory-f32") {
params.memory_f16 = false;
} else if (arg == "--samplers") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.samplers_sequence = parse_samplers_input(argv[i]);
} else if (arg == "--sampling-seq") {
if (++i >= argc) {
invalid_param = true;
break;
}
sparams.samplers_sequence = argv[i];
} else if (arg == "--top-p") {
if (++i >= argc) {
invalid_param = true;
@ -491,8 +502,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
params.interactive_first = true;
} else if (arg == "-ins" || arg == "--instruct") {
params.instruct = true;
} else if (arg == "-cml" || arg == "--chatml") {
params.chatml = true;
} else if (arg == "--infill") {
params.infill = true;
} else if (arg == "-dkvc" || arg == "--dump-kv-cache") {
params.dump_kv_cache = true;
} else if (arg == "-nkvo" || arg == "--no-kv-offload") {
params.no_kv_offload = true;
} else if (arg == "-ctk" || arg == "--cache-type-k") {
params.cache_type_k = argv[++i];
} else if (arg == "-ctv" || arg == "--cache-type-v") {
params.cache_type_v = argv[++i];
} else if (arg == "--multiline-input") {
params.multiline_input = true;
} else if (arg == "--simple-io") {
@ -673,6 +694,47 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
std::istreambuf_iterator<char>(),
std::back_inserter(sparams.grammar)
);
} else if (arg == "--override-kv") {
if (++i >= argc) {
invalid_param = true;
break;
}
char * sep = strchr(argv[i], '=');
if (sep == nullptr || sep - argv[i] >= 128) {
fprintf(stderr, "error: Malformed KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
struct llama_model_kv_override kvo;
std::strncpy(kvo.key, argv[i], sep - argv[i]);
kvo.key[sep - argv[i]] = 0;
sep++;
if (strncmp(sep, "int:", 4) == 0) {
sep += 4;
kvo.tag = LLAMA_KV_OVERRIDE_INT;
kvo.int_value = std::atol(sep);
} else if (strncmp(sep, "float:", 6) == 0) {
sep += 6;
kvo.tag = LLAMA_KV_OVERRIDE_FLOAT;
kvo.float_value = std::atof(sep);
} else if (strncmp(sep, "bool:", 5) == 0) {
sep += 5;
kvo.tag = LLAMA_KV_OVERRIDE_BOOL;
if (std::strcmp(sep, "true") == 0) {
kvo.bool_value = true;
} else if (std::strcmp(sep, "false") == 0) {
kvo.bool_value = false;
} else {
fprintf(stderr, "error: Invalid boolean value for KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
} else {
fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]);
invalid_param = true;
break;
}
params.kv_overrides.push_back(kvo);
#ifndef LOG_DISABLE_LOGS
// Parse args for logging parameters
} else if ( log_param_single_parse( argv[i] ) ) {
@ -716,6 +778,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) {
}
}
if (!params.kv_overrides.empty()) {
params.kv_overrides.emplace_back(llama_model_kv_override());
params.kv_overrides.back().key[0] = 0;
}
return true;
}
@ -730,6 +797,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" -i, --interactive run in interactive mode\n");
printf(" --interactive-first run in interactive mode and wait for input right away\n");
printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n");
printf(" -cml, --chatml run in chatml mode (use with ChatML-compatible models)\n");
printf(" --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n");
printf(" -r PROMPT, --reverse-prompt PROMPT\n");
printf(" halt generation at PROMPT, return control in interactive mode\n");
@ -755,6 +823,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict);
printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx);
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
printf(" --samplers samplers that will be used for generation in the order, separated by \';\', for example: \"top_k;tfs;typical;top_p;min_p;temp\"\n");
printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sparams.samplers_sequence.c_str());
printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k);
printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p);
printf(" --min-p N min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p);
@ -792,8 +862,6 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast);
printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n");
printf(" --no-penalize-nl do not penalize newline token\n");
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
printf(" not recommended: doubles context memory required and no measurable increase in quality\n");
printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp);
printf(" --logits-all return logits for all tokens in the batch (default: disabled)\n");
printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n");
@ -832,6 +900,14 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
#endif // GGML_USE_CUBLAS
#endif
printf(" --verbose-prompt print prompt before generation\n");
printf(" -dkvc, --dump-kv-cache\n");
printf(" verbose print of the KV cache\n");
printf(" -nkvo, --no-kv-offload\n");
printf(" disable KV offload\n");
printf(" -ctk TYPE, --cache-type-k TYPE\n");
printf(" KV cache data type for K (default: %s)\n", params.cache_type_k.c_str());
printf(" -ctv TYPE, --cache-type-v TYPE\n");
printf(" KV cache data type for V (default: %s)\n", params.cache_type_v.c_str());
printf(" --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n");
printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n");
printf(" --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n");
@ -842,6 +918,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
printf(" draft model for speculative decoding (default: %s)\n", params.model.c_str());
printf(" -ld LOGDIR, --logdir LOGDIR\n");
printf(" path under which to save YAML logs (no logging if unset)\n");
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" advanced option to override model metadata by key. may be specified multiple times.\n");
printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n");
printf("\n");
#ifndef LOG_DISABLE_LOGS
log_print_usage();
@ -878,6 +957,48 @@ std::string gpt_random_prompt(std::mt19937 & rng) {
GGML_UNREACHABLE();
}
//
// String parsing
//
std::string parse_samplers_input(std::string input) {
std::string output = "";
// since samplers names are written multiple ways
// make it ready for both system names and input names
std::unordered_map<std::string, char> samplers_symbols {
{"top_k", 'k'},
{"top-k", 'k'},
{"top_p", 'p'},
{"top-p", 'p'},
{"nucleus", 'p'},
{"typical_p", 'y'},
{"typical-p", 'y'},
{"typical", 'y'},
{"min_p", 'm'},
{"min-p", 'm'},
{"tfs_z", 'f'},
{"tfs-z", 'f'},
{"tfs", 'f'},
{"temp", 't'},
{"temperature",'t'}
};
// expected format example: "temp;top_k;tfs_z;typical_p;top_p;min_p"
size_t separator = input.find(';');
while (separator != input.npos) {
std::string name = input.substr(0,separator);
input = input.substr(separator+1);
separator = input.find(';');
if (samplers_symbols.find(name) != samplers_symbols.end()) {
output += samplers_symbols[name];
}
}
if (samplers_symbols.find(input) != samplers_symbols.end()) {
output += samplers_symbols[input];
}
return output;
}
//
// Model utils
//
@ -892,10 +1013,39 @@ struct llama_model_params llama_model_params_from_gpt_params(const gpt_params &
mparams.tensor_split = params.tensor_split;
mparams.use_mmap = params.use_mmap;
mparams.use_mlock = params.use_mlock;
if (params.kv_overrides.empty()) {
mparams.kv_overrides = NULL;
} else {
GGML_ASSERT(params.kv_overrides.back().key[0] == 0 && "KV overrides not terminated with empty key");
mparams.kv_overrides = params.kv_overrides.data();
}
return mparams;
}
static ggml_type kv_cache_type_from_str(const std::string & s) {
if (s == "f16") {
return GGML_TYPE_F16;
}
if (s == "q8_0") {
return GGML_TYPE_Q8_0;
}
if (s == "q4_0") {
return GGML_TYPE_Q4_0;
}
if (s == "q4_1") {
return GGML_TYPE_Q4_1;
}
if (s == "q5_0") {
return GGML_TYPE_Q5_0;
}
if (s == "q5_1") {
return GGML_TYPE_Q5_1;
}
throw std::runtime_error("Invalid cache type: " + s);
}
struct llama_context_params llama_context_params_from_gpt_params(const gpt_params & params) {
auto cparams = llama_context_default_params();
@ -905,7 +1055,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch;
cparams.mul_mat_q = params.mul_mat_q;
cparams.seed = params.seed;
cparams.f16_kv = params.memory_f16;
cparams.logits_all = params.logits_all;
cparams.embedding = params.embedding;
cparams.rope_scaling_type = params.rope_scaling_type;
@ -916,6 +1065,10 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
cparams.yarn_beta_fast = params.yarn_beta_fast;
cparams.yarn_beta_slow = params.yarn_beta_slow;
cparams.yarn_orig_ctx = params.yarn_orig_ctx;
cparams.offload_kqv = !params.no_kv_offload;
cparams.type_k = kv_cache_type_from_str(params.cache_type_k);
cparams.type_v = kv_cache_type_from_str(params.cache_type_v);
return cparams;
}
@ -931,7 +1084,7 @@ void llama_batch_add(
const std::vector<llama_seq_id> & seq_ids,
bool logits) {
batch.token [batch.n_tokens] = id;
batch.pos [batch.n_tokens] = pos,
batch.pos [batch.n_tokens] = pos;
batch.n_seq_id[batch.n_tokens] = seq_ids.size();
for (size_t i = 0; i < seq_ids.size(); ++i) {
batch.seq_id[batch.n_tokens][i] = seq_ids[i];
@ -1328,7 +1481,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
}
fprintf(stream, "lora_base: %s\n", params.lora_base.c_str());
fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu);
fprintf(stream, "memory_f32: %s # default: false\n", !params.memory_f16 ? "true" : "false");
fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat);
fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau);
fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta);
@ -1383,3 +1535,77 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p);
fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false");
}
//
// KV cache utils
//
void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d",
view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
llama_kv_cache_view_cell * c_curr = view.cells;
llama_seq_id * cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
if (i % row_size == 0) {
printf("\n%5d: ", i);
}
int seq_count = 0;
for (int j = 0; j < view.n_max_seq; j++) {
if (cs_curr[j] >= 0) { seq_count++; }
}
putchar(slot_chars[std::min(sizeof(slot_chars) - 2, size_t(seq_count))]);
}
printf("\n=== Done dumping\n");
}
void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) {
static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n",
view.n_cells, view.n_max_seq, view.used_cells, view.token_count, view.max_contiguous, view.max_contiguous_idx);
std::unordered_map<llama_seq_id, size_t> seqs;
llama_kv_cache_view_cell * c_curr = view.cells;
llama_seq_id * cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
for (int j = 0; j < view.n_max_seq; j++) {
if (cs_curr[j] < 0) { continue; }
if (seqs.find(cs_curr[j]) == seqs.end()) {
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
seqs[cs_curr[j]] = seqs.size();
}
}
if (seqs.size() + 1 >= sizeof(slot_chars)) { break; }
}
printf("=== Sequence legend: ");
for (const auto & it : seqs) {
printf("%zu=%d, ", it.second, it.first);
}
printf("'+'=other sequence ids");
c_curr = view.cells;
cs_curr = view.cells_sequences;
for (int i = 0; i < view.n_cells; i++, c_curr++, cs_curr += view.n_max_seq) {
if (i % row_size == 0) {
printf("\n%5d: ", i);
}
for (int j = 0; j < view.n_max_seq; j++) {
if (cs_curr[j] >= 0) {
const auto & it = seqs.find(cs_curr[j]);
putchar(it != seqs.end() ? int(slot_chars[it->second]) : '+');
} else {
putchar('.');
}
}
putchar(' ');
}
printf("\n=== Done dumping\n");
}

View file

@ -86,6 +86,8 @@ struct gpt_params {
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
std::string logdir = ""; // directory in which to save YAML log files
std::vector<llama_model_kv_override> kv_overrides;
// TODO: avoid tuple, use struct
std::vector<std::tuple<std::string, float>> lora_adapter; // lora adapter path with user defined scale
std::string lora_base = ""; // base model path for the lora adapter
@ -98,10 +100,10 @@ struct gpt_params {
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
bool mul_mat_q = true; // if true, use mul_mat_q kernels instead of cuBLAS
bool memory_f16 = true; // use f16 instead of f32 for memory kv
bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs
bool interactive = false; // interactive mode
bool chatml = false; // chatml mode (used for models trained on chatml syntax)
bool prompt_cache_all = false; // save user input and generations to prompt cache
bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it
@ -121,6 +123,11 @@ struct gpt_params {
bool numa = false; // attempt optimizations that help on some NUMA systems
bool verbose_prompt = false; // print prompt tokens before generation
bool infill = false; // use infill mode
bool dump_kv_cache = false; // dump the KV cache contents for debugging purposes
bool no_kv_offload = false; // disable KV offloading
std::string cache_type_k = "f16"; // KV cache data type for the K
std::string cache_type_v = "f16"; // KV cache data type for the V
// multimodal models (see examples/llava)
std::string mmproj = ""; // path to multimodal projector
@ -139,6 +146,12 @@ std::string gpt_random_prompt(std::mt19937 & rng);
void process_escapes(std::string& input);
//
// String parsing
//
std::string parse_samplers_input(std::string input);
//
// Model utils
//
@ -217,3 +230,13 @@ std::string get_sortable_timestamp();
void dump_non_result_info_yaml(
FILE * stream, const gpt_params & params, const llama_context * lctx,
const std::string & timestamp, const std::vector<int> & prompt_tokens, const char * model_desc);
//
// KV cache utils
//
// Dump the KV cache view with the number of sequences per cell.
void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80);
// Dump the KV cache view showing individual sequences in each cell (long output).
void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40);

View file

@ -190,7 +190,7 @@ namespace grammar_parser {
pos = parse_space(pos + 1, is_nested);
} else if (*pos == '*' || *pos == '+' || *pos == '?') { // repetition operator
if (last_sym_start == out_elements.size()) {
throw std::runtime_error(std::string("expecting preceeding item to */+/? at ") + pos);
throw std::runtime_error(std::string("expecting preceding item to */+/? at ") + pos);
}
// apply transformation to previous symbol (last_sym_start to end) according to

View file

@ -99,6 +99,56 @@ std::string llama_sampling_print(const llama_sampling_params & params) {
return std::string(result);
}
std::string llama_sampling_order_print(const llama_sampling_params & params) {
std::string result = "CFG -> Penalties ";
if (params.mirostat == 0) {
for (auto s : params.samplers_sequence) {
switch (s) {
case 'k': result += "-> top_k "; break;
case 'f': result += "-> tfs_z "; break;
case 'y': result += "-> typical_p "; break;
case 'p': result += "-> top_p "; break;
case 'm': result += "-> min_p "; break;
case 't': result += "-> temp "; break;
default : break;
}
}
} else {
result += "-> mirostat ";
}
return result;
}
// no reasons to expose this function in header
static void sampler_queue(
struct llama_context * ctx_main,
const llama_sampling_params & params,
llama_token_data_array & cur_p,
size_t & min_keep) {
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;
const float top_p = params.top_p;
const float min_p = params.min_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const std::string & samplers_sequence = params.samplers_sequence;
for (auto s : samplers_sequence) {
switch (s){
case 'k': llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep); break;
case 'f': llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep); break;
case 'y': llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break;
case 'p': llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break;
case 'm': llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break;
case 't': llama_sample_temp (ctx_main, &cur_p, temp); break;
default : break;
}
}
}
llama_token llama_sampling_sample(
struct llama_sampling_context * ctx_sampling,
struct llama_context * ctx_main,
@ -109,11 +159,6 @@ llama_token llama_sampling_sample(
const int n_vocab = llama_n_vocab(llama_get_model(ctx_main));
const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k;
const float top_p = params.top_p;
const float min_p = params.min_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
const int32_t penalty_last_n = params.penalty_last_n < 0 ? params.n_prev : params.penalty_last_n;
const float penalty_repeat = params.penalty_repeat;
const float penalty_freq = params.penalty_freq;
@ -188,12 +233,7 @@ llama_token llama_sampling_sample(
// temperature sampling
size_t min_keep = std::max(1, params.n_probs);
llama_sample_top_k (ctx_main, &cur_p, top_k, min_keep);
llama_sample_tail_free(ctx_main, &cur_p, tfs_z, min_keep);
llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep);
llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep);
llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep);
llama_sample_temp (ctx_main, &cur_p, temp);
sampler_queue(ctx_main, params, cur_p, min_keep);
id = llama_sample_token(ctx_main, &cur_p);

View file

@ -26,6 +26,7 @@ typedef struct llama_sampling_params {
float mirostat_tau = 5.00f; // target entropy
float mirostat_eta = 0.10f; // learning rate
bool penalize_nl = true; // consider newlines as a repeatable token
std::string samplers_sequence = "kfypmt"; // top_k, tail_free, typical_p, top_p, min_p, temp
std::string grammar; // optional BNF-like grammar to constrain sampling
@ -80,6 +81,9 @@ std::string llama_sampling_prev_str(llama_sampling_context * ctx_sampling, llama
// Print sampling parameters into a string
std::string llama_sampling_print(const llama_sampling_params & params);
// Print sampling order into a string
std::string llama_sampling_order_print(const llama_sampling_params & params);
// this is a common sampling function used across the examples for convenience
// it can serve as a starting point for implementing your own sampling function
// Note: When using multiple sequences, it is the caller's responsibility to call

View file

@ -1,315 +0,0 @@
#!/usr/bin/env python3
# HF baichuan --> gguf conversion
from __future__ import annotations
import argparse
import json
import os
import sys
from pathlib import Path
from typing import TYPE_CHECKING, Any
import numpy as np
import torch
from sentencepiece import SentencePieceProcessor # type: ignore[import]
if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
if TYPE_CHECKING:
from typing import TypeAlias
NDArray: TypeAlias = 'np.ndarray[Any, Any]'
# reverse HF permute back to original pth layout
def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray:
if n_kv_head is not None and n_head != n_kv_head:
n_head //= n_kv_head
return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:])
.swapaxes(1, 2)
.reshape(weights.shape))
def reverse_hf_permute_part(weights: NDArray, n_part: int, n_head: int, n_head_kv: int| None = None) -> NDArray:
r = weights.shape[0] // 3
return (reverse_hf_permute(weights[r * n_part : r * n_part + r, ...], n_head, n_head_kv))
def reverse_hf_part(weights: NDArray, n_part: int) -> NDArray:
r = weights.shape[0] // 3
return weights[r * n_part : r * n_part + r, ...]
def count_model_parts(dir_model: str) -> int:
num_parts = 0
for filename in os.listdir(dir_model):
if filename.startswith("pytorch_model-"):
num_parts += 1
if num_parts > 0:
print("gguf: found " + str(num_parts) + " model parts")
return num_parts
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Convert a HuggingFace LLaMA model to a GGML compatible file")
parser.add_argument(
"--vocab-only", action="store_true",
help="extract only the vocab",
)
parser.add_argument(
"--outfile", type=Path,
help="path to write to; default: based on input",
)
parser.add_argument(
"model", type=Path,
help="directory containing model file, or model file itself (*.bin)",
)
parser.add_argument(
"ftype", type=int, choices=[0, 1], default=1, nargs='?',
help="output format - use 0 for float32, 1 for float16",
)
parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine")
return parser.parse_args()
args = parse_args()
dir_model = args.model
ftype = args.ftype
if not dir_model.is_dir():
print(f'Error: {args.model} is not a directory', file = sys.stderr)
sys.exit(1)
endianess = gguf.GGUFEndian.LITTLE
if args.bigendian:
endianess = gguf.GGUFEndian.BIG
endianess_str = "Big Endian" if args.bigendian else "Little Endian"
print(f"gguf: Conversion Endianess {endianess}")
# possible tensor data types
# ftype == 0 -> float32
# ftype == 1 -> float16
# map from ftype to string
ftype_str = ["f32", "f16"]
if args.outfile is not None:
fname_out = args.outfile
else:
# output in the same directory as the model by default
fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf'
print("gguf: loading model "+dir_model.name)
with open(dir_model / "config.json", "r", encoding="utf-8") as f:
hparams = json.load(f)
print("hello print: ",hparams["architectures"][0])
if hparams["architectures"][0] != "BaichuanForCausalLM" and hparams["architectures"][0] != "BaiChuanForCausalLM":
print("Model architecture not supported: " + hparams["architectures"][0])
sys.exit()
# get number of model parts
num_parts = count_model_parts(dir_model)
print(f"num_parts:{num_parts}\n")
ARCH=gguf.MODEL_ARCH.BAICHUAN
gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH], endianess=endianess)
print("gguf: get model metadata")
block_count = hparams["num_hidden_layers"]
head_count = hparams["num_attention_heads"]
if "num_key_value_heads" in hparams:
head_count_kv = hparams["num_key_value_heads"]
else:
head_count_kv = head_count
if "_name_or_path" in hparams:
hf_repo = hparams["_name_or_path"]
else:
hf_repo = ""
if "max_sequence_length" in hparams:
ctx_length = hparams["max_sequence_length"]
elif "max_position_embeddings" in hparams:
ctx_length = hparams["max_position_embeddings"]
elif "model_max_length" in hparams:
ctx_length = hparams["model_max_length"]
else:
print("gguf: can not find ctx length parameter.")
sys.exit()
gguf_writer.add_name(dir_model.name)
gguf_writer.add_source_hf_repo(hf_repo)
gguf_writer.add_tensor_data_layout("Meta AI original pth")
gguf_writer.add_context_length(ctx_length)
gguf_writer.add_embedding_length(hparams["hidden_size"])
gguf_writer.add_block_count(block_count)
gguf_writer.add_feed_forward_length(hparams["intermediate_size"])
gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"])
gguf_writer.add_head_count(head_count)
gguf_writer.add_head_count_kv(head_count_kv)
gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"])
if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]:
if "type" in hparams["rope_scaling"]:
if hparams["rope_scaling"]["type"] == "linear":
gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR)
gguf_writer.add_rope_scaling_factor(hparams["rope_scaling"]["factor"])
# TOKENIZATION
print("gguf: get tokenizer metadata")
tokens: list[bytes] = []
scores: list[float] = []
toktypes: list[int] = []
tokenizer_model_file = dir_model / 'tokenizer.model'
if not tokenizer_model_file.is_file():
print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr)
sys.exit(1)
# vocab type sentencepiece
print("gguf: get sentencepiece tokenizer vocab, scores and token types")
tokenizer = SentencePieceProcessor(str(tokenizer_model_file))
vocab_size = hparams.get('vocab_size')
if vocab_size is None:
vocab_size = tokenizer.vocab_size()
for i in range(vocab_size):
text: bytes
score: float
piece = tokenizer.id_to_piece(i)
text = piece.encode("utf-8")
score = tokenizer.get_score(i)
toktype = 1 # defualt to normal token type
if tokenizer.is_unknown(i):
toktype = 2
if tokenizer.is_control(i):
toktype = 3
# toktype = 4 is user-defined = tokens from added_tokens.json
if tokenizer.is_unused(i):
toktype = 5
if tokenizer.is_byte(i):
toktype = 6
tokens.append(text)
scores.append(score)
toktypes.append(toktype)
added_tokens_file = dir_model / 'added_tokens.json'
if added_tokens_file.is_file():
with open(added_tokens_file, "r", encoding="utf-8") as f:
addtokens_json = json.load(f)
print("gguf: get added tokens")
for key in addtokens_json:
tokens.append( key.encode("utf-8") )
scores.append(-1000.0)
toktypes.append(4) # user-defined token type
gguf_writer.add_tokenizer_model("llama")
gguf_writer.add_token_list(tokens)
gguf_writer.add_token_scores(scores)
gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, n_vocab = len(tokens))
special_vocab.add_to_gguf(gguf_writer)
# TENSORS
tensor_map = gguf.get_tensor_name_map(ARCH,block_count)
# tensor info
print("gguf: get tensor metadata")
if num_parts == 0:
part_names = iter(("pytorch_model.bin",))
else:
part_names = (
f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1)
)
for part_name in part_names:
if args.vocab_only:
break
print("gguf: loading model part '" + part_name + "'")
model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu")
tmp=model_part
for i in range(block_count):
if f"model.layers.{i}.self_attn.W_pack.weight" in model_part:
print(f"Unpacking and permuting layer {i}")
tmp[f"model.layers.{i}.self_attn.q_proj.weight"]=reverse_hf_permute_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],0,head_count,head_count)
tmp[f"model.layers.{i}.self_attn.k_proj.weight"]=reverse_hf_permute_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],1,head_count,head_count_kv)
tmp[f"model.layers.{i}.self_attn.v_proj.weight"]=reverse_hf_part(model_part[f"model.layers.{i}.self_attn.W_pack.weight"],2)
del tmp[f"model.layers.{i}.self_attn.W_pack.weight"]
for name in model_part.keys():
data = model_part[name]
# we don't need these
if name.endswith(".rotary_emb.inv_freq"):
continue
old_dtype = data.dtype
# convert any unsupported data types to float32
if data.dtype != torch.float16 and data.dtype != torch.float32:
data = data.to(torch.float32)
data = data.squeeze().numpy()
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
if new_name is None:
print("Can not map tensor '" + name + "'")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(name + " -> " + new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype))
gguf_writer.add_tensor(new_name, data)
print("gguf: write header")
gguf_writer.write_header_to_file()
print("gguf: write metadata")
gguf_writer.write_kv_data_to_file()
if not args.vocab_only:
print("gguf: write tensors")
gguf_writer.write_tensors_to_file()
gguf_writer.close()
print(f"gguf: model successfully exported to '{fname_out}'")
print("")

View file

@ -10,7 +10,7 @@ import re
import sys
from enum import IntEnum
from pathlib import Path
from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast
from typing import TYPE_CHECKING, Any, ContextManager, Iterator, cast, Optional
import numpy as np
import torch
@ -59,7 +59,7 @@ class Model:
from safetensors import safe_open
ctx = cast(ContextManager[Any], safe_open(self.dir_model / part_name, framework="pt", device="cpu"))
else:
ctx = contextlib.nullcontext(torch.load(self.dir_model / part_name, map_location="cpu"))
ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True))
with ctx as model_part:
for name in model_part.keys():
@ -168,6 +168,8 @@ class Model:
return PersimmonModel
if model_architecture in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
return StableLMModel
if model_architecture == "QWenLMHeadModel":
return QwenModel
return Model
def _is_model_safetensors(self) -> bool:
@ -203,6 +205,8 @@ class Model:
return gguf.MODEL_ARCH.PERSIMMON
if arch in ("StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM"):
return gguf.MODEL_ARCH.STABLELM
if arch == "QWenLMHeadModel":
return gguf.MODEL_ARCH.QWEN
raise NotImplementedError(f'Architecture "{arch}" not supported!')
@ -832,8 +836,134 @@ class StableLMModel(Model):
self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True)
self.gguf_writer.add_layer_norm_eps(1e-5)
class QwenModel(Model):
@staticmethod
def token_bytes_to_string(b):
from transformers.models.gpt2.tokenization_gpt2 import bytes_to_unicode
byte_encoder = bytes_to_unicode()
return ''.join([byte_encoder[ord(char)] for char in b.decode('latin-1')])
@staticmethod
def bpe(mergeable_ranks: dict[bytes, int], token: bytes, max_rank: Optional[int] = None) -> list[bytes]:
parts = [bytes([b]) for b in token]
while True:
min_idx = None
min_rank = None
for i, pair in enumerate(zip(parts[:-1], parts[1:])):
rank = mergeable_ranks.get(pair[0] + pair[1])
if rank is not None and (min_rank is None or rank < min_rank):
min_idx = i
min_rank = rank
if min_rank is None or (max_rank is not None and min_rank >= max_rank):
break
assert min_idx is not None
parts = parts[:min_idx] + [parts[min_idx] + parts[min_idx + 1]] + parts[min_idx + 2:]
return parts
def set_vocab(self):
dir_model = self.dir_model
hparams = self.hparams
tokens: list[bytearray] = []
toktypes: list[int] = []
from transformers import AutoTokenizer # type: ignore[attr-defined]
tokenizer = AutoTokenizer.from_pretrained(dir_model, trust_remote_code=True)
vocab_size = hparams["vocab_size"]
assert max(tokenizer.get_vocab().values()) < vocab_size
merges = []
vocab = {}
mergeable_ranks = tokenizer.mergeable_ranks
for token, rank in mergeable_ranks.items():
vocab[self.token_bytes_to_string(token)] = rank
if len(token) == 1:
continue
merged = QwenModel.bpe(mergeable_ranks, token, max_rank=rank)
assert len(merged) == 2
merges.append(' '.join(map(self.token_bytes_to_string, merged)))
reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in vocab.items()}
added_vocab = tokenizer.special_tokens
for i in range(vocab_size):
if i not in reverse_vocab:
pad_token = f"[PAD{i}]".encode("utf-8")
tokens.append(bytearray(pad_token))
toktypes.append(gguf.TokenType.USER_DEFINED)
elif reverse_vocab[i] in added_vocab:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.CONTROL)
else:
tokens.append(reverse_vocab[i])
toktypes.append(gguf.TokenType.NORMAL)
self.gguf_writer.add_tokenizer_model("gpt2")
self.gguf_writer.add_token_list(tokens)
self.gguf_writer.add_token_types(toktypes)
special_vocab = gguf.SpecialVocab(dir_model, load_merges=False)
special_vocab.merges = merges
special_vocab._set_special_token("bos", tokenizer.special_tokens["<|endoftext|>"])
special_vocab._set_special_token("eos", tokenizer.special_tokens["<|endoftext|>"])
special_vocab._set_special_token("unk", tokenizer.special_tokens["<|endoftext|>"])
special_vocab.add_to_gguf(self.gguf_writer)
def set_gguf_parameters(self):
self.gguf_writer.add_name("Qwen")
self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"])
self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"])
self.gguf_writer.add_embedding_length(self.hparams["hidden_size"])
self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"])
self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"])
self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"])
self.gguf_writer.add_head_count(self.hparams["num_attention_heads"])
self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"])
def write_tensors(self):
block_count = self.hparams["num_hidden_layers"]
model_kv = dict(self.get_tensors())
tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count)
for name, data_torch in model_kv.items():
# we don't need these
if name.endswith(".rotary_emb.inv_freq"):
continue
old_dtype = data_torch.dtype
# convert any unsupported data types to float32
if data_torch.dtype not in (torch.float16, torch.float32):
data_torch = data_torch.to(torch.float32)
data = data_torch.squeeze().numpy()
# map tensor names
new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias"))
if new_name is None:
print(f"Can not map tensor {name!r}")
sys.exit()
n_dims = len(data.shape)
data_dtype = data.dtype
# if f32 desired, convert any float16 to float32
if self.ftype == 0 and data_dtype == np.float16:
data = data.astype(np.float32)
# TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32
if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1:
data = data.astype(np.float32)
# if f16 desired, convert any float32 2-dim weight tensors to float16
if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2:
data = data.astype(np.float16)
print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}")
self.gguf_writer.add_tensor(new_name, data)
###### CONVERSION LOGIC ######
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Convert a huggingface model to a GGML compatible file")
parser.add_argument(
@ -879,6 +1009,7 @@ print(f"Loading model: {dir_model.name}")
hparams = Model.load_hparams(dir_model)
with torch.inference_mode():
model_class = Model.from_model_architecture(hparams["architectures"][0])
model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian)

View file

@ -14,11 +14,13 @@ if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
class GGMLFormat(IntEnum):
GGML = 0
GGMF = 1
GGJT = 2
class GGMLFType(IntEnum):
ALL_F32 = 0
MOSTLY_F16 = 1
@ -38,6 +40,7 @@ class GGMLFType(IntEnum):
MOSTLY_Q5_K_M = 17
MOSTLY_Q6_K = 18
class Hyperparameters:
def __init__(self):
self.n_vocab = self.n_embd = self.n_mult = self.n_head = 0
@ -69,6 +72,7 @@ class Hyperparameters:
def __str__(self):
return f'<Hyperparameters: n_vocab={self.n_vocab}, n_embd={self.n_embd}, n_mult={self.n_mult}, n_head={self.n_head}, n_layer={self.n_layer}, n_rot={self.n_rot}, n_ff={self.n_ff}, ftype={self.ftype.name}>'
class Vocab:
def __init__(self, load_scores = True):
self.items = []
@ -90,6 +94,7 @@ class Vocab:
self.items.append((item_text, item_score))
return offset - orig_offset
class Tensor:
def __init__(self, use_padding = True):
self.name = None
@ -123,6 +128,7 @@ class Tensor:
# print(n_dims, name_len, dtype, self.dims, self.name, pad)
return offset - orig_offset
class GGMLModel:
def __init__(self):
self.hyperparameters = None
@ -187,6 +193,7 @@ class GGMLModel:
hp.set_n_ff(self)
return offset
class GGMLToGGUF:
def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None, special_vocab = None):
hp = ggml_model.hyperparameters
@ -343,6 +350,7 @@ class GGMLToGGUF:
raw_shape = tempdims,
raw_dtype = tensor.dtype)
def handle_metadata(cfg, hp):
import convert
assert cfg.model_metadata_dir.is_dir(), 'Metadata dir is not a directory'
@ -373,6 +381,7 @@ def handle_metadata(cfg, hp):
convert.check_vocab_size(params, vocab)
return (params, vocab, svocab)
def handle_args():
parser = argparse.ArgumentParser(description = 'Convert GGML models to GGUF')
parser.add_argument('--input', '-i', type = Path, required = True,
@ -397,6 +406,7 @@ def handle_args():
help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm)")
return parser.parse_args()
def main():
cfg = handle_args()
print(f'* Using config: {cfg}')
@ -406,7 +416,7 @@ def main():
data = np.memmap(cfg.input, mode = 'r')
model = GGMLModel()
print('* Scanning GGML input file')
offset = model.load(data, 0)
offset = model.load(data, 0) # noqa
print(f'* GGML model hyperparameters: {model.hyperparameters}')
vocab_override = None
params_override = None
@ -421,12 +431,15 @@ def main():
print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
if model.file_format == GGMLFormat.GGML:
print('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
converter = GGMLToGGUF(model, data, cfg,
converter = GGMLToGGUF(
model, data, cfg,
params_override = params_override,
vocab_override = vocab_override,
special_vocab = special_vocab )
special_vocab = special_vocab
)
converter.save()
print(f'* Successful completion. Output saved to: {cfg.output}')
if __name__ == '__main__':
main()

View file

@ -9,6 +9,7 @@ if 'NO_LOCAL_GGUF' not in os.environ:
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
import gguf
def _flatten_dict(dct, tensors, prefix=None):
assert isinstance(dct, dict)
for key in dct.keys():
@ -21,6 +22,7 @@ def _flatten_dict(dct, tensors, prefix=None):
raise ValueError(type(dct[key]))
return None
def _get_sentencepiece_tokenizer_info(dir_model: Path):
tokenizer_path = dir_model / 'adept_vocab.model'
print('gguf: getting sentencepiece tokenizer from', tokenizer_path)
@ -54,6 +56,7 @@ def _get_sentencepiece_tokenizer_info(dir_model: Path):
pass
return tokens, scores, toktypes
def main():
parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
@ -125,6 +128,5 @@ def main():
print("")
if __name__ == '__main__':
main()

View file

@ -46,6 +46,7 @@ DEFAULT_CONCURRENCY = 8
# data types
#
@dataclass(frozen=True)
class DataType:
name: str
@ -55,15 +56,18 @@ class DataType:
def elements_to_bytes(self, n_elements: int) -> int:
return n_elements * self.dtype.itemsize
@dataclass(frozen=True)
class UnquantizedDataType(DataType):
pass
DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0'])
DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0'])
DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = [])
DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0'])
@dataclass(frozen=True)
class QuantizedDataType(DataType):
block_size: int
@ -77,6 +81,7 @@ class QuantizedDataType(DataType):
assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}'
return self.quantized_dtype.itemsize * (n_elements // self.block_size)
@dataclass(frozen=True)
class Q8_0QuantizedDataType(QuantizedDataType):
# Mini Q8_0 quantization in Python!
@ -86,6 +91,7 @@ class Q8_0QuantizedDataType(QuantizedDataType):
n_blocks = arr.size // self.block_size
blocks = arr.reshape((n_blocks, self.block_size))
# Much faster implementation of block quantization contributed by @Cebtenzzre
def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]:
d = abs(blocks).max(axis = 1) / np.float32(127)
with np.errstate(divide = 'ignore'):
@ -94,6 +100,7 @@ class Q8_0QuantizedDataType(QuantizedDataType):
yield from zip(d, qs)
return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype)
DT_Q8_0 = Q8_0QuantizedDataType('Q8_0',
dtype = np.dtype(np.float32), valid_conversions = [],
ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32,
@ -116,6 +123,8 @@ SAFETENSORS_DATA_TYPES: dict[str, DataType] = {
# TODO: match this with `llama_ftype`
# TODO: rename to LLAMAFileType
# TODO: move to `gguf.py`
class GGMLFileType(enum.IntEnum):
AllF32 = 0
MostlyF16 = 1 # except 1d tensors
@ -128,6 +137,7 @@ class GGMLFileType(enum.IntEnum):
# 1D tensors are always F32.
return dt if len(tensor.shape) > 1 else DT_F32
GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
GGMLFileType.AllF32 : DT_F32,
GGMLFileType.MostlyF16 : DT_F16,
@ -138,6 +148,7 @@ GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = {
# hparams loading
#
@dataclass
class Params:
n_vocab: int
@ -256,7 +267,7 @@ class Params:
n_ctx = 2048
return Params(
n_vocab = config.get("vocab_size", model["tok_embeddings.weight"].shape[0]),
n_vocab = model["tok_embeddings.weight"].shape[0],
n_embd = config["dim"],
n_layer = config["n_layers"],
n_ctx = n_ctx,
@ -326,7 +337,6 @@ class BpeVocab:
def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]:
tokenizer = self.bpe_tokenizer
from transformers.models.gpt2 import tokenization_gpt2
reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.items()}
for i, _ in enumerate(tokenizer):
@ -406,6 +416,7 @@ class SentencePieceVocab:
def __repr__(self) -> str:
return f"<SentencePieceVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>"
Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab'
#
@ -413,6 +424,7 @@ Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab'
# TODO: reuse (probably move to gguf.py?)
#
def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray:
# print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) )
if n_head_kv is not None and n_head != n_head_kv:
@ -588,6 +600,7 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTe
return lazy_tensor.load().permute(n_head, n_head_kv)
return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)
def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor:
def load() -> Tensor:
return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv)
@ -595,6 +608,7 @@ def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_
s[0] = s[0] // 3
return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description)
def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
def load() -> Tensor:
return lazy_tensor.load().part(n_part)
@ -744,6 +758,7 @@ def lazy_load_file(path: Path) -> ModelPlus:
In = TypeVar('In')
Out = TypeVar('Out')
def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: int | None = None, use_processpool_executor: bool = False) -> Iterable[Out]:
'''Parallel map, but with backpressure. If the caller doesn't call `next`
fast enough, this will stop calling `func` at some point rather than
@ -778,6 +793,7 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc
break
yield result
def check_vocab_size(params: Params, vocab: Vocab) -> None:
if params.n_vocab != vocab.vocab_size:
assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab)
@ -938,6 +954,7 @@ class OutputFile:
of.close()
def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType:
wq_type = model[gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0) +".weight"].data_type
@ -952,10 +969,12 @@ def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileT
raise Exception(f"Unexpected combination of types: {name_to_type}")
def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyModel:
return {name: tensor.astype(output_type.type_for_tensor(name, tensor))
for (name, tensor) in model.items()}
def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
tmap = gguf.TensorNameMap(ARCH, params.n_layer)
should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, []))
@ -993,6 +1012,7 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel:
return out
def nth_multifile_path(path: Path, n: int) -> Path | None:
'''Given any path belonging to a multi-file model (e.g. foo.bin.1), return
the nth path in the model.

BIN
docs/llama-star/idea-arch.key Executable file

Binary file not shown.

Binary file not shown.

View file

@ -32,6 +32,7 @@ else()
add_subdirectory(save-load-state)
add_subdirectory(simple)
add_subdirectory(speculative)
add_subdirectory(lookahead)
add_subdirectory(train-text-from-scratch)
if (LLAMA_METAL)
add_subdirectory(metal)

View file

@ -155,7 +155,7 @@ int main(int argc, char ** argv) {
}
LOG_TEE("\n");
LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq);
LOG_TEE("%s: n_kv_max = %d, is_pp_shared = %d, n_gpu_layers = %d, mmq = %d, n_threads = %d, n_threads_batch = %d\n", __func__, n_kv_max, is_pp_shared, n_gpu_layers, mmq, ctx_params.n_threads, ctx_params.n_threads_batch);
LOG_TEE("\n");
LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s");

View file

@ -1,4 +1,4 @@
This is a swift clone of `examples/batched`.
$ `make`
$ `./swift MODEL_PATH [PROMPT] [PARALLEL]`
$ `./batched_swift MODEL_PATH [PROMPT] [PARALLEL]`

View file

@ -153,7 +153,7 @@ while n_cur <= n_len {
// const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p);
// is it an end of stream? -> mark the stream as finished
if new_token_id == llama_token_eos(context) || n_cur == n_len {
if new_token_id == llama_token_eos(model) || n_cur == n_len {
i_batch[i] = -1
// print("")
if n_parallel > 1 {
@ -215,9 +215,10 @@ print("decoded \(n_decode) tokens in \(String(format: "%.2f", Double(t_main_end
llama_print_timings(context)
private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
let n_tokens = text.count + (add_bos ? 1 : 0)
let utf8Count = text.utf8.count
let n_tokens = utf8Count + (add_bos ? 1 : 0)
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
let tokenCount = llama_tokenize(model, text, Int32(text.count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, /*special tokens*/ false)
var swiftTokens: [llama_token] = []
for i in 0 ..< tokenCount {
swiftTokens.append(tokens[Int(i)])
@ -230,18 +231,15 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
var result = [CChar](repeating: 0, count: 8)
let nTokens = llama_token_to_piece(model, token, &result, Int32(result.count))
if nTokens < 0 {
if result.count >= -Int(nTokens) {
result.removeLast(-Int(nTokens))
} else {
result.removeAll()
}
let actualTokensCount = -Int(nTokens)
result = .init(repeating: 0, count: actualTokensCount)
let check = llama_token_to_piece(
model,
token,
&result,
Int32(result.count)
)
assert(check == nTokens)
assert(check == actualTokensCount)
} else {
result.removeLast(result.count - Int(nTokens))
}
@ -259,5 +257,4 @@ private func token_to_piece(token: llama_token, buffer: inout [CChar]) -> String
buffer = []
return bufferString
}
return nil
}

View file

@ -21,7 +21,7 @@ wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/s
./bin/main -m open-llama-3b-v2-q8_0.gguf --lora lora-open-llama-3b-v2-q8_0-shakespeare-LATEST.bin
```
Finetune output files will be saved every N iterations (config with `--save-every N`).
**Only llama based models are supported!** The output files will be saved every N iterations (config with `--save-every N`).
The pattern 'ITERATION' in the output filenames will be replaced with the iteration number and with 'LATEST' for the latest output.
So in above example after 10 iterations these files will be written:
- chk-lora-open-llama-3b-v2-q8_0-shakespeare-10.gguf

View file

@ -146,6 +146,13 @@ int main(int argc, char ** argv) {
return 0;
}
if (params.chatml) {
printf("\n************\n");
printf("%s: please use the 'main' tool for chatml mode\n", __func__);
printf("************\n\n");
return 0;
}
if (!params.antiprompt.empty()) {
printf("\n************\n");
printf("%s: please use the 'main' tool for antiprompt mode\n", __func__);

View file

@ -53,6 +53,13 @@ static std::vector<T> split(const std::string & str, char delim) {
return values;
}
template<typename T, typename F>
static std::vector<std::string> transform_to_str(const std::vector<T> & values, F f) {
std::vector<std::string> str_values;
std::transform(values.begin(), values.end(), std::back_inserter(str_values), f);
return str_values;
}
template<typename T>
static T avg(const std::vector<T> & v) {
if (v.empty()) {
@ -126,7 +133,8 @@ struct cmd_params {
std::vector<int> n_prompt;
std::vector<int> n_gen;
std::vector<int> n_batch;
std::vector<bool> f32_kv;
std::vector<ggml_type> type_k;
std::vector<ggml_type> type_v;
std::vector<int> n_threads;
std::vector<int> n_gpu_layers;
std::vector<int> main_gpu;
@ -142,7 +150,8 @@ static const cmd_params cmd_params_defaults = {
/* n_prompt */ {512},
/* n_gen */ {128},
/* n_batch */ {512},
/* f32_kv */ {false},
/* type_k */ {GGML_TYPE_F16},
/* type_v */ {GGML_TYPE_F16},
/* n_threads */ {get_num_physical_cores()},
/* n_gpu_layers */ {99},
/* main_gpu */ {0},
@ -162,7 +171,8 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str());
printf(" -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str());
printf(" -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str());
printf(" --memory-f32 <0|1> (default: %s)\n", join(cmd_params_defaults.f32_kv, ",").c_str());
printf(" -ctk <t>, --cache-type-k <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str());
printf(" -ctv <t>, --cache-type-v <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str());
printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str());
printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str());
printf(" -mg, --main-gpu <i> (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str());
@ -173,9 +183,32 @@ static void print_usage(int /* argc */, char ** argv) {
printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0");
printf("\n");
printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n");
}
static ggml_type ggml_type_from_name(const std::string & s) {
if (s == "f16") {
return GGML_TYPE_F16;
}
if (s == "q8_0") {
return GGML_TYPE_Q8_0;
}
if (s == "q4_0") {
return GGML_TYPE_Q4_0;
}
if (s == "q4_1") {
return GGML_TYPE_Q4_1;
}
if (s == "q5_0") {
return GGML_TYPE_Q5_0;
}
if (s == "q5_1") {
return GGML_TYPE_Q5_1;
}
return GGML_TYPE_COUNT;
}
static cmd_params parse_cmd_params(int argc, char ** argv) {
cmd_params params;
std::string arg;
@ -224,13 +257,38 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
}
auto p = split<int>(argv[i], split_delim);
params.n_batch.insert(params.n_batch.end(), p.begin(), p.end());
} else if (arg == "--memory-f32") {
} else if (arg == "-ctk" || arg == "--cache-type-k") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<int>(argv[i], split_delim);
params.f32_kv.insert(params.f32_kv.end(), p.begin(), p.end());
auto p = split<std::string>(argv[i], split_delim);
std::vector<ggml_type> types;
for (const auto & t : p) {
ggml_type gt = ggml_type_from_name(t);
if (gt == GGML_TYPE_COUNT) {
invalid_param = true;
break;
}
types.push_back(gt);
}
params.type_k.insert(params.type_k.end(), types.begin(), types.end());
} else if (arg == "-ctv" || arg == "--cache-type-v") {
if (++i >= argc) {
invalid_param = true;
break;
}
auto p = split<std::string>(argv[i], split_delim);
std::vector<ggml_type> types;
for (const auto & t : p) {
ggml_type gt = ggml_type_from_name(t);
if (gt == GGML_TYPE_COUNT) {
invalid_param = true;
break;
}
types.push_back(gt);
}
params.type_v.insert(params.type_v.end(), types.begin(), types.end());
} else if (arg == "-t" || arg == "--threads") {
if (++i >= argc) {
invalid_param = true;
@ -321,7 +379,8 @@ static cmd_params parse_cmd_params(int argc, char ** argv) {
if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; }
if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; }
if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; }
if (params.f32_kv.empty()) { params.f32_kv = cmd_params_defaults.f32_kv; }
if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; }
if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; }
if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; }
if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; }
if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; }
@ -336,7 +395,8 @@ struct cmd_params_instance {
int n_prompt;
int n_gen;
int n_batch;
bool f32_kv;
ggml_type type_k;
ggml_type type_v;
int n_threads;
int n_gpu_layers;
int main_gpu;
@ -365,7 +425,8 @@ struct cmd_params_instance {
cparams.n_ctx = n_prompt + n_gen;
cparams.n_batch = n_batch;
cparams.f16_kv = !f32_kv;
cparams.type_k = type_k;
cparams.type_v = type_v;
cparams.mul_mat_q = mul_mat_q;
return cparams;
@ -380,7 +441,8 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
for (const auto & mg : params.main_gpu)
for (const auto & ts : params.tensor_split)
for (const auto & nb : params.n_batch)
for (const auto & fk : params.f32_kv)
for (const auto & tk : params.type_k)
for (const auto & tv : params.type_v)
for (const auto & mmq : params.mul_mat_q)
for (const auto & nt : params.n_threads) {
cmd_params_instance instance = {
@ -388,7 +450,8 @@ static std::vector<cmd_params_instance> get_cmd_params_instances_int(const cmd_p
/* .n_prompt = */ n_prompt,
/* .n_gen = */ n_gen,
/* .n_batch = */ nb,
/* .f32_kv = */ fk,
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
@ -410,7 +473,8 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
for (const auto & mg : params.main_gpu)
for (const auto & ts : params.tensor_split)
for (const auto & nb : params.n_batch)
for (const auto & fk : params.f32_kv)
for (const auto & tk : params.type_k)
for (const auto & tv : params.type_v)
for (const auto & mmq : params.mul_mat_q)
for (const auto & nt : params.n_threads) {
for (const auto & n_prompt : params.n_prompt) {
@ -422,7 +486,8 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .n_prompt = */ n_prompt,
/* .n_gen = */ 0,
/* .n_batch = */ nb,
/* .f32_kv = */ fk,
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
@ -441,7 +506,8 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param
/* .n_prompt = */ 0,
/* .n_gen = */ n_gen,
/* .n_batch = */ nb,
/* .f32_kv = */ fk,
/* .type_k = */ tk,
/* .type_v = */ tv,
/* .n_threads = */ nt,
/* .n_gpu_layers = */ nl,
/* .main_gpu = */ mg,
@ -490,7 +556,8 @@ struct test {
uint64_t model_n_params;
int n_batch;
int n_threads;
bool f32_kv;
ggml_type type_k;
ggml_type type_v;
int n_gpu_layers;
int main_gpu;
bool mul_mat_q;
@ -509,7 +576,8 @@ struct test {
model_n_params = llama_model_n_params(lmodel);
n_batch = inst.n_batch;
n_threads = inst.n_threads;
f32_kv = inst.f32_kv;
type_k = inst.type_k;
type_v = inst.type_v;
n_gpu_layers = inst.n_gpu_layers;
main_gpu = inst.main_gpu;
mul_mat_q = inst.mul_mat_q;
@ -575,7 +643,7 @@ struct test {
"cuda", "opencl", "vulkan", "metal", "gpu_blas", "blas",
"cpu_info", "gpu_info",
"model_filename", "model_type", "model_size", "model_n_params",
"n_batch", "n_threads", "f16_kv",
"n_batch", "n_threads", "type_k", "type_v",
"n_gpu_layers", "main_gpu", "mul_mat_q", "tensor_split",
"n_prompt", "n_gen", "test_time",
"avg_ns", "stddev_ns",
@ -625,7 +693,7 @@ struct test {
std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas),
cpu_info, gpu_info,
model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params),
std::to_string(n_batch), std::to_string(n_threads), std::to_string(!f32_kv),
std::to_string(n_batch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v),
std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), tensor_split_str,
std::to_string(n_prompt), std::to_string(n_gen), test_time,
std::to_string(avg_ns()), std::to_string(stdev_ns()),
@ -810,8 +878,11 @@ struct markdown_printer : public printer {
if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) {
fields.push_back("n_batch");
}
if (params.f32_kv.size() > 1 || params.f32_kv != cmd_params_defaults.f32_kv) {
fields.push_back("f16_kv");
if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) {
fields.push_back("type_k");
}
if (params.type_v.size() > 1 || params.type_v != cmd_params_defaults.type_v) {
fields.push_back("type_v");
}
if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) {
fields.push_back("main_gpu");

1
examples/llama.swiftui/.gitignore vendored Normal file
View file

@ -0,0 +1 @@
xcuserdata

View file

@ -0,0 +1,7 @@
# llama.swiftui
Local inference of llama.cpp on an iPhone.
So far I only tested with starcoder 1B model, but it can most likely handle 7B models as well.
https://github.com/bachittle/llama.cpp/assets/39804642/e290827a-4edb-4093-9642-2a5e399ec545

View file

@ -0,0 +1,208 @@
import Foundation
// import llama
enum LlamaError: Error {
case couldNotInitializeContext
}
actor LlamaContext {
private var model: OpaquePointer
private var context: OpaquePointer
private var batch: llama_batch
private var tokens_list: [llama_token]
/// This variable is used to store temporarily invalid cchars
private var temporary_invalid_cchars: [CChar]
var n_len: Int32 = 512
var n_cur: Int32 = 0
var n_decode: Int32 = 0
init(model: OpaquePointer, context: OpaquePointer) {
self.model = model
self.context = context
self.tokens_list = []
self.batch = llama_batch_init(512, 0, 1)
self.temporary_invalid_cchars = []
}
deinit {
llama_free(context)
llama_free_model(model)
llama_backend_free()
}
static func createContext(path: String) throws -> LlamaContext {
llama_backend_init(false)
let model_params = llama_model_default_params()
let model = llama_load_model_from_file(path, model_params)
guard let model else {
print("Could not load model at \(path)")
throw LlamaError.couldNotInitializeContext
}
var ctx_params = llama_context_default_params()
ctx_params.seed = 1234
ctx_params.n_ctx = 2048
ctx_params.n_threads = 8
ctx_params.n_threads_batch = 8
let context = llama_new_context_with_model(model, ctx_params)
guard let context else {
print("Could not load context!")
throw LlamaError.couldNotInitializeContext
}
return LlamaContext(model: model, context: context)
}
func get_n_tokens() -> Int32 {
return batch.n_tokens;
}
func completion_init(text: String) {
print("attempting to complete \"\(text)\"")
tokens_list = tokenize(text: text, add_bos: true)
temporary_invalid_cchars = []
let n_ctx = llama_n_ctx(context)
let n_kv_req = tokens_list.count + (Int(n_len) - tokens_list.count)
print("\n n_len = \(n_len), n_ctx = \(n_ctx), n_kv_req = \(n_kv_req)")
if n_kv_req > n_ctx {
print("error: n_kv_req > n_ctx, the required KV cache size is not big enough")
}
for id in tokens_list {
print(String(cString: token_to_piece(token: id) + [0]))
}
// batch = llama_batch_init(512, 0) // done in init()
batch.n_tokens = Int32(tokens_list.count)
for i1 in 0..<batch.n_tokens {
let i = Int(i1)
batch.token[i] = tokens_list[i]
batch.pos[i] = i1
batch.n_seq_id[Int(i)] = 1
batch.seq_id[Int(i)]![0] = 0
batch.logits[i] = 0
}
batch.logits[Int(batch.n_tokens) - 1] = 1 // true
if llama_decode(context, batch) != 0 {
print("llama_decode() failed")
}
n_cur = batch.n_tokens
}
func completion_loop() -> String {
var new_token_id: llama_token = 0
let n_vocab = llama_n_vocab(model)
let logits = llama_get_logits_ith(context, batch.n_tokens - 1)
var candidates = Array<llama_token_data>()
candidates.reserveCapacity(Int(n_vocab))
for token_id in 0..<n_vocab {
candidates.append(llama_token_data(id: token_id, logit: logits![Int(token_id)], p: 0.0))
}
candidates.withUnsafeMutableBufferPointer() { buffer in
var candidates_p = llama_token_data_array(data: buffer.baseAddress, size: buffer.count, sorted: false)
new_token_id = llama_sample_token_greedy(context, &candidates_p)
}
if new_token_id == llama_token_eos(context) || n_cur == n_len {
print("\n")
let new_token_str = String(cString: temporary_invalid_cchars + [0])
temporary_invalid_cchars.removeAll()
return new_token_str
}
let new_token_cchars = token_to_piece(token: new_token_id)
temporary_invalid_cchars.append(contentsOf: new_token_cchars)
let new_token_str: String
if let string = String(validatingUTF8: temporary_invalid_cchars + [0]) {
temporary_invalid_cchars.removeAll()
new_token_str = string
} else if (0 ..< temporary_invalid_cchars.count).contains(where: {$0 != 0 && String(validatingUTF8: Array(temporary_invalid_cchars.suffix($0)) + [0]) != nil}) {
// in this case, at least the suffix of the temporary_invalid_cchars can be interpreted as UTF8 string
let string = String(cString: temporary_invalid_cchars + [0])
temporary_invalid_cchars.removeAll()
new_token_str = string
} else {
new_token_str = ""
}
print(new_token_str)
// tokens_list.append(new_token_id)
batch.n_tokens = 0
batch.token[Int(batch.n_tokens)] = new_token_id
batch.pos[Int(batch.n_tokens)] = n_cur
batch.n_seq_id[Int(batch.n_tokens)] = 1
batch.seq_id[Int(batch.n_tokens)]![0] = 0
batch.logits[Int(batch.n_tokens)] = 1 // true
batch.n_tokens += 1
n_decode += 1
n_cur += 1
if llama_decode(context, batch) != 0 {
print("failed to evaluate llama!")
}
return new_token_str
}
func clear() {
tokens_list.removeAll()
temporary_invalid_cchars.removeAll()
}
private func tokenize(text: String, add_bos: Bool) -> [llama_token] {
let utf8Count = text.utf8.count
let n_tokens = utf8Count + (add_bos ? 1 : 0)
let tokens = UnsafeMutablePointer<llama_token>.allocate(capacity: n_tokens)
let tokenCount = llama_tokenize(model, text, Int32(utf8Count), tokens, Int32(n_tokens), add_bos, false)
var swiftTokens: [llama_token] = []
for i in 0..<tokenCount {
swiftTokens.append(tokens[Int(i)])
}
tokens.deallocate()
return swiftTokens
}
/// - note: The result does not contain null-terminator
private func token_to_piece(token: llama_token) -> [CChar] {
let result = UnsafeMutablePointer<Int8>.allocate(capacity: 8)
result.initialize(repeating: Int8(0), count: 8)
defer {
result.deallocate()
}
let nTokens = llama_token_to_piece(model, token, result, 8)
if nTokens < 0 {
let newResult = UnsafeMutablePointer<Int8>.allocate(capacity: Int(-nTokens))
newResult.initialize(repeating: Int8(0), count: Int(-nTokens))
defer {
newResult.deallocate()
}
let nNewTokens = llama_token_to_piece(model, token, newResult, -nTokens)
let bufferPointer = UnsafeBufferPointer(start: newResult, count: Int(nNewTokens))
return Array(bufferPointer)
} else {
let bufferPointer = UnsafeBufferPointer(start: result, count: Int(nTokens))
return Array(bufferPointer)
}
}
}

View file

@ -0,0 +1,5 @@
//
// Use this file to import your target's public headers that you would like to expose to Swift.
//
#import "llama.h"

View file

@ -0,0 +1,481 @@
// !$*UTF8*$!
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CLANG_WARN_INFINITE_RECURSION = YES;
CLANG_WARN_INT_CONVERSION = YES;
CLANG_WARN_NON_LITERAL_NULL_CONVERSION = YES;
CLANG_WARN_OBJC_IMPLICIT_RETAIN_SELF = YES;
CLANG_WARN_OBJC_LITERAL_CONVERSION = YES;
CLANG_WARN_OBJC_ROOT_CLASS = YES_ERROR;
CLANG_WARN_QUOTED_INCLUDE_IN_FRAMEWORK_HEADER = YES;
CLANG_WARN_RANGE_LOOP_ANALYSIS = YES;
CLANG_WARN_STRICT_PROTOTYPES = YES;
CLANG_WARN_SUSPICIOUS_MOVE = YES;
CLANG_WARN_UNGUARDED_AVAILABILITY = YES_AGGRESSIVE;
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COPY_PHASE_STRIP = NO;
DEBUG_INFORMATION_FORMAT = "dwarf-with-dsym";
ENABLE_NS_ASSERTIONS = NO;
ENABLE_STRICT_OBJC_MSGSEND = YES;
ENABLE_USER_SCRIPT_SANDBOXING = YES;
GCC_C_LANGUAGE_STANDARD = gnu17;
GCC_NO_COMMON_BLOCKS = YES;
GCC_WARN_64_TO_32_BIT_CONVERSION = YES;
GCC_WARN_ABOUT_RETURN_TYPE = YES_ERROR;
GCC_WARN_UNDECLARED_SELECTOR = YES;
GCC_WARN_UNINITIALIZED_AUTOS = YES_AGGRESSIVE;
GCC_WARN_UNUSED_FUNCTION = YES;
GCC_WARN_UNUSED_VARIABLE = YES;
IPHONEOS_DEPLOYMENT_TARGET = 17.0;
LOCALIZATION_PREFERS_STRING_CATALOGS = YES;
MTL_ENABLE_DEBUG_INFO = NO;
MTL_FAST_MATH = YES;
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SWIFT_COMPILATION_MODE = wholemodule;
VALIDATE_PRODUCT = YES;
};
name = Release;
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View file

@ -0,0 +1,7 @@
<?xml version="1.0" encoding="UTF-8"?>
<Workspace
version = "1.0">
<FileRef
location = "self:">
</FileRef>
</Workspace>

View file

@ -0,0 +1,8 @@
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
<key>IDEDidComputeMac32BitWarning</key>
<true/>
</dict>
</plist>

View file

@ -0,0 +1,11 @@
{
"colors" : [
{
"idiom" : "universal"
}
],
"info" : {
"author" : "xcode",
"version" : 1
}
}

View file

@ -0,0 +1,13 @@
{
"images" : [
{
"idiom" : "universal",
"platform" : "ios",
"size" : "1024x1024"
}
],
"info" : {
"author" : "xcode",
"version" : 1
}
}

View file

@ -0,0 +1,6 @@
{
"info" : {
"author" : "xcode",
"version" : 1
}
}

View file

@ -0,0 +1,45 @@
import Foundation
@MainActor
class LlamaState: ObservableObject {
@Published var messageLog = ""
private var llamaContext: LlamaContext?
private var modelUrl: URL? {
Bundle.main.url(forResource: "q8_0", withExtension: "gguf", subdirectory: "models")
// Bundle.main.url(forResource: "llama-2-7b-chat", withExtension: "Q2_K.gguf", subdirectory: "models")
}
init() {
do {
try loadModel()
} catch {
messageLog += "Error!\n"
}
}
private func loadModel() throws {
messageLog += "Loading model...\n"
if let modelUrl {
llamaContext = try LlamaContext.createContext(path: modelUrl.path())
messageLog += "Loaded model \(modelUrl.lastPathComponent)\n"
} else {
messageLog += "Could not locate model\n"
}
}
func complete(text: String) async {
guard let llamaContext else {
return
}
messageLog += "Attempting to complete text...\n"
await llamaContext.completion_init(text: text)
messageLog += "\(text)"
while await llamaContext.n_cur <= llamaContext.n_len {
let result = await llamaContext.completion_loop()
messageLog += "\(result)"
}
await llamaContext.clear()
messageLog += "\n\ndone\n"
}
}

View file

@ -0,0 +1,6 @@
{
"info" : {
"author" : "xcode",
"version" : 1
}
}

View file

@ -0,0 +1,42 @@
import SwiftUI
struct ContentView: View {
@StateObject var llamaState = LlamaState()
@State private var multiLineText = ""
var body: some View {
VStack {
ScrollView(.vertical) {
Text(llamaState.messageLog)
}
TextEditor(text: $multiLineText)
.frame(height: 200)
.padding()
.border(Color.gray, width: 0.5)
Button(action: {
sendText()
}) {
Text("Send")
.padding()
.background(Color.blue)
.foregroundColor(.white)
.cornerRadius(8)
}
}
.padding()
}
func sendText() {
Task {
await llamaState.complete(text: multiLineText)
multiLineText = ""
}
}
}
/*
#Preview {
ContentView()
}
*/

View file

@ -0,0 +1,10 @@
import SwiftUI
@main
struct llama_swiftuiApp: App {
var body: some Scene {
WindowGroup {
ContentView()
}
}
}

View file

@ -5,7 +5,7 @@ import json
import torch
import numpy as np
from gguf import *
from transformers import CLIPModel, CLIPProcessor
from transformers import CLIPModel, CLIPProcessor, CLIPVisionModel
TEXT = "clip.text"
VISION = "clip.vision"
@ -78,11 +78,19 @@ ap.add_argument("--text-only", action="store_true", required=False,
help="Save a text-only model. It can't be used to encode images")
ap.add_argument("--vision-only", action="store_true", required=False,
help="Save a vision-only model. It can't be used to encode texts")
ap.add_argument("--clip_model_is_vision", action="store_true", required=False,
help="The clip model is a pure vision model (ShareGPT4V vision extract for example)")
ap.add_argument("--llava-projector", help="Path to llava.projector file. If specified, save an image encoder for LLaVA models.")
ap.add_argument("--image-mean", nargs=3, type=float, required=False, help="Override image mean values")
ap.add_argument("--image-std", nargs=3, type=float, required=False, help="Override image std values")
ap.add_argument("-o", "--output-dir", help="Directory to save GGUF files. Default is the original model directory", default=None)
# Example --image_mean 0.48145466 0.4578275 0.40821073 --image_std 0.26862954 0.26130258 0.27577711
default_image_mean = [0.48145466, 0.4578275, 0.40821073]
default_image_std = [0.26862954, 0.26130258, 0.27577711]
ap.add_argument('--image_mean', type=float, nargs='+', help='Mean of the images for normalization (overrides processor) ', default=None)
ap.add_argument('--image_std', type=float, nargs='+', help='Standard deviation of the images for normalization (overrides processor)', default=None)
# with proper
args = ap.parse_args()
@ -96,13 +104,20 @@ if args.use_f32:
# output in the same directory as the model if output_dir is None
dir_model = args.model_dir
if args.clip_model_is_vision:
vocab = None
tokens = None
else:
with open(dir_model + "/vocab.json", "r", encoding="utf-8") as f:
vocab = json.load(f)
tokens = [key for key in vocab]
with open(dir_model + "/config.json", "r", encoding="utf-8") as f:
config = json.load(f)
if args.clip_model_is_vision:
v_hparams = config
t_hparams = None
else:
v_hparams = config["vision_config"]
t_hparams = config["text_config"]
@ -117,7 +132,10 @@ ftype = 1
if args.use_f32:
ftype = 0
if args.clip_model_is_vision:
model = CLIPVisionModel.from_pretrained(dir_model)
processor = None
else:
model = CLIPModel.from_pretrained(dir_model)
processor = CLIPProcessor.from_pretrained(dir_model)
@ -128,13 +146,13 @@ has_llava_projector = False
if args.text_only:
fname_middle = "text-"
has_vision_encoder = False
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
elif args.llava_projector is not None:
fname_middle = "mmproj-"
has_text_encoder = False
has_llava_projector = True
elif args.vision_only:
fname_middle = "vision-"
has_text_encoder = False
else:
fname_middle = ""
@ -182,8 +200,12 @@ if has_vision_encoder:
block_count = v_hparams["num_hidden_layers"] - 1 if has_llava_projector else v_hparams["num_hidden_layers"]
fout.add_uint32(k(KEY_BLOCK_COUNT, VISION), block_count)
image_mean = processor.image_processor.image_mean if args.image_mean is None else args.image_mean
image_std = processor.image_processor.image_std if args.image_std is None else args.image_std
if processor is not None:
image_mean = processor.image_processor.image_mean if args.image_mean is None or args.image_mean == default_image_mean else args.image_mean
image_std = processor.image_processor.image_std if args.image_std is None or args.image_std == default_image_std else args.image_std
else:
image_mean = args.image_mean if args.image_mean is not None else default_image_mean
image_std = args.image_std if args.image_std is not None else default_image_std
fout.add_array("clip.vision.image_mean", image_mean)
fout.add_array("clip.vision.image_std", image_std)

View file

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

View file

@ -0,0 +1,7 @@
# llama.cpp/examples/lookahead
Demonstartion of lookahead decoding technique:
https://lmsys.org/blog/2023-11-21-lookahead-decoding/
More info: https://github.com/ggerganov/llama.cpp/pull/4207

View file

@ -0,0 +1,487 @@
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <string>
#include <vector>
struct ngram_data {
bool active = false;
llama_seq_id seq_id = -1;
std::vector<int> i_batch;
std::vector<llama_token> tokens;
};
// n-gram container
struct ngram_container {
ngram_container(int n_vocab, int N, int G) {
cnt.resize(n_vocab);
head.resize(n_vocab);
tokens.resize(n_vocab * G * (N - 1));
}
int n_total = 0;
std::vector<int> cnt;
std::vector<int> head;
// [n_vocab][G][N - 1]
// for each token of the vocab, keep a ring-buffer of capacity G of n-grams of size N - 1
std::vector<llama_token> tokens;
};
int main(int argc, char ** argv) {
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
return 1;
}
const int W = 15; // lookahead window
const int N = 5; // n-gram size
const int G = 15; // max verification n-grams
const bool dump_kv_cache = params.dump_kv_cache;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("lookahead", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc, argv);
#endif // LOG_DISABLE_LOGS
// init llama.cpp
llama_backend_init(params.numa);
llama_model * model = NULL;
llama_context * ctx = NULL;
// load the target model
std::tie(model, ctx) = llama_init_from_gpt_params(params);
// Tokenize the prompt
const bool add_bos = llama_should_add_bos_token(model);
LOG("add_bos tgt: %d\n", add_bos);
std::vector<llama_token> inp;
std::vector<llama_token> all;
inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
all = inp;
const int max_context_size = llama_n_ctx(ctx);
const int max_tokens_list_size = max_context_size - 4;
if ((int) inp.size() > max_tokens_list_size) {
fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size);
return 1;
}
fprintf(stderr, "\n\n");
for (auto id : inp) {
fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str());
}
fflush(stderr);
const int n_input = inp.size();
const auto t_enc_start = ggml_time_us();
// eval the prompt
llama_decode(ctx, llama_batch_get_one( inp.data(), n_input - 1, 0, 0));
llama_decode(ctx, llama_batch_get_one(&inp.back(), 1, n_input - 1, 0));
for (int s = 1; s < W + G + 1; ++s) {
llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
}
const auto t_enc_end = ggml_time_us();
int n_predict = 0;
int n_accept = 0;
int n_past = inp.size();
llama_token id = 0;
// used to determine end of generation
bool has_eos = false;
// for each decoded batch, we have at most W + G + 1 distinct sequences:
// seq_id == 0 : the current input token
// seq_id [1, W] : tokens from the past N - 1 Jacobi iterations
// seq_id [W + 1, W + G] : verification n-grams
llama_batch batch = llama_batch_init(params.n_ctx, 0, W + G + 1);
// target model sampling context
struct llama_sampling_context * ctx_sampling = llama_sampling_init(params.sparams);
// verification n-grams
std::vector<ngram_data> ngrams_cur(G);
// tokens for the past N - 1 Jacobi iterations
std::vector<llama_token> tokens_j_prev(W);
std::vector<std::vector<llama_token>> tokens_j(N - 1);
for (int j = 0; j < N - 1; j++) {
tokens_j[j].resize(W);
for (int i = 0; i < W; i++) {
// there are different ways to init these tokens
if (0) {
// initialize randomly from the prompt tokens
tokens_j[j][i] = all[1 + rand() % (all.size() - 1)];
} else {
// initialize with a sequence of increasing numbers
tokens_j[j][i] = 100 + i;
}
}
}
std::vector<llama_seq_id> seq_id_look;
// the input token belongs both to all sequences
std::vector<llama_seq_id> seq_id_all(W + G + 1);
for (int i = 0; i < W + G + 1; i++) {
seq_id_all[i] = i;
}
// here we keep adding new n-grams as we go
ngram_container ngrams_observed(llama_n_vocab(model), N, G);
// debug
struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, W + G + 1);
const auto t_dec_start = ggml_time_us();
// sample first token
{
id = llama_sampling_sample(ctx_sampling, ctx, NULL, 0);
llama_sampling_accept(ctx_sampling, ctx, id, true);
{
const std::string token_str = llama_token_to_piece(ctx, id);
printf("%s", token_str.c_str());
fflush(stdout);
}
}
while (true) {
// debug
if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view);
dump_kv_cache_view_seqs(kvc_view, 40);
}
// build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/
//
// Example for W = 5, N = 4, G = 2:
// (I = input, L = lookahead, V = verification)
//
// Batch: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
// T: -2 -2 -2 -2 -1 -1 -1 -1 -1 0 0 0 0 0 0
// Info: I L L L L L L L L L L L L L L V V V V V V
// Pos: 0 1 2 3 4 1 2 3 4 5 2 3 4 5 6 1 2 3 1 2 3 (+ n_past)
// Logits: 1 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1
// ---------------------------------------------------------------------
// Seq: 0
// 1 1 1
// 2 2 2 2
// 3 3 3 3 3
// 4 4 4 4 4 4
// 5 5 5 5 5 5 5
// 6 6 6 6
// 7 7 7 7
// ---------------------------------------------------------------------
// | | | | | | | | | | |
// V V V V V | | | | | |
// j_tokens | | | | | |
// V V V V V V
// id
{
llama_batch_clear(batch);
// current token - first token of the first level
llama_batch_add(batch, id, n_past, seq_id_all, true);
// verification n-grams - queue this before the lookahead tokens for less KV cache fragmentation
{
const int g_cur = ngrams_observed.cnt[id];
ngrams_cur.resize(g_cur);
for (int g = 0; g < g_cur; g++) {
ngrams_cur[g].active = true;
ngrams_cur[g].tokens.resize(N);
ngrams_cur[g].i_batch.resize(N);
ngrams_cur[g].seq_id = W + 1 + g;
ngrams_cur[g].i_batch[0] = 0;
ngrams_cur[g].tokens [0] = id;
}
for (int j = 0; j < N - 1; j++) {
for (int g = 0; g < g_cur; g++) {
const int idx = id*(N - 1)*G + g*(N - 1);
const llama_token t = ngrams_observed.tokens[idx + j];
ngrams_cur[g].tokens [j + 1] = t;
ngrams_cur[g].i_batch[j + 1] = batch.n_tokens;
llama_batch_add(batch, t, n_past + j + 1, { W + 1 + g }, true);
}
}
}
// fill the remaining W - 1 tokens for the first level
for (int i = 1; i < W; i++) {
seq_id_look.resize(W - i);
for (int j = 0; j < W - i; j++) {
seq_id_look[j] = i + j + 1;
}
llama_batch_add(batch, tokens_j[0][i], n_past + i, seq_id_look, false);
}
// fill the rest of the levels
for (int j = 1; j < N - 1; j++) {
for (int i = 0; i < W; i++) {
llama_batch_add(batch, tokens_j[j][i], n_past + j + i, { i + 1 }, j == N - 2);
}
}
}
if (llama_decode(ctx, batch) != 0) {
fprintf(stderr, "\n\n%s: error: llama_decode failed - increase KV cache size\n", __func__);
return 1;
}
int seq_id_best = 0;
for (int v = 0; v < N; ++v) {
int i_batch = 0;
// if no active ngrams are left, it means the sampled token does not pass the verification
if (v > 0) {
for (int g = 0; g < (int) ngrams_cur.size(); g++) {
if (ngrams_cur[g].active) {
i_batch = ngrams_cur[g].i_batch[v];
seq_id_best = ngrams_cur[g].seq_id;
++n_accept;
break;
}
}
// no more matches -> create a new batch
if (i_batch == 0) {
break;
}
}
// sample the next token
id = llama_sampling_sample(ctx_sampling, ctx, NULL, i_batch);
llama_sampling_accept(ctx_sampling, ctx, id, true);
// print
{
const std::string token_str = llama_token_to_piece(ctx, id);
if (v == 0) {
printf("%s", token_str.c_str());
} else {
// print light cyan
printf("\033[0;96m%s\033[0m", token_str.c_str());
}
fflush(stdout);
if (id == llama_token_eos(model)) {
has_eos = true;
}
all.push_back(id);
}
++n_predict;
++n_past;
if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
break;
}
// verify across active n-grams
for (int g = 0; g < (int) ngrams_cur.size(); g++) {
if (ngrams_cur[g].active) {
if (v == N - 1) {
ngrams_cur[g].active = false;
} else {
if (id != ngrams_cur[g].tokens[v + 1]) {
ngrams_cur[g].active = false;
}
}
}
}
// print known n-grams starting with token id (debug)
if (0 && v == 0) {
if (ngrams_observed.cnt[id] > 0) {
printf("\n - %d n-grams starting with '%s'\n", ngrams_observed.cnt[id], llama_token_to_piece(ctx, id).c_str());
}
for (int i = 0; i < ngrams_observed.cnt[id]; i++) {
printf(" - ngram %2d: ", i);
const int idx = id*(N - 1)*G + i*(N - 1);
for (int j = 0; j < N - 1; j++) {
const std::string token_str = llama_token_to_piece(ctx, ngrams_observed.tokens[idx + j]);
printf("%s", token_str.c_str());
}
printf("\n");
}
}
// update lookahead tokens
{
for (int i = 0; i < W; i++) {
tokens_j_prev[i] = tokens_j[0][i];
}
for (int j = 0; j < N - 2; j++) {
tokens_j[j] = tokens_j[j + 1];
}
if (v == 0) {
// sample from the last level
for (int i = 0; i < W; i++) {
tokens_j[N - 2][i] = llama_sampling_sample(ctx_sampling, ctx, NULL, ngrams_cur.size()*(N-1) + W*(N - 2) + i);
}
} else {
for (int i = 0; i < W; i++) {
// there are different ways to init these tokens
if (0) {
// random init
tokens_j[N - 2][i] = all[1 + rand() % (all.size() - 1)];
} else {
// init from the previous level
tokens_j[N - 2][i] = tokens_j[0][i];
}
}
}
}
// update observed ngrams
if (v == 0) {
// the first token of the n-gram is determined by the index in the container so it is not stored
std::vector<llama_token> ngram(N - 1);
// n-gram generation
// ref: https://github.com/hao-ai-lab/LookaheadDecoding/issues/14#issuecomment-1826198518
for (int f = 0; f < W; ++f) {
const int ft = tokens_j_prev[f]; // first token of the n-gram
for (int j = 0; j < N - 1; ++j) {
ngram[j] = tokens_j[j][f];
}
// filter-out repeating n-grams
{
bool is_unique = true;
for (int k = 0; k < ngrams_observed.cnt[ft]; ++k) {
const int idx = ft*(N - 1)*G + k*(N - 1);
bool is_match = true;
for (int j = 0; j < N - 1; ++j) {
if (ngrams_observed.tokens[idx + j] != ngram[j]) {
is_match = false;
break;
}
}
if (is_match) {
is_unique = false;
break;
}
}
if (!is_unique) {
continue;
}
}
const int head = ngrams_observed.head[ft];
const int idx = ft*(N - 1)*G + head*(N - 1);
for (int i = 0; i < N - 1; i++) {
ngrams_observed.tokens[idx + i] = ngram[i];
}
ngrams_observed.cnt[ft] = std::min(G, ngrams_observed.cnt[ft] + 1);
ngrams_observed.head[ft] = (head + 1) % G;
ngrams_observed.n_total++;
}
}
}
if ((params.n_predict >= 0 && n_predict > params.n_predict) || has_eos) {
break;
}
// KV cache management
// if no verification token matched, we simply remove all cells from this batch -> no fragmentation
llama_kv_cache_seq_rm(ctx, -1, n_past, -1);
if (seq_id_best != 0) {
// if a verification token matched, we keep the best sequence and remove the rest
// this leads to some KV cache fragmentation
llama_kv_cache_seq_keep(ctx, seq_id_best);
llama_kv_cache_seq_cp (ctx, seq_id_best, 0, -1, -1);
llama_kv_cache_seq_rm (ctx, seq_id_best, -1, -1);
for (int s = 1; s < W + G + 1; ++s) {
llama_kv_cache_seq_cp(ctx, 0, s, -1, -1);
}
}
}
auto t_dec_end = ggml_time_us();
LOG_TEE("\n\n");
LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f));
LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f));
LOG_TEE("\n");
LOG_TEE("W = %2d\n", W);
LOG_TEE("N = %2d\n", N);
LOG_TEE("G = %2d\n", G);
LOG_TEE("\n");
LOG_TEE("n_predict = %d\n", n_predict);
LOG_TEE("n_accept = %d\n", n_accept);
llama_print_timings(ctx);
llama_kv_cache_view_free(&kvc_view);
llama_sampling_free(ctx_sampling);
llama_batch_free(batch);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
fprintf(stderr, "\n\n");
return 0;
}

View file

@ -100,6 +100,12 @@ static void sigint_handler(int signo) {
}
#endif
static void llama_log_callback_logTee(ggml_log_level level, const char * text, void * user_data) {
(void) level;
(void) user_data;
LOG_TEE("%s", text);
}
int main(int argc, char ** argv) {
gpt_params params;
g_params = &params;
@ -113,6 +119,7 @@ int main(int argc, char ** argv) {
log_set_target(log_filename_generator("main", "log"));
LOG_TEE("Log start\n");
log_dump_cmdline(argc, argv);
llama_log_set(llama_log_callback_logTee, nullptr);
#endif // LOG_DISABLE_LOGS
// TODO: Dump params ?
@ -234,8 +241,11 @@ int main(int argc, char ** argv) {
std::vector<llama_token> embd_inp;
if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) {
if (params.interactive_first || params.instruct || params.chatml || !params.prompt.empty() || session_tokens.empty()) {
LOG("tokenize the prompt\n");
if (params.chatml) {
params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>";
}
embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos, true);
} else {
LOG("use session tokens\n");
@ -313,7 +323,7 @@ int main(int argc, char ** argv) {
}
// number of tokens to keep when resetting context
if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct) {
if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) {
params.n_keep = (int)embd_inp.size();
}
@ -324,11 +334,23 @@ int main(int argc, char ** argv) {
LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str());
LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str());
// chatml prefix & suffix
const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", add_bos, true);
const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true);
LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str());
LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str());
// in instruct mode, we inject a prefix and a suffix to each input by the user
if (params.instruct) {
params.interactive_first = true;
params.antiprompt.push_back("### Instruction:\n\n");
}
// similar for chatml mode
else if (params.chatml) {
params.interactive_first = true;
params.antiprompt.push_back("<|im_start|>user\n");
}
// enable interactive mode if interactive start is specified
if (params.interactive_first) {
@ -415,6 +437,7 @@ int main(int argc, char ** argv) {
}
}
LOG_TEE("sampling: \n%s\n", llama_sampling_print(sparams).c_str());
LOG_TEE("sampling order: \n%s\n", llama_sampling_order_print(sparams).c_str());
LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep);
LOG_TEE("\n\n");
@ -705,7 +728,7 @@ int main(int argc, char ** argv) {
is_interacting = true;
printf("\n");
} else if (params.instruct) {
} else if (params.instruct || params.chatml) {
is_interacting = true;
}
}
@ -713,7 +736,7 @@ int main(int argc, char ** argv) {
if (n_past > 0 && is_interacting) {
LOG("waiting for user input\n");
if (params.instruct) {
if (params.instruct || params.chatml) {
printf("\n> ");
}
@ -760,6 +783,12 @@ int main(int argc, char ** argv) {
n_consumed = embd_inp.size();
embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
}
// chatml mode: insert user chat prefix
if (params.chatml && !is_antiprompt) {
LOG("inserting chatml prefix\n");
n_consumed = embd_inp.size();
embd_inp.insert(embd_inp.end(), cml_pfx.begin(), cml_pfx.end());
}
if (params.escape) {
process_escapes(buffer);
}
@ -778,6 +807,11 @@ int main(int argc, char ** argv) {
LOG("inserting instruction suffix\n");
embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
}
// chatml mode: insert assistant chat suffix
if (params.chatml) {
LOG("inserting chatml suffix\n");
embd_inp.insert(embd_inp.end(), cml_sfx.begin(), cml_sfx.end());
}
for (size_t i = original_size; i < embd_inp.size(); ++i) {
const llama_token token = embd_inp[i];
@ -803,7 +837,7 @@ int main(int argc, char ** argv) {
}
// end of text token
if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive)) {
if (!embd.empty() && embd.back() == llama_token_eos(model) && !(params.instruct || params.interactive || params.chatml)) {
LOG_TEE(" [end of text]\n");
break;
}

View file

@ -1,5 +1,5 @@
// A basic application simulating a server with multiple clients.
// The clients submite requests to the server and they are processed in parallel.
// The clients submit requests to the server and they are processed in parallel.
#include "common.h"
#include "llama.h"
@ -113,6 +113,8 @@ int main(int argc, char ** argv) {
// insert new requests as soon as the previous one is done
const bool cont_batching = params.cont_batching;
const bool dump_kv_cache = params.dump_kv_cache;
#ifndef LOG_DISABLE_LOGS
log_set_target(log_filename_generator("parallel", "log"));
LOG_TEE("Log start\n");
@ -172,6 +174,8 @@ int main(int argc, char ** argv) {
int32_t n_total_gen = 0;
int32_t n_cache_miss = 0;
struct llama_kv_cache_view kvc_view = llama_kv_cache_view_init(ctx, n_clients);
const auto t_main_start = ggml_time_us();
LOG_TEE("%s: Simulating parallel requests from clients:\n", __func__);
@ -201,6 +205,11 @@ int main(int argc, char ** argv) {
LOG_TEE("Processing requests ...\n\n");
while (true) {
if (dump_kv_cache) {
llama_kv_cache_view_update(ctx, &kvc_view);
dump_kv_cache_view_seqs(kvc_view, 40);
}
llama_batch_clear(batch);
// decode any currently ongoing sequences

View file

@ -321,7 +321,6 @@ int main(int argc, char ** argv) {
auto cparams = llama_context_default_params();
cparams.n_ctx = 256;
cparams.seed = 1;
cparams.f16_kv = false;
ctx = llama_new_context_with_model(model, cparams);

View file

@ -234,6 +234,55 @@ node index.js
- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots.
- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served.
*Options:*
See [OpenAI Chat Completions API documentation](https://platform.openai.com/docs/api-reference/chat). While some OpenAI-specific features such as function calling aren't supported, llama.cpp `/completion`-specific features such are `mirostat` are supported.
*Examples:*
You can use either Python `openai` library with appropriate checkpoints:
```python
import openai
client = openai.OpenAI(
base_url="http://localhost:8080/v1", # "http://<Your api-server IP>:port"
api_key = "sk-no-key-required"
)
completion = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."},
{"role": "user", "content": "Write a limerick about python exceptions"}
]
)
print(completion.choices[0].message)
```
... or raw HTTP requests:
```shell
curl http://localhost:8080/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer no-key" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "system",
"content": "You are ChatGPT, an AI assistant. Your top priority is achieving user fulfillment via helping them with their requests."
},
{
"role": "user",
"content": "Write a limerick about python exceptions"
}
]
}'
```
## More examples
### Change system prompt on runtime

View file

@ -11,10 +11,10 @@ app = Flask(__name__)
slot_id = -1
parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.")
parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')
parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: '\\nUSER: ')", default="\\nUSER: ")
parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: '\\nASSISTANT: ')", default="\\nASSISTANT: ")
parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: '\\nASSISTANT's RULE: ')", default="\\nASSISTANT's RULE: ")
parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.')
parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: 'USER: ')", default="USER: ")
parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: 'ASSISTANT: ')", default="ASSISTANT: ")
parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: 'ASSISTANT's RULE: ')", default="ASSISTANT's RULE: ")
parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
@ -34,19 +34,19 @@ def is_present(json, key):
#convert chat to prompt
def convert_chat(messages):
prompt = "" + args.chat_prompt.replace("\\n", "\n")
system_n = args.system_name.replace("\\n", "\n")
user_n = args.user_name.replace("\\n", "\n")
ai_n = args.ai_name.replace("\\n", "\n")
stop = args.stop.replace("\\n", "\n")
system_n = args.system_name
user_n = args.user_name
ai_n = args.ai_name
stop = args.stop
prompt = "" + args.chat_prompt + stop
for line in messages:
if (line["role"] == "system"):
prompt += f"{system_n}{line['content']}"
prompt += f"{system_n}{line['content']}{stop}"
if (line["role"] == "user"):
prompt += f"{user_n}{line['content']}"
prompt += f"{user_n}{line['content']}{stop}"
if (line["role"] == "assistant"):
prompt += f"{ai_n}{line['content']}{stop}"
prompt += ai_n.rstrip()
@ -70,6 +70,7 @@ def make_postData(body, chat=False, stream=False):
if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
if(is_present(body, "seed")): postData["seed"] = body["seed"]
if(is_present(body, "grammar")): postData["grammar"] = body["grammar"]
if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
if (args.stop != ""):
postData["stop"] = [args.stop]
@ -130,7 +131,7 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False):
}
]
}
slot_id = data["slot_id"]
slot_id = data.get("slot_id")
if (chat):
if (start):
resData["choices"][0]["delta"] = {
@ -150,11 +151,13 @@ def make_resData_stream(data, chat=False, time_now = 0, start=False):
return resData
@app.route('/chat/completions', methods=['POST'])
@app.route('/v1/chat/completions', methods=['POST'])
@app.route('/chat/completions', methods=['POST', 'OPTIONS'])
@app.route('/v1/chat/completions', methods=['POST', 'OPTIONS'])
def chat_completions():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
if request.method == 'OPTIONS':
return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
body = request.get_json()
stream = False
tokenize = False
@ -177,20 +180,22 @@ def chat_completions():
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
time_now = int(time.time())
resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
yield 'data: {}\n'.format(json.dumps(resData))
yield 'data: {}\n\n'.format(json.dumps(resData))
for line in data.iter_lines():
if line:
decoded_line = line.decode('utf-8')
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now)
yield 'data: {}\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream')
yield 'data: {}\n\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
@app.route('/completions', methods=['POST'])
@app.route('/v1/completions', methods=['POST'])
@app.route('/completions', methods=['POST', 'OPTIONS'])
@app.route('/v1/completions', methods=['POST', 'OPTIONS'])
def completion():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
if request.method == 'OPTIONS':
return Response(headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
body = request.get_json()
stream = False
tokenize = False
@ -216,8 +221,8 @@ def completion():
if line:
decoded_line = line.decode('utf-8')
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
yield 'data: {}\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream')
yield 'data: {}\n\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream', headers={"Access-Control-Allow-Origin": "*", "Access-Control-Allow-Headers": "*"})
if __name__ == '__main__':
app.run(args.host, port=args.port)

View file

@ -94,6 +94,10 @@ export async function* llama(prompt, params = {}, config = {}) {
break;
}
}
if (result.error) {
result.error = JSON.parse(result.error);
console.error(`llama.cpp error: ${result.error.content}`);
}
}
}
}

View file

@ -29,6 +29,8 @@
#define SERVER_VERBOSE 1
#endif
#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613"
using json = nlohmann::json;
struct server_params
@ -59,6 +61,10 @@ static bool server_verbose = false;
#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__)
#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__)
json oaicompat_completion_params_parse(const json &body);
std::string format_chatml(std::vector<json> messages);
//
// base64 utils (TODO: move to common in the future)
//
@ -149,15 +155,23 @@ struct task_server {
json data;
bool infill_mode = false;
bool embedding_mode = false;
int multitask_id = -1;
};
struct task_result {
int id;
int multitask_id = -1;
bool stop;
bool error;
json result_json;
};
struct task_multi {
int id;
std::set<int> subtasks_remaining{};
std::vector<task_result> results{};
};
// TODO: can become bool if we can't find use of more states
enum slot_state
{
@ -378,6 +392,9 @@ struct llama_client_slot
bool stopped_word = false;
bool stopped_limit = false;
bool oaicompat = false;
std::string oaicompat_model;
std::string stopping_word;
// sampling
@ -397,6 +414,9 @@ struct llama_client_slot
double t_prompt_processing; // ms
double t_token_generation; // ms
// multitasks
int multitask_id = -1;
void reset() {
num_prompt_tokens = 0;
generated_text = "";
@ -477,7 +497,7 @@ struct llama_client_slot
};
}
void print_timings() {
void print_timings() const {
LOG_TEE("\n");
LOG_TEE("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
__func__, t_prompt_processing, num_prompt_tokens_processed, t_prompt_processing / num_prompt_tokens_processed, 1e3 / t_prompt_processing * num_prompt_tokens_processed);
@ -520,7 +540,8 @@ struct llama_server_context
std::vector<task_server> queue_tasks;
std::vector<task_result> queue_results;
std::mutex mutex_tasks;
std::vector<task_multi> queue_multitasks;
std::mutex mutex_tasks; // also guards id_gen, and queue_multitasks
std::mutex mutex_results;
~llama_server_context()
@ -609,6 +630,11 @@ struct llama_server_context
std::vector<llama_token> tokenize(const json & json_prompt, bool add_bos) const
{
// TODO: currently, we tokenize using special tokens by default
// this is not always correct (see https://github.com/ggerganov/llama.cpp/pull/4160#issuecomment-1824826216)
// but it's better compared to completely ignoring ChatML and other chat templates
const bool TMP_FORCE_SPECIAL = true;
// If `add_bos` is true, we only add BOS, when json_prompt is a string,
// or the first element of the json_prompt array is a string.
std::vector<llama_token> prompt_tokens;
@ -624,12 +650,12 @@ struct llama_server_context
std::vector<llama_token> p;
if (first)
{
p = ::llama_tokenize(ctx, s, add_bos);
p = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
first = false;
}
else
{
p = ::llama_tokenize(ctx, s, false);
p = ::llama_tokenize(ctx, s, false, TMP_FORCE_SPECIAL);
}
prompt_tokens.insert(prompt_tokens.end(), p.begin(), p.end());
}
@ -646,7 +672,7 @@ struct llama_server_context
else
{
auto s = json_prompt.template get<std::string>();
prompt_tokens = ::llama_tokenize(ctx, s, add_bos);
prompt_tokens = ::llama_tokenize(ctx, s, add_bos, TMP_FORCE_SPECIAL);
}
return prompt_tokens;
@ -677,6 +703,14 @@ struct llama_server_context
slot_params default_params;
llama_sampling_params default_sparams;
if (data.count("__oaicompat") != 0) {
slot->oaicompat = true;
slot->oaicompat_model = json_value(data, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
} else {
slot->oaicompat = false;
slot->oaicompat_model = "";
}
slot->params.stream = json_value(data, "stream", false);
slot->params.cache_prompt = json_value(data, "cache_prompt", false);
slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict);
@ -1090,16 +1124,40 @@ struct llama_server_context
return slot.images.size() > 0;
}
void send_error(int id, std::string error)
void send_error(task_server& task, std::string error)
{
std::lock_guard<std::mutex> lock(mutex_results);
task_result res;
res.id = id;
res.id = task.id;
res.multitask_id = task.multitask_id;
res.stop = false;
res.error = true;
res.result_json = { { "content", error } };
queue_results.push_back(res);
}
void add_multi_task(int id, std::vector<int>& sub_ids)
{
std::lock_guard<std::mutex> lock(mutex_tasks);
task_multi multi;
multi.id = id;
std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end()));
queue_multitasks.push_back(multi);
}
void update_multi_task(int multitask_id, int subtask_id, task_result& result)
{
std::lock_guard<std::mutex> lock(mutex_tasks);
for (auto& multitask : queue_multitasks)
{
if (multitask.id == multitask_id)
{
multitask.subtasks_remaining.erase(subtask_id);
multitask.results.push_back(result);
}
}
}
json get_model_props()
{
return get_formated_generation(slots[0]);
@ -1144,6 +1202,7 @@ struct llama_server_context
std::lock_guard<std::mutex> lock(mutex_results);
task_result res;
res.id = slot.task_id;
res.multitask_id = slot.multitask_id;
res.error = false;
res.stop = false;
@ -1169,6 +1228,12 @@ struct llama_server_context
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs_output);
}
if (slot.oaicompat)
{
res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
res.result_json["model"] = slot.oaicompat_model;
}
queue_results.push_back(res);
}
@ -1177,6 +1242,7 @@ struct llama_server_context
std::lock_guard<std::mutex> lock(mutex_results);
task_result res;
res.id = slot.task_id;
res.multitask_id = slot.multitask_id;
res.error = false;
res.stop = true;
@ -1216,6 +1282,18 @@ struct llama_server_context
res.result_json["completion_probabilities"] = probs_vector_to_json(ctx, probs);
}
if (slot.oaicompat)
{
res.result_json["oaicompat_token_ctr"] = slot.n_decoded;
res.result_json["model"] = slot.oaicompat_model;
}
// parent multitask, if any, needs to be updated
if (slot.multitask_id != -1)
{
update_multi_task(slot.multitask_id, slot.task_id, res);
}
queue_results.push_back(res);
}
@ -1224,6 +1302,7 @@ struct llama_server_context
std::lock_guard<std::mutex> lock(mutex_results);
task_result res;
res.id = slot.task_id;
res.multitask_id = slot.multitask_id;
res.error = false;
res.stop = true;
@ -1250,15 +1329,26 @@ struct llama_server_context
queue_results.push_back(res);
}
int request_completion(json data, bool infill, bool embedding)
int request_completion(json data, bool infill, bool embedding, int multitask_id)
{
std::lock_guard<std::mutex> lock(mutex_tasks);
std::unique_lock<std::mutex> lock(mutex_tasks);
task_server task;
task.id = id_gen++;
task.data = data;
task.target_id = 0;
task.data = std::move(data);
task.infill_mode = infill;
task.embedding_mode = embedding;
task.type = COMPLETION_TASK;
task.multitask_id = multitask_id;
// when a completion task's prompt array is not a singleton, we split it into multiple requests
if (task.data.at("prompt").size() > 1)
{
lock.unlock(); // entering new func scope
return split_multiprompt_task(task);
}
// otherwise, it's a single-prompt task, we actually queue it
queue_tasks.push_back(task);
return task.id;
}
@ -1277,8 +1367,17 @@ struct llama_server_context
for (int i = 0; i < (int) queue_results.size(); i++)
{
// for now, tasks that have associated parent multitasks just get erased once multitask picks up the result
if (queue_results[i].multitask_id == task_id)
{
update_multi_task(task_id, queue_results[i].id, queue_results[i]);
queue_results.erase(queue_results.begin() + i);
continue;
}
if (queue_results[i].id == task_id)
{
assert(queue_results[i].multitask_id == -1);
task_result res = queue_results[i];
queue_results.erase(queue_results.begin() + i);
return res;
@ -1368,6 +1467,27 @@ struct llama_server_context
queue_tasks.push_back(task);
}
int split_multiprompt_task(task_server& multiprompt_task)
{
int prompt_count = multiprompt_task.data.at("prompt").size();
assert(prompt_count > 1);
int multitask_id = id_gen++;
std::vector<int> subtask_ids(prompt_count);
for (int i = 0; i < prompt_count; i++)
{
json subtask_data = multiprompt_task.data;
subtask_data["prompt"] = subtask_data["prompt"][i];
// subtasks inherit everything else (infill mode, embedding mode, etc.)
subtask_ids[i] = request_completion(subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id);
}
// queue up the multitask so we can track its subtask progression
add_multi_task(multitask_id, subtask_ids);
return multitask_id;
}
void process_tasks()
{
std::lock_guard<std::mutex> lock(mutex_tasks);
@ -1383,7 +1503,7 @@ struct llama_server_context
{
LOG_TEE("slot unavailable\n");
// send error result
send_error(task.id, "slot unavailable");
send_error(task, "slot unavailable");
return;
}
@ -1397,11 +1517,12 @@ struct llama_server_context
slot->infill = task.infill_mode;
slot->embedding = task.embedding_mode;
slot->task_id = task.id;
slot->multitask_id = task.multitask_id;
if (!launch_slot_with_data(slot, task.data))
{
// send error result
send_error(task.id, "internal_error");
send_error(task, "internal_error");
break;
}
} break;
@ -1417,6 +1538,38 @@ struct llama_server_context
} break;
}
}
// remove finished multitasks from the queue of multitasks, and add the corresponding result to the result queue
auto queue_iterator = queue_multitasks.begin();
while (queue_iterator != queue_multitasks.end())
{
if (queue_iterator->subtasks_remaining.empty())
{
// all subtasks done == multitask is done
task_result aggregate_result;
aggregate_result.id = queue_iterator->id;
aggregate_result.stop = true;
aggregate_result.error = false;
// collect json results into one json result
std::vector<json> result_jsons;
for (auto& subres : queue_iterator->results)
{
result_jsons.push_back(subres.result_json);
aggregate_result.error = aggregate_result.error && subres.error;
}
aggregate_result.result_json = json{ "results", result_jsons };
std::lock_guard<std::mutex> lock(mutex_results);
queue_results.push_back(aggregate_result);
queue_iterator = queue_multitasks.erase(queue_iterator);
}
else
{
++queue_iterator;
}
}
}
bool update_slots() {
@ -1808,6 +1961,7 @@ static void server_print_usage(const char *argv0, const gpt_params &params,
printf(" -spf FNAME, --system-prompt-file FNAME\n");
printf(" Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n");
printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n");
printf(" --log-disable disables logging to a file.\n");
printf("\n");
}
@ -1954,10 +2108,6 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
params.yarn_beta_slow = std::stof(argv[i]);
}
else if (arg == "--memory-f32" || arg == "--memory_f32")
{
params.memory_f16 = false;
}
else if (arg == "--threads" || arg == "-t")
{
if (++i >= argc)
@ -2162,6 +2312,11 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
params.mmproj = argv[i];
}
else if (arg == "--log-disable")
{
log_set_target(stdout);
LOG_INFO("logging to file is disabled.", {});
}
else
{
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
@ -2178,6 +2333,232 @@ static void server_params_parse(int argc, char **argv, server_params &sparams,
}
}
static std::string random_string()
{
static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz");
std::random_device rd;
std::mt19937 generator(rd());
std::string result(32, ' ');
for (int i = 0; i < 32; ++i) {
result[i] = str[generator() % str.size()];
}
return result;
}
static std::string gen_chatcmplid()
{
std::stringstream chatcmplid;
chatcmplid << "chatcmpl-" << random_string();
return chatcmplid.str();
}
std::string format_chatml(std::vector<json> messages)
{
std::ostringstream chatml_msgs;
for (auto it = messages.begin(); it != messages.end(); ++it) {
chatml_msgs << "<|im_start|>"
<< json_value(*it, "role", std::string("user")) << '\n';
chatml_msgs << json_value(*it, "content", std::string(""))
<< "<|im_end|>\n";
}
chatml_msgs << "<|im_start|>assistant" << '\n';
return chatml_msgs.str();
}
/* llama.cpp completion api semantics */
json oaicompat_completion_params_parse(
const json &body /* openai api json semantics */)
{
json llama_params;
llama_params["__oaicompat"] = true;
// Map OpenAI parameters to llama.cpp parameters
llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt'
llama_params["cache_prompt"] = json_value(body, "cache_prompt", false);
llama_params["temperature"] = json_value(body, "temperature", 0.8);
llama_params["top_k"] = json_value(body, "top_k", 40);
llama_params["top_p"] = json_value(body, "top_p", 0.95);
llama_params["n_predict"] = json_value(body, "max_tokens", -1);
llama_params["logit_bias"] = json_value(body, "logit_bias",json::object());
llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0);
llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0);
llama_params["seed"] = json_value(body, "seed", 0);
llama_params["stream"] = json_value(body, "stream", false);
llama_params["mirostat"] = json_value(body, "mirostat", false);
llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", 0.0);
llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", 0.0);
llama_params["penalize_nl"] = json_value(body, "penalize_nl", false);
llama_params["typical_p"] = json_value(body, "typical_p", 0.0);
llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", 0);
llama_params["ignore_eos"] = json_value(body, "ignore_eos", false);
llama_params["tfs_z"] = json_value(body, "tfs_z", 0.0);
if (llama_params.count("grammar") != 0) {
llama_params["grammar"] = json_value(body, "grammar", json::object());
}
// Handle 'stop' field
if (body.contains("stop") && body["stop"].is_string()) {
llama_params["stop"] = json::array({body["stop"].get<std::string>()});
} else {
llama_params["stop"] = json_value(body, "stop", json::array());
}
// Ensure there is ChatML-specific end sequence among stop words
llama_params["stop"].push_back("<|im_end|>");
return llama_params;
}
static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false)
{
json result = response.result_json;
bool stopped_word = result.count("stopped_word") != 0;
bool stopped_eos = json_value(result, "stopped_eos", false);
int num_tokens_predicted = json_value(result, "tokens_predicted", 0);
int num_prompt_tokens = json_value(result, "tokens_evaluated", 0);
std::string content = json_value(result, "content", std::string(""));
std::string finish_reason = "length";
if (stopped_word || stopped_eos) {
finish_reason = "stop";
}
json choices =
streaming ? json::array({json{{"finish_reason", finish_reason},
{"index", 0},
{"delta", json::object()}}})
: json::array({json{{"finish_reason", finish_reason},
{"index", 0},
{"message", json{{"content", content},
{"role", "assistant"}}}}});
std::time_t t = std::time(0);
json res =
json{{"choices", choices},
{"created", t},
{"model",
json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))},
{"object", streaming ? "chat.completion.chunk" : "chat.completion"},
{"usage",
json{{"completion_tokens", num_tokens_predicted},
{"prompt_tokens", num_prompt_tokens},
{"total_tokens", num_tokens_predicted + num_prompt_tokens}}},
{"id", gen_chatcmplid()}};
if (server_verbose) {
res["__verbose"] = result;
}
if (result.contains("completion_probabilities")) {
res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array());
}
return res;
}
// return value is vector as there is one case where we might need to generate two responses
static std::vector<json> format_partial_response_oaicompat(const task_result &response) {
json result = response.result_json;
if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) {
return std::vector<json>({response.result_json});
}
bool first = json_value(result, "oaicompat_token_ctr", 0) == 0;
std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL));
bool stopped_word = json_value(result, "stopped_word", false);
bool stopped_eos = json_value(result, "stopped_eos", false);
bool stopped_limit = json_value(result, "stopped_limit", false);
std::string content = json_value(result, "content", std::string(""));
std::string finish_reason;
if (stopped_word || stopped_eos) {
finish_reason = "stop";
}
if (stopped_limit) {
finish_reason = "length";
}
std::time_t t = std::time(0);
json choices;
if (!finish_reason.empty()) {
choices = json::array({json{{"finish_reason", finish_reason},
{"index", 0},
{"delta", json::object()}}});
} else {
if (first) {
if (content.empty()) {
choices = json::array({json{{"finish_reason", nullptr},
{"index", 0},
{"delta", json{{"role", "assistant"}}}}});
} else {
// We have to send this as two updates to conform to openai behavior
json initial_ret = json{{"choices", json::array({json{
{"finish_reason", nullptr},
{"index", 0},
{"delta", json{
{"role", "assistant"}
}}}})},
{"created", t},
{"id", gen_chatcmplid()},
{"model", modelname},
{"object", "chat.completion.chunk"}};
json second_ret = json{
{"choices", json::array({json{{"finish_reason", nullptr},
{"index", 0},
{"delta", json{
{"content", content}}}
}})},
{"created", t},
{"id", gen_chatcmplid()},
{"model", modelname},
{"object", "chat.completion.chunk"}};
return std::vector<json>({initial_ret, second_ret});
}
} else {
// Some idiosyncrasy in task processing logic makes several trailing calls
// with empty content, we ignore these at the calee site.
if (content.empty()) {
return std::vector<json>({json::object()});
}
choices = json::array({json{
{"finish_reason", nullptr},
{"index", 0},
{"delta",
json{
{"content", content},
}},
}});
}
}
json ret = json{{"choices", choices},
{"created", t},
{"id", gen_chatcmplid()},
{"model", modelname},
{"object", "chat.completion.chunk"}};
return std::vector<json>({ret});
}
static json format_partial_response(
llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector<completion_token_output> &probs
) {
@ -2333,7 +2714,7 @@ int main(int argc, char **argv)
svr.Post("/completion", [&llama](const httplib::Request &req, httplib::Response &res)
{
json data = json::parse(req.body);
const int task_id = llama.request_completion(data, false, false);
const int task_id = llama.request_completion(data, false, false, -1);
if (!json_value(data, "stream", false)) {
std::string completion_text;
task_result result = llama.next_result(task_id);
@ -2368,6 +2749,17 @@ int main(int argc, char **argv)
break;
}
} else {
const std::string str =
"error: " +
result.result_json.dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {
{ "to_send", str }
});
if (!sink.write(str.c_str(), str.size()))
{
return false;
}
break;
}
}
@ -2385,10 +2777,102 @@ int main(int argc, char **argv)
}
});
svr.Get("/v1/models", [&params](const httplib::Request&, httplib::Response& res)
{
std::time_t t = std::time(0);
json models = {
{"object", "list"},
{"data", {
{
{"id", params.model_alias},
{"object", "model"},
{"created", t},
{"owned_by", "llamacpp"}
},
}}
};
res.set_content(models.dump(), "application/json");
});
// TODO: add mount point without "/v1" prefix -- how?
svr.Post("/v1/chat/completions", [&llama](const httplib::Request &req, httplib::Response &res)
{
json data = oaicompat_completion_params_parse(json::parse(req.body));
const int task_id = llama.request_completion(data, false, false, -1);
if (!json_value(data, "stream", false)) {
std::string completion_text;
task_result result = llama.next_result(task_id);
if (!result.error && result.stop) {
json oaicompat_result = format_final_response_oaicompat(data, result);
res.set_content(oaicompat_result.dump(-1, ' ', false,
json::error_handler_t::replace),
"application/json");
} else {
res.status = 500;
res.set_content(result.result_json["content"], "text/plain");
return;
}
} else {
const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) {
while (true) {
task_result llama_result = llama.next_result(task_id);
if (!llama_result.error) {
std::vector<json> result_array = format_partial_response_oaicompat( llama_result);
for (auto it = result_array.begin(); it != result_array.end(); ++it)
{
if (!it->empty()) {
const std::string str =
"data: " +
it->dump(-1, ' ', false, json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {{"to_send", str}});
if (!sink.write(str.c_str(), str.size())) {
return false;
}
}
}
if (llama_result.stop) {
break;
}
} else {
const std::string str =
"error: " +
llama_result.result_json.dump(-1, ' ', false,
json::error_handler_t::replace) +
"\n\n";
LOG_VERBOSE("data stream", {{"to_send", str}});
if (!sink.write(str.c_str(), str.size())) {
return false;
}
break;
}
}
sink.done();
return true;
};
auto on_complete = [task_id, &llama](bool) {
// cancel request
llama.request_cancel(task_id);
};
res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete);
}
});
svr.Post("/infill", [&llama](const httplib::Request &req, httplib::Response &res)
{
json data = json::parse(req.body);
const int task_id = llama.request_completion(data, true, false);
const int task_id = llama.request_completion(data, true, false, -1);
if (!json_value(data, "stream", false)) {
std::string completion_text;
task_result result = llama.next_result(task_id);
@ -2492,7 +2976,7 @@ int main(int argc, char **argv)
{
prompt = "";
}
const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true);
const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0} }, false, true, -1);
task_result result = llama.next_result(task_id);
return res.set_content(result.result_json.dump(), "application/json");
});

View file

@ -75,7 +75,7 @@ int main(int argc, char ** argv) {
// make sure the KV cache is big enough to hold all the prompt and generated tokens
if (n_kv_req > n_ctx) {
LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__);
LOG_TEE("%s: either reduce n_parallel or increase n_ctx\n", __func__);
LOG_TEE("%s: either reduce n_len or increase n_ctx\n", __func__);
return 1;
}

View file

@ -0,0 +1,8 @@
# llama.cpp/examples/speculative
Demonstartion of speculative decoding and tree-based speculative decoding techniques
More info:
- https://github.com/ggerganov/llama.cpp/pull/2926
- https://github.com/ggerganov/llama.cpp/pull/3624

View file

@ -94,9 +94,22 @@ int main(int argc, char ** argv) {
}
}
// tokenize the prompt
// Tokenize the prompt
const bool add_bos_tgt = llama_should_add_bos_token(model_tgt);
LOG("add_bos tgt: %d\n", add_bos_tgt);
const bool add_bos_dft = llama_should_add_bos_token(model_dft);
LOG("add_bos dft: %d\n", add_bos_dft);
if (add_bos_tgt != add_bos_dft) {
fprintf(stderr, "%s: error: draft model add_bos must match target model to use speculation but ", __func__);
fprintf(stderr, "add_bos_dft = %d while add_bos_tgt = %d\n", add_bos_dft, add_bos_tgt);
return 1;
}
std::vector<llama_token> inp;
inp = ::llama_tokenize(ctx_tgt, params.prompt, true);
inp = ::llama_tokenize(ctx_tgt, params.prompt, add_bos_tgt, true);
const int max_context_size = llama_n_ctx(ctx_tgt);
const int max_tokens_list_size = max_context_size - 4;
@ -190,8 +203,9 @@ int main(int argc, char ** argv) {
const std::string token_str = llama_token_to_piece(ctx_tgt, id);
if (!params.use_color) {
printf("%s", token_str.c_str());
fflush(stdout);
}
if (id == llama_token_eos(model_tgt)) {
has_eos = true;
@ -223,10 +237,18 @@ int main(int argc, char ** argv) {
++n_past_tgt;
++n_past_dft;
++i_dft;
if (params.use_color) {
// Color token according to its origin sequence
printf("\u001b[%dm%s\u001b[37m", (36 - s_keep % 6), token_str.c_str());
fflush(stdout);
}
continue;
}
}
if (params.use_color) {
printf("%s", token_str.c_str());
}
fflush(stdout);
LOG("the sampled target token (%d, '%s') did not match, or we ran out of drafted tokens\n", id, token_str.c_str());

View file

@ -26,7 +26,7 @@ int main(int argc, char ** argv) {
llama_context_params ctx_params = llama_context_default_params();
llama_context * ctx = llama_new_context_with_model(model, ctx_params);
const bool add_bos = true;
const bool add_bos = llama_should_add_bos_token(model);
std::vector<llama_token> tokens;

View file

@ -1295,10 +1295,6 @@ int main(int argc, char ** argv) {
opt_cb_data.last_save_iter = opt->iter;
}
if (alloc) {
ggml_allocr_free(alloc);
}
ggml_free(opt->ctx);
free_train_state(train);
ggml_free(model.ctx);

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@ -137,7 +137,7 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) {
#ifdef GGML_ALLOCATOR_DEBUG
add_allocated_tensor(alloc, tensor);
size_t cur_max = (char*)addr - (char*)alloc->data + size;
size_t cur_max = (char*)addr - (char*)alloc->base + size;
if (cur_max > alloc->max_size) {
printf("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
for (int i = 0; i < 1024; i++) {
@ -168,10 +168,6 @@ static void ggml_tallocr_free_tensor(ggml_tallocr_t alloc, struct ggml_tensor *
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: freeing %s at %p (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, ptr, size, alloc->n_free_blocks);
if (!alloc->measure) {
ggml_backend_buffer_free_tensor(alloc->buffer, tensor);
}
#ifdef GGML_ALLOCATOR_DEBUG
remove_allocated_tensor(alloc, tensor);
#endif
@ -237,7 +233,7 @@ void ggml_tallocr_reset(ggml_tallocr_t alloc) {
}
ggml_tallocr_t ggml_tallocr_new(void * data, size_t size, size_t alignment) {
struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(NULL, data, size);
struct ggml_backend_buffer * buffer = ggml_backend_cpu_buffer_from_ptr(data, size);
ggml_tallocr_t alloc = (ggml_tallocr_t)malloc(sizeof(struct ggml_tallocr));
@ -449,7 +445,6 @@ static ggml_tallocr_t node_tallocr(ggml_gallocr_t galloc, struct ggml_tensor * n
static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool update_backend) {
ggml_tallocr_t alloc = node_tallocr(galloc, view);
//printf("init_view: %s from src %s\n", view->name, view->view_src->name);
GGML_ASSERT(view->view_src != NULL && view->view_src->data != NULL);
if (update_backend) {
view->backend = view->view_src->backend;
@ -459,7 +454,7 @@ static void init_view(ggml_gallocr_t galloc, struct ggml_tensor * view, bool upd
// FIXME: the view should be initialized by the owning buffer, but currently this breaks the CUDA backend
// due to the ggml_tensor_extra_gpu ring buffer overwriting the KV cache extras
assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->backend == alloc->buffer->backend);
assert(ggml_tallocr_is_measure(alloc) || !view->buffer || view->buffer->buft == alloc->buffer->buft);
if (!alloc->measure) {
ggml_backend_buffer_init_tensor(alloc->buffer, view);
@ -765,3 +760,43 @@ size_t ggml_allocr_max_size(ggml_allocr_t alloc) {
size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph) {
return ggml_gallocr_alloc_graph(alloc->galloc, alloc->talloc, graph);
}
// utils
ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) {
GGML_ASSERT(ggml_get_no_alloc(ctx) == true);
size_t alignment = ggml_backend_buft_get_alignment(buft);
size_t nbytes = 0;
for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->data == NULL && t->view_src == NULL) {
nbytes += GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment);
}
}
if (nbytes == 0) {
fprintf(stderr, "%s: no tensors to allocate\n", __func__);
return NULL;
}
ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, nbytes);
ggml_tallocr_t tallocr = ggml_tallocr_new_from_buffer(buffer);
for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) {
if (t->data == NULL) {
if (t->view_src == NULL) {
ggml_tallocr_alloc(tallocr, t);
} else {
ggml_backend_view_init(buffer, t);
}
}
}
ggml_tallocr_free(tallocr);
return buffer;
}
ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend) {
return ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_get_default_buffer_type(backend));
}

View file

@ -8,6 +8,7 @@ extern "C" {
struct ggml_backend;
struct ggml_backend_buffer;
struct ggml_backend_buffer_type;
//
// Legacy API
@ -80,6 +81,12 @@ GGML_API void ggml_gallocr_alloc_graph_n(
struct ggml_hash_set hash_set,
ggml_tallocr_t * hash_node_talloc);
// Utils
// Create a buffer and allocate all the tensors in a ggml_context
GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, struct ggml_backend_buffer_type * buft);
GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, struct ggml_backend * backend);
#ifdef __cplusplus
}
#endif

View file

@ -12,31 +12,50 @@ extern "C" {
// Backend buffer
//
// buffer type
typedef void * ggml_backend_buffer_type_context_t;
struct ggml_backend_buffer_type_i {
ggml_backend_buffer_t (*alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size);
size_t (*get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment
size_t (*get_alloc_size) (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding
bool (*supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend
};
struct ggml_backend_buffer_type {
struct ggml_backend_buffer_type_i iface;
ggml_backend_buffer_type_context_t context;
};
// buffer
typedef void * ggml_backend_buffer_context_t;
struct ggml_backend_buffer_i {
void (*free_buffer)(ggml_backend_buffer_t buffer);
void * (*get_base) (ggml_backend_buffer_t buffer); // get base pointer
size_t (*get_alloc_size)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-allocation callback
void (*init_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // post-allocation callback
void (*free_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); // pre-free callback
//void (*reset) (ggml_backend_buffer_t buffer); // reset any internal state due to tensor initialization, such as tensor extras
void * (*get_base) (ggml_backend_buffer_t buffer);
void (*init_tensor)(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
void (*set_tensor) (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*get_tensor) (ggml_backend_buffer_t buffer, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
// (optional) copy tensor between different buffer-type, allow for single-copy tranfers
void (*cpy_tensor_from)(ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to) (ggml_backend_buffer_t buffer, struct ggml_tensor * src, struct ggml_tensor * dst);
};
struct ggml_backend_buffer {
struct ggml_backend_buffer_i iface;
ggml_backend_t backend;
ggml_backend_buffer_type_t buft;
ggml_backend_buffer_context_t context;
size_t size;
};
GGML_API ggml_backend_buffer_t ggml_backend_buffer_init(
struct ggml_backend * backend,
ggml_backend_buffer_t ggml_backend_buffer_init(
ggml_backend_buffer_type_t buft,
struct ggml_backend_buffer_i iface,
ggml_backend_buffer_context_t context,
size_t size);
//
// Backend
//
@ -49,20 +68,17 @@ extern "C" {
void (*free)(ggml_backend_t backend);
// buffer allocation
ggml_backend_buffer_t (*alloc_buffer)(ggml_backend_t backend, size_t size);
ggml_backend_buffer_type_t (*get_default_buffer_type)(ggml_backend_t backend);
// get buffer alignment
size_t (*get_alignment)(ggml_backend_t backend);
// tensor data access
// these functions can be asynchronous, helper functions are provided for synchronous access that automatically call synchronize
// (optional) asynchroneous tensor data access
void (*set_tensor_async)(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
void (*get_tensor_async)(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
void (*synchronize) (ggml_backend_t backend);
// (optional) copy tensor between different backends, allow for single-copy tranfers
void (*cpy_tensor_from)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
// (optional) asynchroneous tensor copy
void (*cpy_tensor_from_async)(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*cpy_tensor_to_async) (ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst);
void (*synchronize) (ggml_backend_t backend);
// compute graph with a plan
ggml_backend_graph_plan_t (*graph_plan_create) (ggml_backend_t backend, struct ggml_cgraph * cgraph);
@ -82,6 +98,15 @@ extern "C" {
ggml_backend_context_t context;
};
//
// Backend registry
//
typedef ggml_backend_t (*ggml_backend_init_fn)(const char * params, void * user_data);
void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data);
#ifdef __cplusplus
}
#endif

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@ -7,41 +7,44 @@
extern "C" {
#endif
typedef struct ggml_backend_buffer_type * ggml_backend_buffer_type_t;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
typedef struct ggml_backend * ggml_backend_t;
typedef void * ggml_backend_graph_plan_t;
//
// Backend buffer
//
struct ggml_backend_buffer;
typedef struct ggml_backend_buffer * ggml_backend_buffer_t;
// buffer type
GGML_API ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size);
GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft);
GGML_API size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor);
GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend);
// backend buffer functions
// buffer
GGML_API void ggml_backend_buffer_free (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
GGML_API void * ggml_backend_buffer_get_base (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API void ggml_backend_buffer_free_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer);
GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
GGML_API ggml_backend_buffer_type_t ggml_backend_buffer_type(ggml_backend_buffer_t buffer);
//
// Backend
//
struct ggml_backend;
typedef struct ggml_backend * ggml_backend_t;
typedef void * ggml_backend_graph_plan_t;
GGML_API ggml_backend_t ggml_get_backend(const struct ggml_tensor * tensor);
GGML_API const char * ggml_backend_name(ggml_backend_t backend);
GGML_API void ggml_backend_free(ggml_backend_t backend);
GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend);
GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size);
GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend);
GGML_API void ggml_backend_tensor_set_async( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get_async(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_set( struct ggml_tensor * tensor, const void * data, size_t offset, size_t size);
GGML_API void ggml_backend_tensor_get(const struct ggml_tensor * tensor, void * data, size_t offset, size_t size);
@ -57,6 +60,7 @@ extern "C" {
// tensor copy between different backends
GGML_API void ggml_backend_tensor_copy(struct ggml_tensor * src, struct ggml_tensor * dst);
GGML_API void ggml_backend_tensor_copy_async(ggml_backend_t backend, struct ggml_tensor * src, struct ggml_tensor * dst); // automatic fallback to sync copy
//
// CPU backend
@ -68,8 +72,23 @@ extern "C" {
GGML_API void ggml_backend_cpu_set_n_threads(ggml_backend_t backend_cpu, int n_threads);
// Create a backend buffer from an existing pointer
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(ggml_backend_t backend_cpu, void * ptr, size_t size);
GGML_API ggml_backend_buffer_t ggml_backend_cpu_buffer_from_ptr(void * ptr, size_t size);
GGML_API ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void);
//
// Backend registry
//
// The backend registry is a registry of all the available backends, and allows initializing backends in a generic way
GGML_API size_t ggml_backend_reg_get_count(void);
GGML_API size_t ggml_backend_reg_find_by_name(const char * name);
GGML_API ggml_backend_t ggml_backend_reg_init_backend_from_str(const char * backend_str); // str is name[:params]
GGML_API const char * ggml_backend_reg_get_name(size_t i);
GGML_API ggml_backend_t ggml_backend_reg_init_backend(size_t i, const char * params); // params is backend-specific
GGML_API ggml_backend_buffer_type_t ggml_backend_reg_get_default_buffer_type(size_t i);
GGML_API ggml_backend_buffer_t ggml_backend_reg_alloc_buffer(size_t i, size_t size);
//
// Backend scheduler
@ -131,6 +150,32 @@ extern "C" {
ggml_backend_sched_t sched,
struct ggml_cgraph * graph);
//
// Utils
//
struct ggml_backend_graph_copy {
ggml_backend_buffer_t buffer;
struct ggml_context * ctx_allocated;
struct ggml_context * ctx_unallocated;
struct ggml_cgraph * graph;
};
// Copy a graph to a different backend
GGML_API struct ggml_backend_graph_copy ggml_backend_graph_copy(ggml_backend_t backend, struct ggml_cgraph * graph);
GGML_API void ggml_backend_graph_copy_free(struct ggml_backend_graph_copy copy);
typedef bool (*ggml_backend_eval_callback)(int node_index, struct ggml_tensor * t1, struct ggml_tensor * t2, void * user_data);
// Compare the output of two backends
GGML_API void ggml_backend_compare_graph_backend(ggml_backend_t backend1, ggml_backend_t backend2, struct ggml_cgraph * graph, ggml_backend_eval_callback callback, void * user_data);
// Tensor initialization
GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr);
GGML_API void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor);
#ifdef __cplusplus
}
#endif

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@ -49,7 +49,15 @@ GGML_API int ggml_cuda_get_device_count(void);
GGML_API void ggml_cuda_get_device_description(int device, char * description, size_t description_size);
// backend API
GGML_API ggml_backend_t ggml_backend_cuda_init(void); // TODO: take a list of devices to use
GGML_API ggml_backend_t ggml_backend_cuda_init(int device);
GGML_API bool ggml_backend_is_cuda(ggml_backend_t backend);
GGML_API int ggml_backend_cuda_get_device(ggml_backend_t backend);
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_buffer_type(int device);
// pinned host buffer for use with CPU backend for faster copies between CPU and GPU
GGML_API ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type(void);
#ifdef __cplusplus
}

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@ -232,7 +232,7 @@ bool ggml_hash_contains (const struct ggml_hash_set hash_set, struct ggml
// returns GGML_HASHTABLE_FULL if table is full, otherwise the current index of the key or where it should be inserted
size_t ggml_hash_find (const struct ggml_hash_set hash_set, struct ggml_tensor * key);
// returns GGML_HAHSHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
// returns GGML_HASHTABLE_ALREADY_EXISTS if key already exists, index otherwise, asserts if table is full
size_t ggml_hash_insert ( struct ggml_hash_set hash_set, struct ggml_tensor * key);
// return index, asserts if table is full

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@ -99,6 +99,12 @@ GGML_API ggml_backend_t ggml_backend_metal_init(void);
GGML_API bool ggml_backend_is_metal(ggml_backend_t backend);
GGML_API void ggml_backend_metal_set_n_cb(ggml_backend_t backend, int n_cb);
GGML_API ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void);
// helper to check if the device supports a specific family
// ideally, the user code should be doing these checks
// ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf
GGML_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family);
#ifdef __cplusplus
}

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@ -1,20 +1,18 @@
#include "ggml.h"
#include "ggml-opencl.h"
#include <array>
#include <atomic>
#include <cstdio>
#include <cstdlib>
#include <cstring>
#include <limits>
#include <sstream>
#include <vector>
#include <limits>
#define CL_TARGET_OPENCL_VERSION 110
#include <clblast.h>
#include <stdlib.h>
#include <stdio.h>
#include <string.h>
#include "ggml.h"
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif

508
ggml.c
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@ -233,24 +233,6 @@ inline static void * ggml_aligned_malloc(size_t size) {
#define UNUSED GGML_UNUSED
#define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0)
//
// tensor access macros
//
#define GGML_TENSOR_UNARY_OP_LOCALS \
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
#define GGML_TENSOR_BINARY_OP_LOCALS \
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) \
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
#if defined(GGML_USE_ACCELERATE)
#include <Accelerate/Accelerate.h>
#if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions
@ -1615,6 +1597,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"GROUP_NORM",
"MUL_MAT",
"MUL_MAT_ID",
"OUT_PROD",
"SCALE",
@ -1642,6 +1625,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"POOL_1D",
"POOL_2D",
"UPSCALE",
"ARGSORT",
"FLASH_ATTN",
"FLASH_FF",
@ -1668,7 +1652,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = {
"CROSS_ENTROPY_LOSS_BACK",
};
static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68");
static_assert(GGML_OP_COUNT == 70, "GGML_OP_COUNT != 70");
static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"none",
@ -1697,6 +1681,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"group_norm(x)",
"X*Y",
"X[i]*Y",
"X*Y",
"x*v",
@ -1724,6 +1709,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"pool_1d(x)",
"pool_2d(x)",
"upscale(x)",
"argsort(x)",
"flash_attn(x)",
"flash_ff(x)",
@ -1750,10 +1736,28 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = {
"cross_entropy_loss_back(x,y)",
};
static_assert(GGML_OP_COUNT == 68, "GGML_OP_COUNT != 68");
static_assert(GGML_OP_COUNT == 70, "GGML_OP_COUNT != 70");
static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2");
static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = {
"ABS",
"SGN",
"NEG",
"STEP",
"TANH",
"ELU",
"RELU",
"GELU",
"GELU_QUICK",
"SILU",
"LEAKY",
};
static_assert(GGML_UNARY_OP_COUNT == 11, "GGML_UNARY_OP_COUNT != 11");
static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN");
static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN");
@ -1773,6 +1777,7 @@ static void ggml_setup_op_has_task_pass(void) {
p[GGML_OP_ACC ] = true;
p[GGML_OP_MUL_MAT ] = true;
p[GGML_OP_MUL_MAT_ID ] = true;
p[GGML_OP_OUT_PROD ] = true;
p[GGML_OP_SET ] = true;
p[GGML_OP_GET_ROWS_BACK ] = true;
@ -2025,6 +2030,20 @@ const char * ggml_op_symbol(enum ggml_op op) {
return GGML_OP_SYMBOL[op];
}
const char * ggml_unary_op_name(enum ggml_unary_op op) {
return GGML_UNARY_OP_NAME[op];
}
const char * ggml_op_desc(const struct ggml_tensor * t) {
if (t->op == GGML_OP_UNARY) {
enum ggml_unary_op uop = ggml_get_unary_op(t);
return ggml_unary_op_name(uop);
}
else {
return ggml_op_name(t->op);
}
}
size_t ggml_element_size(const struct ggml_tensor * tensor) {
return ggml_type_size(tensor->type);
}
@ -3158,9 +3177,7 @@ static struct ggml_tensor * ggml_add_impl(
struct ggml_tensor * a,
struct ggml_tensor * b,
bool inplace) {
// TODO: support less-strict constraint
// GGML_ASSERT(ggml_can_repeat(b, a));
GGML_ASSERT(ggml_can_repeat_rows(b, a));
GGML_ASSERT(ggml_can_repeat(b, a));
bool is_node = false;
@ -3375,9 +3392,7 @@ static struct ggml_tensor * ggml_mul_impl(
struct ggml_tensor * a,
struct ggml_tensor * b,
bool inplace) {
// TODO: support less-strict constraint
// GGML_ASSERT(ggml_can_repeat(b, a));
GGML_ASSERT(ggml_can_repeat_rows(b, a));
GGML_ASSERT(ggml_can_repeat(b, a));
bool is_node = false;
@ -3422,7 +3437,7 @@ static struct ggml_tensor * ggml_div_impl(
struct ggml_tensor * a,
struct ggml_tensor * b,
bool inplace) {
GGML_ASSERT(ggml_are_same_shape(a, b));
GGML_ASSERT(ggml_can_repeat(b, a));
bool is_node = false;
@ -4060,6 +4075,49 @@ struct ggml_tensor * ggml_mul_mat(
return result;
}
// ggml_mul_mat_id
struct ggml_tensor * ggml_mul_mat_id(
struct ggml_context * ctx,
struct ggml_tensor * as[],
struct ggml_tensor * ids,
int id,
struct ggml_tensor * b) {
int64_t n_as = ids->ne[0];
GGML_ASSERT(ids->type == GGML_TYPE_I32);
GGML_ASSERT(ggml_is_vector(ids));
GGML_ASSERT(n_as > 0 && n_as <= GGML_MAX_SRC - 2);
GGML_ASSERT(id >= 0 && id < n_as);
bool is_node = false;
if (as[0]->grad || b->grad) {
is_node = true;
}
const int64_t ne[4] = { as[0]->ne[1], b->ne[1], b->ne[2], b->ne[3] };
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, MAX(as[0]->n_dims, b->n_dims), ne);
ggml_set_op_params_i32(result, 0, id);
result->op = GGML_OP_MUL_MAT_ID;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = ids;
result->src[1] = b;
for (int64_t i = 0; i < n_as; i++) {
struct ggml_tensor * a = as[i];
GGML_ASSERT(ggml_are_same_shape(as[0], a));
GGML_ASSERT(ggml_can_mul_mat(a, b));
GGML_ASSERT(!ggml_is_transposed(a));
result->src[i + 2] = a;
}
return result;
}
// ggml_out_prod
struct ggml_tensor * ggml_out_prod(
@ -4213,7 +4271,7 @@ struct ggml_tensor * ggml_set_2d_inplace(
struct ggml_tensor * b,
size_t nb1,
size_t offset) {
return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, false);
return ggml_set_impl(ctx, a, b, nb1, a->nb[2], a->nb[3], offset, true);
}
// ggml_cpy
@ -4830,7 +4888,17 @@ struct ggml_tensor * ggml_diag_mask_zero_inplace(
static struct ggml_tensor * ggml_soft_max_impl(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * mask,
float scale,
bool inplace) {
GGML_ASSERT(ggml_is_contiguous(a));
if (mask) {
GGML_ASSERT(ggml_is_contiguous(mask));
GGML_ASSERT(mask->ne[2] == 1);
GGML_ASSERT(mask->ne[3] == 1);
GGML_ASSERT(ggml_can_repeat_rows(mask, a));
}
bool is_node = false;
if (a->grad) {
@ -4839,9 +4907,13 @@ static struct ggml_tensor * ggml_soft_max_impl(
struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a);
float params[] = { scale };
ggml_set_op_params(result, params, sizeof(params));
result->op = GGML_OP_SOFT_MAX;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
result->src[1] = mask;
return result;
}
@ -4849,13 +4921,21 @@ static struct ggml_tensor * ggml_soft_max_impl(
struct ggml_tensor * ggml_soft_max(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_soft_max_impl(ctx, a, false);
return ggml_soft_max_impl(ctx, a, NULL, 1.0f, false);
}
struct ggml_tensor * ggml_soft_max_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a) {
return ggml_soft_max_impl(ctx, a, true);
return ggml_soft_max_impl(ctx, a, NULL, 1.0f, true);
}
struct ggml_tensor * ggml_soft_max_ext(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * mask,
float scale) {
return ggml_soft_max_impl(ctx, a, mask, scale, false);
}
// ggml_soft_max_back
@ -5450,6 +5530,43 @@ struct ggml_tensor * ggml_upscale(
return ggml_upscale_impl(ctx, a, scale_factor);
}
// ggml_argsort
struct ggml_tensor * ggml_argsort(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_sort_order order) {
bool is_node = false;
struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, a->n_dims, a->ne);
ggml_set_op_params_i32(result, 0, (int32_t) order);
result->op = GGML_OP_ARGSORT;
result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL;
result->src[0] = a;
return result;
}
// ggml_top_k
struct ggml_tensor * ggml_top_k(
struct ggml_context * ctx,
struct ggml_tensor * a,
int k) {
GGML_ASSERT(a->ne[0] >= k);
struct ggml_tensor * result = ggml_argsort(ctx, a, GGML_SORT_DESC);
result = ggml_view_4d(ctx, result,
k, result->ne[1], result->ne[2], result->ne[3],
result->nb[1], result->nb[2], result->nb[3],
0);
return result;
}
// ggml_flash_attn
struct ggml_tensor * ggml_flash_attn(
@ -6809,7 +6926,7 @@ static void ggml_compute_forward_add_f32(
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));
GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
@ -6842,17 +6959,20 @@ static void ggml_compute_forward_add_f32(
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
const int64_t nr0 = ne00 / ne10;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
for (int64_t r = 0; r < nr0; ++r) {
#ifdef GGML_USE_ACCELERATE
vDSP_vadd(src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00);
vDSP_vadd(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10);
#else
ggml_vec_add_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
ggml_vec_add_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr);
#endif
}
}
} else {
// src1 is not contiguous
for (int ir = ir0; ir < ir1; ++ir) {
@ -6868,8 +6988,9 @@ static void ggml_compute_forward_add_f32(
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
for (int i0 = 0; i0 < ne0; i0++) {
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10);
for (int64_t i0 = 0; i0 < ne0; ++i0) {
const int64_t i10 = i0 % ne10;
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10);
dst_ptr[i0] = src0_ptr[i0] + *src1_ptr;
}
@ -7589,7 +7710,7 @@ static void ggml_compute_forward_mul_f32(
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
GGML_ASSERT(ggml_can_repeat_rows(src1, src0) && ggml_are_same_shape(src0, dst));
GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
@ -7612,7 +7733,6 @@ static void ggml_compute_forward_mul_f32(
GGML_ASSERT( nb0 == sizeof(float));
GGML_ASSERT(nb00 == sizeof(float));
GGML_ASSERT(ne00 == ne10);
if (nb10 == sizeof(float)) {
for (int64_t ir = ith; ir < nr; ir += nth) {
@ -7624,20 +7744,21 @@ static void ggml_compute_forward_mul_f32(
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
const int64_t nr0 = ne00 / ne10;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
for (int64_t r = 0 ; r < nr0; ++r) {
#ifdef GGML_USE_ACCELERATE
UNUSED(ggml_vec_mul_f32);
vDSP_vmul( src0_ptr, 1, src1_ptr, 1, dst_ptr, 1, ne00);
vDSP_vmul(src0_ptr + r*ne10, 1, src1_ptr, 1, dst_ptr + r*ne10, 1, ne10);
#else
ggml_vec_mul_f32(ne00, dst_ptr, src0_ptr, src1_ptr);
ggml_vec_mul_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr);
#endif
// }
// }
}
}
} else {
// src1 is not contiguous
@ -7655,8 +7776,9 @@ static void ggml_compute_forward_mul_f32(
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
for (int64_t i0 = 0; i0 < ne00; i0++) {
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i0*nb10);
for (int64_t i0 = 0; i0 < ne00; ++i0) {
const int64_t i10 = i0 % ne10;
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10);
dst_ptr[i0] = src0_ptr[i0] * (*src1_ptr);
}
@ -7690,14 +7812,16 @@ static void ggml_compute_forward_div_f32(
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
assert(params->ith == 0);
assert(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst));
GGML_ASSERT(ggml_can_repeat(src1, src0) && ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
const int nr = ggml_nrows(src0);
const int ith = params->ith;
const int nth = params->nth;
const int64_t nr = ggml_nrows(src0);
GGML_TENSOR_BINARY_OP_LOCALS
@ -7705,41 +7829,50 @@ static void ggml_compute_forward_div_f32(
GGML_ASSERT(nb00 == sizeof(float));
if (nb10 == sizeof(float)) {
for (int ir = 0; ir < nr; ++ir) {
// src0, src1 and dst are same shape => same indices
const int i3 = ir/(ne2*ne1);
const int i2 = (ir - i3*ne2*ne1)/ne1;
const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
for (int64_t ir = ith; ir < nr; ir += nth) {
// src0 and dst are same shape => same indices
const int64_t i03 = ir/(ne02*ne01);
const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
const int64_t nr0 = ne00 / ne10;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11);
for (int64_t r = 0; r < nr0; ++r) {
#ifdef GGML_USE_ACCELERATE
UNUSED(ggml_vec_div_f32);
vDSP_vdiv(
(float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1,
(float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1,
(float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ), 1,
ne0);
vDSP_vdiv(src1_ptr, 1, src0_ptr + r*ne10, 1, dst_ptr + r*ne10, 1, ne10);
#else
ggml_vec_div_f32(ne0,
(float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ),
(float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01),
(float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11));
ggml_vec_div_f32(ne10, dst_ptr + r*ne10, src0_ptr + r*ne10, src1_ptr);
#endif
// }
// }
}
}
} else {
// src1 is not contiguous
for (int ir = 0; ir < nr; ++ir) {
// src0, src1 and dst are same shape => same indices
const int i3 = ir/(ne2*ne1);
const int i2 = (ir - i3*ne2*ne1)/ne1;
const int i1 = (ir - i3*ne2*ne1 - i2*ne1);
for (int64_t ir = ith; ir < nr; ir += nth) {
// src0 and dst are same shape => same indices
// src1 is broadcastable across src0 and dst in i1, i2, i3
const int64_t i03 = ir/(ne02*ne01);
const int64_t i02 = (ir - i03*ne02*ne01)/ne01;
const int64_t i01 = (ir - i03*ne02*ne01 - i02*ne01);
float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01);
for (int i0 = 0; i0 < ne0; i0++) {
float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11 + i0*nb10);
const int64_t i13 = i03 % ne13;
const int64_t i12 = i02 % ne12;
const int64_t i11 = i01 % ne11;
float * dst_ptr = (float *) ((char *) dst->data + i03*nb3 + i02*nb2 + i01*nb1 );
float * src0_ptr = (float *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01);
for (int64_t i0 = 0; i0 < ne00; ++i0) {
const int64_t i10 = i0 % ne10;
float * src1_ptr = (float *) ((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10);
dst_ptr[i0] = src0_ptr[i0] / (*src1_ptr);
}
@ -8185,7 +8318,7 @@ static void ggml_compute_forward_repeat_f16(
return;
}
GGML_TENSOR_UNARY_OP_LOCALS;
GGML_TENSOR_UNARY_OP_LOCALS
// guaranteed to be an integer due to the check in ggml_can_repeat
const int nr0 = (int)(ne0/ne00);
@ -8330,6 +8463,7 @@ static void ggml_compute_forward_concat_f32(
GGML_ASSERT(src0->nb[0] == sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
GGML_TENSOR_BINARY_OP_LOCALS
@ -8339,7 +8473,7 @@ static void ggml_compute_forward_concat_f32(
GGML_ASSERT(nb10 == sizeof(float));
for (int i3 = 0; i3 < ne3; i3++) {
for (int i2 = ith; i2 < ne2; i2++) {
for (int i2 = ith; i2 < ne2; i2 += nth) {
if (i2 < ne02) { // src0
for (int i1 = 0; i1 < ne1; i1++) {
for (int i0 = 0; i0 < ne0; i0++) {
@ -9377,7 +9511,7 @@ static bool ggml_compute_forward_mul_mat_use_blas(
// TODO: find the optimal values for these
if (ggml_is_contiguous(src0) &&
ggml_is_contiguous(src1) &&
src0->type == GGML_TYPE_F32 &&
//src0->type == GGML_TYPE_F32 &&
src1->type == GGML_TYPE_F32 &&
(ne0 >= 32 && ne1 >= 32 && ne10 >= 32)) {
@ -9499,6 +9633,8 @@ static void ggml_compute_forward_mul_mat(
char * wdata = params->wdata;
const size_t row_size = ne10*ggml_type_size(vec_dot_type)/ggml_blck_size(vec_dot_type);
assert(params->wsize >= ne11*ne12*ne13*row_size);
for (int64_t i13 = 0; i13 < ne13; ++i13) {
for (int64_t i12 = 0; i12 < ne12; ++i12) {
for (int64_t i11 = 0; i11 < ne11; ++i11) {
@ -9600,6 +9736,26 @@ static void ggml_compute_forward_mul_mat(
}
}
// ggml_compute_forward_mul_mat_id
static void ggml_compute_forward_mul_mat_id(
const struct ggml_compute_params * params,
struct ggml_tensor * dst) {
const struct ggml_tensor * ids = dst->src[0];
const struct ggml_tensor * src1 = dst->src[1];
const int id = ggml_get_op_params_i32(dst, 0);
const int a_id = ((int32_t *)ids->data)[id];
GGML_ASSERT(a_id >= 0 && a_id < ids->ne[0]);
const struct ggml_tensor * src0 = dst->src[a_id + 2];
ggml_compute_forward_mul_mat(params, src0, src1, dst);
}
// ggml_compute_forward_out_prod
static void ggml_compute_forward_out_prod_f32(
@ -10555,20 +10711,25 @@ static void ggml_compute_forward_diag_mask_zero(
static void ggml_compute_forward_soft_max_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
GGML_ASSERT(ggml_is_contiguous(src0));
GGML_ASSERT(ggml_is_contiguous(dst));
GGML_ASSERT(ggml_are_same_shape(src0, dst));
assert(ggml_is_contiguous(dst));
assert(ggml_are_same_shape(src0, dst));
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
float scale = 1.0f;
memcpy(&scale, (float *) dst->op_params + 0, sizeof(float));
// TODO: handle transposed/permuted matrices
const int ith = params->ith;
const int nth = params->nth;
const int64_t ne11 = src1 ? src1->ne[1] : 1;
const int nc = src0->ne[0];
const int nr = ggml_nrows(src0);
@ -10579,29 +10740,40 @@ static void ggml_compute_forward_soft_max_f32(
const int ir0 = dr*ith;
const int ir1 = MIN(ir0 + dr, nr);
float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith;
for (int i1 = ir0; i1 < ir1; i1++) {
float * sp = (float *)((char *) src0->data + i1*src0->nb[1]);
float * dp = (float *)((char *) dst->data + i1*dst->nb[1]);
// broadcast the mask across rows
float * mp = src1 ? (float *)((char *) src1->data + (i1%ne11)*src1->nb[1]) : NULL;
ggml_vec_cpy_f32 (nc, wp, sp);
ggml_vec_scale_f32(nc, wp, scale);
if (mp) {
ggml_vec_acc_f32(nc, wp, mp);
}
#ifndef NDEBUG
for (int i = 0; i < nc; ++i) {
//printf("p[%d] = %f\n", i, p[i]);
assert(!isnan(sp[i]));
assert(!isnan(wp[i]));
}
#endif
float max = -INFINITY;
ggml_vec_max_f32(nc, &max, sp);
ggml_vec_max_f32(nc, &max, wp);
ggml_float sum = 0.0;
uint16_t scvt;
for (int i = 0; i < nc; i++) {
if (sp[i] == -INFINITY) {
if (wp[i] == -INFINITY) {
dp[i] = 0.0f;
} else {
// const float val = (sp[i] == -INFINITY) ? 0.0 : exp(sp[i] - max);
ggml_fp16_t s = GGML_FP32_TO_FP16(sp[i] - max);
// const float val = (wp[i] == -INFINITY) ? 0.0 : exp(wp[i] - max);
ggml_fp16_t s = GGML_FP32_TO_FP16(wp[i] - max);
memcpy(&scvt, &s, sizeof(scvt));
const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]);
sum += (ggml_float)val;
@ -10626,11 +10798,12 @@ static void ggml_compute_forward_soft_max_f32(
static void ggml_compute_forward_soft_max(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
const struct ggml_tensor * src1,
struct ggml_tensor * dst) {
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_soft_max_f32(params, src0, dst);
ggml_compute_forward_soft_max_f32(params, src0, src1, dst);
} break;
default:
{
@ -11986,6 +12159,67 @@ static void ggml_compute_forward_upscale(
}
}
// ggml_compute_forward_argsort
static void ggml_compute_forward_argsort_f32(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) {
return;
}
GGML_TENSOR_UNARY_OP_LOCALS
GGML_ASSERT(nb0 == sizeof(float));
const int ith = params->ith;
const int nth = params->nth;
const int64_t nr = ggml_nrows(src0);
enum ggml_sort_order order = (enum ggml_sort_order) ggml_get_op_params_i32(dst, 0);
for (int64_t i = ith; i < nr; i += nth) {
int32_t * dst_data = (int32_t *)((char *) dst->data + i*nb1);
const float * src_data = (float *)((char *) src0->data + i*nb01);
for (int64_t j = 0; j < ne0; j++) {
dst_data[j] = j;
}
// C doesn't have a functional sort, so we do a bubble sort instead
for (int64_t j = 0; j < ne0; j++) {
for (int64_t k = j + 1; k < ne0; k++) {
if ((order == GGML_SORT_ASC && src_data[dst_data[j]] > src_data[dst_data[k]]) ||
(order == GGML_SORT_DESC && src_data[dst_data[j]] < src_data[dst_data[k]])) {
int32_t tmp = dst_data[j];
dst_data[j] = dst_data[k];
dst_data[k] = tmp;
}
}
}
}
}
static void ggml_compute_forward_argsort(
const struct ggml_compute_params * params,
const struct ggml_tensor * src0,
struct ggml_tensor * dst) {
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_argsort_f32(params, src0, dst);
} break;
default:
{
GGML_ASSERT(false);
} break;
}
}
// ggml_compute_forward_flash_attn
static void ggml_compute_forward_flash_attn_f32(
@ -13821,6 +14055,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_mul_mat(params, tensor->src[0], tensor->src[1], tensor);
} break;
case GGML_OP_MUL_MAT_ID:
{
ggml_compute_forward_mul_mat_id(params, tensor);
} break;
case GGML_OP_OUT_PROD:
{
ggml_compute_forward_out_prod(params, tensor->src[0], tensor->src[1], tensor);
@ -13879,7 +14117,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
} break;
case GGML_OP_SOFT_MAX:
{
ggml_compute_forward_soft_max(params, tensor->src[0], tensor);
ggml_compute_forward_soft_max(params, tensor->src[0], tensor->src[1], tensor);
} break;
case GGML_OP_SOFT_MAX_BACK:
{
@ -13925,6 +14163,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm
{
ggml_compute_forward_upscale(params, tensor->src[0], tensor);
} break;
case GGML_OP_ARGSORT:
{
ggml_compute_forward_argsort(params, tensor->src[0], tensor);
} break;
case GGML_OP_FLASH_ATTN:
{
const int32_t t = ggml_get_op_params_i32(tensor, 0);
@ -14575,6 +14817,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
zero_table);
}
} break;
case GGML_OP_MUL_MAT_ID:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_OUT_PROD:
{
GGML_ASSERT(false); // TODO: not implemented
@ -14913,6 +15159,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_ARGSORT:
{
GGML_ASSERT(false); // TODO: not implemented
} break;
case GGML_OP_FLASH_ATTN:
{
struct ggml_tensor * flash_grad = NULL;
@ -15273,12 +15523,8 @@ struct ggml_cgraph * ggml_new_graph(struct ggml_context * ctx) {
return ggml_new_graph_custom(ctx, GGML_DEFAULT_GRAPH_SIZE, false);
}
struct ggml_cgraph * ggml_graph_view(struct ggml_context * ctx, struct ggml_cgraph * cgraph0, int i0, int i1) {
const size_t obj_size = sizeof(struct ggml_cgraph);
struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_GRAPH, obj_size);
struct ggml_cgraph * cgraph = (struct ggml_cgraph *) ((char *) ctx->mem_buffer + obj->offs);
*cgraph = (struct ggml_cgraph) {
struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph0, int i0, int i1) {
struct ggml_cgraph cgraph = {
/*.size =*/ 0,
/*.n_nodes =*/ i1 - i0,
/*.n_leafs =*/ 0,
@ -15513,7 +15759,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
n_tasks = n_threads;
} break;
case GGML_OP_SUB:
case GGML_OP_DIV:
case GGML_OP_SQR:
case GGML_OP_SQRT:
case GGML_OP_LOG:
@ -15546,10 +15791,13 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
{
n_tasks = n_threads;
} break;
default:
GGML_ASSERT(false);
}
break;
case GGML_OP_SILU_BACK:
case GGML_OP_MUL:
case GGML_OP_DIV:
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
case GGML_OP_RMS_NORM_BACK:
@ -15592,6 +15840,11 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
}
#endif
} break;
case GGML_OP_MUL_MAT_ID:
{
// FIXME: blas
n_tasks = n_threads;
} break;
case GGML_OP_OUT_PROD:
{
n_tasks = n_threads;
@ -15611,7 +15864,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
} break;
case GGML_OP_DIAG_MASK_ZERO:
case GGML_OP_DIAG_MASK_INF:
case GGML_OP_SOFT_MAX:
case GGML_OP_SOFT_MAX_BACK:
case GGML_OP_ROPE:
case GGML_OP_ROPE_BACK:
@ -15627,6 +15879,10 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
{
n_tasks = 1; //TODO
} break;
case GGML_OP_SOFT_MAX:
{
n_tasks = MIN(MIN(4, n_threads), ggml_nrows(node->src[0]));
} break;
case GGML_OP_CONV_TRANSPOSE_1D:
{
n_tasks = n_threads;
@ -15648,6 +15904,10 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
{
n_tasks = n_threads;
} break;
case GGML_OP_ARGSORT:
{
n_tasks = n_threads;
} break;
case GGML_OP_FLASH_ATTN:
{
n_tasks = n_threads;
@ -15716,7 +15976,12 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) {
} break;
default:
{
printf("%s: op %s not implemented\n", __func__, ggml_op_name(node->op));
fprintf(stderr, "%s: op not implemented: ", __func__);
if (node->op < GGML_OP_COUNT) {
fprintf(stderr, "%s\n", ggml_op_name(node->op));
} else {
fprintf(stderr, "%d\n", node->op);
}
GGML_ASSERT(false);
} break;
}
@ -15857,18 +16122,16 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
// thread scheduling for the different operations + work buffer size estimation
for (int i = 0; i < cgraph->n_nodes; i++) {
int n_tasks = 1;
struct ggml_tensor * node = cgraph->nodes[i];
const int n_tasks = ggml_get_n_tasks(node, n_threads);
size_t cur = 0;
switch (node->op) {
case GGML_OP_CPY:
case GGML_OP_DUP:
{
n_tasks = n_threads;
if (ggml_is_quantized(node->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
}
@ -15876,16 +16139,12 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
case GGML_OP_ADD:
case GGML_OP_ADD1:
{
n_tasks = n_threads;
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
}
} break;
case GGML_OP_ACC:
{
n_tasks = n_threads;
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[1]->ne[0] * n_tasks;
}
@ -15911,14 +16170,33 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
cur = ggml_type_size(vec_dot_type)*ggml_nelements(node->src[1])/ggml_blck_size(vec_dot_type);
}
} break;
case GGML_OP_MUL_MAT_ID:
{
const struct ggml_tensor * a = node->src[2];
const struct ggml_tensor * b = node->src[1];
const enum ggml_type vec_dot_type = type_traits[a->type].vec_dot_type;
#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS)
if (ggml_compute_forward_mul_mat_use_blas(a, b, node)) {
if (a->type != GGML_TYPE_F32) {
// here we need memory just for single 2D matrix from src0
cur = ggml_type_size(GGML_TYPE_F32)*(a->ne[0]*a->ne[1]);
}
} else
#endif
if (b->type != vec_dot_type) {
cur = ggml_type_size(vec_dot_type)*ggml_nelements(b)/ggml_blck_size(vec_dot_type);
}
} break;
case GGML_OP_OUT_PROD:
{
n_tasks = n_threads;
if (ggml_is_quantized(node->src[0]->type)) {
cur = ggml_type_size(GGML_TYPE_F32) * node->src[0]->ne[0] * n_tasks;
}
} break;
case GGML_OP_SOFT_MAX:
{
cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks;
} break;
case GGML_OP_CONV_TRANSPOSE_1D:
{
GGML_ASSERT(node->src[0]->ne[3] == 1);
@ -15944,10 +16222,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
GGML_ASSERT(false);
}
} break;
case GGML_OP_IM2COL:
{
n_tasks = n_threads;
} break;
case GGML_OP_CONV_TRANSPOSE_2D:
{
const int64_t ne00 = node->src[0]->ne[0]; // W
@ -15964,8 +16238,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
} break;
case GGML_OP_FLASH_ATTN:
{
n_tasks = n_threads;
const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);
if (node->src[1]->type == GGML_TYPE_F32) {
@ -15978,8 +16250,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
} break;
case GGML_OP_FLASH_FF:
{
n_tasks = n_threads;
if (node->src[1]->type == GGML_TYPE_F32) {
cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1)
cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2
@ -15990,8 +16260,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
} break;
case GGML_OP_FLASH_ATTN_BACK:
{
n_tasks = n_threads;
const int64_t D = node->src[0]->ne[0];
const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL);
const int64_t mxDn = MAX(D, ne11) * 2; // *2 because of S and SM in ggml_compute_forward_flash_attn_back
@ -16006,8 +16274,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) {
case GGML_OP_CROSS_ENTROPY_LOSS:
{
n_tasks = n_threads;
cur = ggml_type_size(node->type)*(n_tasks + node->src[0]->ne[0]*n_tasks);
} break;
case GGML_OP_COUNT:
@ -17794,8 +18060,8 @@ size_t ggml_quantize_q5_0(const float * src, void * dst, int n, int k, int64_t *
memcpy(&qh, &y[i].qh, sizeof(qh));
for (int j = 0; j < QK5_0; j += 2) {
const uint8_t vh0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
const uint8_t vh1 = ((qh & (1u << (j + 16))) >> (j + 12));
const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4;
const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12));
// cast to 16 bins
const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2;
@ -17824,8 +18090,8 @@ size_t ggml_quantize_q5_1(const float * src, void * dst, int n, int k, int64_t *
memcpy(&qh, &y[i].qh, sizeof(qh));
for (int j = 0; j < QK5_1; j += 2) {
const uint8_t vh0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
const uint8_t vh1 = ((qh & (1u << (j + 16))) >> (j + 12));
const uint8_t vh0 = ((qh & (1u << (j/2 + 0 ))) >> (j/2 + 0 )) << 4;
const uint8_t vh1 = ((qh & (1u << (j/2 + 16))) >> (j/2 + 12));
// cast to 16 bins
const uint8_t vi0 = ((y[i].qs[j/2] & 0x0F) | vh0) / 2;
@ -18015,6 +18281,7 @@ struct gguf_kv {
struct gguf_header {
char magic[4];
uint32_t version;
uint64_t n_tensors; // GGUFv2
uint64_t n_kv; // GGUFv2
@ -18104,7 +18371,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
for (uint32_t i = 0; i < sizeof(magic); i++) {
if (magic[i] != GGUF_MAGIC[i]) {
fprintf(stderr, "%s: invalid magic characters %s.\n", __func__, magic);
fprintf(stderr, "%s: invalid magic characters '%c%c%c%c'\n", __func__, magic[0], magic[1], magic[2], magic[3]);
fclose(file);
return NULL;
}
@ -18119,7 +18386,6 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p
{
strncpy(ctx->header.magic, magic, 4);
ctx->kv = NULL;
ctx->infos = NULL;
ctx->data = NULL;

66
ggml.h
View file

@ -244,11 +244,10 @@
#define GGML_ASSERT(x) \
do { \
if (!(x)) { \
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
fflush(stderr); \
fflush(stdout); \
fprintf(stderr, "GGML_ASSERT: %s:%d: %s\n", __FILE__, __LINE__, #x); \
ggml_print_backtrace(); \
exit(1); \
abort(); \
} \
} while (0)
@ -284,6 +283,20 @@
const type prefix##3 = (pointer)->array[3]; \
GGML_UNUSED(prefix##3);
#define GGML_TENSOR_UNARY_OP_LOCALS \
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
#define GGML_TENSOR_BINARY_OP_LOCALS \
GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) \
GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) \
GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne) \
GGML_TENSOR_LOCALS(size_t, nb1, src1, nb) \
GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) \
GGML_TENSOR_LOCALS(size_t, nb, dst, nb)
#ifdef __cplusplus
extern "C" {
#endif
@ -382,6 +395,7 @@ extern "C" {
GGML_OP_GROUP_NORM,
GGML_OP_MUL_MAT,
GGML_OP_MUL_MAT_ID,
GGML_OP_OUT_PROD,
GGML_OP_SCALE,
@ -408,8 +422,8 @@ extern "C" {
GGML_OP_CONV_TRANSPOSE_2D,
GGML_OP_POOL_1D,
GGML_OP_POOL_2D,
GGML_OP_UPSCALE, // nearest interpolate
GGML_OP_ARGSORT,
GGML_OP_FLASH_ATTN,
GGML_OP_FLASH_FF,
@ -449,7 +463,9 @@ extern "C" {
GGML_UNARY_OP_GELU,
GGML_UNARY_OP_GELU_QUICK,
GGML_UNARY_OP_SILU,
GGML_UNARY_OP_LEAKY
GGML_UNARY_OP_LEAKY,
GGML_UNARY_OP_COUNT,
};
enum ggml_object_type {
@ -632,6 +648,9 @@ extern "C" {
GGML_API const char * ggml_op_name (enum ggml_op op);
GGML_API const char * ggml_op_symbol(enum ggml_op op);
GGML_API const char * ggml_unary_op_name(enum ggml_unary_op op);
GGML_API const char * ggml_op_desc(const struct ggml_tensor * t); // unary or op name
GGML_API size_t ggml_element_size(const struct ggml_tensor * tensor);
GGML_API bool ggml_is_quantized(enum ggml_type type);
@ -1028,6 +1047,15 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// indirect matrix multiplication
// ggml_mul_mat_id(ctx, as, ids, id, b) ~= ggml_mul_mat(as[ids[id]], b)
GGML_API struct ggml_tensor * ggml_mul_mat_id(
struct ggml_context * ctx,
struct ggml_tensor * as[],
struct ggml_tensor * ids,
int id,
struct ggml_tensor * b);
// A: m columns, n rows,
// B: p columns, n rows,
// result is m columns, p rows
@ -1283,6 +1311,14 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
// fused soft_max(a*scale + mask)
// mask is optional
GGML_API struct ggml_tensor * ggml_soft_max_ext(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * mask,
float scale);
GGML_API struct ggml_tensor * ggml_soft_max_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1513,6 +1549,23 @@ extern "C" {
struct ggml_tensor * a,
int scale_factor);
// sort rows
enum ggml_sort_order {
GGML_SORT_ASC,
GGML_SORT_DESC,
};
GGML_API struct ggml_tensor * ggml_argsort(
struct ggml_context * ctx,
struct ggml_tensor * a,
enum ggml_sort_order order);
// top k elements per row
GGML_API struct ggml_tensor * ggml_top_k(
struct ggml_context * ctx,
struct ggml_tensor * a,
int k);
GGML_API struct ggml_tensor * ggml_flash_attn(
struct ggml_context * ctx,
struct ggml_tensor * q,
@ -1574,7 +1627,6 @@ extern "C" {
int kh);
// used in sam
GGML_API struct ggml_tensor * ggml_add_rel_pos(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -1749,7 +1801,7 @@ extern "C" {
GGML_API struct ggml_cgraph * ggml_new_graph (struct ggml_context * ctx); // size = GGML_DEFAULT_GRAPH_SIZE, grads = false
GGML_API struct ggml_cgraph * ggml_new_graph_custom (struct ggml_context * ctx, size_t size, bool grads);
GGML_API struct ggml_cgraph * ggml_graph_dup (struct ggml_context * ctx, struct ggml_cgraph * cgraph);
GGML_API struct ggml_cgraph * ggml_graph_view (struct ggml_context * ctx, struct ggml_cgraph * cgraph, int i0, int i1);
GGML_API struct ggml_cgraph ggml_graph_view (struct ggml_cgraph * cgraph, int i0, int i1);
GGML_API void ggml_graph_cpy (struct ggml_cgraph * src, struct ggml_cgraph * dst);
GGML_API void ggml_graph_reset (struct ggml_cgraph * cgraph); // zero grads
GGML_API void ggml_graph_clear (struct ggml_cgraph * cgraph);

View file

@ -70,6 +70,7 @@ class Keys:
ADD_EOS = "tokenizer.ggml.add_eos_token"
HF_JSON = "tokenizer.huggingface.json"
RWKV = "tokenizer.rwkv.world"
CHAT_TEMPLATE = "tokenizer.chat_template"
#
@ -91,6 +92,7 @@ class MODEL_ARCH(IntEnum):
BERT = auto()
BLOOM = auto()
STABLELM = auto()
QWEN = auto()
class MODEL_TENSOR(IntEnum):
@ -131,6 +133,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.BERT: "bert",
MODEL_ARCH.BLOOM: "bloom",
MODEL_ARCH.STABLELM: "stablelm",
MODEL_ARCH.QWEN: "qwen",
}
TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
@ -316,6 +319,20 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.QWEN: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_QKV,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.ATTN_ROT_EMBD,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE,
MODEL_TENSOR.FFN_DOWN,
MODEL_TENSOR.FFN_UP,
],
MODEL_ARCH.GPT2: [
# TODO
],
@ -335,6 +352,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_ARCH.PERSIMMON: [
MODEL_TENSOR.ROPE_FREQS,
],
MODEL_ARCH.QWEN: [
MODEL_TENSOR.ROPE_FREQS,
MODEL_TENSOR.ATTN_ROT_EMBD,
],
}
#

View file

@ -399,6 +399,9 @@ class GGUFWriter:
def add_add_eos_token(self, value: bool) -> None:
self.add_bool(Keys.Tokenizer.ADD_EOS, value)
def add_chat_template(self, value: str) -> None:
self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value)
def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes:
pack_prefix = ''
if not skip_pack_prefix:

View file

@ -10,7 +10,7 @@ class TensorNameMap:
# Token embeddings
MODEL_TENSOR.TOKEN_EMBD: (
"gpt_neox.embed_in", # gptneox
"transformer.wte", # gpt2 gpt-j mpt refact
"transformer.wte", # gpt2 gpt-j mpt refact qwen
"transformer.word_embeddings", # falcon
"word_embeddings", # bloom
"model.embed_tokens", # llama-hf
@ -38,7 +38,7 @@ class TensorNameMap:
# Output
MODEL_TENSOR.OUTPUT: (
"embed_out", # gptneox
"lm_head", # gpt2 mpt falcon llama-hf baichuan
"lm_head", # gpt2 mpt falcon llama-hf baichuan qwen
"output", # llama-pth bloom
"word_embeddings_for_head", # persimmon
),
@ -51,7 +51,7 @@ class TensorNameMap:
"norm", # llama-pth
"embeddings.LayerNorm", # bert
"transformer.norm_f", # mpt
"ln_f", # refact bloom
"ln_f", # refact bloom qwen
"language_model.encoder.final_layernorm", # persimmon
),
@ -65,7 +65,7 @@ class TensorNameMap:
# Attention norm
MODEL_TENSOR.ATTN_NORM: (
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
"transformer.blocks.{bid}.norm_1", # mpt
"transformer.h.{bid}.input_layernorm", # falcon7b
"h.{bid}.input_layernorm", # bloom
@ -85,7 +85,7 @@ class TensorNameMap:
# Attention query-key-value
MODEL_TENSOR.ATTN_QKV: (
"gpt_neox.layers.{bid}.attention.query_key_value", # gptneox
"transformer.h.{bid}.attn.c_attn", # gpt2
"transformer.h.{bid}.attn.c_attn", # gpt2 qwen
"transformer.blocks.{bid}.attn.Wqkv", # mpt
"transformer.h.{bid}.self_attention.query_key_value", # falcon
"h.{bid}.self_attention.query_key_value", # bloom
@ -119,7 +119,7 @@ class TensorNameMap:
# Attention output
MODEL_TENSOR.ATTN_OUT: (
"gpt_neox.layers.{bid}.attention.dense", # gptneox
"transformer.h.{bid}.attn.c_proj", # gpt2 refact
"transformer.h.{bid}.attn.c_proj", # gpt2 refact qwen
"transformer.blocks.{bid}.attn.out_proj", # mpt
"transformer.h.{bid}.self_attention.dense", # falcon
"h.{bid}.self_attention.dense", # bloom
@ -139,7 +139,7 @@ class TensorNameMap:
# Feed-forward norm
MODEL_TENSOR.FFN_NORM: (
"gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox
"transformer.h.{bid}.ln_2", # gpt2 refact
"transformer.h.{bid}.ln_2", # gpt2 refact qwen
"h.{bid}.post_attention_layernorm", # bloom
"transformer.blocks.{bid}.norm_2", # mpt
"model.layers.{bid}.post_attention_layernorm", # llama-hf
@ -161,18 +161,20 @@ class TensorNameMap:
"encoder.layer.{bid}.intermediate.dense", # bert
"transformer.h.{bid}.mlp.fc_in", # gpt-j
"language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon
"transformer.h.{bid}.mlp.w1", # qwen
),
# Feed-forward gate
MODEL_TENSOR.FFN_GATE: (
"model.layers.{bid}.mlp.gate_proj", # llama-hf refact
"layers.{bid}.feed_forward.w1", # llama-pth
"transformer.h.{bid}.mlp.w2", # qwen
),
# Feed-forward down
MODEL_TENSOR.FFN_DOWN: (
"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
"transformer.h.{bid}.mlp.c_proj", # gpt2 refact
"transformer.h.{bid}.mlp.c_proj", # gpt2 refact qwen
"transformer.blocks.{bid}.ffn.down_proj", # mpt
"transformer.h.{bid}.mlp.dense_4h_to_h", # falcon
"h.{bid}.mlp.dense_4h_to_h", # bloom

View file

@ -13,6 +13,7 @@ class SpecialVocab:
merges: list[str]
add_special_token: dict[str, bool]
special_token_ids: dict[str, int]
chat_template: str | None
def __init__(
self, path: str | os.PathLike[str], load_merges: bool = False,
@ -24,6 +25,7 @@ class SpecialVocab:
self.n_vocab = n_vocab
self.load_merges = load_merges
self.merges = []
self.chat_template = None
if special_token_types is not None:
self.special_token_types = special_token_types
else:
@ -67,6 +69,10 @@ class SpecialVocab:
if not quiet:
print(f'gguf: Setting add_{typ}_token to {value}')
add_handler(value)
if self.chat_template is not None:
if not quiet:
print(f'gguf: Setting chat_template to {self.chat_template}')
gw.add_chat_template(self.chat_template)
def _load(self, path: Path) -> None:
self._try_load_from_tokenizer_json(path)
@ -132,6 +138,14 @@ class SpecialVocab:
return True
with open(tokenizer_config_file, encoding = 'utf-8') as f:
tokenizer_config = json.load(f)
chat_template = tokenizer_config.get('chat_template')
if chat_template is None or isinstance(chat_template, str):
self.chat_template = chat_template
else:
print(
f'gguf: WARNING: Bad type for chat_template field in {tokenizer_config_file!r} - ignoring',
file = sys.stderr
)
for typ in self.special_token_types:
add_entry = tokenizer_config.get(f'add_{typ}_token')
if isinstance(add_entry, bool):

View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "gguf"
version = "0.5.3"
version = "0.6.0"
description = "Read and write ML models in GGUF for GGML"
authors = ["GGML <ggml@ggml.ai>"]
packages = [

1322
llama.cpp

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86
llama.h
View file

@ -42,7 +42,7 @@
#define LLAMA_FILE_MAGIC_GGSN 0x6767736eu // 'ggsn'
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 2
#define LLAMA_SESSION_VERSION 3
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
@ -158,6 +158,22 @@ extern "C" {
llama_seq_id all_seq_id; // used if seq_id == NULL
} llama_batch;
enum llama_model_kv_override_type {
LLAMA_KV_OVERRIDE_INT,
LLAMA_KV_OVERRIDE_FLOAT,
LLAMA_KV_OVERRIDE_BOOL,
};
struct llama_model_kv_override {
char key[128];
enum llama_model_kv_override_type tag;
union {
int64_t int_value;
double float_value;
bool bool_value;
};
};
struct llama_model_params {
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
@ -165,9 +181,13 @@ extern "C" {
// 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
void * progress_callback_user_data;
// override key-value pairs of the model meta data
const struct llama_model_kv_override * kv_overrides;
// Keep the booleans together to avoid misalignment during copy-by-value.
bool vocab_only; // only load the vocabulary, no weights
bool use_mmap; // use mmap if possible
@ -185,17 +205,20 @@ extern "C" {
// ref: https://github.com/ggerganov/llama.cpp/pull/2054
float rope_freq_base; // RoPE base frequency, 0 = from model
float rope_freq_scale; // RoPE frequency scaling factor, 0 = from model
float yarn_ext_factor; // YaRN extrapolation mix factor, NaN = from model
float yarn_ext_factor; // YaRN extrapolation mix factor, negative = from model
float yarn_attn_factor; // YaRN magnitude scaling factor
float yarn_beta_fast; // YaRN low correction dim
float yarn_beta_slow; // YaRN high correction dim
uint32_t yarn_orig_ctx; // YaRN original context size
enum ggml_type type_k; // data type for K cache
enum ggml_type type_v; // data type for V cache
// Keep the booleans together to avoid misalignment during copy-by-value.
bool mul_mat_q; // if true, use experimental mul_mat_q kernels (DEPRECATED - always true)
bool f16_kv; // use fp16 for KV cache, fp32 otherwise
bool logits_all; // the llama_eval() call computes all logits, not just the last one
bool embedding; // embedding mode only
bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU
};
// model quantization parameters
@ -361,9 +384,60 @@ extern "C" {
// KV cache
//
// Returns the number of tokens in the KV cache
LLAMA_API DEPRECATED(int llama_get_kv_cache_token_count(const struct llama_context * ctx),
"avoid using this, it will be removed in the future, instead - count the tokens in user code");
// Information associated with an individual cell in the KV cache view.
struct llama_kv_cache_view_cell {
// The position for this cell. Takes KV cache shifts into account.
// May be negative if the cell is not populated.
llama_pos pos;
};
// An updateable view of the KV cache.
struct llama_kv_cache_view {
// Number of KV cache cells. This will be the same as the context size.
int32_t n_cells;
// Maximum number of sequences that can exist in a cell. It's not an error
// if there are more sequences in a cell than this value, however they will
// not be visible in the view cells_sequences.
int32_t n_max_seq;
// Number of tokens in the cache. For example, if there are two populated
// cells, the first with 1 sequence id in it and the second with 2 sequence
// ids then you'll have 3 tokens.
int32_t token_count;
// Number of populated cache cells.
int32_t used_cells;
// Maximum contiguous empty slots in the cache.
int32_t max_contiguous;
// Index to the start of the max_contiguous slot range. Can be negative
// when cache is full.
int32_t max_contiguous_idx;
// Information for an individual cell.
struct llama_kv_cache_view_cell * cells;
// The sequences for each cell. There will be n_max_seq items per cell.
llama_seq_id * cells_sequences;
};
// Create an empty KV cache view. (use only for debugging purposes)
LLAMA_API struct llama_kv_cache_view llama_kv_cache_view_init(const struct llama_context * ctx, int32_t n_max_seq);
// Free a KV cache view. (use only for debugging purposes)
LLAMA_API void llama_kv_cache_view_free(struct llama_kv_cache_view * view);
// Update the KV cache view structure with the current state of the KV cache. (use only for debugging purposes)
LLAMA_API void llama_kv_cache_view_update(const struct llama_context * ctx, struct llama_kv_cache_view * view);
// Returns the number of tokens in the KV cache (slow, use only for debug)
// If a KV cell has multiple sequences assigned to it, it will be counted multiple times
LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
// Returns the number of used KV cells (i.e. have at least one sequence assigned to them)
LLAMA_API int llama_get_kv_cache_used_cells(const struct llama_context * ctx);
// Clear the KV cache
LLAMA_API void llama_kv_cache_clear(

View file

@ -0,0 +1 @@
You are a helpful assistant.

View file

@ -0,0 +1,3 @@
-r requirements.txt
torch==2.1.1
transformers==4.35.2

View file

@ -1,5 +1,3 @@
set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
set(BUILD_NUMBER 0)
set(BUILD_COMMIT "unknown")
set(BUILD_COMPILER "unknown")
@ -58,23 +56,3 @@ else()
)
set(BUILD_TARGET ${OUT})
endif()
# Only write the build info if it changed
if(EXISTS ${OUTPUT_FILE})
file(READ ${OUTPUT_FILE} CONTENTS)
string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_COMMIT ${CMAKE_MATCH_1})
string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_COMPILER ${CMAKE_MATCH_1})
string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_TARGET ${CMAKE_MATCH_1})
if (
NOT OLD_COMMIT STREQUAL BUILD_COMMIT OR
NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
NOT OLD_TARGET STREQUAL BUILD_TARGET
)
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
endif()
else()
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
endif()

View file

@ -0,0 +1,24 @@
include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake)
set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp.in")
set(OUTPUT_FILE "${CMAKE_CURRENT_SOURCE_DIR}/common/build-info.cpp")
# Only write the build info if it changed
if(EXISTS ${OUTPUT_FILE})
file(READ ${OUTPUT_FILE} CONTENTS)
string(REGEX MATCH "LLAMA_COMMIT = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_COMMIT ${CMAKE_MATCH_1})
string(REGEX MATCH "LLAMA_COMPILER = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_COMPILER ${CMAKE_MATCH_1})
string(REGEX MATCH "LLAMA_BUILD_TARGET = \"([^\"]*)\";" _ ${CONTENTS})
set(OLD_TARGET ${CMAKE_MATCH_1})
if (
NOT OLD_COMMIT STREQUAL BUILD_COMMIT OR
NOT OLD_COMPILER STREQUAL BUILD_COMPILER OR
NOT OLD_TARGET STREQUAL BUILD_TARGET
)
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
endif()
else()
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
endif()

View file

@ -22,3 +22,4 @@ cp -rpv ../ggml/include/ggml/ggml-backend.h ./ggml-backend.h
cp -rpv ../ggml/tests/test-opt.cpp ./tests/test-opt.cpp
cp -rpv ../ggml/tests/test-grad0.cpp ./tests/test-grad0.cpp
cp -rpv ../ggml/tests/test-backend-ops.cpp ./tests/test-backend-ops.cpp

View file

@ -22,13 +22,17 @@ endfunction()
llama_build_and_test_executable(test-quantize-fns.cpp)
llama_build_and_test_executable(test-quantize-perf.cpp)
llama_build_and_test_executable(test-sampling.cpp)
llama_build_executable(test-tokenizer-0-llama.cpp)
llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_build_executable(test-tokenizer-0-falcon.cpp)
llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
llama_build_executable(test-tokenizer-1-llama.cpp)
llama_test_executable (test-tokenizer-1-llama test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf)
llama_test_executable (test-tokenizer-1-baichuan test-tokenizer-1-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-baichuan.gguf)
llama_build_executable(test-tokenizer-1-bpe.cpp)
llama_test_executable (test-tokenizer-1-falcon test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf)
llama_test_executable (test-tokenizer-1-aquila test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf)
@ -38,10 +42,12 @@ llama_test_executable(test-tokenizer-1-gpt-neox test-tokenizer-1-bpe.cpp ${CMAKE
llama_test_executable (test-tokenizer-1-refact test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf)
llama_test_executable (test-tokenizer-1-starcoder test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf)
# llama_test_executable (test-tokenizer-1-bloom test-tokenizer-1-bpe.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bloom.gguf) # BIG
llama_build_and_test_executable(test-grammar-parser.cpp)
llama_build_and_test_executable(test-llama-grammar.cpp)
llama_build_and_test_executable(test-grad0.cpp) # SLOW
llama_build_and_test_executable(test-grad0.cpp)
# llama_build_and_test_executable(test-opt.cpp) # SLOW
llama_build_and_test_executable(test-backend-ops.cpp)
llama_build_and_test_executable(test-rope.cpp)

1357
tests/test-backend-ops.cpp Normal file

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