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Merge remote-tracking branch 'upstream/master' into grammar

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Evan Jones 2023-07-05 22:07:51 -04:00
commit 38fbd4001e
79 changed files with 21579 additions and 4836 deletions
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2
.flake8 Normal file
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@ -0,0 +1,2 @@
[flake8]
max-line-length = 125

9
.github/workflows/build.yml vendored
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@ -10,10 +10,10 @@ on:
push:
branches:
- master
paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp']
paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu']
pull_request:
types: [opened, synchronize, reopened]
paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp']
paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.hpp', '**/*.c', '**/*.cpp', '**/*.cu']
env:
BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
@ -111,6 +111,7 @@ jobs:
- name: Dependencies
id: depends
continue-on-error: true
run: |
brew update
@ -129,15 +130,17 @@ jobs:
- name: Dependencies
id: depends
continue-on-error: true
run: |
brew update
- name: Build
id: cmake_build
run: |
sysctl -a
mkdir build
cd build
cmake -DLLAMA_AVX2=OFF ..
cmake -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF ..
cmake --build . --config Release
- name: Test

8
.gitignore vendored
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@ -1,5 +1,6 @@
*.o
*.a
*.so
.DS_Store
.build/
.cache/
@ -22,6 +23,7 @@ build-metal/
build-no-accel/
build-sanitize-addr/
build-sanitize-thread/
out/
models/*
*.bin
@ -32,14 +34,18 @@ models/*
/result
/perplexity
/embedding
/train-text-from-scratch
/simple
/benchmark-matmult
/vdot
/server
/Pipfile
/embd-input-test
/libllama.so
build-info.h
arm_neon.h
compile_commands.json
CMakeSettings.json
__pycache__

15
.pre-commit-config.yaml Normal file
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@ -0,0 +1,15 @@
# See https://pre-commit.com for more information
# See https://pre-commit.com/hooks.html for more hooks
exclude: prompts/.*.txt
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v3.2.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-yaml
- id: check-added-large-files
- repo: https://github.com/PyCQA/flake8
rev: 6.0.0
hooks:
- id: flake8

110
CMakeLists.txt
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@ -68,15 +68,19 @@ option(LLAMA_ACCELERATE "llama: enable Accelerate framework
option(LLAMA_BLAS "llama: use BLAS" OFF)
set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor")
option(LLAMA_CUBLAS "llama: use cuBLAS" OFF)
option(LLAMA_CUDA_FORCE_DMMV "llama: use dmmv instead of mmvq CUDA kernels" OFF)
set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels")
set(LLAMA_CUDA_DMMV_Y "1" CACHE STRING "llama: y block size for dmmv CUDA kernels")
set(LLAMA_CUDA_MMV_Y "1" CACHE STRING "llama: y block size for mmv CUDA kernels")
option(LLAMA_CUDA_DMMV_F16 "llama: use 16 bit floats for dmmv CUDA kernels" OFF)
set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
option(LLAMA_METAL "llama: use Metal" OFF)
option(LLAMA_K_QUANTS "llama: use k-quants" ON)
option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_SERVER "llama: build server example" OFF)
option(LLAMA_BUILD_SERVER "llama: build server example" ON)
#
# Build info header
@ -158,17 +162,64 @@ if (LLAMA_BLAS)
if ($(CMAKE_VERSION) VERSION_GREATER_EQUAL 3.22)
set(BLA_SIZEOF_INTEGER 8)
endif()
set(BLA_VENDOR ${LLAMA_BLAS_VENDOR})
find_package(BLAS)
if (BLAS_FOUND)
message(STATUS "BLAS found, Libraries: ${BLAS_LIBRARIES}")
if ("${BLAS_INCLUDE_DIRS}" STREQUAL "")
# BLAS_INCLUDE_DIRS is missing in FindBLAS.cmake.
# see https://gitlab.kitware.com/cmake/cmake/-/issues/20268
find_package(PkgConfig REQUIRED)
if (${LLAMA_BLAS_VENDOR} MATCHES "Generic")
pkg_check_modules(DepBLAS REQUIRED blas)
elseif (${LLAMA_BLAS_VENDOR} MATCHES "OpenBLAS")
pkg_check_modules(DepBLAS REQUIRED openblas)
elseif (${LLAMA_BLAS_VENDOR} MATCHES "FLAME")
pkg_check_modules(DepBLAS REQUIRED blis)
elseif (${LLAMA_BLAS_VENDOR} MATCHES "ATLAS")
pkg_check_modules(DepBLAS REQUIRED blas-atlas)
elseif (${LLAMA_BLAS_VENDOR} MATCHES "FlexiBLAS")
pkg_check_modules(DepBLAS REQUIRED flexiblas_api)
elseif (${LLAMA_BLAS_VENDOR} MATCHES "Intel")
# all Intel* libraries share the same include path
pkg_check_modules(DepBLAS REQUIRED mkl-sdl)
elseif (${LLAMA_BLAS_VENDOR} MATCHES "NVHPC")
# this doesn't provide pkg-config
# suggest to assign BLAS_INCLUDE_DIRS on your own
if ("${NVHPC_VERSION}" STREQUAL "")
message(WARNING "Better to set NVHPC_VERSION")
else()
set(DepBLAS_FOUND ON)
set(DepBLAS_INCLUDE_DIRS "/opt/nvidia/hpc_sdk/${CMAKE_SYSTEM_NAME}_${CMAKE_SYSTEM_PROCESSOR}/${NVHPC_VERSION}/math_libs/include")
endif()
endif()
if (DepBLAS_FOUND)
set(BLAS_INCLUDE_DIRS ${DepBLAS_INCLUDE_DIRS})
else()
message(WARNING "BLAS_INCLUDE_DIRS neither been provided nor been automatically"
" detected by pkgconfig, trying to find cblas.h from possible paths...")
find_path(BLAS_INCLUDE_DIRS
NAMES cblas.h
HINTS
/usr/include
/usr/local/include
/usr/include/openblas
/opt/homebrew/opt/openblas/include
/usr/local/opt/openblas/include
/usr/include/x86_64-linux-gnu/openblas/include
)
endif()
endif()
message(STATUS "BLAS found, Includes: ${BLAS_INCLUDE_DIRS}")
add_compile_options(${BLAS_LINKER_FLAGS})
add_compile_definitions(GGML_USE_OPENBLAS)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${BLAS_LIBRARIES})
set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${BLAS_INCLUDE_DIRS})
message("${BLAS_LIBRARIES} ${BLAS_INCLUDE_DIRS}")
include_directories(${BLAS_INCLUDE_DIRS})
else()
message(WARNING "BLAS not found, please refer to "
"https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors"
@ -176,6 +227,14 @@ if (LLAMA_BLAS)
endif()
endif()
if (LLAMA_K_QUANTS)
set(GGML_SOURCES_EXTRA ${GGML_SOURCES_EXTRA} k_quants.c k_quants.h)
add_compile_definitions(GGML_USE_K_QUANTS)
if (LLAMA_QKK_64)
add_compile_definitions(GGML_QKK_64)
endif()
endif()
if (LLAMA_CUBLAS)
cmake_minimum_required(VERSION 3.17)
@ -188,8 +247,18 @@ if (LLAMA_CUBLAS)
set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h)
add_compile_definitions(GGML_USE_CUBLAS)
if (LLAMA_CUDA_FORCE_DMMV)
add_compile_definitions(GGML_CUDA_FORCE_DMMV)
endif()
add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X})
add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y})
add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y})
if (DEFINED LLAMA_CUDA_DMMV_Y)
add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility
endif()
if (LLAMA_CUDA_DMMV_F16)
add_compile_definitions(GGML_CUDA_DMMV_F16)
endif()
add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER})
if (LLAMA_STATIC)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static)
@ -197,6 +266,15 @@ if (LLAMA_CUBLAS)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt)
endif()
if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES)
if (LLAMA_CUDA_DMMV_F16)
set(CMAKE_CUDA_ARCHITECTURES "61") # needed for f16 CUDA intrinsics
else()
set(CMAKE_CUDA_ARCHITECTURES "52;61") # lowest CUDA 12 standard + lowest for integer intrinsics
endif()
endif()
message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}")
else()
message(WARNING "cuBLAS not found")
endif()
@ -227,11 +305,6 @@ if (LLAMA_METAL)
)
endif()
if (LLAMA_K_QUANTS)
set(GGML_SOURCES_EXTRA ${GGML_SOURCES_EXTRA} k_quants.c k_quants.h)
add_compile_definitions(GGML_USE_K_QUANTS)
endif()
if (LLAMA_CLBLAST)
find_package(CLBlast)
if (CLBlast_FOUND)
@ -320,11 +393,6 @@ if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES
if (MSVC)
# TODO: arm msvc?
else()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
# Apple M1, M2, etc.
# Raspberry Pi 3, 4, Zero 2 (64-bit)
add_compile_options(-mcpu=native)
endif()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6")
# Raspberry Pi 1, Zero
add_compile_options(-mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access)
@ -408,12 +476,15 @@ add_library(ggml OBJECT
${GGML_SOURCES_EXTRA}
)
target_include_directories(ggml PUBLIC .)
target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES})
target_compile_features(ggml PUBLIC c_std_11) # don't bump
target_link_libraries(ggml PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS})
add_library(ggml_static STATIC $<TARGET_OBJECTS:ggml>)
if (BUILD_SHARED_LIBS)
set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON)
add_library(ggml_shared SHARED $<TARGET_OBJECTS:ggml>)
target_link_libraries(ggml_shared PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS})
endif()
add_library(llama
@ -437,13 +508,6 @@ if (BUILD_SHARED_LIBS)
endif()
endif()
if (GGML_SOURCES_CUDA)
message(STATUS "GGML CUDA sources found, configuring CUDA architecture")
set_property(TARGET ggml PROPERTY CUDA_ARCHITECTURES OFF)
set_property(TARGET ggml PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto")
set_property(TARGET llama PROPERTY CUDA_ARCHITECTURES OFF)
endif()
#
# programs, examples and tests

62
Makefile
View file

@ -1,9 +1,5 @@
# Define the default target now so that it is always the first target
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot
ifdef LLAMA_BUILD_SERVER
BUILD_TARGETS += server
endif
BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server libembdinput.so embd-input-test
default: $(BUILD_TARGETS)
@ -41,8 +37,11 @@ endif
# keep standard at C11 and C++11
# -Ofast tends to produce faster code, but may not be available for some compilers.
#OPT = -Ofast
ifdef LLAMA_FAST
OPT = -Ofast
else
OPT = -O3
endif
CFLAGS = -I. $(OPT) -std=c11 -fPIC
CXXFLAGS = -I. -I./examples $(OPT) -std=c++11 -fPIC
LDFLAGS =
@ -56,6 +55,10 @@ else
CXXFLAGS += -DNDEBUG
endif
ifdef LLAMA_SERVER_VERBOSE
CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE)
endif
# warnings
CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith
CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar
@ -127,7 +130,12 @@ endif
ifndef LLAMA_NO_K_QUANTS
CFLAGS += -DGGML_USE_K_QUANTS
CXXFLAGS += -DGGML_USE_K_QUANTS
OBJS += k_quants.o
ifdef LLAMA_QKK_64
CFLAGS += -DGGML_QKK_64
CXXFLAGS += -DGGML_QKK_64
endif
endif
ifndef LLAMA_NO_ACCELERATE
@ -141,11 +149,7 @@ endif # LLAMA_NO_ACCELERATE
ifdef LLAMA_OPENBLAS
CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas -I/usr/include/openblas
ifneq ($(shell grep -e "Arch Linux" -e "ID_LIKE=arch" /etc/os-release 2>/dev/null),)
LDFLAGS += -lopenblas -lcblas
else
LDFLAGS += -lopenblas
endif
endif # LLAMA_OPENBLAS
ifdef LLAMA_BLIS
@ -160,16 +164,29 @@ ifdef LLAMA_CUBLAS
OBJS += ggml-cuda.o
NVCC = nvcc
NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native
ifdef LLAMA_CUDA_FORCE_DMMV
NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV
endif # LLAMA_CUDA_FORCE_DMMV
ifdef LLAMA_CUDA_DMMV_X
NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X)
else
NVCCFLAGS += -DGGML_CUDA_DMMV_X=32
endif # LLAMA_CUDA_DMMV_X
ifdef LLAMA_CUDA_DMMV_Y
NVCCFLAGS += -DGGML_CUDA_DMMV_Y=$(LLAMA_CUDA_DMMV_Y)
ifdef LLAMA_CUDA_MMV_Y
NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y)
else ifdef LLAMA_CUDA_DMMV_Y
NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility
else
NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1
endif # LLAMA_CUDA_DMMV_Y
NVCCFLAGS += -DGGML_CUDA_MMV_Y=1
endif # LLAMA_CUDA_MMV_Y
ifdef LLAMA_CUDA_DMMV_F16
NVCCFLAGS += -DGGML_CUDA_DMMV_F16
endif # LLAMA_CUDA_DMMV_F16
ifdef LLAMA_CUDA_KQUANTS_ITER
NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER)
else
NVCCFLAGS += -DK_QUANTS_PER_ITERATION=2
endif
ggml-cuda.o: ggml-cuda.cu ggml-cuda.h
$(NVCC) $(NVCCFLAGS) $(CXXFLAGS) -Wno-pedantic -c $< -o $@
endif # LLAMA_CUBLAS
@ -248,7 +265,7 @@ $(info )
ggml.o: ggml.c ggml.h ggml-cuda.h
$(CC) $(CFLAGS) -c $< -o $@
llama.o: llama.cpp ggml.h ggml-cuda.h llama.h llama-util.h
llama.o: llama.cpp ggml.h ggml-cuda.h ggml-metal.h llama.h llama-util.h
$(CXX) $(CXXFLAGS) -c $< -o $@
common.o: examples/common.cpp examples/common.h
@ -261,7 +278,7 @@ libllama.so: llama.o ggml.o $(OBJS)
$(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS)
clean:
rm -vf *.o main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot build-info.h
rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test build-info.h
#
# Examples
@ -273,6 +290,9 @@ main: examples/main/main.cpp build-info.h ggml.
@echo '==== Run ./main -h for help. ===='
@echo
simple: examples/simple/simple.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
quantize: examples/quantize/quantize.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
@ -291,6 +311,16 @@ save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml.
server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
libembdinput.so: examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) --shared $(CXXFLAGS) $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS)
embd-input-test: libembdinput.so examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o common.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.so,$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput
train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp build-info.h ggml.o llama.o $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
build-info.h: $(wildcard .git/index) scripts/build-info.sh
@sh scripts/build-info.sh > $@.tmp
@if ! cmp -s $@.tmp $@; then \

1
Package.swift
View file

@ -11,6 +11,7 @@ let package = Package(
.target(
name: "llama",
path: ".",
exclude: ["ggml-metal.metal"],
sources: ["ggml.c", "llama.cpp"],
publicHeadersPath: "spm-headers",
cSettings: [.unsafeFlags(["-Wno-shorten-64-to-32"]), .define("GGML_USE_ACCELERATE")],

87
README.md
View file

@ -5,16 +5,17 @@
[![Actions Status](https://github.com/ggerganov/llama.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/llama.cpp/actions)
[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[Roadmap](https://github.com/users/ggerganov/projects/7) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml)
Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
**Hot topics:**
- Roadmap June 2023: https://github.com/ggerganov/llama.cpp/discussions/1729
- GPU support with Metal (Apple Silicon): https://github.com/ggerganov/llama.cpp/pull/1642
- High-quality 2,3,4,5,6-bit quantization: https://github.com/ggerganov/llama.cpp/pull/1684
- Multi-GPU support: https://github.com/ggerganov/llama.cpp/pull/1607
- Training LLaMA models from scratch: https://github.com/ggerganov/llama.cpp/pull/1652
- CPU threading improvements: https://github.com/ggerganov/llama.cpp/pull/1632
- Simple web chat example: https://github.com/ggerganov/llama.cpp/pull/1998
- k-quants now support super-block size of 64: https://github.com/ggerganov/llama.cpp/pull/2001
- New roadmap: https://github.com/users/ggerganov/projects/7
- Azure CI brainstorming: https://github.com/ggerganov/llama.cpp/discussions/1985
- p1 : LLM-based code completion engine at the edge : https://github.com/ggml-org/p1/discussions/1
<details>
<summary>Table of Contents</summary>
@ -33,6 +34,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
<li><a href="#quantization">Quantization</a></li>
<li><a href="#interactive-mode">Interactive mode</a></li>
<li><a href="#instruction-mode-with-alpaca">Instruction mode with Alpaca</a></li>
<li><a href="#using-openllama">Using OpenLLaMA</a></li>
<li><a href="#using-gpt4all">Using GPT4All</a></li>
<li><a href="#using-pygmalion-7b--metharme-7b">Using Pygmalion 7B & Metharme 7B</a></li>
<li><a href="#obtaining-the-facebook-llama-original-model-and-stanford-alpaca-model-data">Obtaining the Facebook LLaMA original model and Stanford Alpaca model data</a></li>
@ -84,6 +86,7 @@ as the main playground for developing new features for the [ggml](https://github
- [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy)
- [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b)
- [X] [WizardLM](https://github.com/nlpxucan/WizardLM)
- [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B)
**Bindings:**
@ -92,6 +95,7 @@ as the main playground for developing new features for the [ggml](https://github
- Node.js: [hlhr202/llama-node](https://github.com/hlhr202/llama-node)
- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
**UI:**
@ -336,9 +340,16 @@ Building the program with BLAS support may lead to some performance improvements
cmake .. -DLLAMA_CUBLAS=ON
cmake --build . --config Release
```
Note: Because llama.cpp uses multiple CUDA streams for matrix multiplication results [are not guaranteed to be reproducible](https://docs.nvidia.com/cuda/cublas/index.html#results-reproducibility). If you need reproducibility, set `GGML_CUDA_MAX_STREAMS` in the file `ggml-cuda.cu` to 1.
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used.
The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance:
| Option | Legal values | Default | Description |
|-------------------------|------------------------|---------|-------------|
| LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 7.0/Turing/RTX 2000 or higher). Does not affect k-quants. |
| LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. |
| LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. |
| LLAMA_CUDA_DMMV_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. |
| LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. |
- #### CLBlast
@ -372,7 +383,7 @@ Building the program with BLAS support may lead to some performance improvements
```sh
git clone https://github.com/CNugteren/CLBlast.git
mkdir CLBlast/build
cd CLBLast/build
cd CLBlast/build
cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF
cmake --build . --config Release
cmake --install . --prefix /some/path
@ -541,6 +552,13 @@ cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
>
```
### Using [OpenLLaMA](https://github.com/openlm-research/open_llama)
OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. It uses the same architecture and is a drop-in replacement for the original LLaMA weights.
- Download the [3B](https://huggingface.co/openlm-research/open_llama_3b), [7B](https://huggingface.co/openlm-research/open_llama_7b), or [13B](https://huggingface.co/openlm-research/open_llama_13b) model from Hugging Face.
- Convert the model to ggml FP16 format using `python convert.py <path to OpenLLaMA directory>`
### Using [GPT4All](https://github.com/nomic-ai/gpt4all)
- Obtain the `tokenizer.model` file from LLaMA model and put it to `models`
@ -616,8 +634,14 @@ And after 4.45 hours, you will have the final perplexity.
### Android
#### Building the Project using Android NDK
You can easily run `llama.cpp` on Android device with [termux](https://termux.dev/).
First, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake:
First, install the essential packages for termux:
```
pkg install clang wget git cmake
```
Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake:
```
$ mkdir build-android
$ cd build-android
@ -630,6 +654,49 @@ Finally, copy the `llama` binary and the model files to your device storage. Her
https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
#### Building the Project using Termux (F-Droid)
Termux from F-Droid offers an alternative route to execute the project on an Android device. This method empowers you to construct the project right from within the terminal, negating the requirement for a rooted device or SD Card.
Outlined below are the directives for installing the project using OpenBLAS and CLBlast. This combination is specifically designed to deliver peak performance on recent devices that feature a GPU.
If you opt to utilize OpenBLAS, you'll need to install the corresponding package.
```
apt install libopenblas
```
Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages:
```
apt install ocl-icd opencl-headers opencl-clhpp clinfo
```
In order to compile CLBlast, you'll need to first clone the respective Git repository, which can be found at this URL: https://github.com/CNugteren/CLBlast. Alongside this, clone this repository into your home directory. Once this is done, navigate to the CLBlast folder and execute the commands detailed below:
```
cmake .
make
cp libclblast.so* $PREFIX/lib
cp ./include/clblast.h ../llama.cpp
```
Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below:
```
cp /data/data/com.termux/files/usr/include/openblas/cblas.h .
cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h .
make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice)
```
Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below:
```
GGML_OPENCL_PLATFORM=0
GGML_OPENCL_DEVICE=0
export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH
```
(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ )
For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle.
Place your desired model into the `/llama.cpp/models/` directory and execute the `./main (...)` script.
### Docker
#### Prerequisites

79
build.zig
View file

@ -1,61 +1,58 @@
const std = @import("std");
// Zig Version: 0.11.0-dev.3379+629f0d23b
pub fn build(b: *std.build.Builder) void {
const target = b.standardTargetOptions(.{});
const optimize = b.standardReleaseOptions();
const want_lto = b.option(bool, "lto", "Want -fLTO");
const lib = b.addStaticLibrary("llama", null);
lib.want_lto = want_lto;
lib.setTarget(target);
lib.setBuildMode(optimize);
const optimize = b.standardOptimizeOption(.{});
const lib = b.addStaticLibrary(.{
.name = "llama",
.target = target,
.optimize = optimize,
});
lib.linkLibC();
lib.linkLibCpp();
lib.addIncludePath(".");
lib.addIncludePath("examples");
lib.addIncludePath("./examples");
lib.addCSourceFiles(&.{
"ggml.c",
}, &.{"-std=c11"});
lib.addCSourceFiles(&.{
"llama.cpp",
}, &.{"-std=c++11"});
lib.install();
b.installArtifact(lib);
const build_args = .{ .b = b, .lib = lib, .target = target, .optimize = optimize, .want_lto = want_lto };
const examples = .{
"main",
"baby-llama",
"embedding",
// "metal",
"perplexity",
"quantize",
"quantize-stats",
"save-load-state",
// "server",
"simple",
"train-text-from-scratch",
};
const exe = build_example("main", build_args);
_ = build_example("quantize", build_args);
_ = build_example("perplexity", build_args);
_ = build_example("embedding", build_args);
// create "zig build run" command for ./main
const run_cmd = exe.run();
run_cmd.step.dependOn(b.getInstallStep());
if (b.args) |args| {
run_cmd.addArgs(args);
}
const run_step = b.step("run", "Run the app");
run_step.dependOn(&run_cmd.step);
}
fn build_example(comptime name: []const u8, args: anytype) *std.build.LibExeObjStep {
const b = args.b;
const lib = args.lib;
const want_lto = args.want_lto;
const exe = b.addExecutable(name, null);
exe.want_lto = want_lto;
lib.setTarget(args.target);
lib.setBuildMode(args.optimize);
inline for (examples) |example_name| {
const exe = b.addExecutable(.{
.name = example_name,
.target = target,
.optimize = optimize,
});
exe.addIncludePath(".");
exe.addIncludePath("examples");
exe.addIncludePath("./examples");
exe.addCSourceFiles(&.{
std.fmt.comptimePrint("examples/{s}/{s}.cpp", .{name, name}),
std.fmt.comptimePrint("examples/{s}/{s}.cpp", .{example_name, example_name}),
"examples/common.cpp",
}, &.{"-std=c++11"});
exe.linkLibrary(lib);
exe.install();
return exe;
b.installArtifact(exe);
const run_cmd = b.addRunArtifact(exe);
run_cmd.step.dependOn(b.getInstallStep());
if (b.args) |args| run_cmd.addArgs(args);
const run_step = b.step("run_" ++ example_name, "Run the app");
run_step.dependOn(&run_cmd.step);
}
}

6
convert-lora-to-ggml.py
View file

@ -113,6 +113,10 @@ with open(output_path, "wb") as fout:
write_file_header(fout, params)
for k, v in model.items():
if k.endswith(".default.weight"):
k = k.replace(".default.weight", ".weight")
if k in ["llama_proj.weight", "llama_proj.bias"]:
continue
if k.endswith("lora_A.weight"):
if v.dtype != torch.float16 and v.dtype != torch.float32:
v = v.float()
@ -120,7 +124,7 @@ with open(output_path, "wb") as fout:
else:
v = v.float()
t = v.numpy()
t = v.detach().numpy()
tname = translate_tensor_name(k)
print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB")
write_tensor_header(fout, tname, t.shape, t.dtype)

150
convert.py
View file

@ -130,6 +130,14 @@ TENSORS_LIST = make_tensors_list()
TENSORS_SET = set(TENSORS_LIST)
def find_n_mult(n_ff: int, n_embd: int) -> int:
# hardcoded magic range
for n_mult in range(256, 1, -1):
calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult
if calc_ff == n_ff:
return n_mult
raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).")
@dataclass
class Params:
n_vocab: int
@ -137,21 +145,61 @@ class Params:
n_mult: int
n_head: int
n_layer: int
file_type: GGMLFileType
@staticmethod
def guessed(model: 'LazyModel', file_type: GGMLFileType) -> 'Params':
n_vocab, n_embd = model["tok_embeddings.weight"].shape
def guessed(model: 'LazyModel') -> 'Params':
# try transformer naming first
n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape
# try transformer naming first
if "model.layers.0.self_attn.q_proj.weight" in model:
n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model)
else:
n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model)
n_head=n_embd // 128 # guessed
return Params(
n_vocab=n_vocab,
n_embd=n_embd,
n_mult=256,
n_head=n_embd // 128,
n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model),
file_type=file_type,
n_head=n_head,
n_layer=n_layer,
)
@staticmethod
def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params':
config = json.load(open(config_path))
n_vocab = config["vocab_size"];
n_embd = config["hidden_size"];
n_head = config["num_attention_heads"];
n_layer = config["num_hidden_layers"];
n_ff = config["intermediate_size"];
n_mult = find_n_mult(n_ff, n_embd);
return Params(
n_vocab=n_vocab,
n_embd=n_embd,
n_mult=n_mult,
n_head=n_head,
n_layer=n_layer,
)
@staticmethod
def load(model_plus: 'ModelPlus') -> 'Params':
orig_config_path = model_plus.paths[0].parent / "params.json"
hf_transformer_config_path = model_plus.paths[0].parent / "config.json"
if hf_transformer_config_path.exists():
params = Params.loadHFTransformerJson(model_plus.model, hf_transformer_config_path)
else:
params = Params.guessed(model_plus.model)
print(f'params: n_vocab:{params.n_vocab} n_embd:{params.n_embd} n_mult:{params.n_mult} n_head:{params.n_head} n_layer:{params.n_layer}')
return params
class SentencePieceVocab:
def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None:
@ -273,6 +321,10 @@ class Tensor(metaclass=ABCMeta):
@abstractmethod
def permute(self, n_head: int) -> 'Tensor': ...
@abstractmethod
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ...
@abstractmethod
def part(self, n_part: int) -> 'UnquantizedTensor': ...
@abstractmethod
def to_ggml(self) -> 'GGMLCompatibleTensor': ...
@ -297,6 +349,14 @@ class UnquantizedTensor(Tensor):
def to_ggml(self) -> 'UnquantizedTensor':
return self
def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor':
r = self.ndarray.shape[0] // 3
return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head))
def part(self, n_part: int) -> 'UnquantizedTensor':
r = self.ndarray.shape[0] // 3
return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...])
def permute(self, n_head: int) -> 'UnquantizedTensor':
return UnquantizedTensor(permute(self.ndarray, n_head))
@ -512,7 +572,11 @@ class LazyTensor:
if not isinstance(self.data_type, QuantizedDataType):
raise Exception(f"Can't turn an unquantized tensor into a quantized type ({data_type})")
if self.data_type.have_g_idx:
sys.stderr.write("Error: Input uses the newer GPTQ-for-LLaMa format (using g_idx), which is not yet natively supported by GGML. For now you can still convert this model by passing `--outtype f16` to dequantize, but that will result in a much larger output file for no quality benefit.\n")
sys.stderr.write(
"Error: Input uses the newer GPTQ-for-LLaMa format (using g_idx), "
"which is not yet natively supported by GGML. "
"For now you can still convert this model by passing `--outtype f16` to dequantize, "
"but that will result in a much larger output file for no quality benefit.\n")
sys.exit(1)
assert not data_type.have_g_idx and self.data_type.have_addends and data_type.have_addends
@ -590,20 +654,38 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor:
return lazy_tensor.load().permute(n_head)
return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor:
def load() -> Tensor:
return lazy_tensor.load().permute_part(n_part, n_head)
s = lazy_tensor.shape.copy()
s[0] = s[0] // 3
return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description)
def convert_transformers_to_orig(model: LazyModel) -> LazyModel:
def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor:
def load() -> Tensor:
return lazy_tensor.load().part(n_part)
s = lazy_tensor.shape.copy()
s[0] = s[0] // 3
return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description)
def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel:
out: LazyModel = {}
out["tok_embeddings.weight"] = model["model.embed_tokens.weight"]
out["norm.weight"] = model["model.norm.weight"]
out["output.weight"] = model["lm_head.weight"]
n_head = model["model.layers.0.self_attn.q_proj.weight"].shape[1] // 128
for i in itertools.count():
if f"model.layers.{i}.self_attn.q_proj.weight" not in model:
break
out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], n_head)
out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], n_head)
if f"model.layers.{i}.self_attn.q_proj.weight" in model:
out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head)
out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head)
out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"]
elif f"model.layers.{i}.self_attn.W_pack.weight" in model:
out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head)
out[f"layers.{i}.attention.wk.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head)
out[f"layers.{i}.attention.wv.weight"] = part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 2)
else:
break
out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"]
out[f"layers.{i}.feed_forward.w1.weight"] = model[f"model.layers.{i}.mlp.gate_proj.weight"]
@ -695,7 +777,8 @@ class LazyUnpickler(pickle.Unpickler):
return LazyStorage(load=load, kind=pid[1], description=description)
# @staticmethod
def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any, # pyright: ignore[reportSelfClsParameterName]
def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any,
# pyright: ignore[reportSelfClsParameterName]
requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor:
assert isinstance(storage, LazyStorage)
@ -915,7 +998,7 @@ class OutputFile:
def __init__(self, fname_out: Path) -> None:
self.fout = open(fname_out, "wb")
def write_file_header(self, params: Params) -> None:
def write_file_header(self, params: Params, file_type: GGMLFileType) -> None:
self.fout.write(b"ggjt"[::-1]) # magic
values = [
1, # file version
@ -925,7 +1008,7 @@ class OutputFile:
params.n_head,
params.n_layer,
params.n_embd // params.n_head, # rot (obsolete)
params.file_type.value,
file_type.value,
]
self.fout.write(struct.pack("i" * len(values), *values))
@ -946,17 +1029,17 @@ class OutputFile:
def write_vocab_only(fname_out: Path, vocab: Vocab) -> None:
of = OutputFile(fname_out)
params = Params(n_vocab=vocab.vocab_size, n_embd=0, n_mult=0,
n_head=1, n_layer=0, file_type=GGMLFileType.AllF32)
n_head=1, n_layer=0)
of = OutputFile(fname_out)
of.write_file_header(params)
of.write_file_header(params, file_type=GGMLFileType.AllF32)
of.write_vocab(vocab)
of.fout.close()
@staticmethod
def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None:
def write_all(fname_out: Path, params: Params, file_type: GGMLFileType, model: LazyModel, vocab: Vocab) -> None:
check_vocab_size(params, vocab)
of = OutputFile(fname_out)
of.write_file_header(params)
of.write_file_header(params, file_type)
print("Writing vocab...")
of.write_vocab(vocab)
@ -992,11 +1075,11 @@ def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFi
raise Exception(f"Unexpected combination of types: {name_to_type}")
def do_necessary_conversions(model: LazyModel) -> LazyModel:
def do_necessary_conversions(model: LazyModel, params: Params) -> LazyModel:
model = handle_quantization(model)
if "lm_head.weight" in model:
model = convert_transformers_to_orig(model)
model = convert_transformers_to_orig(model, params)
model = filter_and_sort_tensors(model)
return model
@ -1094,23 +1177,27 @@ def load_vocab(path: Path) -> SentencePieceVocab:
elif path3.exists():
path = path3
else:
raise FileNotFoundError(f"Could not find tokenizer.model in {path} or its parent; if it's in another directory, pass the directory as --vocab-dir")
raise FileNotFoundError(
f"Could not find tokenizer.model in {path} or its parent; "
"if it's in another directory, pass the directory as --vocab-dir")
added_tokens_path = path.parent / "added_tokens.json"
print(f"Loading vocab file {path}")
return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None)
def default_outfile(model_paths: List[Path], params: Params) -> Path:
def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path:
namestr = {
GGMLFileType.AllF32: "f32",
GGMLFileType.MostlyF16: "f16",
GGMLFileType.MostlyQ4_0: "q4_0",
GGMLFileType.MostlyQ4_1: "q4_1",
GGMLFileType.PerLayerIsQ4_1: "q4_1",
}[params.file_type]
}[file_type]
ret = model_paths[0].parent / f"ggml-model-{namestr}.bin"
if ret in model_paths:
sys.stderr.write(f"Error: Default output path ({ret}) would overwrite the input. Please explicitly specify a path using --outfile.\n")
sys.stderr.write(
f"Error: Default output path ({ret}) would overwrite the input. "
"Please explicitly specify a path using --outfile.\n")
sys.exit(1)
return ret
@ -1131,7 +1218,8 @@ def main(args_in: Optional[List[str]] = None) -> None:
parser.add_argument("--outtype", choices=["f32", "f16", "q4_1", "q4_0"], help="output format (default: based on input)")
parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file")
parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input")
parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
parser.add_argument("model", type=Path,
help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)")
args = parser.parse_args(args_in)
vocab: Vocab
@ -1154,13 +1242,13 @@ def main(args_in: Optional[List[str]] = None) -> None:
else:
vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent
vocab = load_vocab(vocab_dir)
params = Params.load(model_plus)
model = model_plus.model
model = do_necessary_conversions(model)
model = do_necessary_conversions(model, params)
output_type = pick_output_type(model, args.outtype)
model = convert_to_output_type(model, output_type)
params = Params.guessed(model, output_type)
outfile = args.outfile or default_outfile(model_plus.paths, params)
OutputFile.write_all(outfile, params, model, vocab)
outfile = args.outfile or default_outfile(model_plus.paths, output_type)
OutputFile.write_all(outfile, params, output_type, model, vocab)
print(f"Wrote {outfile}")

3
examples/CMakeLists.txt
View file

@ -39,6 +39,9 @@ else()
add_subdirectory(save-load-state)
add_subdirectory(benchmark)
add_subdirectory(baby-llama)
add_subdirectory(train-text-from-scratch)
add_subdirectory(simple)
add_subdirectory(embd-input)
if (LLAMA_METAL)
add_subdirectory(metal)
endif()

35
examples/baby-llama/baby-llama.cpp
View file

@ -4,6 +4,10 @@
#include <random>
#include <cstring>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
float frand() {
return (float)rand()/(float)RAND_MAX;
}
@ -79,34 +83,39 @@ struct ggml_tensor * randomize_tensor_normal(
int ndims,
const int64_t ne[],
struct random_normal_distribution * rnd) {
float scale = 1.0; // xavier
switch (ndims) {
case 1:
scale /= sqrtf(ne[0]);
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)tensor->data)[i0] = frand_normal(rnd);
((float *)tensor->data)[i0] = scale * frand_normal(rnd);
}
break;
case 2:
scale /= sqrtf(ne[0]+ne[1]);
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)tensor->data)[i1*ne[0] + i0] = frand_normal(rnd);
((float *)tensor->data)[i1*ne[0] + i0] = scale * frand_normal(rnd);
}
}
break;
case 3:
scale /= sqrtf(ne[0]+ne[1]);
for (int i2 = 0; i2 < ne[2]; i2++) {
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)tensor->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand_normal(rnd);
((float *)tensor->data)[i2*ne[1]*ne[0] + i1*ne[0] + i0] = scale * frand_normal(rnd);
}
}
}
break;
case 4:
scale /= sqrtf(ne[0]+ne[1]);
for (int i3 = 0; i3 < ne[3]; i3++) {
for (int i2 = 0; i2 < ne[2]; i2++) {
for (int i1 = 0; i1 < ne[1]; i1++) {
for (int i0 = 0; i0 < ne[0]; i0++) {
((float *)tensor->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = frand_normal(rnd);
((float *)tensor->data)[i3*ne[2]*ne[1]*ne[0] + i2*ne[1]*ne[0] + i1*ne[0] + i0] = scale * frand_normal(rnd);
}
}
}
@ -148,8 +157,8 @@ struct llama_hparams_lora {
uint32_t n_rot = 64;
uint32_t n_lora = 64;
bool operator!=(const llama_hparams & other) const {
return memcmp(this, &other, sizeof(llama_hparams));
bool operator!=(const llama_hparams_lora & other) const {
return memcmp(this, &other, sizeof(llama_hparams_lora)) != 0;
}
};
@ -557,8 +566,8 @@ struct ggml_tensor * forward(
// wk shape [n_embd, n_embd, 1, 1]
// Qcur shape [n_embd/n_head, n_head, N, 1]
// Kcur shape [n_embd/n_head, n_head, N, 1]
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0);
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0);
// store key and value to memory
{
@ -814,8 +823,8 @@ struct ggml_tensor * forward_batch(
// wk shape [n_embd, n_embd, 1, 1]
// Qcur shape [n_embd/n_head, n_head, N, n_batch]
// Kcur shape [n_embd/n_head, n_head, N, n_batch]
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0);
struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0);
assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch);
assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch);
@ -1107,7 +1116,7 @@ struct ggml_tensor * forward_lora(
model->layers[il].wqb,
cur)),
n_embd/n_head, n_head, N),
n_past, n_rot, 0);
n_past, n_rot, 0, 0);
struct ggml_tensor * Kcur = ggml_rope(ctx0,
ggml_reshape_3d(ctx0,
ggml_mul_mat(ctx0,
@ -1116,7 +1125,7 @@ struct ggml_tensor * forward_lora(
model->layers[il].wkb,
cur)),
n_embd/n_head, n_head, N),
n_past, n_rot, 0);
n_past, n_rot, 0, 0);
// store key and value to memory
{
@ -1465,7 +1474,7 @@ struct ggml_tensor * square_error_loss(struct ggml_context * ctx, struct ggml_te
}
struct ggml_tensor * cross_entropy_loss(struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) {
const float eps = 1e-3;
const float eps = 1e-3f;
return
ggml_sum(ctx,
ggml_neg(ctx,

10
examples/benchmark/benchmark-matmult.cpp
View file

@ -16,6 +16,10 @@
#include <iterator>
#include <algorithm>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
float tensor_sum_elements(const ggml_tensor * tensor) {
float sum = 0;
if (tensor->type==GGML_TYPE_F32) {
@ -29,9 +33,9 @@ float tensor_sum_elements(const ggml_tensor * tensor) {
}
void tensor_dump(const ggml_tensor * tensor, const char * name) {
printf("%15s: type = %i (%5s) ne = %5d x %5d x %5d, nb = (%5li, %5li, %5li) - ", name,
printf("%15s: type = %i (%5s) ne = %5" PRIi64 " x %5" PRIi64 " x %5" PRIi64 ", nb = (%5zi, %5zi, %5zi) - ", name,
tensor->type, ggml_type_name(tensor->type),
(int) tensor->ne[0], (int) tensor->ne[1], (int) tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]);
tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->nb[0], tensor->nb[1], tensor->nb[2]);
float sum = tensor_sum_elements(tensor);
printf("Sum of tensor %s is %6.2f\n", name, sum);
}
@ -120,7 +124,7 @@ int main(int argc, char ** argv) {
ctx_size += sizex*sizey*ggml_type_sizef(GGML_TYPE_F32); // BLAS
ctx_size += 1024*1024*16;
printf("Allocating Memory of size %li bytes, %li MB\n",ctx_size, (ctx_size/1024/1024));
printf("Allocating Memory of size %zi bytes, %zi MB\n",ctx_size, (ctx_size/1024/1024));
struct ggml_init_params params = {
/*.mem_size =*/ ctx_size,

41
examples/chat-vicuna.sh Executable file
View file

@ -0,0 +1,41 @@
#!/bin/bash
set -e
cd "$(dirname "$0")/.." || exit
MODEL="${MODEL:-./models/ggml-vic13b-uncensored-q5_0.bin}"
PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat.txt}
USER_NAME="### Human"
AI_NAME="### Assistant"
# Adjust to the number of CPU cores you want to use.
N_THREAD="${N_THREAD:-8}"
# Number of tokens to predict (made it larger than default because we want a long interaction)
N_PREDICTS="${N_PREDICTS:-2048}"
# Note: you can also override the generation options by specifying them on the command line:
# For example, override the context size by doing: ./chatLLaMa --ctx_size 1024
GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647}"
DATE_TIME=$(date +%H:%M)
DATE_YEAR=$(date +%Y)
PROMPT_FILE=$(mktemp -t llamacpp_prompt.XXXXXXX.txt)
sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \
-e "s/\[\[AI_NAME\]\]/$AI_NAME/g" \
-e "s/\[\[DATE_TIME\]\]/$DATE_TIME/g" \
-e "s/\[\[DATE_YEAR\]\]/$DATE_YEAR/g" \
$PROMPT_TEMPLATE > $PROMPT_FILE
# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
./bin/main $GEN_OPTIONS \
--model "$MODEL" \
--threads "$N_THREAD" \
--n_predict "$N_PREDICTS" \
--color --interactive \
--file ${PROMPT_FILE} \
--reverse-prompt "### Human:" \
--in-prefix ' ' \
"$@"

49
examples/common.cpp
View file

@ -28,6 +28,10 @@
#include <wchar.h>
#endif
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
int32_t get_num_physical_cores() {
#ifdef __linux__
// enumerate the set of thread siblings, num entries is num cores
@ -102,14 +106,11 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
}
if (arg == "-s" || arg == "--seed") {
#if defined(GGML_USE_CUBLAS)
fprintf(stderr, "WARNING: when using cuBLAS generation results are NOT guaranteed to be reproducible.\n");
#endif
if (++i >= argc) {
invalid_param = true;
break;
}
params.seed = std::stoi(argv[i]);
params.seed = std::stoul(argv[i]);
} else if (arg == "-t" || arg == "--threads") {
if (++i >= argc) {
invalid_param = true;
@ -331,11 +332,19 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
}
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set a tensor split.\n");
#endif // GGML_USE_CUBLAS
} else if (arg == "--low-vram" || arg == "-lv") {
#ifdef GGML_USE_CUBLAS
params.low_vram = true;
#else
fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. It is not possible to set lower vram usage.\n");
#endif // GGML_USE_CUBLAS
} else if (arg == "--no-mmap") {
params.use_mmap = false;
} else if (arg == "--mtest") {
params.mem_test = true;
} else if (arg == "--numa") {
params.numa = true;
} else if (arg == "--export") {
params.export_cgraph = true;
} else if (arg == "--verbose-prompt") {
@ -367,7 +376,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
} else {
throw std::exception();
}
} catch (const std::exception &e) {
} catch (const std::exception&) {
invalid_param = true;
break;
}
@ -428,6 +437,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
gpt_print_usage(argc, argv, default_params);
exit(1);
}
if (escape_prompt) {
process_escapes(params.prompt);
}
@ -497,12 +507,16 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
if (llama_mmap_supported()) {
fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n");
}
fprintf(stderr, " --numa attempt optimizations that help on some NUMA systems\n");
fprintf(stderr, " if run without this previously, it is recommended to drop the system page cache before using this\n");
fprintf(stderr, " see https://github.com/ggerganov/llama.cpp/issues/1437\n");
#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD
fprintf(stderr, " -ngl N, --n-gpu-layers N\n");
fprintf(stderr, " number of layers to store in VRAM\n");
fprintf(stderr, " -ts SPLIT --tensor-split SPLIT\n");
fprintf(stderr, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n");
fprintf(stderr, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n" );
fprintf(stderr, " -lv, --low-vram don't allocate VRAM scratch buffer\n" );
#endif
fprintf(stderr, " --mtest compute maximum memory usage\n");
fprintf(stderr, " --export export the computation graph to 'llama.ggml'\n");
@ -544,7 +558,7 @@ std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::s
return res;
}
struct llama_context * llama_init_from_gpt_params(const gpt_params & params) {
std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(const gpt_params & params) {
auto lparams = llama_context_default_params();
lparams.n_ctx = params.n_ctx;
@ -552,6 +566,7 @@ struct llama_context * llama_init_from_gpt_params(const gpt_params & params) {
lparams.n_gpu_layers = params.n_gpu_layers;
lparams.main_gpu = params.main_gpu;
memcpy(lparams.tensor_split, params.tensor_split, LLAMA_MAX_DEVICES*sizeof(float));
lparams.low_vram = params.low_vram;
lparams.seed = params.seed;
lparams.f16_kv = params.memory_f16;
lparams.use_mmap = params.use_mmap;
@ -559,25 +574,33 @@ struct llama_context * llama_init_from_gpt_params(const gpt_params & params) {
lparams.logits_all = params.perplexity;
lparams.embedding = params.embedding;
llama_context * lctx = llama_init_from_file(params.model.c_str(), lparams);
if (lctx == NULL) {
llama_model * model = llama_load_model_from_file(params.model.c_str(), lparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
return NULL;
return std::make_tuple(nullptr, nullptr);
}
llama_context * lctx = llama_new_context_with_model(model, lparams);
if (lctx == NULL) {
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_free_model(model);
return std::make_tuple(nullptr, nullptr);
}
if (!params.lora_adapter.empty()) {
int err = llama_apply_lora_from_file(lctx,
int err = llama_model_apply_lora_from_file(model,
params.lora_adapter.c_str(),
params.lora_base.empty() ? NULL : params.lora_base.c_str(),
params.n_threads);
if (err != 0) {
fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__);
return NULL;
llama_free(lctx);
llama_free_model(model);
return std::make_tuple(nullptr, nullptr);
}
}
return lctx;
return std::make_tuple(model, lctx);
}
void console_init(console_state & con_st) {

8
examples/common.h
View file

@ -9,6 +9,7 @@
#include <random>
#include <thread>
#include <unordered_map>
#include <tuple>
#if !defined (_WIN32)
#include <stdio.h>
@ -21,7 +22,7 @@
int32_t get_num_physical_cores();
struct gpt_params {
int32_t seed = -1; // RNG seed
uint32_t seed = -1; // RNG seed
int32_t n_threads = get_num_physical_cores();
int32_t n_predict = -1; // new tokens to predict
int32_t n_ctx = 512; // context size
@ -30,6 +31,7 @@ struct gpt_params {
int32_t n_gpu_layers = 0; // number of layers to store in VRAM
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
// sampling parameters
std::unordered_map<llama_token, float> logit_bias; // logit bias for specific tokens
@ -58,6 +60,7 @@ struct gpt_params {
std::string lora_adapter = ""; // lora adapter path
std::string lora_base = ""; // base model path for the lora adapter
bool low_vram = false; // if true, reduce VRAM usage at the cost of performance
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
@ -75,6 +78,7 @@ struct gpt_params {
bool use_mmap = true; // use mmap for faster loads
bool use_mlock = false; // use mlock to keep model in memory
bool mem_test = false; // compute maximum memory usage
bool numa = false; // attempt optimizations that help on some NUMA systems
bool export_cgraph = false; // export the computation graph
bool verbose_prompt = false; // print prompt tokens before generation
};
@ -95,7 +99,7 @@ std::vector<llama_token> llama_tokenize(struct llama_context * ctx, const std::s
// Model utils
//
struct llama_context * llama_init_from_gpt_params(const gpt_params & params);
std::tuple<struct llama_model *, struct llama_context *> llama_init_from_gpt_params(const gpt_params & params);
//
// Console utils

4
examples/embd-input/.gitignore vendored Normal file
View file

@ -0,0 +1,4 @@
PandaGPT
MiniGPT-4
*.pth

15
examples/embd-input/CMakeLists.txt Normal file
View file

@ -0,0 +1,15 @@
set(TARGET embdinput)
add_library(${TARGET} embd-input-lib.cpp embd-input.h)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()
set(TARGET embd-input-test)
add_executable(${TARGET} embd-input-test.cpp)
target_link_libraries(${TARGET} PRIVATE common llama embdinput ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

63
examples/embd-input/README.md Normal file
View file

@ -0,0 +1,63 @@
### Examples for input embedding directly
## Requirement
build `libembdinput.so`
run the following comman in main dir (../../).
```
make
```
## [LLaVA](https://github.com/haotian-liu/LLaVA/) example (llava.py)
1. Obtian LLaVA model (following https://github.com/haotian-liu/LLaVA/ , use https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/).
2. Convert it to ggml format.
3. `llava_projection.pth` is [pytorch_model-00003-of-00003.bin](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin).
```
import torch
bin_path = "../LLaVA-13b-delta-v1-1/pytorch_model-00003-of-00003.bin"
pth_path = "./examples/embd_input/llava_projection.pth"
dic = torch.load(bin_path)
used_key = ["model.mm_projector.weight","model.mm_projector.bias"]
torch.save({k: dic[k] for k in used_key}, pth_path)
```
4. Check the path of LLaVA model and `llava_projection.pth` in `llava.py`.
## [PandaGPT](https://github.com/yxuansu/PandaGPT) example (panda_gpt.py)
1. Obtian PandaGPT lora model from https://github.com/yxuansu/PandaGPT. Rename the file to `adapter_model.bin`. Use [convert-lora-to-ggml.py](../../convert-lora-to-ggml.py) to convert it to ggml format.
The `adapter_config.json` is
```
{
"peft_type": "LORA",
"fan_in_fan_out": false,
"bias": null,
"modules_to_save": null,
"r": 32,
"lora_alpha": 32,
"lora_dropout": 0.1,
"target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"]
}
```
2. Papare the `vicuna` v0 model.
3. Obtain the [ImageBind](https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth) model.
4. Clone the PandaGPT source.
```
git clone https://github.com/yxuansu/PandaGPT
```
5. Install the requirement of PandaGPT.
6. Check the path of PandaGPT source, ImageBind model, lora model and vicuna model in panda_gpt.py.
## [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4/) example (minigpt4.py)
1. Obtain MiniGPT-4 model from https://github.com/Vision-CAIR/MiniGPT-4/ and put it in `embd-input`.
2. Clone the MiniGPT-4 source.
```
git clone https://github.com/Vision-CAIR/MiniGPT-4/
```
3. Install the requirement of PandaGPT.
4. Papare the `vicuna` v0 model.
5. Check the path of MiniGPT-4 source, MiniGPT-4 model and vicuna model in `minigpt4.py`.

223
examples/embd-input/embd-input-lib.cpp Normal file
View file

@ -0,0 +1,223 @@
// Defines sigaction on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include "embd-input.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
static llama_context ** g_ctx;
extern "C" {
struct MyModel* create_mymodel(int argc, char ** argv) {
gpt_params params;
if (gpt_params_parse(argc, argv, params) == false) {
return nullptr;
}
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
llama_init_backend(params.numa);
llama_model * model;
llama_context * ctx;
g_ctx = &ctx;
// load the model and apply lora adapter, if any
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return nullptr;
}
// print system information
{
fprintf(stderr, "\n");
fprintf(stderr, "system_info: n_threads = %d / %d | %s\n",
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
struct MyModel * ret = new MyModel();
ret->ctx = ctx;
ret->params = params;
ret->n_past = 0;
// printf("ctx: %d\n", ret->ctx);
return ret;
}
void free_mymodel(struct MyModel * mymodel) {
llama_context * ctx = mymodel->ctx;
llama_print_timings(ctx);
llama_free(ctx);
delete mymodel;
}
bool eval_float(void * model, float * input, int N){
MyModel * mymodel = (MyModel*)model;
llama_context * ctx = mymodel->ctx;
gpt_params params = mymodel->params;
int n_emb = llama_n_embd(ctx);
int n_past = mymodel->n_past;
int n_batch = N; // params.n_batch;
for (int i = 0; i < (int) N; i += n_batch) {
int n_eval = (int) N - i;
if (n_eval > n_batch) {
n_eval = n_batch;
}
if (llama_eval_embd(ctx, (input+i*n_emb), n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
n_past += n_eval;
}
mymodel->n_past = n_past;
return true;
}
bool eval_tokens(void * model, std::vector<llama_token> tokens) {
MyModel * mymodel = (MyModel* )model;
llama_context * ctx;
ctx = mymodel->ctx;
gpt_params params = mymodel->params;
int n_past = mymodel->n_past;
for (int i = 0; i < (int) tokens.size(); i += params.n_batch) {
int n_eval = (int) tokens.size() - i;
if (n_eval > params.n_batch) {
n_eval = params.n_batch;
}
if (llama_eval(ctx, &tokens[i], n_eval, n_past, params.n_threads)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
n_past += n_eval;
}
mymodel->n_past = n_past;
return true;
}
bool eval_id(struct MyModel* mymodel, int id) {
std::vector<llama_token> tokens;
tokens.push_back(id);
return eval_tokens(mymodel, tokens);
}
bool eval_string(struct MyModel * mymodel,const char* str){
llama_context * ctx = mymodel->ctx;
std::string str2 = str;
std::vector<llama_token> embd_inp = ::llama_tokenize(ctx, str2, true);
eval_tokens(mymodel, embd_inp);
return true;
}
llama_token sampling_id(struct MyModel* mymodel) {
llama_context* ctx = mymodel->ctx;
gpt_params params = mymodel->params;
// int n_ctx = llama_n_ctx(ctx);
// out of user input, sample next token
const float temp = params.temp;
const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
const float top_p = params.top_p;
const float tfs_z = params.tfs_z;
const float typical_p = params.typical_p;
// const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
// const float repeat_penalty = params.repeat_penalty;
// const float alpha_presence = params.presence_penalty;
// const float alpha_frequency = params.frequency_penalty;
const int mirostat = params.mirostat;
const float mirostat_tau = params.mirostat_tau;
const float mirostat_eta = params.mirostat_eta;
// const bool penalize_nl = params.penalize_nl;
llama_token id = 0;
{
auto logits = llama_get_logits(ctx);
auto n_vocab = llama_n_vocab(ctx);
// Apply params.logit_bias map
for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) {
logits[it->first] += it->second;
}
std::vector<llama_token_data> candidates;
candidates.reserve(n_vocab);
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f});
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
// TODO: Apply penalties
// float nl_logit = logits[llama_token_nl()];
// auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
// llama_sample_repetition_penalty(ctx, &candidates_p,
// last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
// last_n_repeat, repeat_penalty);
// llama_sample_frequency_and_presence_penalties(ctx, &candidates_p,
// last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
// last_n_repeat, alpha_frequency, alpha_presence);
// if (!penalize_nl) {
// logits[llama_token_nl()] = nl_logit;
// }
if (temp <= 0) {
// Greedy sampling
id = llama_sample_token_greedy(ctx, &candidates_p);
} else {
if (mirostat == 1) {
static float mirostat_mu = 2.0f * mirostat_tau;
const int mirostat_m = 100;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu);
} else if (mirostat == 2) {
static float mirostat_mu = 2.0f * mirostat_tau;
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
} else {
// Temperature sampling
llama_sample_top_k(ctx, &candidates_p, top_k, 1);
llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
llama_sample_typical(ctx, &candidates_p, typical_p, 1);
llama_sample_top_p(ctx, &candidates_p, top_p, 1);
llama_sample_temperature(ctx, &candidates_p, temp);
id = llama_sample_token(ctx, &candidates_p);
}
}
}
return id;
}
const char * sampling(struct MyModel * mymodel) {
llama_context * ctx = mymodel->ctx;
int id = sampling_id(mymodel);
static std::string ret;
if (id == llama_token_eos()) {
ret = "</s>";
} else {
ret = llama_token_to_str(ctx, id);
}
eval_id(mymodel, id);
return ret.c_str();
}
}

35
examples/embd-input/embd-input-test.cpp Normal file
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@ -0,0 +1,35 @@
#include "embd-input.h"
#include <stdlib.h>
#include <random>
#include <string.h>
int main(int argc, char** argv) {
auto mymodel = create_mymodel(argc, argv);
int N = 10;
int max_tgt_len = 500;
int n_embd = llama_n_embd(mymodel->ctx);
// add random float embd to test evaluation
float * data = new float[N*n_embd];
std::default_random_engine e;
std::uniform_real_distribution<float> u(0,1);
for (int i=0;i<N*n_embd;i++) {
data[i] = u(e);
}
eval_string(mymodel, "user: what is the color of the flag of UN?");
eval_float(mymodel, data, N);
eval_string(mymodel, "assistant:");
eval_string(mymodel, mymodel->params.prompt.c_str());
const char* tmp;
for (int i=0; i<max_tgt_len; i++) {
tmp = sampling(mymodel);
if (strcmp(tmp, "</s>")==0) break;
printf("%s", tmp);
fflush(stdout);
}
printf("\n");
free_mymodel(mymodel);
return 0;
}

28
examples/embd-input/embd-input.h Normal file
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@ -0,0 +1,28 @@
#ifndef _EMBD_INPUT_H_
#define _EMBD_INPUT_H_ 1
#include "common.h"
#include "llama.h"
#include "build-info.h"
extern "C" {
typedef struct MyModel {
llama_context* ctx;
gpt_params params;
int n_past = 0;
} MyModel;
struct MyModel* create_mymodel(int argc, char ** argv);
bool eval_float(void* model, float* input, int N);
bool eval_tokens(void* model, std::vector<llama_token> tokens);
bool eval_id(struct MyModel* mymodel, int id);
bool eval_string(struct MyModel* mymodel, const char* str);
const char * sampling(struct MyModel* mymodel);
llama_token sampling_id(struct MyModel* mymodel);
void free_mymodel(struct MyModel* mymodel);
}
#endif

71
examples/embd-input/embd_input.py Normal file
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@ -0,0 +1,71 @@
import ctypes
from ctypes import cdll, c_char_p, c_void_p, POINTER, c_float, c_int
import numpy as np
import os
libc = cdll.LoadLibrary("./libembdinput.so")
libc.sampling.restype=c_char_p
libc.create_mymodel.restype=c_void_p
libc.eval_string.argtypes=[c_void_p, c_char_p]
libc.sampling.argtypes=[c_void_p]
libc.eval_float.argtypes=[c_void_p, POINTER(c_float), c_int]
class MyModel:
def __init__(self, args):
argc = len(args)
c_str = [c_char_p(i.encode()) for i in args]
args_c = (c_char_p * argc)(*c_str)
self.model = c_void_p(libc.create_mymodel(argc, args_c))
self.max_tgt_len = 512
self.print_string_eval = True
def __del__(self):
libc.free_mymodel(self.model)
def eval_float(self, x):
libc.eval_float(self.model, x.astype(np.float32).ctypes.data_as(POINTER(c_float)), x.shape[1])
def eval_string(self, x):
libc.eval_string(self.model, x.encode()) # c_char_p(x.encode()))
if self.print_string_eval:
print(x)
def eval_token(self, x):
libc.eval_id(self.model, x)
def sampling(self):
s = libc.sampling(self.model)
return s
def stream_generate(self, end="</s>"):
ret = b""
end = end.encode()
for _ in range(self.max_tgt_len):
tmp = self.sampling()
ret += tmp
yield tmp
if ret.endswith(end):
break
def generate_with_print(self, end="</s>"):
ret = b""
for i in self.stream_generate(end=end):
ret += i
print(i.decode(errors="replace"), end="", flush=True)
print("")
return ret.decode(errors="replace")
def generate(self, end="</s>"):
text = b"".join(self.stream_generate(end=end))
return text.decode(errors="replace")
if __name__ == "__main__":
model = MyModel(["main", "--model", "../llama.cpp/models/ggml-vic13b-q4_1.bin", "-c", "2048"])
model.eval_string("""user: what is the color of the flag of UN?""")
x = np.random.random((5120,10))# , dtype=np.float32)
model.eval_float(x)
model.eval_string("""assistant:""")
for i in model.generate():
print(i.decode(errors="replace"), end="", flush=True)

70
examples/embd-input/llava.py Normal file
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@ -0,0 +1,70 @@
import sys
import os
sys.path.insert(0, os.path.dirname(__file__))
from embd_input import MyModel
import numpy as np
from torch import nn
import torch
from transformers import CLIPVisionModel, CLIPImageProcessor
from PIL import Image
# model parameters from 'liuhaotian/LLaVA-13b-delta-v1-1'
vision_tower = "openai/clip-vit-large-patch14"
select_hidden_state_layer = -2
# (vision_config.image_size // vision_config.patch_size) ** 2
image_token_len = (224//14)**2
class Llava:
def __init__(self, args):
self.image_processor = CLIPImageProcessor.from_pretrained(vision_tower)
self.vision_tower = CLIPVisionModel.from_pretrained(vision_tower)
self.mm_projector = nn.Linear(1024, 5120)
self.model = MyModel(["main", *args])
def load_projection(self, path):
state = torch.load(path)
self.mm_projector.load_state_dict({
"weight": state["model.mm_projector.weight"],
"bias": state["model.mm_projector.bias"]})
def chat(self, question):
self.model.eval_string("user: ")
self.model.eval_string(question)
self.model.eval_string("\nassistant: ")
return self.model.generate_with_print()
def chat_with_image(self, image, question):
with torch.no_grad():
embd_image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
image_forward_out = self.vision_tower(embd_image.unsqueeze(0), output_hidden_states=True)
select_hidden_state = image_forward_out.hidden_states[select_hidden_state_layer]
image_feature = select_hidden_state[:, 1:]
embd_image = self.mm_projector(image_feature)
embd_image = embd_image.cpu().numpy()[0]
self.model.eval_string("user: ")
self.model.eval_token(32003-2) # im_start
self.model.eval_float(embd_image.T)
for i in range(image_token_len-embd_image.shape[0]):
self.model.eval_token(32003-3) # im_patch
self.model.eval_token(32003-1) # im_end
self.model.eval_string(question)
self.model.eval_string("\nassistant: ")
return self.model.generate_with_print()
if __name__=="__main__":
# model form liuhaotian/LLaVA-13b-delta-v1-1
a = Llava(["--model", "./models/ggml-llava-13b-v1.1.bin", "-c", "2048"])
# Extract from https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin.
# Also here can use pytorch_model-00003-of-00003.bin directly.
a.load_projection(os.path.join(
os.path.dirname(__file__) ,
"llava_projetion.pth"))
respose = a.chat_with_image(
Image.open("./media/llama1-logo.png").convert('RGB'),
"what is the text in the picture?")
respose
a.chat("what is the color of it?")

128
examples/embd-input/minigpt4.py Normal file
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@ -0,0 +1,128 @@
import sys
import os
sys.path.insert(0, os.path.dirname(__file__))
from embd_input import MyModel
import numpy as np
from torch import nn
import torch
from PIL import Image
minigpt4_path = os.path.join(os.path.dirname(__file__), "MiniGPT-4")
sys.path.insert(0, minigpt4_path)
from minigpt4.models.blip2 import Blip2Base
from minigpt4.processors.blip_processors import Blip2ImageEvalProcessor
class MiniGPT4(Blip2Base):
"""
MiniGPT4 model from https://github.com/Vision-CAIR/MiniGPT-4
"""
def __init__(self,
args,
vit_model="eva_clip_g",
q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
img_size=224,
drop_path_rate=0,
use_grad_checkpoint=False,
vit_precision="fp32",
freeze_vit=True,
freeze_qformer=True,
num_query_token=32,
llama_model="",
prompt_path="",
prompt_template="",
max_txt_len=32,
end_sym='\n',
low_resource=False, # use 8 bit and put vit in cpu
device_8bit=0
):
super().__init__()
self.img_size = img_size
self.low_resource = low_resource
self.preprocessor = Blip2ImageEvalProcessor(img_size)
print('Loading VIT')
self.visual_encoder, self.ln_vision = self.init_vision_encoder(
vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
)
print('Loading VIT Done')
print('Loading Q-Former')
self.Qformer, self.query_tokens = self.init_Qformer(
num_query_token, self.visual_encoder.num_features
)
self.Qformer.cls = None
self.Qformer.bert.embeddings.word_embeddings = None
self.Qformer.bert.embeddings.position_embeddings = None
for layer in self.Qformer.bert.encoder.layer:
layer.output = None
layer.intermediate = None
self.load_from_pretrained(url_or_filename=q_former_model)
print('Loading Q-Former Done')
self.llama_proj = nn.Linear(
self.Qformer.config.hidden_size, 5120 # self.llama_model.config.hidden_size
)
self.max_txt_len = max_txt_len
self.end_sym = end_sym
self.model = MyModel(["main", *args])
# system promt
self.model.eval_string("Give the following image: <Img>ImageContent</Img>. "
"You will be able to see the image once I provide it to you. Please answer my questions."
"###")
def encode_img(self, image):
image = self.preprocessor(image)
image = image.unsqueeze(0)
device = image.device
if self.low_resource:
self.vit_to_cpu()
image = image.to("cpu")
with self.maybe_autocast():
image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
inputs_llama = self.llama_proj(query_output.last_hidden_state)
# atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device)
return inputs_llama
def load_projection(self, path):
state = torch.load(path)["model"]
self.llama_proj.load_state_dict({
"weight": state["llama_proj.weight"],
"bias": state["llama_proj.bias"]})
def chat(self, question):
self.model.eval_string("Human: ")
self.model.eval_string(question)
self.model.eval_string("\n### Assistant:")
return self.model.generate_with_print(end="###")
def chat_with_image(self, image, question):
with torch.no_grad():
embd_image = self.encode_img(image)
embd_image = embd_image.cpu().numpy()[0]
self.model.eval_string("Human: <Img>")
self.model.eval_float(embd_image.T)
self.model.eval_string("</Img> ")
self.model.eval_string(question)
self.model.eval_string("\n### Assistant:")
return self.model.generate_with_print(end="###")
if __name__=="__main__":
a = MiniGPT4(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048"])
a.load_projection(os.path.join(
os.path.dirname(__file__) ,
"pretrained_minigpt4.pth"))
respose = a.chat_with_image(
Image.open("./media/llama1-logo.png").convert('RGB'),
"what is the text in the picture?")
a.chat("what is the color of it?")

98
examples/embd-input/panda_gpt.py Normal file
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@ -0,0 +1,98 @@
import sys
import os
sys.path.insert(0, os.path.dirname(__file__))
from embd_input import MyModel
import numpy as np
from torch import nn
import torch
# use PandaGPT path
panda_gpt_path = os.path.join(os.path.dirname(__file__), "PandaGPT")
imagebind_ckpt_path = "./models/panda_gpt/"
sys.path.insert(0, os.path.join(panda_gpt_path,"code","model"))
from ImageBind.models import imagebind_model
from ImageBind import data
ModalityType = imagebind_model.ModalityType
max_tgt_len = 400
class PandaGPT:
def __init__(self, args):
self.visual_encoder,_ = imagebind_model.imagebind_huge(pretrained=True, store_path=imagebind_ckpt_path)
self.visual_encoder.eval()
self.llama_proj = nn.Linear(1024, 5120) # self.visual_hidden_size, 5120)
self.max_tgt_len = max_tgt_len
self.model = MyModel(["main", *args])
self.generated_text = ""
self.device = "cpu"
def load_projection(self, path):
state = torch.load(path, map_location="cpu")
self.llama_proj.load_state_dict({
"weight": state["llama_proj.weight"],
"bias": state["llama_proj.bias"]})
def eval_inputs(self, inputs):
self.model.eval_string("<Img>")
embds = self.extract_multimoal_feature(inputs)
for i in embds:
self.model.eval_float(i.T)
self.model.eval_string("</Img> ")
def chat(self, question):
return self.chat_with_image(None, question)
def chat_with_image(self, inputs, question):
if self.generated_text == "":
self.model.eval_string("###")
self.model.eval_string(" Human: ")
if inputs:
self.eval_inputs(inputs)
self.model.eval_string(question)
self.model.eval_string("\n### Assistant:")
ret = self.model.generate_with_print(end="###")
self.generated_text += ret
return ret
def extract_multimoal_feature(self, inputs):
features = []
for key in ["image", "audio", "video", "thermal"]:
if key + "_paths" in inputs:
embeds = self.encode_data(key, inputs[key+"_paths"])
features.append(embeds)
return features
def encode_data(self, data_type, data_paths):
type_map = {
"image": ModalityType.VISION,
"audio": ModalityType.AUDIO,
"video": ModalityType.VISION,
"thermal": ModalityType.THERMAL,
}
load_map = {
"image": data.load_and_transform_vision_data,
"audio": data.load_and_transform_audio_data,
"video": data.load_and_transform_video_data,
"thermal": data.load_and_transform_thermal_data
}
load_function = load_map[data_type]
key = type_map[data_type]
inputs = {key: load_function(data_paths, self.device)}
with torch.no_grad():
embeddings = self.visual_encoder(inputs)
embeds = embeddings[key]
embeds = self.llama_proj(embeds).cpu().numpy()
return embeds
if __name__=="__main__":
a = PandaGPT(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048", "--lora", "./models/panda_gpt/ggml-adapter-model.bin","--temp", "0"])
a.load_projection("./models/panda_gpt/adapter_model.bin")
a.chat_with_image(
{"image_paths": ["./media/llama1-logo.png"]},
"what is the text in the picture? 'llama' or 'lambda'?")
a.chat("what is the color of it?")

16
examples/embedding/embedding.cpp
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@ -4,6 +4,10 @@
#include <ctime>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
int main(int argc, char ** argv) {
gpt_params params;
@ -20,24 +24,25 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed < 0) {
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
llama_init_backend();
llama_init_backend(params.numa);
llama_model * model;
llama_context * ctx;
// load the model
ctx = llama_init_from_gpt_params(params);
if (ctx == NULL) {
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return 1;
}
@ -86,6 +91,7 @@ int main(int argc, char ** argv) {
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(model);
return 0;
}

5
examples/jeopardy/graph.py
View file

@ -1,5 +1,5 @@
import matplotlib.pyplot as plt
import sys, os
import os
import csv
labels = []
@ -8,6 +8,7 @@ numEntries = 1
rows = []
def bar_chart(numbers, labels, pos):
plt.bar(pos, numbers, color='blue')
plt.xticks(ticks=pos, labels=labels)
@ -16,6 +17,7 @@ def bar_chart(numbers, labels, pos):
plt.ylabel("Questions Correct")
plt.show()
def calculatecorrect():
directory = os.fsencode("./examples/jeopardy/results/")
csv_reader = csv.reader(open("./examples/jeopardy/qasheet.csv", 'rt'), delimiter=',')
@ -45,7 +47,6 @@ def calculatecorrect():
numbers.append(totalcorrect)
if __name__ == '__main__':
calculatecorrect()
pos = list(range(numEntries))

7
examples/main/README.md
View file

@ -242,7 +242,7 @@ Example usage: `--logit-bias 29905-inf`
### RNG Seed
- `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, < 0 = random seed).
- `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
The RNG seed is used to initialize the random number generator that influences the text generation process. By setting a specific seed value, you can obtain consistent and reproducible results across multiple runs with the same input and settings. This can be helpful for testing, debugging, or comparing the effects of different options on the generated text to see when they diverge. If the seed is set to a value less than 0, a random seed will be used, which will result in different outputs on each run.
@ -262,6 +262,10 @@ These options help improve the performance and memory usage of the LLaMA models.
- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. However, if the model is larger than your total amount of RAM or if your system is low on available memory, using mmap might increase the risk of pageouts, negatively impacting performance. Disabling mmap results in slower load times but may reduce pageouts if you're not using `--mlock`. Note that if the model is larger than the total amount of RAM, turning off mmap would prevent the model from loading at all.
### NUMA support
- `--numa`: Attempt optimizations that help on some systems with non-uniform memory access. This currently consists of pinning an equal proportion of the threads to the cores on each NUMA node, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop\_caches' as root.
### Memory Float 32
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement and cached prompt file size but does not appear to increase generation quality in a measurable way. Not recommended.
@ -288,5 +292,6 @@ These options provide extra functionality and customization when running the LLa
- `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
- `-lv, --low-vram`: Do not allocate a VRAM scratch buffer for holding temporary results. Reduces VRAM usage at the cost of performance, particularly prompt processing speed. Requires cuBLAS.
- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.

29
examples/main/main.cpp
View file

@ -24,11 +24,17 @@
#include <unistd.h>
#elif defined (_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <windows.h>
#include <signal.h>
#endif
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
static console_state con_st;
static llama_context ** g_ctx;
@ -89,25 +95,26 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed < 0) {
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
llama_init_backend();
llama_init_backend(params.numa);
llama_model * model;
llama_context * ctx;
g_ctx = &ctx;
// load the model and apply lora adapter, if any
ctx = llama_init_from_gpt_params(params);
if (ctx == NULL) {
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return 1;
}
@ -134,6 +141,7 @@ int main(int argc, char ** argv) {
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(model);
return 0;
}
@ -142,6 +150,7 @@ int main(int argc, char ** argv) {
if (params.export_cgraph) {
llama_eval_export(ctx, "llama.ggml");
llama_free(ctx);
llama_free_model(model);
return 0;
}
@ -349,6 +358,13 @@ int main(int argc, char ** argv) {
std::vector<llama_token> embd;
// do one empty run to warm up the model
{
const std::vector<llama_token> tmp = { llama_token_bos(), };
llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
llama_reset_timings(ctx);
}
while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
// predict
if (embd.size() > 0) {
@ -359,7 +375,7 @@ int main(int argc, char ** argv) {
if ((int)embd.size() > max_embd_size) {
auto skipped_tokens = embd.size() - max_embd_size;
console_set_color(con_st, CONSOLE_COLOR_ERROR);
printf("<<input too long: skipped %ld token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
printf("<<input too long: skipped %zu token%s>>", skipped_tokens, skipped_tokens != 1 ? "s" : "");
console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
fflush(stdout);
embd.resize(max_embd_size);
@ -691,6 +707,7 @@ int main(int argc, char ** argv) {
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(model);
if (grammar != NULL) {
llama_grammar_free(grammar);

6
examples/metal/metal.cpp
View file

@ -40,8 +40,10 @@ int main(int argc, char ** argv) {
// this allocates all Metal resources and memory buffers
auto * ctx_metal = ggml_metal_init();
ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data));
ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval));
const size_t max_size_data = ggml_get_max_tensor_size(ctx_data);
const size_t max_size_eval = ggml_get_max_tensor_size(ctx_eval);
ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data), max_size_data);
ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval), max_size_eval);
// main
{

16
examples/perplexity/perplexity.cpp
View file

@ -5,6 +5,10 @@
#include <cmath>
#include <ctime>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
std::vector<float> softmax(const std::vector<float>& logits) {
std::vector<float> probs(logits.size());
float max_logit = logits[0];
@ -132,24 +136,25 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
if (params.seed < 0) {
if (params.seed == LLAMA_DEFAULT_SEED) {
params.seed = time(NULL);
}
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
std::mt19937 rng(params.seed);
if (params.random_prompt) {
params.prompt = gpt_random_prompt(rng);
}
llama_init_backend();
llama_init_backend(params.numa);
llama_model * model;
llama_context * ctx;
// load the model and apply lora adapter, if any
ctx = llama_init_from_gpt_params(params);
if (ctx == NULL) {
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\n", __func__);
return 1;
}
@ -165,6 +170,7 @@ int main(int argc, char ** argv) {
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(model);
return 0;
}

33
examples/quantize-stats/quantize-stats.cpp
View file

@ -19,6 +19,10 @@
#include <thread>
#include <mutex>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
struct quantize_stats_params {
std::string model = "models/7B/ggml-model-f16.bin";
bool verbose = false;
@ -143,7 +147,7 @@ void test_roundtrip_on_chunk(
const ggml_tensor * layer,
int64_t offset,
int64_t chunk_size,
const quantize_fns_t & qfns,
const ggml_type_traits_t & qfns,
bool use_reference,
float * input_scratch,
char * quantized_scratch,
@ -159,11 +163,11 @@ void test_roundtrip_on_chunk(
}
if (use_reference) {
qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size);
} else {
qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
qfns.from_float(input_scratch, quantized_scratch, chunk_size);
}
qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
qfns.to_float(quantized_scratch, output_scratch, chunk_size);
update_error_stats(chunk_size, input_scratch, output_scratch, stats);
}
@ -173,7 +177,7 @@ void test_roundtrip_on_chunk(
void test_roundtrip_on_layer(
std::string & name,
bool print_layer_stats,
const quantize_fns_t & qfns,
const ggml_type_traits_t & qfns,
bool use_reference,
const ggml_tensor * layer,
std::vector<float> & input_scratch,
@ -316,6 +320,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "Loading model\n");
const int64_t t_main_start_us = ggml_time_us();
llama_model * model;
llama_context * ctx;
{
@ -326,10 +331,18 @@ int main(int argc, char ** argv) {
lparams.f16_kv = false;
lparams.use_mlock = false;
ctx = llama_init_from_file(params.model.c_str(), lparams);
model = llama_load_model_from_file(params.model.c_str(), lparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
return 1;
}
ctx = llama_new_context_with_model(model, lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str());
fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str());
llama_free_model(model);
return 1;
}
}
@ -353,6 +366,7 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: error: Quantization should be tested with a float model, "
"this model contains already quantized layers (%s is type %d)\n", __func__, kv_tensor.first.c_str(), kv_tensor.second->type);
llama_free(ctx);
llama_free_model(model);
return 1;
}
included_layers++;
@ -374,8 +388,8 @@ int main(int argc, char ** argv) {
if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) {
continue;
}
quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
if (qfns.quantize_row_q && qfns.dequantize_row_q) {
ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
if (qfns.from_float && qfns.to_float) {
if (params.verbose) {
printf("testing %s ...\n", ggml_type_name(type));
}
@ -411,6 +425,7 @@ int main(int argc, char ** argv) {
llama_free(ctx);
llama_free_model(model);
// report timing
{
const int64_t t_main_end_us = ggml_time_us();

162
examples/quantize/quantize.cpp
View file

@ -4,43 +4,135 @@
#include <cstdio>
#include <cstring>
#include <map>
#include <vector>
#include <string>
static const std::map<std::string, llama_ftype> LLAMA_FTYPE_MAP = {
{"q4_0", LLAMA_FTYPE_MOSTLY_Q4_0},
{"q4_1", LLAMA_FTYPE_MOSTLY_Q4_1},
{"q5_0", LLAMA_FTYPE_MOSTLY_Q5_0},
{"q5_1", LLAMA_FTYPE_MOSTLY_Q5_1},
{"q8_0", LLAMA_FTYPE_MOSTLY_Q8_0},
{"q2_K", LLAMA_FTYPE_MOSTLY_Q2_K},
{"q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M},
{"q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S},
{"q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M},
{"q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L},
{"q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M},
{"q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S},
{"q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M},
{"q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M},
{"q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S},
{"q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M},
{"q6_K", LLAMA_FTYPE_MOSTLY_Q6_K},
struct quant_option {
std::string name;
llama_ftype ftype;
std::string desc;
};
bool try_parse_ftype(const std::string & ftype_str, llama_ftype & ftype, std::string & ftype_str_out) {
auto it = LLAMA_FTYPE_MAP.find(ftype_str);
if (it != LLAMA_FTYPE_MAP.end()) {
ftype = it->second;
ftype_str_out = it->first;
static const std::vector<struct quant_option> QUANT_OPTIONS = {
{
"Q4_0",
LLAMA_FTYPE_MOSTLY_Q4_0,
" 3.50G, +0.2499 ppl @ 7B - small, very high quality loss - legacy, prefer using Q3_K_M",
},
{
"Q4_1",
LLAMA_FTYPE_MOSTLY_Q4_1,
" 3.90G, +0.1846 ppl @ 7B - small, substantial quality loss - legacy, prefer using Q3_K_L",
},
{
"Q5_0",
LLAMA_FTYPE_MOSTLY_Q5_0,
" 4.30G, +0.0796 ppl @ 7B - medium, balanced quality - legacy, prefer using Q4_K_M",
},
{
"Q5_1",
LLAMA_FTYPE_MOSTLY_Q5_1,
" 4.70G, +0.0415 ppl @ 7B - medium, low quality loss - legacy, prefer using Q5_K_M",
},
#ifdef GGML_USE_K_QUANTS
{
"Q2_K",
LLAMA_FTYPE_MOSTLY_Q2_K,
" 2.67G, +0.8698 ppl @ 7B - smallest, extreme quality loss - not recommended",
},
{
"Q3_K",
LLAMA_FTYPE_MOSTLY_Q3_K_M,
"alias for Q3_K_M"
},
{
"Q3_K_S",
LLAMA_FTYPE_MOSTLY_Q3_K_S,
" 2.75G, +0.5505 ppl @ 7B - very small, very high quality loss",
},
{
"Q3_K_M",
LLAMA_FTYPE_MOSTLY_Q3_K_M,
" 3.06G, +0.2437 ppl @ 7B - very small, very high quality loss",
},
{
"Q3_K_L",
LLAMA_FTYPE_MOSTLY_Q3_K_L,
" 3.35G, +0.1803 ppl @ 7B - small, substantial quality loss",
},
{
"Q4_K",
LLAMA_FTYPE_MOSTLY_Q4_K_M,
"alias for Q4_K_M",
},
{
"Q4_K_S",
LLAMA_FTYPE_MOSTLY_Q4_K_S,
" 3.56G, +0.1149 ppl @ 7B - small, significant quality loss",
},
{
"Q4_K_M",
LLAMA_FTYPE_MOSTLY_Q4_K_M,
" 3.80G, +0.0535 ppl @ 7B - medium, balanced quality - *recommended*",
},
{
"Q5_K",
LLAMA_FTYPE_MOSTLY_Q5_K_M,
"alias for Q5_K_M",
},
{
"Q5_K_S",
LLAMA_FTYPE_MOSTLY_Q5_K_S,
" 4.33G, +0.0353 ppl @ 7B - large, low quality loss - *recommended*",
},
{
"Q5_K_M",
LLAMA_FTYPE_MOSTLY_Q5_K_M,
" 4.45G, +0.0142 ppl @ 7B - large, very low quality loss - *recommended*",
},
{
"Q6_K",
LLAMA_FTYPE_MOSTLY_Q6_K,
" 5.15G, +0.0044 ppl @ 7B - very large, extremely low quality loss",
},
#endif
{
"Q8_0",
LLAMA_FTYPE_MOSTLY_Q8_0,
" 6.70G, +0.0004 ppl @ 7B - very large, extremely low quality loss - not recommended",
},
{
"F16",
LLAMA_FTYPE_MOSTLY_F16,
"13.00G @ 7B - extremely large, virtually no quality loss - not recommended",
},
{
"F32",
LLAMA_FTYPE_ALL_F32,
"26.00G @ 7B - absolutely huge, lossless - not recommended",
},
};
bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) {
std::string ftype_str;
for (auto ch : ftype_str_in) {
ftype_str.push_back(std::toupper(ch));
}
for (auto & it : QUANT_OPTIONS) {
if (it.name == ftype_str) {
ftype = it.ftype;
ftype_str_out = it.name;
return true;
}
// try to parse as an integer
}
try {
int ftype_int = std::stoi(ftype_str);
for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
if (it->second == ftype_int) {
ftype = it->second;
ftype_str_out = it->first;
for (auto & it : QUANT_OPTIONS) {
if (it.ftype == ftype_int) {
ftype = it.ftype;
ftype_str_out = it.name;
return true;
}
}
@ -52,15 +144,15 @@ bool try_parse_ftype(const std::string & ftype_str, llama_ftype & ftype, std::st
}
// usage:
// ./quantize models/llama/ggml-model.bin [models/llama/ggml-model-quant.bin] type [nthreads]
// ./quantize [--allow-requantize] [--leave-output-tensor] models/llama/ggml-model.bin [models/llama/ggml-model-quant.bin] type [nthreads]
//
void usage(const char * executable) {
fprintf(stderr, "usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.bin [model-quant.bin] type [nthreads]\n", executable);
fprintf(stderr, "usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.bin [model-quant.bin] type [nthreads]\n\n", executable);
fprintf(stderr, " --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
fprintf(stderr, " --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
fprintf(stderr, "Allowed quantization types:\n");
for (auto it = LLAMA_FTYPE_MAP.begin(); it != LLAMA_FTYPE_MAP.end(); it++) {
fprintf(stderr, " type = \"%s\" or %d\n", it->first.c_str(), it->second);
fprintf(stderr, "\nAllowed quantization types:\n");
for (auto & it : QUANT_OPTIONS) {
printf(" %2d or %-6s : %s\n", it.ftype, it.name.c_str(), it.desc.c_str());
}
exit(1);
}
@ -88,7 +180,7 @@ int main(int argc, char ** argv) {
usage(argv[0]);
}
llama_init_backend();
llama_init_backend(false);
// parse command line arguments
const std::string fname_inp = argv[arg_idx];

31
examples/save-load-state/save-load-state.cpp
View file

@ -35,12 +35,22 @@ int main(int argc, char ** argv) {
auto last_n_tokens_data = std::vector<llama_token>(params.repeat_last_n, 0);
// init
auto ctx = llama_init_from_file(params.model.c_str(), lparams);
auto model = llama_load_model_from_file(params.model.c_str(), lparams);
if (model == nullptr) {
return 1;
}
auto ctx = llama_new_context_with_model(model, lparams);
if (ctx == nullptr) {
llama_free_model(model);
return 1;
}
auto tokens = std::vector<llama_token>(params.n_ctx);
auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), tokens.size(), true);
auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), int(tokens.size()), true);
if (n_prompt_tokens < 1) {
fprintf(stderr, "%s : failed to tokenize prompt\n", __func__);
llama_free(ctx);
llama_free_model(model);
return 1;
}
@ -84,6 +94,8 @@ int main(int argc, char ** argv) {
printf("%s", next_token_str);
if (llama_eval(ctx, &next_token, 1, n_past, params.n_threads)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx);
llama_free_model(model);
return 1;
}
n_past += 1;
@ -91,23 +103,27 @@ int main(int argc, char ** argv) {
printf("\n\n");
// free old model
// free old context
llama_free(ctx);
// load new model
auto ctx2 = llama_init_from_file(params.model.c_str(), lparams);
// make new context
auto ctx2 = llama_new_context_with_model(model, lparams);
// Load state (rng, logits, embedding and kv_cache) from file
{
FILE *fp_read = fopen("dump_state.bin", "rb");
if (state_size != llama_get_state_size(ctx2)) {
fprintf(stderr, "\n%s : failed to validate state size\n", __func__);
llama_free(ctx2);
llama_free_model(model);
return 1;
}
const size_t ret = fread(state_mem, 1, state_size, fp_read);
if (ret != state_size) {
fprintf(stderr, "\n%s : failed to read state\n", __func__);
llama_free(ctx2);
llama_free_model(model);
return 1;
}
@ -138,6 +154,8 @@ int main(int argc, char ** argv) {
printf("%s", next_token_str);
if (llama_eval(ctx2, &next_token, 1, n_past, params.n_threads)) {
fprintf(stderr, "\n%s : failed to evaluate\n", __func__);
llama_free(ctx2);
llama_free_model(model);
return 1;
}
n_past += 1;
@ -145,5 +163,8 @@ int main(int argc, char ** argv) {
printf("\n\n");
llama_free(ctx2);
llama_free_model(model);
return 0;
}

4
examples/server/CMakeLists.txt
View file

@ -1,6 +1,10 @@
set(TARGET server)
option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON)
include_directories(${CMAKE_CURRENT_SOURCE_DIR})
add_executable(${TARGET} server.cpp json.hpp httplib.h)
target_compile_definitions(${TARGET} PRIVATE
SERVER_VERBOSE=$<BOOL:${LLAMA_SERVER_VERBOSE}>
)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)

338
examples/server/README.md
View file

@ -1,33 +1,73 @@
# llama.cpp/example/server
This example allow you to have a llama.cpp http server to interact from a web page or consume the API.
This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp.
## Table of Contents
Command line options:
1. [Quick Start](#quick-start)
2. [Node JS Test](#node-js-test)
3. [API Endpoints](#api-endpoints)
4. [More examples](#more-examples)
5. [Common Options](#common-options)
6. [Performance Tuning and Memory Options](#performance-tuning-and-memory-options)
- `--threads N`, `-t N`: Set the number of threads to use during computation.
- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
- `-m ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses.
- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
- `-lv, --low-vram`: Do not allocate a VRAM scratch buffer for holding temporary results. Reduces VRAM usage at the cost of performance, particularly prompt processing speed. Requires cuBLAS.
- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `512`.
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended.
- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped.
- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed.
- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains.
- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation.
- `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`.
- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`.
- `--port`: Set the port to listen. Default: `8080`.
- `--path`: path from which to serve static files (default examples/server/public)
- `--embedding`: Enable embedding extraction, Default: disabled.
## Build
server is build alongside everything else from the root of the project
- Using `make`:
```bash
make
```
- Using `CMake`:
```bash
cmake --build . --config Release
```
## Quick Start
To get started right away, run the following command, making sure to use the correct path for the model you have:
#### Unix-based systems (Linux, macOS, etc.):
### Unix-based systems (Linux, macOS, etc.):
```bash
./server -m models/7B/ggml-model.bin --ctx_size 2048
./server -m models/7B/ggml-model.bin -c 2048
```
#### Windows:
### Windows:
```powershell
server.exe -m models\7B\ggml-model.bin --ctx_size 2048
server.exe -m models\7B\ggml-model.bin -c 2048
```
That will start a server that by default listens on `127.0.0.1:8080`. You can consume the endpoints with Postman or NodeJS with axios library.
The above command will start a server that by default listens on `127.0.0.1:8080`.
You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url.
## Testing with CURL
Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS.
```sh
curl --request POST \
--url http://localhost:8080/completion \
--data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}'
```
## Node JS Test
@ -50,7 +90,6 @@ const prompt = `Building a website can be done in 10 simple steps:`;
async function Test() {
let result = await axios.post("http://127.0.0.1:8080/completion", {
prompt,
batch_size: 128,
n_predict: 512,
});
@ -69,246 +108,129 @@ node .
## API Endpoints
You can interact with this API Endpoints. This implementations just support chat style interaction.
- **POST** `hostname:port/completion`: Setting up the Llama Context to begin the completions tasks.
- **POST** `/completion`: Given a prompt, it returns the predicted completion.
*Options:*
`batch_size`: Set the batch size for prompt processing (default: 512).
`temperature`: Adjust the randomness of the generated text (default: 0.8).
`top_k`: Limit the next token selection to the K most probable tokens (default: 40).
`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).
`n_predict`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity).
`n_predict`: Set the number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: 128, -1 = infinity).
`threads`: Set the number of threads to use during computation.
`n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context.
By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt.
`n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt.
`stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
`as_loop`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
`prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate. A space is inserted in the front like main.cpp does.
`interactive`: It allows interacting with the completion, and the completion stops as soon as it encounters a `stop word`. To enable this, set to `true`.
`stop`: Specify a JSON array of stopping strings.
These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []).
`prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate.
`tfs_z`: Enable tail free sampling with parameter z (default: 1.0, 1.0 = disabled).
`stop`: Specify the words or characters that indicate a stop. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration.
`typical_p`: Enable locally typical sampling with parameter p (default: 1.0, 1.0 = disabled).
`exclude`: Specify the words or characters you do not want to appear in the completion. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration.
`repeat_penalty`: Control the repetition of token sequences in the generated text (default: 1.1).
- **POST** `hostname:port/embedding`: Generate embedding of a given text
`repeat_last_n`: Last n tokens to consider for penalizing repetition (default: 64, 0 = disabled, -1 = ctx-size).
*Options:*
`penalize_nl`: Penalize newline tokens when applying the repeat penalty (default: true).
`content`: Set the text to get generate the embedding.
`presence_penalty`: Repeat alpha presence penalty (default: 0.0, 0.0 = disabled).
`threads`: Set the number of threads to use during computation.
`frequency_penalty`: Repeat alpha frequency penalty (default: 0.0, 0.0 = disabled);
To use this endpoint, you need to start the server with the `--embedding` option added.
`mirostat`: Enable Mirostat sampling, controlling perplexity during text generation (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0).
- **POST** `hostname:port/tokenize`: Tokenize a given text
`mirostat_tau`: Set the Mirostat target entropy, parameter tau (default: 5.0).
`mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1).
`seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed).
`ignore_eos`: Ignore end of stream token and continue generating (default: false).
`logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced (default: []).
- **POST** `/tokenize`: Tokenize a given text.
*Options:*
`content`: Set the text to tokenize.
- **GET** `hostname:port/next-token`: Receive the next token predicted, execute this request in a loop. Make sure set `as_loop` as `true` in the completion request.
Note that the special `BOS` token is not added in fron of the text and also a space character is not inserted automatically as it is for `/completion`.
- **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does.
*Options:*
`stop`: Set `hostname:port/next-token?stop=true` to stop the token generation.
`content`: Set the text to process.
## More examples
### Interactive mode
This mode allows interacting in a chat-like manner. It is recommended for models designed as assistants such as `Vicuna`, `WizardLM`, `Koala`, among others. Make sure to add the correct stop word for the corresponding model.
Check the sample in [chat.mjs](chat.mjs).
Run with NodeJS version 16 or later:
The prompt should be generated by you, according to the model's guidelines. You should keep adding the model's completions to the context as well.
This example works well for `Vicuna - version 1`.
```javascript
const axios = require("axios");
let prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
### Human: Hello, Assistant.
### Assistant: Hello. How may I help you today?
### Human: Please tell me the largest city in Europe.
### Assistant: Sure. The largest city in Europe is Moscow, the capital of Russia.`;
async function ChatCompletion(answer) {
// the user's next question to the prompt
prompt += `\n### Human: ${answer}\n`
result = await axios.post("http://127.0.0.1:8080/completion", {
prompt,
batch_size: 128,
temperature: 0.2,
top_k: 40,
top_p: 0.9,
n_keep: -1,
n_predict: 2048,
stop: ["\n### Human:"], // when detect this, stop completion
exclude: ["### Assistant:"], // no show in the completion
threads: 8,
as_loop: true, // use this to request the completion token by token
interactive: true, // enable the detection of a stop word
});
// create a loop to receive every token predicted
// note: this operation is blocking, avoid use this in a ui thread
let message = "";
while (true) {
// you can stop the inference adding '?stop=true' like this http://127.0.0.1:8080/next-token?stop=true
result = await axios.get("http://127.0.0.1:8080/next-token");
process.stdout.write(result.data.content);
message += result.data.content;
// to avoid an infinite loop
if (result.data.stop) {
console.log("Completed");
// make sure to add the completion to the prompt.
prompt += `### Assistant: ${message}`;
break;
}
}
}
// This function should be called every time a question to the model is needed.
async function Test() {
// the server can't inference in paralell
await ChatCompletion("Write a long story about a time magician in a fantasy world");
await ChatCompletion("Summary the story");
}
Test();
```sh
node chat.mjs
```
### Alpaca example
Another sample in [chat.sh](chat.sh).
Requires [bash](https://www.gnu.org/software/bash/), [curl](https://curl.se) and [jq](https://jqlang.github.io/jq/).
Run with bash:
**Temporaly note:** no tested, if you have the model, please test it and report me some issue
```javascript
const axios = require("axios");
let prompt = `Below is an instruction that describes a task. Write a response that appropriately completes the request.
`;
async function DoInstruction(instruction) {
prompt += `\n\n### Instruction:\n\n${instruction}\n\n### Response:\n\n`;
result = await axios.post("http://127.0.0.1:8080/completion", {
prompt,
batch_size: 128,
temperature: 0.2,
top_k: 40,
top_p: 0.9,
n_keep: -1,
n_predict: 2048,
stop: ["### Instruction:\n\n"], // when detect this, stop completion
exclude: [], // no show in the completion
threads: 8,
as_loop: true, // use this to request the completion token by token
interactive: true, // enable the detection of a stop word
});
// create a loop to receive every token predicted
// note: this operation is blocking, avoid use this in a ui thread
let message = "";
while (true) {
result = await axios.get("http://127.0.0.1:8080/next-token");
process.stdout.write(result.data.content);
message += result.data.content;
// to avoid an infinite loop
if (result.data.stop) {
console.log("Completed");
// make sure to add the completion and the user's next question to the prompt.
prompt += message;
break;
}
}
}
// This function should be called every time a instruction to the model is needed.
DoInstruction("Destroy the world"); // as joke
```sh
bash chat.sh
```
### Embeddings
### API like OAI
First, run the server with `--embedding` option:
API example using Python Flask: [api_like_OAI.py](api_like_OAI.py)
This example must be used with server.cpp
```bash
server -m models/7B/ggml-model.bin --ctx_size 2048 --embedding
```sh
python api_like_OAI.py
```
Run this code in NodeJS:
After running the API server, you can use it in Python by setting the API base URL.
```python
openai.api_base = "http://<Your api-server IP>:port"
```
```javascript
const axios = require('axios');
Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API
async function Test() {
let result = await axios.post("http://127.0.0.1:8080/embedding", {
content: `Hello`,
threads: 5
});
// print the embedding array
console.log(result.data.embedding);
### Extending or building alternative Web Front End
The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method.
Read the documentation in `/completion.js` to see convenient ways to access llama.
A simple example is below:
```html
<html>
<body>
<pre>
<script type="module">
import { llama } from '/completion.js'
const prompt = `### Instruction:
Write dad jokes, each one paragraph.
You can use html formatting if needed.
### Response:`
for await (const chunk of llama(prompt)) {
document.write(chunk.data.content)
}
Test();
</script>
</pre>
</body>
</html>
```
### Tokenize
Run this code in NodeJS:
```javascript
const axios = require('axios');
async function Test() {
let result = await axios.post("http://127.0.0.1:8080/tokenize", {
content: `Hello`
});
// print the embedding array
console.log(result.data.tokens);
}
Test();
```
## Common Options
- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
- `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
- `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS.
- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS.
- `--embedding`: Enable the embedding mode. **Completion function doesn't work in this mode**.
- `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`;
- `--port`: Set the port to listen. Default: `8080`.
### RNG Seed
- `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, < 0 = random seed).
The RNG seed is used to initialize the random number generator that influences the text generation process. By setting a specific seed value, you can obtain consistent and reproducible results across multiple runs with the same input and settings. This can be helpful for testing, debugging, or comparing the effects of different options on the generated text to see when they diverge. If the seed is set to a value less than 0, a random seed will be used, which will result in different outputs on each run.
## Performance Tuning and Memory Options
### No Memory Mapping
- `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. However, if the model is larger than your total amount of RAM or if your system is low on available memory, using mmap might increase the risk of pageouts, negatively impacting performance.
### Memory Float 32
- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement but does not appear to increase generation quality in a measurable way. Not recommended.
## Limitations:
- The actual implementation of llama.cpp need a `llama-state` for handle multiple contexts and clients, but this could require more powerful hardware.

219
examples/server/api_like_OAI.py Executable file
View file

@ -0,0 +1,219 @@
import argparse
from flask import Flask, jsonify, request, Response
import urllib.parse
import requests
import time
import json
app = Flask(__name__)
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("--stop", type=str, help="the end of response in chat completions(default: '</s>')", default="</s>")
parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080')
parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="")
parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1')
parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081)
args = parser.parse_args()
def is_present(json, key):
try:
buf = json[key]
except KeyError:
return False
return True
#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")
for line in messages:
if (line["role"] == "system"):
prompt += f"{system_n}{line['content']}"
if (line["role"] == "user"):
prompt += f"{user_n}{line['content']}"
if (line["role"] == "assistant"):
prompt += f"{ai_n}{line['content']}{stop}"
prompt += ai_n.rstrip()
return prompt
def make_postData(body, chat=False, stream=False):
postData = {}
if (chat):
postData["prompt"] = convert_chat(body["messages"])
else:
postData["prompt"] = body["prompt"]
if(is_present(body, "temperature")): postData["temperature"] = body["temperature"]
if(is_present(body, "top_k")): postData["top_k"] = body["top_k"]
if(is_present(body, "top_p")): postData["top_p"] = body["top_p"]
if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"]
if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"]
if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"]
if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"]
if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"]
if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"]
if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"]
if(is_present(body, "seed")): postData["seed"] = body["seed"]
if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()]
if (args.stop != ""):
postData["stop"] = [args.stop]
else:
postData["stop"] = []
if(is_present(body, "stop")): postData["stop"] += body["stop"]
postData["n_keep"] = -1
postData["stream"] = stream
return postData
def make_resData(data, chat=False, promptToken=[]):
resData = {
"id": "chatcmpl" if (chat) else "cmpl",
"object": "chat.completion" if (chat) else "text_completion",
"created": int(time.time()),
"truncated": data["truncated"],
"model": "LLaMA_CPP",
"usage": {
"prompt_tokens": data["tokens_evaluated"],
"completion_tokens": data["tokens_predicted"],
"total_tokens": data["tokens_evaluated"] + data["tokens_predicted"]
}
}
if (len(promptToken) != 0):
resData["promptToken"] = promptToken
if (chat):
#only one choice is supported
resData["choices"] = [{
"index": 0,
"message": {
"role": "assistant",
"content": data["content"],
},
"finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
}]
else:
#only one choice is supported
resData["choices"] = [{
"text": data["content"],
"index": 0,
"logprobs": None,
"finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
}]
return resData
def make_resData_stream(data, chat=False, time_now = 0, start=False):
resData = {
"id": "chatcmpl" if (chat) else "cmpl",
"object": "chat.completion.chunk" if (chat) else "text_completion.chunk",
"created": time_now,
"model": "LLaMA_CPP",
"choices": [
{
"finish_reason": None,
"index": 0
}
]
}
if (chat):
if (start):
resData["choices"][0]["delta"] = {
"role": "assistant"
}
else:
resData["choices"][0]["delta"] = {
"content": data["content"]
}
if (data["stop"]):
resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
else:
resData["choices"][0]["text"] = data["content"]
if (data["stop"]):
resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length"
return resData
@app.route('/chat/completions', methods=['POST'])
@app.route('/v1/chat/completions', methods=['POST'])
def chat_completions():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
body = request.get_json()
stream = False
tokenize = False
if(is_present(body, "stream")): stream = body["stream"]
if(is_present(body, "tokenize")): tokenize = body["tokenize"]
postData = make_postData(body, chat=True, stream=stream)
promptToken = []
if (tokenize):
tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
promptToken = tokenData["tokens"]
if (not stream):
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
print(data.json())
resData = make_resData(data.json(), chat=True, promptToken=promptToken)
return jsonify(resData)
else:
def generate():
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
time_now = int(time.time())
resData = make_resData_stream({}, chat=True, time_now=time_now, start=True)
yield 'data: {}\n'.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')
@app.route('/completions', methods=['POST'])
@app.route('/v1/completions', methods=['POST'])
def completion():
if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key):
return Response(status=403)
body = request.get_json()
stream = False
tokenize = False
if(is_present(body, "stream")): stream = body["stream"]
if(is_present(body, "tokenize")): tokenize = body["tokenize"]
postData = make_postData(body, chat=False, stream=stream)
promptToken = []
if (tokenize):
tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json()
promptToken = tokenData["tokens"]
if (not stream):
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData))
print(data.json())
resData = make_resData(data.json(), chat=False, promptToken=promptToken)
return jsonify(resData)
else:
def generate():
data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True)
time_now = int(time.time())
for line in data.iter_lines():
if line:
decoded_line = line.decode('utf-8')
resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now)
yield 'data: {}\n'.format(json.dumps(resData))
return Response(generate(), mimetype='text/event-stream')
if __name__ == '__main__':
app.run(args.host, port=args.port)

89
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import * as readline from 'node:readline'
import { stdin, stdout } from 'node:process'
const API_URL = 'http://127.0.0.1:8080'
const chat = [
{
human: "Hello, Assistant.",
assistant: "Hello. How may I help you today?"
},
{
human: "Please tell me the largest city in Europe.",
assistant: "Sure. The largest city in Europe is Moscow, the capital of Russia."
},
]
const instruction = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.`
function format_prompt(question) {
return `${instruction}\n${
chat.map(m =>`### Human: ${m.human}\n### Assistant: ${m.assistant}`).join("\n")
}\n### Human: ${question}\n### Assistant:`
}
async function tokenize(content) {
const result = await fetch(`${API_URL}/tokenize`, {
method: 'POST',
body: JSON.stringify({ content })
})
if (!result.ok) {
return []
}
return await result.json().tokens
}
const n_keep = await tokenize(instruction).length
async function chat_completion(question) {
const result = await fetch(`${API_URL}/completion`, {
method: 'POST',
body: JSON.stringify({
prompt: format_prompt(question),
temperature: 0.2,
top_k: 40,
top_p: 0.9,
n_keep: n_keep,
n_predict: 256,
stop: ["\n### Human:"], // stop completion after generating this
stream: true,
})
})
if (!result.ok) {
return
}
let answer = ''
for await (var chunk of result.body) {
const t = Buffer.from(chunk).toString('utf8')
if (t.startsWith('data: ')) {
const message = JSON.parse(t.substring(6))
answer += message.content
process.stdout.write(message.content)
if (message.stop) {
if (message.truncated) {
chat.shift()
}
break
}
}
}
process.stdout.write('\n')
chat.push({ human: question, assistant: answer.trimStart() })
}
const rl = readline.createInterface({ input: stdin, output: stdout });
const readlineQuestion = (rl, query, options) => new Promise((resolve, reject) => {
rl.question(query, options, resolve)
});
while(true) {
const question = await readlineQuestion(rl, '> ')
await chat_completion(question)
}

77
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#!/bin/bash
API_URL="${API_URL:-http://127.0.0.1:8080}"
CHAT=(
"Hello, Assistant."
"Hello. How may I help you today?"
"Please tell me the largest city in Europe."
"Sure. The largest city in Europe is Moscow, the capital of Russia."
)
INSTRUCTION="A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions."
trim() {
shopt -s extglob
set -- "${1##+([[:space:]])}"
printf "%s" "${1%%+([[:space:]])}"
}
trim_trailing() {
shopt -s extglob
printf "%s" "${1%%+([[:space:]])}"
}
format_prompt() {
echo -n "${INSTRUCTION}"
printf "\n### Human: %s\n### Assistant: %s" "${CHAT[@]}" "$1"
}
tokenize() {
curl \
--silent \
--request POST \
--url "${API_URL}/tokenize" \
--data-raw "$(jq -ns --arg content "$1" '{content:$content}')" \
| jq '.tokens[]'
}
N_KEEP=$(tokenize "${INSTRUCTION}" | wc -l)
chat_completion() {
PROMPT="$(trim_trailing "$(format_prompt "$1")")"
DATA="$(echo -n "$PROMPT" | jq -Rs --argjson n_keep $N_KEEP '{
prompt: .,
temperature: 0.2,
top_k: 40,
top_p: 0.9,
n_keep: $n_keep,
n_predict: 256,
stop: ["\n### Human:"],
stream: true
}')"
ANSWER=''
while IFS= read -r LINE; do
if [[ $LINE = data:* ]]; then
CONTENT="$(echo "${LINE:5}" | jq -r '.content')"
printf "%s" "${CONTENT}"
ANSWER+="${CONTENT}"
fi
done < <(curl \
--silent \
--no-buffer \
--request POST \
--url "${API_URL}/completion" \
--data-raw "${DATA}")
printf "\n"
CHAT+=("$1" "$(trim "$ANSWER")")
}
while true; do
read -r -e -p "> " QUESTION
chat_completion "${QUESTION}"
done

375
examples/server/completion.js.hpp Normal file
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@ -0,0 +1,375 @@
unsigned char completion_js[] = {
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};
unsigned int completion_js_len = 4462;

18
examples/server/deps.sh Executable file
View file

@ -0,0 +1,18 @@
#!/bin/bash
# Download and update deps for binary
# get the directory of this script file
DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
PUBLIC=$DIR/public
echo "download js bundle files"
curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js
echo >> $PUBLIC/index.js # add newline
FILES=$(ls $PUBLIC)
for FILE in $FILES; do
func=$(echo $FILE | tr '.' '_')
echo "generate $FILE.hpp ($func)"
xxd -n $func -i $PUBLIC/$FILE > $DIR/$FILE.hpp
done

899
examples/server/index.html.hpp Normal file
View file

@ -0,0 +1,899 @@
unsigned char index_html[] = {
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};
unsigned int index_html_len = 10752;

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examples/server/index.js.hpp Normal file
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const paramDefaults = {
stream: true,
n_predict: 500,
temperature: 0.2,
stop: ["</s>"]
};
let generation_settings = null;
// Completes the prompt as a generator. Recommended for most use cases.
//
// Example:
//
// import { llama } from '/completion.js'
//
// const request = llama("Tell me a joke", {n_predict: 800})
// for await (const chunk of request) {
// document.write(chunk.data.content)
// }
//
export async function* llama(prompt, params = {}, config = {}) {
let controller = config.controller;
if (!controller) {
controller = new AbortController();
}
const completionParams = { ...paramDefaults, ...params, prompt };
const response = await fetch("/completion", {
method: 'POST',
body: JSON.stringify(completionParams),
headers: {
'Connection': 'keep-alive',
'Content-Type': 'application/json',
'Accept': 'text/event-stream'
},
signal: controller.signal,
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
let content = "";
try {
let cont = true;
while (cont) {
const result = await reader.read();
if (result.done) {
break;
}
// sse answers in the form multiple lines of: value\n with data always present as a key. in our case we
// mainly care about the data: key here, which we expect as json
const text = decoder.decode(result.value);
// parse all sse events and add them to result
const regex = /^(\S+):\s(.*)$/gm;
for (const match of text.matchAll(regex)) {
result[match[1]] = match[2]
}
// since we know this is llama.cpp, let's just decode the json in data
result.data = JSON.parse(result.data);
content += result.data.content;
// yield
yield result;
// if we got a stop token from server, we will break here
if (result.data.stop) {
if (result.data.generation_settings) {
generation_settings = result.data.generation_settings;
}
break;
}
}
} catch (e) {
if (e.name !== 'AbortError') {
console.error("llama error: ", e);
}
throw e;
}
finally {
controller.abort();
}
return content;
}
// Call llama, return an event target that you can subcribe to
//
// Example:
//
// import { llamaEventTarget } from '/completion.js'
//
// const conn = llamaEventTarget(prompt)
// conn.addEventListener("message", (chunk) => {
// document.write(chunk.detail.content)
// })
//
export const llamaEventTarget = (prompt, params = {}, config = {}) => {
const eventTarget = new EventTarget();
(async () => {
let content = "";
for await (const chunk of llama(prompt, params, config)) {
if (chunk.data) {
content += chunk.data.content;
eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data }));
}
if (chunk.data.generation_settings) {
eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings }));
}
if (chunk.data.timings) {
eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings }));
}
}
eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } }));
})();
return eventTarget;
}
// Call llama, return a promise that resolves to the completed text. This does not support streaming
//
// Example:
//
// llamaPromise(prompt).then((content) => {
// document.write(content)
// })
//
// or
//
// const content = await llamaPromise(prompt)
// document.write(content)
//
export const llamaPromise = (prompt, params = {}, config = {}) => {
return new Promise(async (resolve, reject) => {
let content = "";
try {
for await (const chunk of llama(prompt, params, config)) {
content += chunk.data.content;
}
resolve(content);
} catch (error) {
reject(error);
}
});
};
/**
* (deprecated)
*/
export const llamaComplete = async (params, controller, callback) => {
for await (const chunk of llama(params.prompt, params, { controller })) {
callback(chunk);
}
}
// Get the model info from the server. This is useful for getting the context window and so on.
export const llamaModelInfo = async () => {
if (!generation_settings) {
generation_settings = await fetch("/model.json").then(r => r.json());
}
return generation_settings;
}

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<html>
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" />
<title>llama.cpp - chat</title>
<style>
body {
background-color: #fff;
color: #000;
font-family: system-ui;
font-size: 90%;
}
#container {
margin: 0em auto;
display: flex;
flex-direction: column;
justify-content: space-between;
height: 100%;
}
main {
margin: 3px;
display: flex;
flex-direction: column;
justify-content: space-between;
gap: 1em;
flex-grow: 1;
overflow-y: auto;
border: 1px solid #ccc;
border-radius: 5px;
padding: 0.5em;
}
body {
max-width: 600px;
min-width: 300px;
line-height: 1.2;
margin: 0 auto;
padding: 0 0.5em;
}
p {
overflow-wrap: break-word;
word-wrap: break-word;
hyphens: auto;
margin-top: 0.5em;
margin-bottom: 0.5em;
}
#write form {
margin: 1em 0 0 0;
display: flex;
flex-direction: column;
gap: 0.5em;
align-items: stretch;
}
.right {
display: flex;
flex-direction: row;
gap: 0.5em;
justify-content: flex-end;
}
fieldset {
border: none;
padding: 0;
margin: 0;
}
textarea {
padding: 5px;
flex-grow: 1;
width: 100%;
}
pre code {
display: block;
background-color: #222;
color: #ddd;
}
code {
font-family: monospace;
padding: 0.1em 0.3em;
border-radius: 3px;
}
fieldset label {
margin: 0.5em 0;
display: block;
}
header, footer {
text-align: center;
}
footer {
font-size: 80%;
color: #888;
}
</style>
<script type="module">
import {
html, h, signal, effect, computed, render, useSignal, useEffect, useRef
} from '/index.js';
import { llama } from '/completion.js';
const session = signal({
prompt: "This is a conversation between user and llama, a friendly chatbot. respond in simple markdown.",
template: "{{prompt}}\n\n{{history}}\n{{char}}:",
historyTemplate: "{{name}}: {{message}}",
transcript: [],
type: "chat",
char: "llama",
user: "User",
})
const params = signal({
n_predict: 400,
temperature: 0.7,
repeat_last_n: 256,
repeat_penalty: 1.18,
top_k: 40,
top_p: 0.5,
})
const llamaStats = signal(null)
const controller = signal(null)
const generating = computed(() => controller.value == null )
const chatStarted = computed(() => session.value.transcript.length > 0)
const transcriptUpdate = (transcript) => {
session.value = {
...session.value,
transcript
}
}
// simple template replace
const template = (str, extraSettings) => {
let settings = session.value;
if (extraSettings) {
settings = { ...settings, ...extraSettings };
}
return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key]));
}
// send message to server
const chat = async (msg) => {
if (controller.value) {
console.log('already running...');
return;
}
controller.value = new AbortController();
transcriptUpdate([...session.value.transcript, ["{{user}}", msg]])
const prompt = template(session.value.template, {
message: msg,
history: session.value.transcript.flatMap(([name, message]) => template(session.value.historyTemplate, {name, message})).join("\n"),
});
let currentMessage = '';
const history = session.value.transcript
const llamaParams = {
...params.value,
stop: ["</s>", template("{{char}}:"), template("{{user}}:")],
}
for await (const chunk of llama(prompt, llamaParams, { controller: controller.value })) {
const data = chunk.data;
currentMessage += data.content;
// remove leading whitespace
currentMessage = currentMessage.replace(/^\s+/, "")
transcriptUpdate([...history, ["{{char}}", currentMessage]])
if (data.stop) {
console.log("Completion finished: '", currentMessage, "', summary: ", data);
}
if (data.timings) {
llamaStats.value = data.timings;
}
}
controller.value = null;
}
function MessageInput() {
const message = useSignal("")
const stop = (e) => {
e.preventDefault();
if (controller.value) {
controller.value.abort();
controller.value = null;
}
}
const reset = (e) => {
stop(e);
transcriptUpdate([]);
}
const submit = (e) => {
stop(e);
chat(message.value);
message.value = "";
}
const enterSubmits = (event) => {
if (event.which === 13 && !event.shiftKey) {
submit(event);
}
}
return html`
<form onsubmit=${submit}>
<div>
<textarea type="text" rows=2 onkeypress=${enterSubmits} value="${message}" oninput=${(e) => message.value = e.target.value} placeholder="Say something..."/>
</div>
<div class="right">
<button type="submit" disabled=${!generating.value} >Send</button>
<button onclick=${stop} disabled=${generating}>Stop</button>
<button onclick=${reset}>Reset</button>
</div>
</form>
`
}
const ChatLog = (props) => {
const messages = session.value.transcript;
const container = useRef(null)
useEffect(() => {
// scroll to bottom (if needed)
if (container.current && container.current.scrollHeight <= container.current.scrollTop + container.current.offsetHeight + 300) {
container.current.scrollTo(0, container.current.scrollHeight)
}
}, [messages])
const chatLine = ([user, msg]) => {
return html`<p key=${msg}><strong>${template(user)}:</strong> <${Markdownish} text=${template(msg)} /></p>`
};
return html`
<section id="chat" ref=${container}>
${messages.flatMap(chatLine)}
</section>`;
};
const ConfigForm = (props) => {
const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value }
const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value }
const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) }
return html`
<form>
<fieldset>
<div>
<label for="prompt">Prompt</label>
<textarea type="text" name="prompt" value="${session.value.prompt}" rows=4 oninput=${updateSession}/>
</div>
<div>
<label for="user">User name</label>
<input type="text" name="user" value="${session.value.user}" oninput=${updateSession} />
</div>
<div>
<label for="bot">Bot name</label>
<input type="text" name="char" value="${session.value.char}" oninput=${updateSession} />
</div>
<div>
<label for="template">Prompt template</label>
<textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/>
</div>
<div>
<label for="template">Chat history template</label>
<textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/>
</div>
<div>
<label for="temperature">Temperature</label>
<input type="range" id="temperature" min="0.0" max="1.0" step="0.01" name="temperature" value="${params.value.temperature}" oninput=${updateParamsFloat} />
<span>${params.value.temperature}</span>
</div>
<div>
<label for="nPredict">Predictions</label>
<input type="range" id="nPredict" min="1" max="2048" step="1" name="n_predict" value="${params.value.n_predict}" oninput=${updateParamsFloat} />
<span>${params.value.n_predict}</span>
</div>
<div>
<label for="repeat_penalty">Penalize repeat sequence</label>
<input type="range" id="repeat_penalty" min="0.0" max="2.0" step="0.01" name="repeat_penalty" value="${params.value.repeat_penalty}" oninput=${updateParamsFloat} />
<span>${params.value.repeat_penalty}</span>
</div>
<div>
<label for="repeat_last_n">Consider N tokens for penalize</label>
<input type="range" id="repeat_last_n" min="0.0" max="2048" name="repeat_last_n" value="${params.value.repeat_last_n}" oninput=${updateParamsFloat} />
<span>${params.value.repeat_last_n}</span>
</div>
</fieldset>
</form>
`
}
// poor mans markdown replacement
const Markdownish = (params) => {
const md = params.text
.replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>')
.replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>')
.replace(/__(.*?)__/g, '<strong>$1</strong>')
.replace(/\*(.*?)\*/g, '<em>$1</em>')
.replace(/_(.*?)_/g, '<em>$1</em>')
.replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>')
.replace(/`(.*?)`/g, '<code>$1</code>')
.replace(/\n/gim, '<br />');
return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`;
};
const ModelGenerationInfo = (params) => {
if (!llamaStats.value) {
return html`<span/>`
}
return html`
<span>
${llamaStats.value.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.predicted_per_second.toFixed(2)} tokens per second
</span>
`
}
function App(props) {
return html`
<div id="container">
<header>
<h1>llama.cpp</h1>
</header>
<main id="content">
<${chatStarted.value ? ChatLog : ConfigForm} />
</main>
<section id="write">
<${MessageInput} />
</section>
<footer>
<p><${ModelGenerationInfo} /></p>
<p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p>
</footer>
</div>
`;
}
render(h(App), document.body);
</script>
</head>
<body>
</body>
</html>

1
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7
examples/simple/CMakeLists.txt Normal file
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@ -0,0 +1,7 @@
set(TARGET simple)
add_executable(${TARGET} simple.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)
if(TARGET BUILD_INFO)
add_dependencies(${TARGET} BUILD_INFO)
endif()

179
examples/simple/simple.cpp Normal file
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@ -0,0 +1,179 @@
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif
#include "common.h"
#include "llama.h"
#include "build-info.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined (_WIN32)
#define WIN32_LEAN_AND_MEAN
#define NOMINMAX
#include <windows.h>
#include <signal.h>
#endif
int main(int argc, char ** argv)
{
gpt_params params;
//---------------------------------
// Print help :
//---------------------------------
if ( argc == 1 || argv[1][0] == '-' )
{
printf( "usage: %s MODEL_PATH [PROMPT]\n" , argv[0] );
return 1 ;
}
//---------------------------------
// Load parameters :
//---------------------------------
if ( argc >= 2 )
{
params.model = argv[1];
}
if ( argc >= 3 )
{
params.prompt = argv[2];
}
if ( params.prompt.empty() )
{
params.prompt = "Hello my name is";
}
//---------------------------------
// Init LLM :
//---------------------------------
llama_init_backend(params.numa);
llama_model * model;
llama_context * ctx;
std::tie(model, ctx) = llama_init_from_gpt_params( params );
if ( model == NULL )
{
fprintf( stderr , "%s: error: unable to load model\n" , __func__ );
return 1;
}
//---------------------------------
// Tokenize the prompt :
//---------------------------------
std::vector<llama_token> tokens_list;
tokens_list = ::llama_tokenize( ctx , params.prompt , true );
const int max_context_size = llama_n_ctx( ctx );
const int max_tokens_list_size = max_context_size - 4 ;
if ( (int)tokens_list.size() > max_tokens_list_size )
{
fprintf( stderr , "%s: error: prompt too long (%d tokens, max %d)\n" ,
__func__ , (int)tokens_list.size() , max_tokens_list_size );
return 1;
}
fprintf( stderr, "\n\n" );
// Print the tokens from the prompt :
for( auto id : tokens_list )
{
printf( "%s" , llama_token_to_str( ctx , id ) );
}
fflush(stdout);
//---------------------------------
// Main prediction loop :
//---------------------------------
// The LLM keeps a contextual cache memory of previous token evaluation.
// Usually, once this cache is full, it is required to recompute a compressed context based on previous
// tokens (see "infinite text generation via context swapping" in the main example), but in this minimalist
// example, we will just stop the loop once this cache is full or once an end of stream is detected.
while ( llama_get_kv_cache_token_count( ctx ) < max_context_size )
{
//---------------------------------
// Evaluate the tokens :
//---------------------------------
if ( llama_eval( ctx , tokens_list.data() , tokens_list.size() , llama_get_kv_cache_token_count( ctx ) , params.n_threads ) )
{
fprintf( stderr, "%s : failed to eval\n" , __func__ );
return 1;
}
tokens_list.clear();
//---------------------------------
// Select the best prediction :
//---------------------------------
llama_token new_token_id = 0;
auto logits = llama_get_logits( ctx );
auto n_vocab = llama_n_vocab( ctx ); // the size of the LLM vocabulary (in tokens)
std::vector<llama_token_data> candidates;
candidates.reserve( n_vocab );
for( llama_token token_id = 0 ; token_id < n_vocab ; token_id++ )
{
candidates.emplace_back( llama_token_data{ token_id , logits[ token_id ] , 0.0f } );
}
llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false };
// Select it using the "Greedy sampling" method :
new_token_id = llama_sample_token_greedy( ctx , &candidates_p );
// is it an end of stream ?
if ( new_token_id == llama_token_eos() )
{
fprintf(stderr, " [end of text]\n");
break;
}
// Print the new token :
printf( "%s" , llama_token_to_str( ctx , new_token_id ) );
fflush( stdout );
// Push this new token for next evaluation :
tokens_list.push_back( new_token_id );
} // wend of main loop
llama_free( ctx );
llama_free_model( model );
return 0;
}
// EOF

4
examples/train-text-from-scratch/CMakeLists.txt Normal file
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@ -0,0 +1,4 @@
set(TARGET train-text-from-scratch)
add_executable(${TARGET} train-text-from-scratch.cpp)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

22
examples/train-text-from-scratch/README.md Normal file
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@ -0,0 +1,22 @@
# train-text-from-scratch
Basic usage instructions:
```bash
# get training data
wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt
# train
./bin/train-text-from-scratch \
--vocab-model ../models/ggml-vocab.bin \
--ctx 64 --embd 256 --head 8 --layer 16 \
--checkpoint-in chk-shakespeare-256x16.bin \
--checkpoint-out chk-shakespeare-256x16.bin \
--model-out ggml-shakespeare-256x16-f32.bin \
--train-data "shakespeare.txt" \
-t 6 -b 16 -n 32 --seed 1 --adam-iter 16 \
--print-details-interval 0 --predict 16 --use-flash
# predict
./bin/main -m ggml-shakespeare-256x16-f32.bin
```

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63
flake.nix
View file

@ -9,27 +9,33 @@
inherit (pkgs.stdenv) isAarch64 isDarwin;
inherit (pkgs.lib) optionals;
isM1 = isAarch64 && isDarwin;
osSpecific =
if isM1 then with pkgs.darwin.apple_sdk_11_0.frameworks; [ Accelerate MetalKit MetalPerformanceShaders MetalPerformanceShadersGraph ]
else if isDarwin then with pkgs.darwin.apple_sdk.frameworks; [ Accelerate CoreGraphics CoreVideo ]
else [ ];
pkgs = import nixpkgs {
inherit system;
};
llama-python = pkgs.python310.withPackages (ps: with ps; [
numpy
sentencepiece
]);
in
{
osSpecific = if isM1 then
with pkgs.darwin.apple_sdk_11_0.frameworks; [
Accelerate
MetalKit
MetalPerformanceShaders
MetalPerformanceShadersGraph
]
else if isDarwin then
with pkgs.darwin.apple_sdk.frameworks; [
Accelerate
CoreGraphics
CoreVideo
]
else
[ ];
pkgs = import nixpkgs { inherit system; };
llama-python =
pkgs.python310.withPackages (ps: with ps; [ numpy sentencepiece ]);
in {
packages.default = pkgs.stdenv.mkDerivation {
name = "llama.cpp";
src = ./.;
postPatch =
if isM1 then ''
postPatch = if isM1 then ''
substituteInPlace ./ggml-metal.m \
--replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/ggml-metal.metal\";"
'' else "";
--replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";"
'' else
"";
nativeBuildInputs = with pkgs; [ cmake ];
buildInputs = osSpecific;
cmakeFlags = [ "-DLLAMA_BUILD_SERVER=ON" ] ++ (optionals isM1 [
@ -48,12 +54,21 @@
'';
meta.mainProgram = "llama";
};
devShells.default = pkgs.mkShell {
packages = with pkgs; [
cmake
llama-python
] ++ osSpecific;
apps.llama-server = {
type = "app";
program = "${self.packages.${system}.default}/bin/llama-server";
};
}
);
apps.llama-embedding = {
type = "app";
program = "${self.packages.${system}.default}/bin/embedding";
};
apps.llama = {
type = "app";
program = "${self.packages.${system}.default}/bin/llama";
};
apps.default = self.apps.${system}.llama;
devShells.default = pkgs.mkShell {
packages = with pkgs; [ cmake llama-python ] ++ osSpecific;
};
});
}

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10
ggml-cuda.h
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@ -8,10 +8,6 @@ extern "C" {
#define GGML_CUDA_MAX_DEVICES 16
struct ggml_tensor_extra_gpu {
void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors
};
void ggml_init_cublas(void);
void ggml_cuda_set_tensor_split(const float * tensor_split);
@ -24,11 +20,15 @@ void ggml_cuda_mul_mat(const struct ggml_tensor * src0, const struct ggml_tens
void * ggml_cuda_host_malloc(size_t size);
void ggml_cuda_host_free(void * ptr);
void ggml_cuda_load_data(const char * fname, struct ggml_tensor * tensors, size_t offset);
void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor);
void ggml_cuda_free_data(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor);
void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor);
void ggml_cuda_set_main_device(int main_device);
void ggml_cuda_set_scratch_size(size_t scratch_size);
void ggml_cuda_free_scratch(void);
bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor);
#ifdef __cplusplus

6
ggml-metal.h
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@ -41,12 +41,15 @@ void ggml_metal_free(struct ggml_metal_context * ctx);
// - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute
// - the mapping is used during computation to determine the arguments of the compute kernels
// - you don't need to keep the host memory buffer allocated as it is never accessed by Metal
// - max_size specifies the maximum size of a tensor and is used to create shared views such
// that it is guaranteed that the tensor will fit in at least one of the views
//
bool ggml_metal_add_buffer(
struct ggml_metal_context * ctx,
const char * name,
void * data,
size_t size);
size_t size,
size_t max_size);
// set data from host memory into the device
void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
@ -55,6 +58,7 @@ void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor *
void ggml_metal_get_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t);
// same as ggml_graph_compute but uses Metal
// creates gf->n_threads command buffers in parallel
void ggml_metal_graph_compute(struct ggml_metal_context * ctx, struct ggml_cgraph * gf);
#ifdef __cplusplus

293
ggml-metal.m
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@ -51,19 +51,26 @@ struct ggml_metal_context {
GGML_METAL_DECL_KERNEL(get_rows_f16);
GGML_METAL_DECL_KERNEL(get_rows_q4_0);
GGML_METAL_DECL_KERNEL(get_rows_q4_1);
GGML_METAL_DECL_KERNEL(get_rows_q2_k);
GGML_METAL_DECL_KERNEL(get_rows_q4_k);
GGML_METAL_DECL_KERNEL(get_rows_q6_k);
GGML_METAL_DECL_KERNEL(get_rows_q2_K);
GGML_METAL_DECL_KERNEL(get_rows_q3_K);
GGML_METAL_DECL_KERNEL(get_rows_q4_K);
GGML_METAL_DECL_KERNEL(get_rows_q5_K);
GGML_METAL_DECL_KERNEL(get_rows_q6_K);
GGML_METAL_DECL_KERNEL(rms_norm);
GGML_METAL_DECL_KERNEL(norm);
GGML_METAL_DECL_KERNEL(mul_mat_f16_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q2_k_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q4_k_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q6_k_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q2_K_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q3_K_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q4_K_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q5_K_f32);
GGML_METAL_DECL_KERNEL(mul_mat_q6_K_f32);
GGML_METAL_DECL_KERNEL(rope);
GGML_METAL_DECL_KERNEL(alibi_f32);
GGML_METAL_DECL_KERNEL(cpy_f32_f16);
GGML_METAL_DECL_KERNEL(cpy_f32_f32);
GGML_METAL_DECL_KERNEL(cpy_f16_f16);
#undef GGML_METAL_DECL_KERNEL
};
@ -86,6 +93,7 @@ struct ggml_metal_context * ggml_metal_init(void) {
ctx->device = MTLCreateSystemDefaultDevice();
ctx->queue = [ctx->device newCommandQueue];
ctx->n_buffers = 0;
// determine if we can use MPS
if (MPSSupportsMTLDevice(ctx->device)) {
@ -124,7 +132,13 @@ struct ggml_metal_context * ggml_metal_init(void) {
exit(1);
}
#ifdef GGML_QKK_64
MTLCompileOptions* options = [MTLCompileOptions new];
options.preprocessorMacros = @{ @"QK_K" : @(64) };
ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error];
#else
ctx->library = [ctx->device newLibraryWithSource:src options:nil error:&error];
#endif
if (error) {
fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]);
exit(1);
@ -151,29 +165,46 @@ struct ggml_metal_context * ggml_metal_init(void) {
GGML_METAL_ADD_KERNEL(get_rows_f16);
GGML_METAL_ADD_KERNEL(get_rows_q4_0);
GGML_METAL_ADD_KERNEL(get_rows_q4_1);
GGML_METAL_ADD_KERNEL(get_rows_q2_k);
GGML_METAL_ADD_KERNEL(get_rows_q4_k);
GGML_METAL_ADD_KERNEL(get_rows_q6_k);
GGML_METAL_ADD_KERNEL(get_rows_q2_K);
GGML_METAL_ADD_KERNEL(get_rows_q3_K);
GGML_METAL_ADD_KERNEL(get_rows_q4_K);
GGML_METAL_ADD_KERNEL(get_rows_q5_K);
GGML_METAL_ADD_KERNEL(get_rows_q6_K);
GGML_METAL_ADD_KERNEL(rms_norm);
GGML_METAL_ADD_KERNEL(norm);
GGML_METAL_ADD_KERNEL(mul_mat_f16_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q2_k_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q4_k_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q6_k_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q2_K_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q3_K_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q4_K_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q5_K_f32);
GGML_METAL_ADD_KERNEL(mul_mat_q6_K_f32);
GGML_METAL_ADD_KERNEL(rope);
GGML_METAL_ADD_KERNEL(alibi_f32);
GGML_METAL_ADD_KERNEL(cpy_f32_f16);
GGML_METAL_ADD_KERNEL(cpy_f32_f32);
GGML_METAL_ADD_KERNEL(cpy_f16_f16);
#undef GGML_METAL_ADD_KERNEL
}
fprintf(stderr, "%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
fprintf(stderr, "%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false");
if (ctx->device.maxTransferRate != 0) {
fprintf(stderr, "%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0);
} else {
fprintf(stderr, "%s: maxTransferRate = built-in GPU\n", __func__);
}
return ctx;
}
void ggml_metal_free(struct ggml_metal_context * ctx) {
fprintf(stderr, "%s: deallocating\n", __func__);
for (int i = 0; i < ctx->n_buffers; ++i) {
[ctx->buffers[i].metal release];
}
free(ctx);
}
@ -184,10 +215,13 @@ void ggml_metal_free(struct ggml_metal_context * ctx) {
static id<MTLBuffer> ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) {
//fprintf(stderr, "%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach);
const int64_t tsize = ggml_nbytes(t);
// find the view that contains the tensor fully
for (int i = 0; i < ctx->n_buffers; ++i) {
const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data;
if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) {
if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) {
*offs = (size_t) ioffs;
//fprintf(stderr, "%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs);
@ -205,7 +239,8 @@ bool ggml_metal_add_buffer(
struct ggml_metal_context * ctx,
const char * name,
void * data,
size_t size) {
size_t size,
size_t max_size) {
if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) {
fprintf(stderr, "%s: too many buffers\n", __func__);
return false;
@ -222,31 +257,69 @@ bool ggml_metal_add_buffer(
}
}
size_t page_size = getpagesize();
size_t aligned_size = size;
if ((aligned_size % page_size) != 0) {
aligned_size += (page_size - (aligned_size % page_size));
const size_t size_page = getpagesize();
size_t size_aligned = size;
if ((size_aligned % size_page) != 0) {
size_aligned += (size_page - (size_aligned % size_page));
}
// the buffer fits into the max buffer size allowed by the device
if (size_aligned <= ctx->device.maxBufferLength) {
ctx->buffers[ctx->n_buffers].name = name;
ctx->buffers[ctx->n_buffers].data = data;
ctx->buffers[ctx->n_buffers].size = size;
if (ctx->device.maxBufferLength < aligned_size) {
fprintf(stderr, "%s: buffer '%s' size %zu is larger than buffer maximum of %zu\n", __func__, name, aligned_size, ctx->device.maxBufferLength);
return false;
}
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:aligned_size options:MTLResourceStorageModeShared deallocator:nil];
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) {
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0);
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0);
return false;
}
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0);
++ctx->n_buffers;
} else {
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0);
// this overlap between the views will guarantee that the tensor with the maximum size will fully fit into
// one of the views
const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case
const size_t size_step = ctx->device.maxBufferLength - size_ovlp;
const size_t size_view = ctx->device.maxBufferLength;
for (size_t i = 0; i < size; i += size_step) {
const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i);
ctx->buffers[ctx->n_buffers].name = name;
ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i);
ctx->buffers[ctx->n_buffers].size = size_step_aligned;
ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil];
if (ctx->buffers[ctx->n_buffers].metal == nil) {
fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0);
return false;
}
fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i);
if (i + size_step < size) {
fprintf(stderr, "\n");
}
++ctx->n_buffers;
}
}
fprintf(stderr, ", (%8.2f / %8.2f)",
ctx->device.currentAllocatedSize / 1024.0 / 1024.0,
ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0);
if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) {
fprintf(stderr, ", warning: current allocated size is greater than the recommended max working set size\n");
} else {
fprintf(stderr, "\n");
}
}
return true;
}
@ -278,15 +351,40 @@ void ggml_metal_graph_compute(
struct ggml_cgraph * gf) {
metal_printf("%s: evaluating graph\n", __func__);
// create multiple command buffers and enqueue them
// then, we encode the graph into the command buffers in parallel
const int n_cb = gf->n_threads;
NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb];
for (int i = 0; i < n_cb; ++i) {
command_buffers[i] = [ctx->queue commandBuffer];
// enqueue the command buffers in order to specify their execution order
[command_buffers[i] enqueue];
}
// TODO: is this the best way to start threads?
dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT);
for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) {
const int n_nodes_per_cb = (gf->n_nodes + n_cb - 1) / n_cb;
dispatch_async(queue, ^{
size_t offs_src0 = 0;
size_t offs_src1 = 0;
size_t offs_dst = 0;
id<MTLCommandBuffer> command_buffer = [ctx->queue commandBuffer];
id<MTLCommandBuffer> command_buffer = command_buffers[cb_idx];
id<MTLComputeCommandEncoder> encoder = nil;
for (int i = 0; i < gf->n_nodes; ++i) {
//metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
const int node_start = (cb_idx + 0) * n_nodes_per_cb;
const int node_end = (cb_idx == n_cb - 1) ? gf->n_nodes : (cb_idx + 1) * n_nodes_per_cb;
for (int i = node_start; i < node_end; ++i) {
metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op));
struct ggml_tensor * src0 = gf->nodes[i]->src0;
struct ggml_tensor * src1 = gf->nodes[i]->src1;
@ -572,7 +670,16 @@ void ggml_metal_graph_compute(
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_k_f32];
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_K_f32];
} break;
case GGML_TYPE_Q3_K:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q3_K_f32];
} break;
case GGML_TYPE_Q4_K:
{
@ -581,7 +688,16 @@ void ggml_metal_graph_compute(
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_k_f32];
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_K_f32];
} break;
case GGML_TYPE_Q5_K:
{
GGML_ASSERT(ne02 == 1);
GGML_ASSERT(ne12 == 1);
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q5_K_f32];
} break;
case GGML_TYPE_Q6_K:
{
@ -590,7 +706,7 @@ void ggml_metal_graph_compute(
nth0 = 4;
nth1 = 16;
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q6_k_f32];
[encoder setComputePipelineState:ctx->pipeline_mul_mat_q6_K_f32];
} break;
default:
{
@ -599,7 +715,6 @@ void ggml_metal_graph_compute(
}
};
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_src1 offset:offs_src1 atIndex:1];
[encoder setBuffer:id_dst offset:offs_dst atIndex:2];
@ -619,15 +734,14 @@ void ggml_metal_graph_compute(
if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else if (src0t == GGML_TYPE_Q2_K) {
}
else if (src0t == GGML_TYPE_Q2_K ||
src0t == GGML_TYPE_Q3_K ||
src0t == GGML_TYPE_Q4_K ||
src0t == GGML_TYPE_Q5_K ||
src0t == GGML_TYPE_Q6_K) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else if (src0t == GGML_TYPE_Q4_K) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else if (src0t == GGML_TYPE_Q6_K) {
[encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
} else {
[encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0];
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)];
@ -644,9 +758,11 @@ void ggml_metal_graph_compute(
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break;
case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break;
case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break;
case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_k]; break;
case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_k]; break;
case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_k]; break;
case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_K]; break;
case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_K]; break;
case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break;
case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_K]; break;
case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break;
default: GGML_ASSERT(false && "not implemented");
}
@ -683,6 +799,70 @@ void ggml_metal_graph_compute(
[encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_NORM:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
const float eps = 1e-5f;
const int nth = 256;
[encoder setComputePipelineState:ctx->pipeline_norm];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:3];
[encoder setBytes:&eps length:sizeof( float) atIndex:4];
[encoder setThreadgroupMemoryLength:nth*sizeof(float) atIndex:0];
const int64_t nrows = ggml_nrows(src0);
[encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ALIBI:
{
if (encoder == nil) {
encoder = [command_buffer computeCommandEncoder];
}
GGML_ASSERT((src0t == GGML_TYPE_F32));
const int n_past = ((int32_t *) src1->data)[0]; UNUSED(n_past);
const int n_head = ((int32_t *) src1->data)[1];
const float max_bias = ((float *) src1->data)[2];
if (__builtin_popcount(n_head) != 1) {
GGML_ASSERT(false && "only power-of-two n_head implemented");
}
const int n_heads_log2_floor = 1 << (int) floor(log2(n_head));
const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor);
[encoder setComputePipelineState:ctx->pipeline_alibi_f32];
[encoder setBuffer:id_src0 offset:offs_src0 atIndex:0];
[encoder setBuffer:id_dst offset:offs_dst atIndex:1];
[encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2];
[encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3];
[encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4];
[encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5];
[encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6];
[encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7];
[encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8];
[encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9];
[encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10];
[encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11];
[encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12];
[encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13];
[encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14];
[encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15];
[encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16];
[encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17];
[encoder setBytes:&m0 length:sizeof( float) atIndex:18];
const int nth = 32;
[encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)];
} break;
case GGML_OP_ROPE:
{
if (encoder == nil) {
@ -736,6 +916,14 @@ void ggml_metal_graph_compute(
default: GGML_ASSERT(false && "not implemented");
};
} break;
case GGML_TYPE_F16:
{
switch (dstt) {
case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_cpy_f16_f16]; break;
case GGML_TYPE_F32: GGML_ASSERT(false && "cpy_f16_f32 not implemented"); break;
default: GGML_ASSERT(false && "not implemented");
};
} break;
default: GGML_ASSERT(false && "not implemented");
}
@ -772,12 +960,21 @@ void ggml_metal_graph_compute(
}
[command_buffer commit];
[command_buffer waitUntilCompleted];
});
}
{
const double time_elapsed = [command_buffer GPUEndTime] - [command_buffer GPUStartTime];
UNUSED(time_elapsed);
// wait for all threads to finish
dispatch_barrier_sync(queue, ^{});
metal_printf("%s: time elapsed = %f ms\n", __func__, time_elapsed * 1000.0);
[command_buffers[n_cb - 1] waitUntilCompleted];
// check status of command buffers
// needed to detect if the device ran out-of-memory for example (#1881)
for (int i = 0; i < n_cb; i++) {
MTLCommandBufferStatus status = (MTLCommandBufferStatus) [command_buffers[i] status];
if (status != MTLCommandBufferStatusCompleted) {
fprintf(stderr, "%s: command buffer %d failed with status %lu\n", __func__, i, status);
GGML_ASSERT(false);
}
}
}

1002
ggml-metal.metal
View file

File diff suppressed because it is too large Load diff

717
ggml-opencl.cpp
View file

@ -15,13 +15,25 @@
#include "ggml.h"
#define CL_DMMV_BLOCK_SIZE 32;
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#define CL_DMMV_BLOCK_SIZE 32
#ifndef K_QUANTS_PER_ITERATION
#define K_QUANTS_PER_ITERATION 1
#else
static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2");
#endif
#define MULTILINE_QUOTE(...) #__VA_ARGS__
static std::string program_source = MULTILINE_QUOTE(
typedef char int8_t;
typedef uchar uint8_t;
typedef short int16_t;
typedef ushort uint16_t;
typedef int int32_t;
typedef uint uint32_t;
@ -59,6 +71,46 @@ struct __attribute__ ((packed)) block_q8_0
int8_t qs[QK8_0];
};
struct __attribute__((packed)) block_q2_K
{
uint8_t scales[16];
uint8_t qs[64];
half d;
half dmin;
};
struct __attribute__((packed)) block_q3_K
{
uint8_t hmask[32];
uint8_t qs[64];
uint8_t scales[12];
half d;
};
struct __attribute__((packed)) block_q4_K
{
half d;
half dmin;
uint8_t scales[12];
uint8_t qs[128];
};
struct __attribute__((packed)) block_q5_K
{
half d;
half dmin;
uint8_t scales[12];
uint8_t qh[32];
uint8_t qs[128];
};
struct __attribute__((packed)) block_q6_K
{
uint8_t ql[128];
uint8_t qh[64];
int8_t scales[16];
half d;
};
__kernel void convert_fp16_to_fp32(__global half* x, __global float* y) {
const uint i = get_global_id(0);
@ -133,6 +185,536 @@ void convert_f16(__global half* x, const int ib, const int iqs, float* v0, float
}
);
static std::string k_quants_source = MULTILINE_QUOTE(
inline void get_scale_min_k4(int j, const __global uint8_t *q, uint8_t *d, uint8_t *m)
{
if (j < 4)
{
*d = q[j] & 63;
*m = q[j + 4] & 63;
}
else
{
*d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4);
*m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4);
}
}
__kernel void dequantize_block_q2_K(__global const struct block_q2_K *x, __global float *yy)
{
const int i = get_group_id(0);
const int tid = get_local_id(0);
const int n = tid / 32;
const int l = tid - 32 * n;
const int is = 8 * n + l / 16;
const uint8_t q = x[i].qs[32 * n + l];
__global float *y = yy + i * QK_K + 128 * n;
const float dall = vload_half(0, &x[i].d);
const float dmin = vload_half(0, &x[i].dmin);
y[l + 0] = dall * (x[i].scales[is + 0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is + 0] >> 4);
y[l + 32] = dall * (x[i].scales[is + 2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is + 2] >> 4);
y[l + 64] = dall * (x[i].scales[is + 4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is + 4] >> 4);
y[l + 96] = dall * (x[i].scales[is + 6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is + 6] >> 4);
}
__kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __global float *yy)
{
int r = get_local_id(0) / 4;
int i = get_group_id(0);
int tid = r / 2;
int is0 = r % 2;
int l0 = 16 * is0 + 4 * (get_local_id(0) % 4);
int n = tid / 4;
int j = tid - 4 * n;
uint8_t m = 1 << (4 * n + j);
int is = 8 * n + 2 * j + is0;
int shift = 2 * j;
int8_t us = is < 4 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 8] >> 0) & 3) << 4)
: is < 8 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 4] >> 2) & 3) << 4)
: is < 12 ? (x[i].scales[is - 8] >> 4) | (((x[i].scales[is + 0] >> 4) & 3) << 4)
: (x[i].scales[is - 8] >> 4) | (((x[i].scales[is - 4] >> 6) & 3) << 4);
float d_all = vload_half(0, &x[i].d);
float dl = d_all * (us - 32);
__global float *y = yy + i * QK_K + 128 * n + 32 * j;
const __global uint8_t *q = x[i].qs + 32 * n;
const __global uint8_t *hm = x[i].hmask;
for (int l = l0; l < l0 + 4; ++l)
y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4));
}
__kernel void dequantize_block_q4_K(__global const struct block_q4_K *x, __global float *yy)
{
const int i = get_group_id(0);
const int tid = get_local_id(0);
const int il = tid / 8;
const int ir = tid % 8;
const int is = 2 * il;
const int n = 4;
__global float *y = yy + i * QK_K + 64 * il + n * ir;
const float dall = vload_half(0, &x[i].d);
const float dmin = vload_half(0, &x[i].dmin);
__global const uint8_t *q = x[i].qs + 32 * il + n * ir;
uint8_t sc, m;
get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
float d1 = dall * sc;
float m1 = dmin * m;
get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
float d2 = dall * sc;
float m2 = dmin * m;
for (int l = 0; l < n; ++l)
{
y[l + 0] = d1 * (q[l] & 0xF) - m1;
y[l + 32] = d2 * (q[l] >> 4) - m2;
}
}
__kernel void dequantize_block_q5_K(__global const struct block_q5_K *x, __global float *yy)
{
const int i = get_group_id(0);
const int tid = get_local_id(0);
const int il = tid / 16;
const int ir = tid % 16;
const int is = 2 * il;
__global float *y = yy + i * QK_K + 64 * il + 2 * ir;
const float dall = vload_half(0, &x[i].d);
const float dmin = vload_half(0, &x[i].dmin);
__global const uint8_t *ql = x[i].qs + 32 * il + 2 * ir;
__global const uint8_t *qh = x[i].qh + 2 * ir;
uint8_t sc, m;
get_scale_min_k4(is + 0, x[i].scales, &sc, &m);
const float d1 = dall * sc;
const float m1 = dmin * m;
get_scale_min_k4(is + 1, x[i].scales, &sc, &m);
const float d2 = dall * sc;
const float m2 = dmin * m;
uint8_t hm = 1 << (2 * il);
y[0] = d1 * ((ql[0] & 0xF) + (qh[0] & hm ? 16 : 0)) - m1;
y[1] = d1 * ((ql[1] & 0xF) + (qh[1] & hm ? 16 : 0)) - m1;
hm <<= 1;
y[32] = d2 * ((ql[0] >> 4) + (qh[0] & hm ? 16 : 0)) - m2;
y[33] = d2 * ((ql[1] >> 4) + (qh[1] & hm ? 16 : 0)) - m2;
}
__kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __global float *yy)
{
const int i = get_group_id(0);
const int tid = get_local_id(0);
const int ip = tid / 32;
const int il = tid - 32 * ip;
const int is = 8 * ip + il / 16;
__global float *y = yy + i * QK_K + 128 * ip + il;
const float d = vload_half(0, &x[i].d);
__global const uint8_t *ql = x[i].ql + 64 * ip + il;
const uint8_t qh = x[i].qh[32 * ip + il];
__global const int8_t *sc = x[i].scales + is;
y[0] = d * sc[0] * ((int8_t)((ql[0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32);
y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32);
y[64] = d * sc[4] * ((int8_t)((ql[0] >> 4) | (((qh >> 4) & 3) << 4)) - 32);
y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32);
}
__kernel void dequantize_mul_mat_vec_q2_K(__global const struct block_q2_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
const int row = get_group_id(0);
const int num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
__global const struct block_q2_K * x = xx + ib0;
const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...15
const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0,1
const int step = 16/K_QUANTS_PER_ITERATION;
const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
const int in = tid - step*im; // 0...15 or 0...7
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2
const int q_offset = 32*im + l0;
const int s_offset = 8*im;
const int y_offset = 128*im + l0;
tmp[16 * ix + tid] = 0;
uint32_t aux[4];
const uint8_t * d = (const uint8_t *)aux;
const uint8_t * m = (const uint8_t *)(aux + 2);
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
__global const float * y = yy + i * QK_K + y_offset;
__global const uint8_t * q = x[i].qs + q_offset;
const float dall = vload_half(0, &x[i].d);
const float dmin = vload_half(0, &x[i].dmin);
__global const uint32_t * a = (__global const uint32_t *)(x[i].scales + s_offset);
aux[0] = a[0] & 0x0f0f0f0f;
aux[1] = a[1] & 0x0f0f0f0f;
aux[2] = (a[0] >> 4) & 0x0f0f0f0f;
aux[3] = (a[1] >> 4) & 0x0f0f0f0f;
float sum1 = 0, sum2 = 0;
for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {
sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3)
+ y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3)
+ y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3)
+ y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3)
+ y[l+16] * d[1] * ((q[l+16] >> 0) & 3)
+ y[l+48] * d[3] * ((q[l+16] >> 2) & 3)
+ y[l+80] * d[5] * ((q[l+16] >> 4) & 3)
+y[l+112] * d[7] * ((q[l+16] >> 6) & 3);
sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6]
+ y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7];
}
tmp[16 * ix + tid] += dall * sum1 - dmin * sum2;
}
// sum up partial sums and write back result
barrier(CLK_LOCAL_MEM_FENCE);
for (int s=16; s>0; s>>=1) {
if (tid < s) {
tmp[tid] += tmp[tid + s];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid == 0) {
dst[row] = tmp[0];
}
}
__kernel void dequantize_mul_mat_vec_q3_K(__global const struct block_q3_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
const uint16_t kmask1 = 0x0303;
const uint16_t kmask2 = 0x0f0f;
const int row = get_group_id(0);
const int num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
__global const struct block_q3_K * x = xx + ib0;
const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0,1
const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop
const int step = 16/K_QUANTS_PER_ITERATION;
const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
const int in = tid - step*im; // 0....15 or 0...7
const uint8_t m = 1 << (4*im);
const int l0 = n*in; // 0...15 or 0...14 in steps of 2
const int q_offset = 32*im + l0;
const int y_offset = 128*im + l0;
uint16_t utmp[4];
const int8_t * s = (const int8_t *)utmp;
const uint16_t s_shift = 4*im;
tmp[16 * ix + tid] = 0;
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
__global const float * y = yy + i * QK_K + y_offset;
__global const uint8_t * q = x[i].qs + q_offset;
__global const uint8_t * h = x[i].hmask + l0;
__global const uint16_t * a = (__global const uint16_t *)x[i].scales;
utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4);
utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4);
utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4);
utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4);
const float d = vload_half(0, &x[i].d);
float sum = 0;
for (int l = 0; l < n; ++l) {
sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4))
+ y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4))
+ y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4))
+ y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4));
sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4))
+ y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4))
+ y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4))
+ y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4));
}
tmp[16 * ix + tid] += d * sum;
}
// sum up partial sums and write back result
barrier(CLK_LOCAL_MEM_FENCE);
for (int s=16; s>0; s>>=1) {
if (tid < s) {
tmp[tid] += tmp[tid + s];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid == 0) {
dst[row] = tmp[0];
}
}
__kernel void dequantize_mul_mat_vec_q4_K(__global const struct block_q4_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
//to rename it later, just to test now
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
const int row = get_group_id(0);
const int num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...15
const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION;
const int step = 8/K_QUANTS_PER_ITERATION;
const int il = tid/step; // 0...3
const int ir = tid - step*il;// 0...3
const int n = 2*K_QUANTS_PER_ITERATION;
const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
const int in = il%2;
const int l0 = n*(2*ir + in);
const int q_offset = 32*im + l0;
const int y_offset = 64*im + l0;
uint16_t aux[4];
const uint8_t * sc = (const uint8_t *)aux;
__global const struct block_q4_K * x = xx + ib0;
tmp[16 * ix + tid] = 0;
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
__global const uint8_t * q1 = x[i].qs + q_offset;
__global const uint8_t * q2 = q1 + 64;
__global const float * y1 = yy + i*QK_K + y_offset;
__global const float * y2 = y1 + 128;
const float dall = vload_half(0, &x[i].d);
const float dmin = vload_half(0, &x[i].dmin);
__global const uint16_t * a = (__global const uint16_t *)x[i].scales;
aux[0] = a[im+0] & kmask1;
aux[1] = a[im+2] & kmask1;
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
float4 s = (float4)(0.f);
float smin = 0;
for (int l = 0; l < n; ++l) {
s.x += y1[l] * (q1[l] & 0xF); s.y += y1[l+32] * (q1[l] >> 4);
s.z += y2[l] * (q2[l] & 0xF); s.w += y2[l+32] * (q2[l] >> 4);
smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7];
}
tmp[16 * ix + tid] += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin;
}
// sum up partial sums and write back result
barrier(CLK_LOCAL_MEM_FENCE);
for (int s=16; s>0; s>>=1) {
if (tid < s) {
tmp[tid] += tmp[tid + s];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid == 0) {
dst[row] = tmp[0];
}
}
__kernel void dequantize_mul_mat_vec_q5_K(__global const struct block_q5_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) {
const uint16_t kmask1 = 0x3f3f;
const uint16_t kmask2 = 0x0f0f;
const uint16_t kmask3 = 0xc0c0;
const int row = get_group_id(0);
const int num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
const int tid = get_local_id(0)/2; // 0...15
const int ix = get_local_id(0)%2;
const int il = tid/4; // 0...3
const int ir = tid - 4*il;// 0...3
const int n = 2;
const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224
const int in = il%2;
const int l0 = n*(2*ir + in);
const int q_offset = 32*im + l0;
const int y_offset = 64*im + l0;
const uint8_t hm1 = 1 << (2*im);
const uint8_t hm2 = hm1 << 4;
uint16_t aux[4];
const uint8_t * sc = (const uint8_t *)aux;
__global const struct block_q5_K * x = xx + ib0;
tmp[16 * ix + tid] = 0;
for (int i = ix; i < num_blocks_per_row; i += 2) {
__global const uint8_t * ql1 = x[i].qs + q_offset;
__global const uint8_t * ql2 = ql1 + 64;
__global const uint8_t * qh = x[i].qh + l0;
__global const float * y1 = yy + i*QK_K + y_offset;
__global const float * y2 = y1 + 128;
const float dall = vload_half(0, &x[i].d);
const float dmin = vload_half(0, &x[i].dmin);
__global const uint16_t * a = (__global const uint16_t *)x[i].scales;
aux[0] = a[im+0] & kmask1;
aux[1] = a[im+2] & kmask1;
aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2);
aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2);
float4 sum = (float4)(0.f);
float smin = 0;
for (int l = 0; l < n; ++l) {
sum.x += y1[l+ 0] * ((ql1[l+ 0] & 0xF) + (qh[l+ 0] & (hm1 << 0) ? 16 : 0))
+ y1[l+16] * ((ql1[l+16] & 0xF) + (qh[l+16] & (hm1 << 0) ? 16 : 0));
sum.y += y1[l+32] * ((ql1[l+ 0] >> 4) + (qh[l+ 0] & (hm1 << 1) ? 16 : 0))
+ y1[l+48] * ((ql1[l+16] >> 4) + (qh[l+16] & (hm1 << 1) ? 16 : 0));
sum.z += y2[l+ 0] * ((ql2[l+ 0] & 0xF) + (qh[l+ 0] & (hm2 << 0) ? 16 : 0))
+ y2[l+16] * ((ql2[l+16] & 0xF) + (qh[l+16] & (hm2 << 0) ? 16 : 0));
sum.w += y2[l+32] * ((ql2[l+ 0] >> 4) + (qh[l+ 0] & (hm2 << 1) ? 16 : 0))
+ y2[l+48] * ((ql2[l+16] >> 4) + (qh[l+16] & (hm2 << 1) ? 16 : 0));
smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3]
+ (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7];
}
tmp[16 * ix + tid] += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin;
}
// sum up partial sums and write back result
barrier(CLK_LOCAL_MEM_FENCE);
for (int s=16; s>0; s>>=1) {
if (tid < s) {
tmp[tid] += tmp[tid + s];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid == 0) {
dst[row] = tmp[0];
}
}
__kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx, __local float* tmp, __global const float * yy, __global float * dst, const int ncols) {
const int row = get_group_id(0);
const int num_blocks_per_row = ncols / QK_K;
const int ib0 = row*num_blocks_per_row;
__global const struct block_q6_K * x = xx + ib0;
const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16
const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0, 1
const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8
const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128...
const int in = tid - step*im; // 0...15 or 0...7
#if K_QUANTS_PER_ITERATION == 1
const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15
const int is = 0;
#else
const int l0 = 4 * in; // 0, 4, 8, ..., 28
const int is = in / 4;
#endif
const int ql_offset = 64*im + l0;
const int qh_offset = 32*im + l0;
const int s_offset = 8*im + is;
const int y_offset = 128*im + l0;
tmp[16 * ix + tid] = 0; // partial sum for thread in warp
for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {
__global const float * y = yy + i * QK_K + y_offset;
__global const uint8_t * ql = x[i].ql + ql_offset;
__global const uint8_t * qh = x[i].qh + qh_offset;
__global const int8_t * s = x[i].scales + s_offset;
const float d = vload_half(0, &x[i].d);
#if K_QUANTS_PER_ITERATION == 1
float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32)
+ y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32)
+ y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32)
+ y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32)
+ y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32)
+ y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32)
+ y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32)
+y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32);
tmp[16 * ix + tid] += sum;
#else
float sum = 0;
for (int l = 0; l < 4; ++l) {
sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32)
+ y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32)
+ y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32)
+ y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32);
}
tmp[16 * ix + tid] += sum;
#endif
}
// sum up partial sums and write back result
barrier(CLK_LOCAL_MEM_FENCE);
for (int s=16; s>0; s>>=1) {
if (tid < s) {
tmp[tid] += tmp[tid + s];
}
barrier(CLK_LOCAL_MEM_FENCE);
}
if (tid == 0) {
dst[row] = tmp[0];
}
}
);
std::string dequant_template = MULTILINE_QUOTE(
__kernel void KERNEL_NAME(__global X_TYPE* x, __global float* y) {
const int i = get_group_id(0)*get_local_size(0) + get_local_id(0)*2;
@ -160,7 +742,7 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __global float* y) {
std::string dequant_mul_mat_vec_template = MULTILINE_QUOTE(
__kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float* y, __global float* dst, const int ncols) {
const int block_size = get_local_size(0);
const int row = get_global_id(0) / block_size;
const int row = get_group_id(0);
const int tid = get_local_id(0);
const uint qk = QUANT_K;
@ -199,6 +781,7 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float
}
);
std::string mul_template = MULTILINE_QUOTE(
__kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y, const int y_offset, __global TYPE* dst, const int dst_offset, const int ky) {
const int i = get_group_id(0)*get_local_size(0) + get_local_id(0);
@ -272,6 +855,7 @@ std::string& replace(std::string& s, const std::string& from, const std::string&
std::string generate_kernels() {
std::stringstream src;
src << program_source << '\n';
src << k_quants_source << '\n';
for (size_t i = 0; i < dequant_str_values.size(); i += dequant_str_keys.size()) {
std::string dequant_kernel = dequant_template;
std::string dmmv_kernel = dequant_mul_mat_vec_template;
@ -289,6 +873,7 @@ std::string generate_kernels() {
}
src << mul_kernel << '\n';
}
return src.str();
}
@ -300,6 +885,8 @@ static cl_program program;
static cl_kernel convert_row_f16_cl;
static cl_kernel dequantize_row_q4_0_cl, dequantize_row_q4_1_cl, dequantize_row_q5_0_cl, dequantize_row_q5_1_cl, dequantize_row_q8_0_cl;
static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl, dequantize_mul_mat_vec_q5_0_cl, dequantize_mul_mat_vec_q5_1_cl, dequantize_mul_mat_vec_q8_0_cl, convert_mul_mat_vec_f16_cl;
static cl_kernel dequantize_block_q2_k_cl, dequantize_block_q3_k_cl, dequantize_block_q4_k_cl, dequantize_block_q5_k_cl, dequantize_block_q6_k_cl;
static cl_kernel dequantize_mul_mat_vec_q2_K_cl, dequantize_mul_mat_vec_q3_K_cl, dequantize_mul_mat_vec_q4_K_cl, dequantize_mul_mat_vec_q5_K_cl, dequantize_mul_mat_vec_q6_K_cl;
static cl_kernel mul_f32_cl;
static bool fp16_support;
@ -318,10 +905,11 @@ static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, co
exit(1);
}
const char* compile_opts = "-cl-mad-enable -cl-unsafe-math-optimizations -cl-finite-math-only -cl-fast-relaxed-math "
"-DQK4_0=32 -DQR4_0=2 -DQK4_1=32 -DQR4_1=2 -DQK5_0=32 -DQR5_0=2 -DQK5_1=32 -DQR5_1=2 -DQK8_0=32 -DQR8_0=1";
std::string compile_opts = "-cl-mad-enable -cl-unsafe-math-optimizations -cl-finite-math-only -cl-fast-relaxed-math "
"-DQK4_0=32 -DQR4_0=2 -DQK4_1=32 -DQR4_1=2 -DQK5_0=32 -DQR5_0=2 -DQK5_1=32 -DQR5_1=2 -DQK8_0=32 -DQR8_0=1 "
"-DQK_K=256 -DK_QUANTS_PER_ITERATION=" + std::to_string(K_QUANTS_PER_ITERATION);
err = clBuildProgram(p, 0, NULL, compile_opts, NULL, NULL);
err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL);
if(err < 0) {
clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size);
@ -529,6 +1117,12 @@ void ggml_cl_init(void) {
CL_CHECK((dequantize_row_q5_0_cl = clCreateKernel(program, "dequantize_row_q5_0", &err), err));
CL_CHECK((dequantize_row_q5_1_cl = clCreateKernel(program, "dequantize_row_q5_1", &err), err));
CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err));
CL_CHECK((dequantize_block_q2_k_cl = clCreateKernel(program, "dequantize_block_q2_K", &err), err));
CL_CHECK((dequantize_block_q3_k_cl = clCreateKernel(program, "dequantize_block_q3_K", &err), err));
CL_CHECK((dequantize_block_q4_k_cl = clCreateKernel(program, "dequantize_block_q4_K", &err), err));
CL_CHECK((dequantize_block_q5_k_cl = clCreateKernel(program, "dequantize_block_q5_K", &err), err));
CL_CHECK((dequantize_block_q6_k_cl = clCreateKernel(program, "dequantize_block_q6_K", &err), err));
// dequant mul mat kernel
CL_CHECK((dequantize_mul_mat_vec_q4_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_0", &err), err));
@ -537,6 +1131,11 @@ void ggml_cl_init(void) {
CL_CHECK((dequantize_mul_mat_vec_q5_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_1", &err), err));
CL_CHECK((dequantize_mul_mat_vec_q8_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q8_0", &err), err));
CL_CHECK((convert_mul_mat_vec_f16_cl = clCreateKernel(program, "convert_mul_mat_vec_f16", &err), err));
CL_CHECK((dequantize_mul_mat_vec_q2_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q2_K", &err), err));
CL_CHECK((dequantize_mul_mat_vec_q3_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q3_K", &err), err));
CL_CHECK((dequantize_mul_mat_vec_q4_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_K", &err), err));
CL_CHECK((dequantize_mul_mat_vec_q5_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_K", &err), err));
CL_CHECK((dequantize_mul_mat_vec_q6_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q6_K", &err), err));
// mul kernel
CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err));
@ -554,6 +1153,16 @@ static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
return &dequantize_row_q5_1_cl;
case GGML_TYPE_Q8_0:
return &dequantize_row_q8_0_cl;
case GGML_TYPE_Q2_K:
return &dequantize_block_q2_k_cl;
case GGML_TYPE_Q3_K:
return &dequantize_block_q3_k_cl;
case GGML_TYPE_Q4_K:
return &dequantize_block_q4_k_cl;
case GGML_TYPE_Q5_K:
return &dequantize_block_q5_k_cl;
case GGML_TYPE_Q6_K:
return &dequantize_block_q6_k_cl;
case GGML_TYPE_F16:
return &convert_row_f16_cl;
default:
@ -561,6 +1170,50 @@ static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) {
}
}
static size_t ggml_cl_global_denom(ggml_type type) {
switch (type) {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
return 1;
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
return 4;
case GGML_TYPE_Q4_K:
return 8;
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
return 4;
case GGML_TYPE_F16:
default:
return 1;
}
}
static size_t ggml_cl_local_size(ggml_type type) {
switch (type) {
case GGML_TYPE_Q4_0:
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q5_0:
case GGML_TYPE_Q5_1:
case GGML_TYPE_Q8_0:
return 0;
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
return 64;
case GGML_TYPE_Q4_K:
return 32;
case GGML_TYPE_Q5_K:
case GGML_TYPE_Q6_K:
return 64;
case GGML_TYPE_F16:
default:
return 0;
}
}
static cl_kernel* ggml_get_dequantize_mul_mat_vec_cl(ggml_type type) {
switch (type) {
case GGML_TYPE_Q4_0:
@ -575,6 +1228,16 @@ static cl_kernel* ggml_get_dequantize_mul_mat_vec_cl(ggml_type type) {
return &dequantize_mul_mat_vec_q8_0_cl;
case GGML_TYPE_F16:
return &convert_mul_mat_vec_f16_cl;
case GGML_TYPE_Q2_K:
return &dequantize_mul_mat_vec_q2_K_cl;
case GGML_TYPE_Q3_K:
return &dequantize_mul_mat_vec_q3_K_cl;
case GGML_TYPE_Q4_K:
return &dequantize_mul_mat_vec_q4_K_cl;
case GGML_TYPE_Q5_K:
return &dequantize_mul_mat_vec_q5_K_cl;
case GGML_TYPE_Q6_K:
return &dequantize_mul_mat_vec_q6_K_cl;
default:
return nullptr;
}
@ -713,7 +1376,7 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1,
const int64_t ne00 = src0->ne[0];
const int64_t ne01 = src0->ne[1];
const int64_t ne02 = src0->ne[2];
const int64_t ne03 = src0->ne[2];
const int64_t ne03 = src0->ne[3];
const int64_t ne0 = ne00 * ne01 * ne02 * ne03;
const int64_t ne10 = src1->ne[0];
const int64_t ne11 = src1->ne[1];
@ -1017,6 +1680,9 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
cl_kernel* dmmv = ggml_get_dequantize_mul_mat_vec_cl(type);
GGML_ASSERT(to_fp32_cl != nullptr);
const size_t global_denom = ggml_cl_global_denom(type);
const size_t local = ggml_cl_local_size(type);
size_t ev_idx = 0;
std::vector<cl_event> events;
@ -1049,10 +1715,10 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor *
CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, NULL, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++));
} else { // general dequantization kernel + CLBlast matrix matrix multiplication
// convert src0 to fp32 on device
const size_t global = x_ne;
const size_t global = x_ne / global_denom;
CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q));
CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X));
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, NULL, &global, NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, NULL, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL));
// copy src1 to device
CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i03, i02, NULL));
@ -1167,7 +1833,7 @@ size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct g
return 0;
}
void ggml_cl_transform_tensor(ggml_tensor * tensor) {
void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) {
const int64_t ne0 = tensor->ne[0];
const int64_t ne1 = tensor->ne[1];
const int64_t ne2 = tensor->ne[2];
@ -1179,6 +1845,7 @@ void ggml_cl_transform_tensor(ggml_tensor * tensor) {
size_t q_size;
cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size);
tensor->data = data;
// copy tensor to device
for (int64_t i3 = 0; i3 < ne3; i3++) {
for (int64_t i2 = 0; i2 < ne2; i2++) {
@ -1190,35 +1857,5 @@ void ggml_cl_transform_tensor(ggml_tensor * tensor) {
CL_CHECK(clFinish(queue));
tensor->data = dst;
tensor->backend = GGML_BACKEND_GPU;
}
void ggml_cl_load_data(const char * fname, struct ggml_tensor * tensor, const size_t offset) {
cl_int err;
FILE * fp = fopen(fname, "rb");
const size_t size = ggml_nbytes(tensor);
cl_mem dst;
CL_CHECK((dst = clCreateBuffer(context, CL_MEM_READ_ONLY, size, nullptr, &err), err));
void * buf_host = malloc(size);
#ifdef _WIN32
int ret = _fseeki64(fp, (__int64) offset, SEEK_SET);
#else
int ret = fseek(fp, (long) offset, SEEK_SET);
#endif
GGML_ASSERT(ret == 0); // same
size_t ret2 = fread(buf_host, size, 1, fp);
if (ret2 != 1) {
fprintf(stderr, "unexpectedly reached end of file");
exit(1);
}
clEnqueueWriteBuffer(queue, dst, CL_TRUE, 0, size, buf_host, 0, nullptr, nullptr);
tensor->data = dst;
free(buf_host);
fclose(fp);
GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU);
}

3
ggml-opencl.h
View file

@ -18,8 +18,7 @@ void ggml_cl_host_free(void * ptr);
void ggml_cl_free_data(const struct ggml_tensor* tensor);
void ggml_cl_transform_tensor(struct ggml_tensor * tensor);
void ggml_cl_load_data(const char * fname, struct ggml_tensor * tensor, size_t offset);
void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor);
#ifdef __cplusplus
}

5666
ggml.c
View file

File diff suppressed because it is too large Load diff

412
ggml.h
View file

@ -198,9 +198,11 @@
#define GGML_MAX_PARAMS 256
#define GGML_MAX_CONTEXTS 64
#define GGML_MAX_OPT 4
#define GGML_MAX_NAME 32
#define GGML_MAX_NAME 48
#define GGML_DEFAULT_N_THREADS 4
#define GGML_UNUSED(x) (void)(x)
#define GGML_ASSERT(x) \
do { \
if (!(x)) { \
@ -209,6 +211,30 @@
} \
} while (0)
// used to copy the number of elements and stride in bytes of tensors into local variables.
// main purpose is to reduce code duplication and improve readability.
//
// example:
//
// GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne);
// GGML_TENSOR_LOCALS(size_t, nb1, src1, nb);
//
#define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \
const type prefix##0 = (pointer)->array[0]; \
GGML_UNUSED(prefix##0);
#define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \
GGML_TENSOR_LOCALS_1 (type, prefix, pointer, array) \
const type prefix##1 = (pointer)->array[1]; \
GGML_UNUSED(prefix##1);
#define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \
GGML_TENSOR_LOCALS_2 (type, prefix, pointer, array) \
const type prefix##2 = (pointer)->array[2]; \
GGML_UNUSED(prefix##2);
#define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \
GGML_TENSOR_LOCALS_3 (type, prefix, pointer, array) \
const type prefix##3 = (pointer)->array[3]; \
GGML_UNUSED(prefix##3);
#ifdef __cplusplus
extern "C" {
#endif
@ -224,8 +250,8 @@ extern "C" {
GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x);
GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x);
GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n);
GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n);
GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n);
GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n);
struct ggml_object;
struct ggml_context;
@ -295,13 +321,18 @@ extern "C" {
GGML_OP_SUM,
GGML_OP_SUM_ROWS,
GGML_OP_MEAN,
GGML_OP_ARGMAX,
GGML_OP_REPEAT,
GGML_OP_REPEAT_BACK,
GGML_OP_ABS,
GGML_OP_SGN,
GGML_OP_NEG,
GGML_OP_STEP,
GGML_OP_TANH,
GGML_OP_ELU,
GGML_OP_RELU,
GGML_OP_GELU,
GGML_OP_GELU_QUICK,
GGML_OP_SILU,
GGML_OP_SILU_BACK,
GGML_OP_NORM, // normalize
@ -309,6 +340,7 @@ extern "C" {
GGML_OP_RMS_NORM_BACK,
GGML_OP_MUL_MAT,
GGML_OP_OUT_PROD,
GGML_OP_SCALE,
GGML_OP_SET,
@ -324,19 +356,30 @@ extern "C" {
GGML_OP_DIAG_MASK_INF,
GGML_OP_DIAG_MASK_ZERO,
GGML_OP_SOFT_MAX,
GGML_OP_SOFT_MAX_BACK,
GGML_OP_ROPE,
GGML_OP_ROPE_BACK,
GGML_OP_ALIBI,
GGML_OP_CLAMP,
GGML_OP_CONV_1D_1S,
GGML_OP_CONV_1D_2S,
GGML_OP_CONV_1D,
GGML_OP_CONV_2D,
GGML_OP_FLASH_ATTN,
GGML_OP_FLASH_FF,
GGML_OP_FLASH_ATTN_BACK,
GGML_OP_WIN_PART,
GGML_OP_WIN_UNPART,
GGML_OP_MAP_UNARY,
GGML_OP_MAP_BINARY,
GGML_OP_MAP_CUSTOM1,
GGML_OP_MAP_CUSTOM2,
GGML_OP_MAP_CUSTOM3,
GGML_OP_CROSS_ENTROPY_LOSS,
GGML_OP_CROSS_ENTROPY_LOSS_BACK,
GGML_OP_COUNT,
};
@ -429,6 +472,9 @@ extern "C" {
// compute types
// NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled.
// This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995.
enum ggml_task_type {
GGML_TASK_INIT = 0,
GGML_TASK_COMPUTE,
@ -454,6 +500,9 @@ extern "C" {
GGML_API int64_t ggml_cycles(void);
GGML_API int64_t ggml_cycles_per_ms(void);
GGML_API void ggml_numa_init(void); // call once for better performance on NUMA systems
GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node
GGML_API void ggml_print_object (const struct ggml_object * obj);
GGML_API void ggml_print_objects(const struct ggml_context * ctx);
@ -478,6 +527,7 @@ extern "C" {
GGML_API bool ggml_is_transposed(const struct ggml_tensor * tensor);
GGML_API bool ggml_is_contiguous(const struct ggml_tensor * tensor);
GGML_API bool ggml_is_permuted (const struct ggml_tensor * tensor);
// use this to compute the memory overhead of a tensor
GGML_API size_t ggml_tensor_overhead(void);
@ -492,8 +542,9 @@ extern "C" {
GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch);
GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc);
GGML_API void * ggml_get_mem_buffer(struct ggml_context * ctx);
GGML_API size_t ggml_get_mem_size (struct ggml_context * ctx);
GGML_API void * ggml_get_mem_buffer (const struct ggml_context * ctx);
GGML_API size_t ggml_get_mem_size (const struct ggml_context * ctx);
GGML_API size_t ggml_get_max_tensor_size(const struct ggml_context * ctx);
GGML_API struct ggml_tensor * ggml_new_tensor(
struct ggml_context * ctx,
@ -549,7 +600,8 @@ extern "C" {
GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor);
GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor);
GGML_API void ggml_set_name(struct ggml_tensor * tensor, const char * name);
GGML_API struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name);
GGML_API struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...);
//
// operations on tensors with backpropagation
@ -574,6 +626,11 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_add1_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_acc(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -597,24 +654,47 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_sub_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_mul(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_mul_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_div(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_div_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_sqr(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_sqr_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_sqrt(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_sqrt_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_log(
struct ggml_context * ctx,
struct ggml_tensor * a);
@ -638,6 +718,11 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
// argmax along rows
GGML_API struct ggml_tensor * ggml_argmax(
struct ggml_context * ctx,
struct ggml_tensor * a);
// if a is the same shape as b, and a is not parameter, return a
// otherwise, return a new tensor: repeat(a) to fit in b
GGML_API struct ggml_tensor * ggml_repeat(
@ -645,35 +730,92 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_repeat_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_abs(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_abs_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_sgn(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_sgn_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_neg(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_neg_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_step(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_step_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_tanh(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_tanh_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_elu(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_elu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_relu(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_relu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
// TODO: double-check this computation is correct
GGML_API struct ggml_tensor * ggml_gelu(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_gelu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_gelu_quick(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_gelu_quick_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_silu(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_silu_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
// a - x
// b - dy
GGML_API struct ggml_tensor * ggml_silu_back(
@ -687,10 +829,18 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_rms_norm(
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_rms_norm_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a);
// a - x
// b - dy
GGML_API struct ggml_tensor * ggml_rms_norm_back(
@ -698,14 +848,22 @@ extern "C" {
struct ggml_tensor * a,
struct ggml_tensor * b);
// A: m rows, n columns
// B: p rows, n columns (i.e. we transpose it internally)
// A: n columns, m rows
// B: n columns, p rows (i.e. we transpose it internally)
// result is m columns, p rows
GGML_API struct ggml_tensor * ggml_mul_mat(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
// A: m columns, n rows,
// B: p columns, n rows,
// result is m columns, p rows
GGML_API struct ggml_tensor * ggml_out_prod(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
//
// operations on tensors without backpropagation
//
@ -916,16 +1074,29 @@ extern "C" {
struct ggml_context * ctx,
struct ggml_tensor * a);
GGML_API struct ggml_tensor * ggml_soft_max_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
// in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_soft_max_back_inplace(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
// rotary position embedding
// if mode & 1 == 1, skip n_past elements
// if mode & 2 == 1, GPT-NeoX style
// if mode & 4 == 1, ChatGLM style
// TODO: avoid creating a new tensor every time
GGML_API struct ggml_tensor * ggml_rope(
struct ggml_context * ctx,
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode);
int mode,
int n_ctx);
// in-place, returns view(a)
GGML_API struct ggml_tensor * ggml_rope_inplace(
@ -933,7 +1104,8 @@ extern "C" {
struct ggml_tensor * a,
int n_past,
int n_dims,
int mode);
int mode,
int n_ctx);
// rotary position embedding backward, i.e compute dx from dy
// a - dy
@ -961,19 +1133,33 @@ extern "C" {
float min,
float max);
// padding = 1
// TODO: we don't support extra parameters for now
// that's why we are hard-coding the stride, padding, and dilation
// not great ..
GGML_API struct ggml_tensor * ggml_conv_1d_1s(
GGML_API struct ggml_tensor * ggml_conv_1d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * b,
int s0, // stride
int p0, // padding
int d0); // dilation
GGML_API struct ggml_tensor * ggml_conv_1d_2s(
GGML_API struct ggml_tensor * ggml_conv_2d(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
struct ggml_tensor * b,
int s0,
int s1,
int p0,
int p1,
int d0,
int d1);
// conv_1d with padding = half
// alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d)
GGML_API struct ggml_tensor* ggml_conv_1d_ph(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
int s,
int d);
GGML_API struct ggml_tensor * ggml_flash_attn(
struct ggml_context * ctx,
@ -982,6 +1168,14 @@ extern "C" {
struct ggml_tensor * v,
bool masked);
GGML_API struct ggml_tensor * ggml_flash_attn_back(
struct ggml_context * ctx,
struct ggml_tensor * q,
struct ggml_tensor * k,
struct ggml_tensor * v,
struct ggml_tensor * d,
bool masked);
GGML_API struct ggml_tensor * ggml_flash_ff(
struct ggml_context * ctx,
struct ggml_tensor * a,
@ -990,21 +1184,106 @@ extern "C" {
struct ggml_tensor * c0,
struct ggml_tensor * c1);
// Mapping operations
// partition into non-overlapping windows with padding if needed
// example:
// a: 768 64 64 1
// w: 14
// res: 768 14 14 25
// used in sam
GGML_API struct ggml_tensor * ggml_win_part(
struct ggml_context * ctx,
struct ggml_tensor * a,
int w);
// reverse of ggml_win_part
// used in sam
GGML_API struct ggml_tensor * ggml_win_unpart(
struct ggml_context * ctx,
struct ggml_tensor * a,
int w0,
int h0,
int w);
// custom operators
typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *);
typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *);
typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *);
GGML_API struct ggml_tensor * ggml_map_unary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_unary_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_unary_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_binary_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_binary_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_binary_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom1_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
ggml_custom1_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom2_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
ggml_custom2_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom3_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_f32_t fun);
GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c,
ggml_custom3_op_f32_t fun);
// loss function
GGML_API struct ggml_tensor * ggml_cross_entropy_loss(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b);
GGML_API struct ggml_tensor * ggml_cross_entropy_loss_back(
struct ggml_context * ctx,
struct ggml_tensor * a,
struct ggml_tensor * b,
struct ggml_tensor * c);
//
// automatic differentiation
//
@ -1099,6 +1378,8 @@ extern "C" {
struct {
int n_iter;
float sched; // schedule multiplier (fixed, decay or warmup)
float decay; // weight decay for AdamW, use 0.0f to disable
float alpha; // learning rate
float beta1;
float beta2;
@ -1123,6 +1404,49 @@ extern "C" {
} lbfgs;
};
struct ggml_opt_context {
struct ggml_context * ctx;
struct ggml_opt_params params;
int iter;
int64_t nx; // number of parameter elements
bool just_initialized;
struct {
struct ggml_tensor * x; // view of the parameters
struct ggml_tensor * g1; // gradient
struct ggml_tensor * g2; // gradient squared
struct ggml_tensor * m; // first moment
struct ggml_tensor * v; // second moment
struct ggml_tensor * mh; // first moment hat
struct ggml_tensor * vh; // second moment hat
struct ggml_tensor * pf; // past function values
float fx_best;
float fx_prev;
int n_no_improvement;
} adam;
struct {
struct ggml_tensor * x; // current parameters
struct ggml_tensor * xp; // previous parameters
struct ggml_tensor * g; // current gradient
struct ggml_tensor * gp; // previous gradient
struct ggml_tensor * d; // search direction
struct ggml_tensor * pf; // past function values
struct ggml_tensor * lmal; // the L-BFGS memory alpha
struct ggml_tensor * lmys; // the L-BFGS memory ys
struct ggml_tensor * lms; // the L-BFGS memory s
struct ggml_tensor * lmy; // the L-BFGS memory y
float fx_best;
float step;
int j;
int k;
int end;
int n_no_improvement;
} lbfgs;
};
GGML_API struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type);
// optimize the function defined by the tensor f
@ -1131,6 +1455,27 @@ extern "C" {
struct ggml_opt_params params,
struct ggml_tensor * f);
// initialize optimizer context
GGML_API void ggml_opt_init(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_opt_params params,
int64_t nx);
// continue optimizing the function defined by the tensor f
GGML_API enum ggml_opt_result ggml_opt_resume(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_tensor * f);
// continue optimizing the function defined by the tensor f
GGML_API enum ggml_opt_result ggml_opt_resume_g(
struct ggml_context * ctx,
struct ggml_opt_context * opt,
struct ggml_tensor * f,
struct ggml_cgraph * gf,
struct ggml_cgraph * gb);
//
// quantization
//
@ -1169,26 +1514,19 @@ extern "C" {
// Internal types and functions exposed for tests and benchmarks
//
#ifdef __cplusplus
// restrict not standard in C++
#define GGML_RESTRICT
#else
#define GGML_RESTRICT restrict
#endif
typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k);
typedef void (*quantize_row_q_t) (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k);
typedef void (*vec_dot_q_t) (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y);
typedef void (*ggml_to_float_t)(const void * x, float * y, int k);
typedef void (*ggml_from_float_t)(const float * x, void * y, int k);
typedef void (*ggml_vec_dot_t)(const int n, float * s, const void * x, const void * y);
typedef struct {
dequantize_row_q_t dequantize_row_q;
quantize_row_q_t quantize_row_q;
quantize_row_q_t quantize_row_q_reference;
quantize_row_q_t quantize_row_q_dot;
vec_dot_q_t vec_dot_q;
ggml_to_float_t to_float;
ggml_from_float_t from_float;
ggml_from_float_t from_float_reference;
ggml_vec_dot_t vec_dot;
enum ggml_type vec_dot_type;
} quantize_fns_t;
} ggml_type_traits_t;
quantize_fns_t ggml_internal_get_quantize_fn(size_t i);
ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i);
#ifdef __cplusplus
}

1686
k_quants.c
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File diff suppressed because it is too large Load diff

51
k_quants.h
View file

@ -7,7 +7,13 @@
#include <stddef.h>
// Super-block size
#ifdef GGML_QKK_64
#define QK_K 64
#define K_SCALE_SIZE 4
#else
#define QK_K 256
#define K_SCALE_SIZE 12
#endif
//
// Super-block quantization structures
@ -29,38 +35,67 @@ static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "w
// weight is represented as x = a * q
// 16 blocks of 16 elemenets each
// Effectively 3.4375 bits per weight
#ifdef GGML_QKK_64
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits
uint8_t scales[2];
ggml_fp16_t d; // super-block scale
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + 11 * QK_K / 64, "wrong q3_K block size/padding");
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding");
#else
typedef struct {
uint8_t hmask[QK_K/8]; // quants - high bit
uint8_t qs[QK_K/4]; // quants - low 2 bits
uint8_t scales[12]; // scales, quantized with 6 bits
ggml_fp16_t d; // super-block scale
} block_q3_K;
static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding");
#endif
// 4-bit quantization
// 16 blocks of 32 elements each
// weight is represented as x = a * q + b
// Effectively 4.5 bits per weight
#ifdef GGML_QKK_64
typedef struct {
ggml_fp16_t d[2]; // super-block scales/mins
uint8_t scales[2]; // 4-bit block scales/mins
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding");
#else
typedef struct {
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits
uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
uint8_t qs[QK_K/2]; // 4--bit quants
} block_q4_K;
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding");
static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding");
#endif
// 5-bit quantization
// 16 blocks of 32 elements each
// weight is represented as x = a * q + b
// Effectively 5.5 bits per weight
#ifdef GGML_QKK_64
typedef struct {
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits
ggml_fp16_t d; // super-block scale
int8_t scales[QK_K/16]; // 8-bit block scales
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding");
#else
typedef struct {
ggml_fp16_t d; // super-block scale for quantized scales
ggml_fp16_t dmin; // super-block scale for quantized mins
uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits
uint8_t qh[QK_K/8]; // quants, high bit
uint8_t qs[QK_K/2]; // quants, low 4 bits
} block_q5_K;
static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding");
#endif
// 6-bit quantization
// weight is represented as x = a * q

22
llama-util.h
View file

@ -172,12 +172,14 @@ struct llama_mmap {
#ifdef _POSIX_MAPPED_FILES
static constexpr bool SUPPORTED = true;
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */) {
llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) {
size = file->size;
int fd = fileno(file->fp);
int flags = MAP_SHARED;
// prefetch/readahead impairs performance on NUMA systems
if (numa) { prefetch = 0; }
#ifdef __linux__
flags |= MAP_POPULATE;
if (prefetch) { flags |= MAP_POPULATE; }
#endif
addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0);
if (addr == MAP_FAILED) {
@ -191,6 +193,14 @@ struct llama_mmap {
strerror(errno));
}
}
if (numa) {
// advise the kernel not to use readahead
// (because the next page might not belong on the same node)
if (madvise(addr, file->size, MADV_RANDOM)) {
fprintf(stderr, "warning: madvise(.., MADV_RANDOM) failed: %s\n",
strerror(errno));
}
}
}
~llama_mmap() {
@ -199,7 +209,9 @@ struct llama_mmap {
#elif defined(_WIN32)
static constexpr bool SUPPORTED = true;
llama_mmap(struct llama_file * file, bool prefetch = true) {
llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) {
(void) numa;
size = file->size;
HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp));
@ -244,8 +256,10 @@ struct llama_mmap {
#else
static constexpr bool SUPPORTED = false;
llama_mmap(struct llama_file *, bool prefetch = true) {
llama_mmap(struct llama_file *, bool prefetch = true, bool numa = false) {
(void) prefetch;
(void) numa;
throw std::runtime_error(std::string("mmap not supported"));
}
#endif

1020
llama.cpp
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File diff suppressed because it is too large Load diff

101
llama.h
View file

@ -26,6 +26,14 @@
# define LLAMA_API
#endif
#ifdef __GNUC__
# define DEPRECATED(func, hint) func __attribute__((deprecated(hint)))
#elif defined(_MSC_VER)
# define DEPRECATED(func, hint) __declspec(deprecated(hint)) func
#else
# define DEPRECATED(func, hint) func
#endif
#define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt'
#define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla'
#define LLAMA_FILE_MAGIC_GGMF 0x67676d66u // 'ggmf'
@ -38,6 +46,8 @@
#define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN
#define LLAMA_SESSION_VERSION 1
#define LLAMA_DEFAULT_SEED 0xFFFFFFFF
#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL)
// Defined when llama.cpp is compiled with support for offloading model layers to GPU.
#define LLAMA_SUPPORTS_GPU_OFFLOAD
@ -53,6 +63,7 @@ extern "C" {
// TODO: show sample usage
//
struct llama_model;
struct llama_context;
typedef int llama_token;
@ -72,26 +83,26 @@ extern "C" {
typedef void (*llama_progress_callback)(float progress, void *ctx);
struct llama_context_params {
int n_ctx; // text context
int n_batch; // prompt processing batch size
int n_gpu_layers; // number of layers to store in VRAM
int main_gpu; // the GPU that is used for scratch and small tensors
uint32_t seed; // RNG seed, -1 for random
int32_t n_ctx; // text context
int32_t n_batch; // prompt processing batch size
int32_t n_gpu_layers; // number of layers to store in VRAM
int32_t main_gpu; // the GPU that is used for scratch and small tensors
float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs
int seed; // RNG seed, -1 for random
// 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;
// Keep the booleans together to avoid misalignment during copy-by-value.
bool low_vram; // if true, reduce VRAM usage at the cost of performance
bool f16_kv; // use fp16 for KV cache
bool logits_all; // the llama_eval() call computes all logits, not just the last one
bool vocab_only; // only load the vocabulary, no weights
bool use_mmap; // use mmap if possible
bool use_mlock; // force system to keep model in RAM
bool embedding; // embedding mode only
// 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;
};
// model file types
enum llama_ftype {
LLAMA_FTYPE_ALL_F32 = 0,
@ -154,6 +165,20 @@ extern "C" {
uint32_t value; // Unicode code point or rule ID
} llama_grammar_element;
// performance timing information
struct llama_timings {
double t_start_ms;
double t_end_ms;
double t_load_ms;
double t_sample_ms;
double t_p_eval_ms;
double t_eval_ms;
int32_t n_sample;
int32_t n_p_eval;
int32_t n_eval;
};
LLAMA_API struct llama_context_params llama_context_default_params();
LLAMA_API struct llama_model_quantize_params llama_model_quantize_default_params();
@ -162,17 +187,29 @@ extern "C" {
// TODO: not great API - very likely to change
// Initialize the llama + ggml backend
// If numa is true, use NUMA optimizations
// Call once at the start of the program
LLAMA_API void llama_init_backend();
LLAMA_API void llama_init_backend(bool numa);
LLAMA_API int64_t llama_time_us();
LLAMA_API struct llama_model * llama_load_model_from_file(
const char * path_model,
struct llama_context_params params);
LLAMA_API void llama_free_model(struct llama_model * model);
LLAMA_API struct llama_context * llama_new_context_with_model(
struct llama_model * model,
struct llama_context_params params);
// Various functions for loading a ggml llama model.
// Allocate (almost) all memory needed for the model.
// Return NULL on failure
LLAMA_API struct llama_context * llama_init_from_file(
LLAMA_API DEPRECATED(struct llama_context * llama_init_from_file(
const char * path_model,
struct llama_context_params params);
struct llama_context_params params),
"please use llama_load_model_from_file combined with llama_new_context_with_model instead");
// Frees all allocated memory
LLAMA_API void llama_free(struct llama_context * ctx);
@ -189,8 +226,15 @@ extern "C" {
// The model needs to be reloaded before applying a new adapter, otherwise the adapter
// will be applied on top of the previous one
// Returns 0 on success
LLAMA_API int llama_apply_lora_from_file(
LLAMA_API DEPRECATED(int llama_apply_lora_from_file(
struct llama_context * ctx,
const char * path_lora,
const char * path_base_model,
int n_threads),
"please use llama_model_apply_lora_from_file instead");
LLAMA_API int llama_model_apply_lora_from_file(
const struct llama_model * model,
const char * path_lora,
const char * path_base_model,
int n_threads);
@ -199,7 +243,7 @@ extern "C" {
LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx);
// Sets the current rng seed.
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed);
LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed);
// Returns the maximum size in bytes of the state (rng, logits, embedding
// and kv_cache) - will often be smaller after compacting tokens
@ -229,6 +273,14 @@ extern "C" {
int n_past,
int n_threads);
// Same as llama_eval, but use float matrix input directly.
LLAMA_API int llama_eval_embd(
struct llama_context * ctx,
const float * embd,
int n_tokens,
int n_past,
int n_threads);
// Export a static computation graph for context of 511 and batch size of 1
// NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these
// parameters here to keep things simple
@ -251,6 +303,14 @@ extern "C" {
LLAMA_API int llama_n_ctx (const struct llama_context * ctx);
LLAMA_API int llama_n_embd (const struct llama_context * ctx);
// Get the vocabulary as output parameters.
// Returns number of results.
LLAMA_API int llama_get_vocab(
const struct llama_context * ctx,
const char * * strings,
float * scores,
int capacity);
// Token logits obtained from the last call to llama_eval()
// The logits for the last token are stored in the last row
// Can be mutated in order to change the probabilities of the next token
@ -266,9 +326,9 @@ extern "C" {
LLAMA_API const char * llama_token_to_str(const struct llama_context * ctx, llama_token token);
// Special tokens
LLAMA_API llama_token llama_token_bos();
LLAMA_API llama_token llama_token_eos();
LLAMA_API llama_token llama_token_nl();
LLAMA_API llama_token llama_token_bos(); // beginning-of-sentence
LLAMA_API llama_token llama_token_eos(); // end-of-sentence
LLAMA_API llama_token llama_token_nl(); // next-line
// Grammar
//
@ -331,6 +391,7 @@ extern "C" {
LLAMA_API llama_token llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token);
// Performance information
LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx);
LLAMA_API void llama_print_timings(struct llama_context * ctx);
LLAMA_API void llama_reset_timings(struct llama_context * ctx);
@ -348,7 +409,7 @@ extern "C" {
#include <string>
struct ggml_tensor;
std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
const std::vector<std::pair<std::string, struct ggml_tensor *>>& llama_internal_get_tensor_map(struct llama_context * ctx);
#endif

6
pocs/vdot/q8dot.cpp
View file

@ -136,7 +136,7 @@ int main(int argc, char** argv) {
auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1;
auto funcs = ggml_internal_get_quantize_fn(ggml_type);
auto funcs = ggml_internal_get_type_traits(ggml_type);
Stat simple, ggml;
@ -156,8 +156,8 @@ int main(int argc, char** argv) {
t1 = std::chrono::high_resolution_clock::now();
float fs;
if (type == 0) funcs.vec_dot_q(kVecSize * QK4_1, &fs, x40.data(), y.data());
else funcs.vec_dot_q(kVecSize * QK4_1, &fs, x41.data(), y.data());
if (type == 0) funcs.vec_dot(kVecSize * QK4_1, &fs, x40.data(), y.data());
else funcs.vec_dot(kVecSize * QK4_1, &fs, x41.data(), y.data());
t2 = std::chrono::high_resolution_clock::now();
t = 1e-3*std::chrono::duration_cast<std::chrono::nanoseconds>(t2-t1).count();
if (iloop > 3) ggml.addResult(fs, t);

17
pocs/vdot/vdot.cpp
View file

@ -10,6 +10,10 @@
#include <ggml.h>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
constexpr int kVecSize = 1 << 18;
float drawFromGaussianPdf(std::mt19937& rndm) {
@ -231,7 +235,7 @@ int main(int argc, char** argv) {
int n4 = useQ4_1 ? kVecSize / QK4_1 : kVecSize / QK4_0; n4 = 64*((n4 + 63)/64);
int n8 = kVecSize / QK8_0; n8 = 64*((n8 + 63)/64);
auto funcs = useQ4_1 ? ggml_internal_get_quantize_fn(GGML_TYPE_Q4_1) : ggml_internal_get_quantize_fn(GGML_TYPE_Q4_0);
auto funcs = useQ4_1 ? ggml_internal_get_type_traits(GGML_TYPE_Q4_1) : ggml_internal_get_type_traits(GGML_TYPE_Q4_0);
std::vector<block_q4_0> q40;
std::vector<block_q4_1> q41;
@ -257,9 +261,9 @@ int main(int argc, char** argv) {
// Note, we do not include this in the timing as in practical application
// we already have the quantized model weights.
if (useQ4_1) {
funcs.quantize_row_q(x1.data(), q41.data(), kVecSize);
funcs.from_float(x1.data(), q41.data(), kVecSize);
} else {
funcs.quantize_row_q(x1.data(), q40.data(), kVecSize);
funcs.from_float(x1.data(), q40.data(), kVecSize);
}
// Now measure time the dot product needs using the "scalar" version above
@ -278,9 +282,10 @@ int main(int argc, char** argv) {
dot_q4_q8(kVecSize, &result, q40.data(), q8.data());
}
else {
funcs.quantize_row_q_dot(y1.data(), q8.data(), kVecSize);
if (useQ4_1) funcs.vec_dot_q(kVecSize, &result, q41.data(), q8.data());
else funcs.vec_dot_q(kVecSize, &result, q40.data(), q8.data());
auto vdot = ggml_internal_get_type_traits(funcs.vec_dot_type);
vdot.from_float(y1.data(), q8.data(), kVecSize);
if (useQ4_1) funcs.vec_dot(kVecSize, &result, q41.data(), q8.data());
else funcs.vec_dot(kVecSize, &result, q40.data(), q8.data());
}
sumq += result;
t2 = std::chrono::high_resolution_clock::now();

10
scripts/sync-ggml.sh
View file

@ -1,6 +1,14 @@
#!/bin/bash
cp -rpv ../ggml/src/ggml.c ./ggml.c
cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu
cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h
cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu
cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h
cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp
cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h
cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m
cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal
cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h
cp -rpv ../ggml/tests/test-opt.c ./tests/test-opt.c
cp -rpv ../ggml/tests/test-grad0.c ./tests/test-grad0.c

2
scripts/verify-checksum-models.py
View file

@ -1,6 +1,7 @@
import os
import hashlib
def sha256sum(file):
block_size = 16 * 1024 * 1024 # 16 MB block size
b = bytearray(block_size)
@ -15,6 +16,7 @@ def sha256sum(file):
return file_hash.hexdigest()
# Define the path to the llama directory (parent folder of script directory)
llama_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.pardir))

1
spm-headers/ggml.h Symbolic link
View file

@ -0,0 +1 @@
../ggml.h

82
tests/test-grad0.c
View file

@ -1,3 +1,4 @@
#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows
#include "ggml.h"
#include <math.h>
@ -5,7 +6,11 @@
#include <stdlib.h>
#include <assert.h>
#define MAX_NARGS 2
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#define MAX_NARGS 3
#undef MIN
#undef MAX
@ -197,8 +202,23 @@ bool check_gradient(
float max_error_abs,
float max_error_rel) {
static int n_threads = -1;
if (n_threads < 0) {
n_threads = GGML_DEFAULT_N_THREADS;
const char *env = getenv("GGML_N_THREADS");
if (env) {
n_threads = atoi(env);
}
printf("GGML_N_THREADS = %d\n", n_threads);
}
struct ggml_cgraph gf = ggml_build_forward (f);
gf.n_threads = n_threads;
struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false);
gb.n_threads = n_threads;
ggml_graph_compute(ctx0, &gf);
ggml_graph_reset (&gf);
@ -1090,6 +1110,25 @@ int main(int argc, const char ** argv) {
}
}
// cross_entropy_loss
{
const int nargs = 1;
int64_t ne2[4];
get_random_dims(ne2, 4);
for (int ndims = 1; ndims <= 3; ++ndims) {
x[0] = get_random_tensor(ctx0, ndims, ne2, -1.0f, 1.0f);
x[1] = get_random_tensor(ctx0, ndims, ne2, 0.0f, 1.0f);
ggml_set_param(ctx0, x[0]);
struct ggml_tensor * f = ggml_sum(ctx0, ggml_cross_entropy_loss(ctx0, x[0], x[1]));
check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-1f, 1e-2f, INFINITY);
// finite differences regularly fails!
}
}
// rope
{
const int nargs = 1;
@ -1115,7 +1154,7 @@ int main(int argc, const char ** argv) {
continue;
}
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode));
struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0));
GGML_PRINT_DEBUG("rope: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode);
check_gradient("rope", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY);
@ -1124,6 +1163,45 @@ int main(int argc, const char ** argv) {
}
}
// flash_attn
{
const int nargs = 3;
int64_t ne2[4];
get_random_dims(ne2, 4);
int64_t D = ne2[0];
int64_t N = ne2[1];
int64_t M = ne2[2] + N;
int64_t B = ne2[3];
for (int masked = 0; masked <= 1; ++masked) {
for (int ndims = 2; ndims <= 4; ++ndims) {
int64_t neq[4] = { D, N, B, ne[3] };
int64_t nek[4] = { D, M, B, ne[3] };
int64_t nev[4] = { M, D, B, ne[3] };
if (ndims == 2) {
neq[2] = 1; neq[3] = 1;
nek[2] = 1; nek[3] = 1;
nev[2] = 1; nev[3] = 1;
} else if (ndims == 3) {
neq[3] = 1;
nek[3] = 1;
nev[3] = 1;
}
x[0] = get_random_tensor(ctx0, ndims, neq, -0.1250f, 0.1250f);
x[1] = get_random_tensor(ctx0, ndims, nek, -0.1250f, 0.1250f);
x[2] = get_random_tensor(ctx0, ndims, nev, -0.1250f, 0.1250f);
ggml_set_param(ctx0, x[0]);
ggml_set_param(ctx0, x[1]);
ggml_set_param(ctx0, x[2]);
struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0)));
check_gradient("flash_attn", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f);
}
}
}
ggml_free(ctx0);
}

43
tests/test-quantize-fns.cpp
View file

@ -9,12 +9,15 @@
#include <string>
#include <vector>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
const float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001;
const float MAX_QUANTIZATION_TOTAL_ERROR = 0.002;
const float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075;
const float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040;
const float MAX_DOT_PRODUCT_ERROR = 0.02;
const float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f;
const float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f;
const float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f;
const float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f;
const float MAX_DOT_PRODUCT_ERROR = 0.02f;
const char* RESULT_STR[] = {"ok", "FAILED"};
@ -37,26 +40,26 @@ float array_rmse(const float * a1, const float * a2, size_t n) {
}
// Total quantization error on test data
float total_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) {
float total_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
std::vector<uint8_t> tmp_q(2*test_size);
std::vector<float> tmp_out(test_size);
qfns.quantize_row_q(test_data, tmp_q.data(), test_size);
qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size);
qfns.from_float(test_data, tmp_q.data(), test_size);
qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
return array_rmse(test_data, tmp_out.data(), test_size);
}
// Total quantization error on test data
float reference_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) {
float reference_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) {
std::vector<uint8_t> tmp_q(2*test_size);
std::vector<float> tmp_out(test_size);
std::vector<float> tmp_out_ref(test_size);
qfns.quantize_row_q(test_data, tmp_q.data(), test_size);
qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size);
qfns.from_float(test_data, tmp_q.data(), test_size);
qfns.to_float(tmp_q.data(), tmp_out.data(), test_size);
qfns.quantize_row_q_reference(test_data, tmp_q.data(), test_size);
qfns.dequantize_row_q(tmp_q.data(), tmp_out_ref.data(), test_size);
qfns.from_float_reference(test_data, tmp_q.data(), test_size);
qfns.to_float(tmp_q.data(), tmp_out_ref.data(), test_size);
return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size);
}
@ -70,15 +73,17 @@ float dot_product(const float * a1, const float * a2, size_t test_size) {
}
// Total dot product error
float dot_product_error(quantize_fns_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) {
float dot_product_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) {
std::vector<uint8_t> tmp_q1(2*test_size);
std::vector<uint8_t> tmp_q2(2*test_size);
qfns.quantize_row_q (test_data1, tmp_q1.data(), test_size);
qfns.quantize_row_q_dot(test_data2, tmp_q2.data(), test_size);
auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
qfns.from_float(test_data1, tmp_q1.data(), test_size);
vdot.from_float(test_data2, tmp_q2.data(), test_size);
float result = INFINITY;
qfns.vec_dot_q(test_size, &result, tmp_q1.data(), tmp_q2.data());
qfns.vec_dot(test_size, &result, tmp_q1.data(), tmp_q2.data());
const float dot_ref = dot_product(test_data1, test_data2, test_size);
@ -120,9 +125,9 @@ int main(int argc, char * argv[]) {
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
ggml_type type = (ggml_type) i;
quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
if (qfns.quantize_row_q && qfns.dequantize_row_q) {
if (qfns.from_float && qfns.to_float) {
const float total_error = total_quantization_error(qfns, test_size, test_data.data());
const float max_quantization_error =
type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS :

96
tests/test-quantize-perf.cpp
View file

@ -13,10 +13,15 @@
#include <string>
#include <vector>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
#define MAX_ALIGNMENT 64
#define QK 32
#define WARMUP 5
#define ITERATIONS 10
#define MAX_ITERATIONS 100000000
#define L1_SIZE 32*128
#define L2_SIZE 32*2048
@ -32,9 +37,9 @@ struct quantize_perf_params {
bool op_dequantize_row_q = false;
bool op_quantize_row_q_dot = false;
bool op_vec_dot_q = false;
int64_t iterations = ITERATIONS;
};
#if defined(__x86_64__) || defined(__i386__)
#include <x86intrin.h>
@ -71,7 +76,7 @@ void * align_with_offset(void * ptr, int offset) {
return (char *) std::align(MAX_ALIGNMENT, MAX_ALIGNMENT, ptr, dummy_size) + offset;
}
void benchmark_function(size_t size, size_t q_size, std::function<size_t(void)> function) {
void benchmark_function(size_t size, size_t q_size, int64_t iterations, std::function<size_t(void)> function) {
int64_t min_time_us = INT64_MAX;
int64_t total_time_us = 0;
int64_t min_time_cycles = INT64_MAX;
@ -82,7 +87,7 @@ void benchmark_function(size_t size, size_t q_size, std::function<size_t(void)>
}
for (int i = 0; i < ITERATIONS; i++) {
for (int i = 0; i < iterations; i++) {
const int64_t start_time = ggml_time_us();
const int64_t start_cycles = cpu_cycles();
@ -98,9 +103,38 @@ void benchmark_function(size_t size, size_t q_size, std::function<size_t(void)>
}
printf(" min cycles/%d vals : %9.2f\n", QK, QK * min_time_cycles / (float) size);
printf(" avg cycles/%d vals : %9.2f\n", QK, QK * total_time_cycles / (float) (size * ITERATIONS));
printf(" float32 throughput : %9.2f GB/s\n", gigabytes_per_second(4 * size * ITERATIONS, total_time_us));
printf(" quantized throughput : %9.2f GB/s\n", gigabytes_per_second(q_size * ITERATIONS, total_time_us));
printf(" avg cycles/%d vals : %9.2f\n", QK, QK * total_time_cycles / (float) (size * iterations));
printf(" float32 throughput : %9.2f GB/s\n", gigabytes_per_second(4 * size * iterations, total_time_us));
printf(" quantized throughput : %9.2f GB/s\n", gigabytes_per_second(q_size * iterations, total_time_us));
}
void usage(char * argv[]) {
printf("Benchmark quantization specific functions on synthetic data\n");
printf("\n");
printf("usage: %s [options]\n", argv[0]);
printf("\n");
printf("options: (default)\n");
printf(" -h, --help show this help message and exit\n");
printf(" --size SIZE set test size, divisible by 32 (L1_SIZE:%d)\n", L1_SIZE);
printf(" -3 use size as L1, L2, L3 sizes (L1:%d L2:%d L3:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE);
printf(" -4 use size as L1, L2, L3, MEM sizes (L1:%d L2:%d L3:%d MEM:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE, MEM_SIZE);
printf(" --op OP set test opration as quantize_row_q_reference, quantize_row_q, dequantize_row_q,\n");
printf(" quantize_row_q_dot, vec_dot_q (all)\n");
printf(" --type TYPE set test type as");
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
ggml_type type = (ggml_type) i;
ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
if (ggml_type_name(type) != NULL) {
if (qfns.from_float && qfns.to_float) {
printf(" %s", ggml_type_name(type));
}
}
}
printf(" (all)\n");
printf(" --alignment-offset OFFSET\n");
printf(" set alignment offset as OFFSET (0)\n");
printf(" -i NUM, --iterations NUM\n");
printf(" set test iteration number (%d)\n", ITERATIONS);
}
int main(int argc, char * argv[]) {
@ -174,6 +208,21 @@ int main(int argc, char * argv[]) {
break;
}
params.alignment_offset = alignment;
} else if ((arg == "-i") || (arg == "--iterations")) {
if (++i >= argc) {
invalid_param = true;
break;
}
int number = std::stoi(argv[i]);
if (number < 0 || number > MAX_ITERATIONS) {
fprintf(stderr, "error: iterations must be less than %d\n", MAX_ITERATIONS);
invalid_param = true;
break;
}
params.iterations = number;
} else if ((arg == "-h") || (arg == "--help")) {
usage(argv);
return 1;
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
return 1;
@ -209,6 +258,8 @@ int main(int argc, char * argv[]) {
generate_data(0, largest, test_data1);
generate_data(1, largest, test_data2);
int64_t iterations = params.iterations;
// Initialize GGML, ensures float conversion tables are initialized
struct ggml_init_params ggml_params = {
@ -220,12 +271,12 @@ int main(int argc, char * argv[]) {
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
ggml_type type = (ggml_type) i;
quantize_fns_t qfns = ggml_internal_get_quantize_fn(i);
if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) {
ggml_type_traits_t qfns = ggml_internal_get_type_traits(type);
if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) {
continue;
}
if (qfns.quantize_row_q && qfns.dequantize_row_q) {
if (qfns.from_float && qfns.to_float) {
printf("%s\n", ggml_type_name(type));
if (params.op_quantize_row_q_reference) {
@ -233,11 +284,11 @@ int main(int argc, char * argv[]) {
for (size_t size : params.test_sizes) {
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
auto quantize_fn = [&](void ) {
qfns.quantize_row_q_reference(test_data1, test_q1, size);
qfns.from_float_reference(test_data1, test_q1, size);
return test_q1[0];
};
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
benchmark_function(size, quantized_size, quantize_fn);
benchmark_function(size, quantized_size, iterations, quantize_fn);
}
printf("\n");
}
@ -247,26 +298,26 @@ int main(int argc, char * argv[]) {
for (size_t size : params.test_sizes) {
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
auto quantize_fn = [&](void ) {
qfns.quantize_row_q(test_data1, test_q1, size);
qfns.from_float(test_data1, test_q1, size);
return test_q1[0];
};
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
benchmark_function(size, quantized_size, quantize_fn);
benchmark_function(size, quantized_size, iterations, quantize_fn);
}
printf("\n");
}
if (params.op_dequantize_row_q) {
printf(" dequantize_row_q\n");
qfns.quantize_row_q(test_data1, test_q1, largest);
qfns.from_float(test_data1, test_q1, largest);
for (size_t size : params.test_sizes) {
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
auto quantize_fn = [&](void ) {
qfns.dequantize_row_q(test_q1, test_out, size);
qfns.to_float(test_q1, test_out, size);
return test_out[0];
};
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
benchmark_function(size, quantized_size, quantize_fn);
benchmark_function(size, quantized_size, iterations, quantize_fn);
}
printf("\n");
}
@ -276,28 +327,29 @@ int main(int argc, char * argv[]) {
for (size_t size : params.test_sizes) {
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
auto quantize_fn = [&](void ) {
qfns.quantize_row_q_dot(test_data1, test_q1, size);
auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type);
vdot.from_float(test_data1, test_q1, size);
return test_q1[0];
};
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
benchmark_function(size, quantized_size, quantize_fn);
benchmark_function(size, quantized_size, iterations, quantize_fn);
}
printf("\n");
}
if (params.op_vec_dot_q) {
printf(" vec_dot_q\n");
qfns.quantize_row_q(test_data1, test_q1, largest);
qfns.quantize_row_q(test_data2, test_q2, largest);
qfns.from_float(test_data1, test_q1, largest);
qfns.from_float(test_data2, test_q2, largest);
for (size_t size : params.test_sizes) {
printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024));
auto quantize_fn = [&](void ) {
float result;
qfns.vec_dot_q(size, &result, test_q1, test_q2);
qfns.vec_dot(size, &result, test_q1, test_q2);
return result;
};
size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type);
benchmark_function(size, quantized_size, quantize_fn);
benchmark_function(size, quantized_size, iterations, quantize_fn);
}
printf("\n");
}

33
tests/test-sampling.cpp
View file

@ -176,27 +176,28 @@ void test_frequency_presence_penalty(
int main(void) {
ggml_time_init();
test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4}, 1);
test_top_k({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2}, 3);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 1);
test_top_k({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 3);
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4}, 0);
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3}, 0.7);
test_top_p({0.1, 0.2, 0.3, 0.4}, {0.4, 0.3, 0.2, 0.1}, 1);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 0.8f);
test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1);
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3}, 0.25);
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.75);
test_tfs({0.1, 0.15, 0.2, 0.25, 0.3}, {0.3, 0.25}, 0.99);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f);
test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f);
test_typical({0.97, 0.01, 0.01, 0.01}, {0.97}, 0.5);
test_typical({0.4, 0.2, 0.2, 0.2}, {0.2, 0.2, 0.2}, 0.5);
test_typical({0.97f, 0.01f, 0.01f, 0.01f}, {0.97f}, 0.5f);
test_typical({0.4f, 0.2f, 0.2f, 0.2f}, {0.2f, 0.2f, 0.2f}, 0.5f);
test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0}, {0.25, 0.25, 0.25, 0.25, 0}, 50.0);
test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2}, {0.5, 0.5, 0, 0, 0}, 50.0);
test_repetition_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2, 0, 0}, {0.5, 0.5, 0, 0, 0}, 50.0);
test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.25f, 0.25f, 0.25f, 0.25f, 0}, 50.0f);
test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
test_repetition_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.5f, 0.5f, 0, 0, 0}, 50.0f);
test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0}, {0.249997, 0.249997, 0.249997, 0.249997, 0.000011}, 5.0, 5.0);
test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2}, {0.499966, 0.499966, 0.000023, 0.000023, 0.000023}, 5.0, 5.0);
test_frequency_presence_penalty({0.2, 0.2, 0.2, 0.2, 0.2}, {0, 1, 2, 0, 0}, {0.499977, 0.499977, 0.000023, 0.000023, 0.000000}, 5.0, 5.0);
test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0}, {0.249997f, 0.249997f, 0.249997f, 0.249997f, 0.000011f}, 5.0f, 5.0f);
test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 5.0f, 5.0f);
test_frequency_presence_penalty({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 5.0f, 5.0f);
printf("OK\n");
}

18
tests/test-tokenizer-0.cpp
View file

@ -28,6 +28,7 @@ int main(int argc, char **argv) {
fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
llama_model * model;
llama_context * ctx;
// load the vocab
@ -36,10 +37,18 @@ int main(int argc, char **argv) {
lparams.vocab_only = true;
ctx = llama_init_from_file(fname.c_str(), lparams);
model = llama_load_model_from_file(fname.c_str(), lparams);
if (model == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
return 1;
}
ctx = llama_new_context_with_model(model, lparams);
if (ctx == NULL) {
fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str());
llama_free_model(model);
return 1;
}
}
@ -48,12 +57,14 @@ int main(int argc, char **argv) {
if (n_vocab != 32000) {
fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab);
llama_free_model(model);
llama_free(ctx);
return 2;
}
for (const auto & test_kv : k_tests()) {
std::vector<llama_token> res(test_kv.first.size());
const int n = llama_tokenize(ctx, test_kv.first.c_str(), res.data(), res.size(), true);
const int n = llama_tokenize(ctx, test_kv.first.c_str(), res.data(), int(res.size()), true);
res.resize(n);
bool correct = res.size() == test_kv.second.size();
@ -77,10 +88,13 @@ int main(int argc, char **argv) {
}
fprintf(stderr, "\n");
llama_free_model(model);
llama_free(ctx);
return 3;
}
}
llama_free_model(model);
llama_free(ctx);
return 0;