Merge pull request #28 from saharNooby/ggml-to-submodule
Move ggml to submodule
This commit is contained in:
Alex
GitHub
commit
0a8157d1ee
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@ -9,41 +9,24 @@ on:
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required: true
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type: boolean
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push:
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paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.c', '**/*.cpp']
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paths: ['.github/workflows/**', '**/CMakeLists.txt', '**/*.h', '**/*.c', '**/*.cpp']
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pull_request:
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types: [opened, synchronize, edited, reopened, review_requested, ready_for_review]
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paths: ['**/CMakeLists.txt', '**/Makefile', '**/*.h', '**/*.c', '**/*.cpp']
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paths: ['**/CMakeLists.txt', '**/*.h', '**/*.c', '**/*.cpp']
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env:
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BRANCH_NAME: ${{ github.head_ref || github.ref_name }}
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jobs:
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ubuntu-latest-make:
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runs-on: ubuntu-latest
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v1
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- name: Dependencies
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id: depends
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run: |
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sudo apt-get update
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sudo apt-get install build-essential
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- name: Build
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id: make_build
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run: |
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make
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ubuntu-latest-cmake:
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runs-on: ubuntu-latest
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v1
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uses: actions/checkout@v3
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with:
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submodules: 'recursive'
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- name: Dependencies
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id: depends
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@ -85,31 +68,15 @@ jobs:
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path: |
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rwkv-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-${{ steps.system-info.outputs.OS_TYPE }}-${{ steps.system-info.outputs.OS_NAME }}-${{ steps.system-info.outputs.OS_VERSION }}-${{ steps.system-info.outputs.CPU_ARCH }}.zip
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macOS-latest-make:
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runs-on: macos-latest
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v1
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- name: Dependencies
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id: depends
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run: |
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brew update
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- name: Build
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id: make_build
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run: |
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make
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macOS-latest-cmake:
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runs-on: macOS-latest
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v1
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uses: actions/checkout@v3
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with:
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submodules: 'recursive'
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- name: Dependencies
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id: depends
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@ -167,7 +134,9 @@ jobs:
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v1
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uses: actions/checkout@v3
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with:
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submodules: 'recursive'
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- name: Build
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id: cmake_build
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@ -222,9 +191,7 @@ jobs:
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runs-on: ubuntu-latest
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needs:
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- ubuntu-latest-make
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- ubuntu-latest-cmake
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- macOS-latest-make
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- macOS-latest-cmake
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- windows-latest-cmake
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@ -0,0 +1,3 @@
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[submodule "ggml"]
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path = ggml
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url = https://github.com/saharNooby/ggml
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@ -222,13 +222,12 @@ endif()
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# Build libraries
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#
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add_library(ggml OBJECT
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ggml.c
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ggml.h)
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if (MSVC)
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add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
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endif()
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add_subdirectory(ggml)
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target_include_directories(ggml PUBLIC .)
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target_compile_features(ggml PUBLIC c_std_11) # don't bump
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target_link_libraries(ggml PRIVATE Threads::Threads ${RWKV_EXTRA_LIBS})
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if (BUILD_SHARED_LIBS)
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set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON)
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endif()
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229
Makefile
229
Makefile
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@ -1,229 +0,0 @@
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ifndef UNAME_S
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UNAME_S := $(shell uname -s)
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endif
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ifndef UNAME_P
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UNAME_P := $(shell uname -p)
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endif
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ifndef UNAME_M
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UNAME_M := $(shell uname -m)
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endif
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CCV := $(shell $(CC) --version | head -n 1)
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CXXV := $(shell $(CXX) --version | head -n 1)
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# Mac OS + Arm can report x86_64
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# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
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ifeq ($(UNAME_S),Darwin)
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ifneq ($(UNAME_P),arm)
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SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null)
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ifeq ($(SYSCTL_M),1)
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# UNAME_P := arm
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# UNAME_M := arm64
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warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789)
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endif
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endif
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endif
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#
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# Compile flags
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#
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# keep standard at C11 and C++11
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CFLAGS = -I. -O3 -DNDEBUG -std=c11 -fPIC
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CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
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LDFLAGS =
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# warnings
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CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith -Wno-unused-function
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CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function
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# OS specific
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# TODO: support Windows
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ifeq ($(UNAME_S),Linux)
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CFLAGS += -pthread
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CXXFLAGS += -pthread
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endif
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ifeq ($(UNAME_S),Darwin)
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CFLAGS += -pthread
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CXXFLAGS += -pthread
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endif
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ifeq ($(UNAME_S),FreeBSD)
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CFLAGS += -pthread
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CXXFLAGS += -pthread
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endif
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ifeq ($(UNAME_S),NetBSD)
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CFLAGS += -pthread
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CXXFLAGS += -pthread
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endif
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ifeq ($(UNAME_S),OpenBSD)
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CFLAGS += -pthread
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CXXFLAGS += -pthread
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endif
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ifeq ($(UNAME_S),Haiku)
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CFLAGS += -pthread
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CXXFLAGS += -pthread
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endif
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# Architecture specific
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# TODO: probably these flags need to be tweaked on some architectures
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# feel free to update the Makefile for your architecture and send a pull request or issue
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ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
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ifeq ($(UNAME_S),Darwin)
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CFLAGS += -mf16c
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AVX1_M := $(shell sysctl machdep.cpu.features)
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ifneq (,$(findstring FMA,$(AVX1_M)))
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CFLAGS += -mfma
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endif
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ifneq (,$(findstring AVX1.0,$(AVX1_M)))
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CFLAGS += -mavx
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endif
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AVX2_M := $(shell sysctl machdep.cpu.leaf7_features)
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ifneq (,$(findstring AVX2,$(AVX2_M)))
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CFLAGS += -mavx2
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endif
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else ifeq ($(UNAME_S),Linux)
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AVX1_M := $(shell grep "avx " /proc/cpuinfo)
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ifneq (,$(findstring avx,$(AVX1_M)))
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CFLAGS += -mavx
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endif
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AVX2_M := $(shell grep "avx2 " /proc/cpuinfo)
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ifneq (,$(findstring avx2,$(AVX2_M)))
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CFLAGS += -mavx2
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endif
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FMA_M := $(shell grep "fma " /proc/cpuinfo)
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ifneq (,$(findstring fma,$(FMA_M)))
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CFLAGS += -mfma
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endif
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F16C_M := $(shell grep "f16c " /proc/cpuinfo)
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ifneq (,$(findstring f16c,$(F16C_M)))
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CFLAGS += -mf16c
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endif
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SSE3_M := $(shell grep "sse3 " /proc/cpuinfo)
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ifneq (,$(findstring sse3,$(SSE3_M)))
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CFLAGS += -msse3
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endif
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AVX512F_M := $(shell grep "avx512f " /proc/cpuinfo)
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ifneq (,$(findstring avx512f,$(AVX512F_M)))
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CFLAGS += -mavx512f
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endif
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AVX512BW_M := $(shell grep "avx512bw " /proc/cpuinfo)
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ifneq (,$(findstring avx512bw,$(AVX512BW_M)))
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CFLAGS += -mavx512bw
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endif
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AVX512DQ_M := $(shell grep "avx512dq " /proc/cpuinfo)
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ifneq (,$(findstring avx512dq,$(AVX512DQ_M)))
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CFLAGS += -mavx512dq
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endif
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AVX512VL_M := $(shell grep "avx512vl " /proc/cpuinfo)
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ifneq (,$(findstring avx512vl,$(AVX512VL_M)))
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CFLAGS += -mavx512vl
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endif
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AVX512CD_M := $(shell grep "avx512cd " /proc/cpuinfo)
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ifneq (,$(findstring avx512cd,$(AVX512CD_M)))
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CFLAGS += -mavx512cd
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endif
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AVX512ER_M := $(shell grep "avx512er " /proc/cpuinfo)
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ifneq (,$(findstring avx512er,$(AVX512ER_M)))
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CFLAGS += -mavx512er
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endif
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AVX512IFMA_M := $(shell grep "avx512ifma " /proc/cpuinfo)
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ifneq (,$(findstring avx512ifma,$(AVX512IFMA_M)))
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CFLAGS += -mavx512ifma
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endif
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AVX512PF_M := $(shell grep "avx512pf " /proc/cpuinfo)
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ifneq (,$(findstring avx512pf,$(AVX512PF_M)))
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CFLAGS += -mavx512pf
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endif
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else ifeq ($(UNAME_S),Haiku)
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AVX1_M := $(shell sysinfo -cpu | grep -w "AVX")
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ifneq (,$(findstring AVX,$(AVX1_M)))
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CFLAGS += -mavx
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endif
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AVX2_M := $(shell sysinfo -cpu | grep -w "AVX2")
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ifneq (,$(findstring AVX2,$(AVX2_M)))
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CFLAGS += -mavx2
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endif
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FMA_M := $(shell sysinfo -cpu | grep -w "FMA")
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ifneq (,$(findstring FMA,$(FMA_M)))
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CFLAGS += -mfma
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endif
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F16C_M := $(shell sysinfo -cpu | grep -w "F16C")
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ifneq (,$(findstring F16C,$(F16C_M)))
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CFLAGS += -mf16c
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endif
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else
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CFLAGS += -mfma -mf16c -mavx -mavx2
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endif
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endif
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ifneq ($(filter ppc64%,$(UNAME_M)),)
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POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
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ifneq (,$(findstring POWER9,$(POWER9_M)))
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CFLAGS += -mcpu=power9
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CXXFLAGS += -mcpu=power9
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endif
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# Require c++23's std::byteswap for big-endian support.
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ifeq ($(UNAME_M),ppc64)
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CXXFLAGS += -std=c++23 -DGGML_BIG_ENDIAN
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endif
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endif
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ifndef RWKV_NO_ACCELERATE
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# Mac M1 - include Accelerate framework.
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# `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time).
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ifeq ($(UNAME_S),Darwin)
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CFLAGS += -DGGML_USE_ACCELERATE
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LDFLAGS += -framework Accelerate
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endif
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endif
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ifdef RWKV_OPENBLAS
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CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/openblas
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LDFLAGS += -lopenblas
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endif
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ifdef RWKV_GPROF
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CFLAGS += -pg
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CXXFLAGS += -pg
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endif
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ifneq ($(filter aarch64%,$(UNAME_M)),)
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CFLAGS += -mcpu=native
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CXXFLAGS += -mcpu=native
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endif
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ifneq ($(filter armv6%,$(UNAME_M)),)
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# Raspberry Pi 1, 2, 3
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CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access
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endif
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ifneq ($(filter armv7%,$(UNAME_M)),)
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# Raspberry Pi 4
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CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations
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endif
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ifneq ($(filter armv8%,$(UNAME_M)),)
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# Raspberry Pi 4
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CFLAGS += -mfp16-format=ieee -mno-unaligned-access
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endif
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#
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# Print build information
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#
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$(info I rwkv.cpp build info: )
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$(info I UNAME_S: $(UNAME_S))
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$(info I UNAME_P: $(UNAME_P))
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$(info I UNAME_M: $(UNAME_M))
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$(info I CFLAGS: $(CFLAGS))
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$(info I CXXFLAGS: $(CXXFLAGS))
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$(info I LDFLAGS: $(LDFLAGS))
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$(info I CC: $(CCV))
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$(info I CXX: $(CXXV))
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$(info )
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default: rwkv.o
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#
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# Build library
|
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#
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ggml.o: ggml.c ggml.h
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$(CC) $(CFLAGS) -c ggml.c -o ggml.o
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rwkv.o: rwkv.cpp rwkv.h
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$(CXX) $(CXXFLAGS) -c rwkv.cpp -o rwkv.o
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10
README.md
10
README.md
|
|
@ -12,7 +12,7 @@ Loading LoRA checkpoints in [Blealtan's format](https://github.com/Blealtan/RWKV
|
|||
|
||||
**TODO (contributions welcome!)**:
|
||||
|
||||
1. Optimize AVX2 implementation of `Q4_1_O` matmul — currently, it is as slow as `FP32`
|
||||
1. Optimize AVX2 implementation of `Q4_1_O` matmul — currently, it is 40% slower than `Q4_1`
|
||||
2. Measure latency and perplexity of different model sizes (169M to 14B) and data types (`FP32`, `FP16`, `Q4_0`, `Q4_1`, `Q4_1_O`)
|
||||
3. Test on Linux (including Colab) and MacOS
|
||||
4. Make required memory calculation more robust (see [#4](https://github.com/saharNooby/rwkv.cpp/issues/4))
|
||||
|
|
@ -24,7 +24,7 @@ Loading LoRA checkpoints in [Blealtan's format](https://github.com/Blealtan/RWKV
|
|||
**Requirements**: [git](https://gitforwindows.org/).
|
||||
|
||||
```commandline
|
||||
git clone https://github.com/saharNooby/rwkv.cpp.git
|
||||
git clone --recursive https://github.com/saharNooby/rwkv.cpp.git
|
||||
cd rwkv.cpp
|
||||
```
|
||||
|
||||
|
|
@ -91,9 +91,9 @@ python rwkv/quantize.py ~/Downloads/rwkv.cpp-169M.bin ~/Downloads/rwkv.cpp-169M-
|
|||
|
||||
Formats available:
|
||||
|
||||
- `4`: `Q4_1_O`, best quality, very slow (as `FP32`).
|
||||
- `3`: `Q4_1`, poor quality, very fast (as `FP16`).
|
||||
- `2`: `Q4_0`, worst quality, breaks larger models, moderately fast (between `FP16` and `FP32`).
|
||||
- `4`: `Q4_1_O`, best quality, slow (30% slower than `FP16`).
|
||||
- `3`: `Q4_1`, poor quality, fast (comparable to `FP16`).
|
||||
- `2`: `Q4_0`, worst quality, breaks larger models, very fast.
|
||||
|
||||
### 4. Run the model
|
||||
|
||||
|
|
|
|||
|
|
@ -0,0 +1 @@
|
|||
Subproject commit 4856b5d30a8985cde31efe4a5bfcc77ac1df61a6
|
||||
840
ggml.h
840
ggml.h
|
|
@ -1,840 +0,0 @@
|
|||
#pragma once
|
||||
|
||||
//
|
||||
// GGML Tensor Library
|
||||
//
|
||||
// This documentation is still a work in progress.
|
||||
// If you wish some specific topics to be covered, feel free to drop a comment:
|
||||
//
|
||||
// https://github.com/ggerganov/whisper.cpp/issues/40
|
||||
//
|
||||
// ## Overview
|
||||
//
|
||||
// This library implements:
|
||||
//
|
||||
// - a set of tensor operations
|
||||
// - automatic differentiation
|
||||
// - basic optimization algorithms
|
||||
//
|
||||
// The aim of this library is to provide a minimalistic approach for various machine learning tasks. This includes,
|
||||
// but is not limited to, the following:
|
||||
//
|
||||
// - linear regression
|
||||
// - support vector machines
|
||||
// - neural networks
|
||||
//
|
||||
// The library allows the user to define a certain function using the available tensor operations. This function
|
||||
// definition is represented internally via a computation graph. Each tensor operation in the function definition
|
||||
// corresponds to a node in the graph. Having the computation graph defined, the user can choose to compute the
|
||||
// function's value and/or its gradient with respect to the input variables. Optionally, the function can be optimized
|
||||
// using one of the available optimization algorithms.
|
||||
//
|
||||
// For example, here we define the function: f(x) = a*x^2 + b
|
||||
//
|
||||
// {
|
||||
// struct ggml_init_params params = {
|
||||
// .mem_size = 16*1024*1024,
|
||||
// .mem_buffer = NULL,
|
||||
// };
|
||||
//
|
||||
// // memory allocation happens here
|
||||
// struct ggml_context * ctx = ggml_init(params);
|
||||
//
|
||||
// struct ggml_tensor * x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
|
||||
//
|
||||
// ggml_set_param(ctx, x); // x is an input variable
|
||||
//
|
||||
// struct ggml_tensor * a = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
|
||||
// struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 1);
|
||||
// struct ggml_tensor * x2 = ggml_mul(ctx, x, x);
|
||||
// struct ggml_tensor * f = ggml_add(ctx, ggml_mul(ctx, a, x2), b);
|
||||
//
|
||||
// ...
|
||||
// }
|
||||
//
|
||||
// Notice that the function definition above does not involve any actual computation. The computation is performed only
|
||||
// when the user explicitly requests it. For example, to compute the function's value at x = 2.0:
|
||||
//
|
||||
// {
|
||||
// ...
|
||||
//
|
||||
// struct ggml_cgraph gf = ggml_build_forward(f);
|
||||
//
|
||||
// // set the input variable and parameter values
|
||||
// ggml_set_f32(x, 2.0f);
|
||||
// ggml_set_f32(a, 3.0f);
|
||||
// ggml_set_f32(b, 4.0f);
|
||||
//
|
||||
// ggml_graph_compute(ctx0, &gf);
|
||||
//
|
||||
// printf("f = %f\n", ggml_get_f32_1d(f, 0));
|
||||
//
|
||||
// ...
|
||||
// }
|
||||
//
|
||||
// The actual computation is performed in the ggml_graph_compute() function.
|
||||
//
|
||||
// The ggml_new_tensor_...() functions create new tensors. They are allocated in the memory buffer provided to the
|
||||
// ggml_init() function. You have to be careful not to exceed the memory buffer size. Therefore, you have to know
|
||||
// in advance how much memory you need for your computation. Alternatively, you can allocate a large enough memory
|
||||
// and after defining the computation graph, call the ggml_used_mem() function to find out how much memory was
|
||||
// actually needed.
|
||||
//
|
||||
// The ggml_set_param() function marks a tensor as an input variable. This is used by the automatic
|
||||
// differentiation and optimization algorithms.
|
||||
//
|
||||
// The described approach allows to define the function graph once and then compute its forward or backward graphs
|
||||
// multiple times. All computations will use the same memory buffer allocated in the ggml_init() function. This way
|
||||
// the user can avoid the memory allocation overhead at runtime.
|
||||
//
|
||||
// The library supports multi-dimensional tensors - up to 4 dimensions. The FP16 and FP32 data types are first class
|
||||
// citizens, but in theory the library can be extended to support FP8 and integer data types.
|
||||
//
|
||||
// Each tensor operation produces a new tensor. Initially the library was envisioned to support only the use of unary
|
||||
// and binary operations. Most of the available operations fall into one of these two categories. With time, it became
|
||||
// clear that the library needs to support more complex operations. The way to support these operations is not clear
|
||||
// yet, but a few examples are demonstrated in the following operations:
|
||||
//
|
||||
// - ggml_permute()
|
||||
// - ggml_conv_1d_1s()
|
||||
// - ggml_conv_1d_2s()
|
||||
//
|
||||
// For each tensor operator, the library implements a forward and backward computation function. The forward function
|
||||
// computes the output tensor value given the input tensor values. The backward function computes the adjoint of the
|
||||
// input tensors given the adjoint of the output tensor. For a detailed explanation of what this means, take a
|
||||
// calculus class, or watch the following video:
|
||||
//
|
||||
// What is Automatic Differentiation?
|
||||
// https://www.youtube.com/watch?v=wG_nF1awSSY
|
||||
//
|
||||
//
|
||||
// ## Tensor data (struct ggml_tensor)
|
||||
//
|
||||
// The tensors are stored in memory via the ggml_tensor struct. The structure provides information about the size of
|
||||
// the tensor, the data type, and the memory buffer where the tensor data is stored. Additionally, it contains
|
||||
// pointers to the "source" tensors - i.e. the tensors that were used to compute the current tensor. For example:
|
||||
//
|
||||
// {
|
||||
// struct ggml_tensor * c = ggml_add(ctx, a, b);
|
||||
//
|
||||
// assert(c->src[0] == a);
|
||||
// assert(c->src[1] == b);
|
||||
// }
|
||||
//
|
||||
// The multi-dimensional tensors are stored in row-major order. The ggml_tensor struct contains fields for the
|
||||
// number of elements in each dimension ("ne") as well as the number of bytes ("nb", a.k.a. stride). This allows
|
||||
// to store tensors that are not contiguous in memory, which is useful for operations such as transposition and
|
||||
// permutation. All tensor operations have to take the stride into account and not assume that the tensor is
|
||||
// contiguous in memory.
|
||||
//
|
||||
// The data of the tensor is accessed via the "data" pointer. For example:
|
||||
//
|
||||
// {
|
||||
// struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2, 3);
|
||||
//
|
||||
// // a[1, 2] = 1.0f;
|
||||
// *(float *) ((char *) a->data + 2*a->nb[1] + 1*a->nb[0]) = 1.0f;
|
||||
//
|
||||
// // a[2, 0] = 2.0f;
|
||||
// *(float *) ((char *) a->data + 0*a->nb[1] + 2*a->nb[0]) = 2.0f;
|
||||
//
|
||||
// ...
|
||||
// }
|
||||
//
|
||||
// Alternatively, there are helper functions, such as ggml_get_f32_1d() and ggml_set_f32_1d() that can be used.
|
||||
//
|
||||
// ## The matrix multiplication operator (ggml_mul_mat)
|
||||
//
|
||||
// TODO
|
||||
//
|
||||
//
|
||||
// ## Multi-threading
|
||||
//
|
||||
// TODO
|
||||
//
|
||||
//
|
||||
// ## Overview of ggml.c
|
||||
//
|
||||
// TODO
|
||||
//
|
||||
//
|
||||
// ## SIMD optimizations
|
||||
//
|
||||
// TODO
|
||||
//
|
||||
//
|
||||
// ## Debugging ggml
|
||||
//
|
||||
// TODO
|
||||
//
|
||||
// ## Adding new operators
|
||||
//
|
||||
// Suppose you want to add e^x unary operator. Following steps need to be done:
|
||||
//
|
||||
// In `ggml.h`:
|
||||
//
|
||||
// 1. Add member `GGML_OP_EXP` to `ggml_op` enum.
|
||||
// 2. Declare the operator function: `struct ggml_tensor * ggml_exp(struct ggml_context * ctx, struct ggml_tensor * x);`.
|
||||
//
|
||||
// In `ggml.c`:
|
||||
//
|
||||
// 1. Implement `ggml_exp` function: it will create result tensor and set its' operator and arguments.
|
||||
// 2. Create forward computation function for FP32: `ggml_compute_forward_exp_f32`: it will do the actual computation.
|
||||
// 3. If needed, create forward computation functions for other types: FP16, INT32, etc.
|
||||
// 4. Create forward dispatch function `ggml_compute_forward_exp`: it would dispatch the call based on tensor data type.
|
||||
// 5. Add `case GGML_OP_EXP`:
|
||||
// - to `ggml_compute_forward` and call the forward dispatch function here.
|
||||
// - to `ggml_compute_backward` and add `GGML_ASSERT(false)` here.
|
||||
// - to `ggml_graph_compute` and add `node->n_tasks = 1` here.
|
||||
// 6. Add operator label to `GGML_OP_LABEL` array and operator symbol to `GGML_OP_SYMBOL` array.
|
||||
// 7. Fix all assertions that check value of `GGML_OP_COUNT`: you've added 1 operator, so increment asserted value by one.
|
||||
//
|
||||
// When in doubt, consult the code of existing operators similar to that you're implementing.
|
||||
// Resulting operator would work for the forward pass, but will lack backward implementation and multi-threading support.
|
||||
//
|
||||
// TODO Implementing backward pass
|
||||
// TODO Implementing multi-threading
|
||||
//
|
||||
|
||||
#ifdef __cplusplus
|
||||
extern "C" {
|
||||
#endif
|
||||
|
||||
#include <stdint.h>
|
||||
#include <stddef.h>
|
||||
#include <stdbool.h>
|
||||
|
||||
#define GGML_MAX_DIMS 4
|
||||
#define GGML_MAX_NODES 4096
|
||||
#define GGML_MAX_PARAMS 16
|
||||
#define GGML_MAX_CONTEXTS 64
|
||||
#define GGML_MAX_OPT 4
|
||||
|
||||
#ifdef __ARM_NEON
|
||||
// we use the built-in 16-bit float type
|
||||
typedef __fp16 ggml_fp16_t;
|
||||
#else
|
||||
typedef uint16_t ggml_fp16_t;
|
||||
#endif
|
||||
|
||||
// convert FP16 <-> FP32
|
||||
float ggml_fp16_to_fp32(ggml_fp16_t x);
|
||||
ggml_fp16_t ggml_fp32_to_fp16(float x);
|
||||
|
||||
struct ggml_object;
|
||||
struct ggml_context;
|
||||
|
||||
enum ggml_type {
|
||||
GGML_TYPE_Q4_0,
|
||||
// Stores min and delta per block, does quantized matmul.
|
||||
GGML_TYPE_Q4_1,
|
||||
// Same as Q4_1, but stores outliers separately, and matmul is done in FP32.
|
||||
// An outlier is the single absmax element in the quantized block.
|
||||
GGML_TYPE_Q4_1_O,
|
||||
GGML_TYPE_I8,
|
||||
GGML_TYPE_I16,
|
||||
GGML_TYPE_I32,
|
||||
GGML_TYPE_F16,
|
||||
GGML_TYPE_F32,
|
||||
GGML_TYPE_COUNT,
|
||||
};
|
||||
|
||||
// available tensor operations:
|
||||
enum ggml_op {
|
||||
GGML_OP_NONE = 0,
|
||||
|
||||
GGML_OP_DUP,
|
||||
GGML_OP_ADD,
|
||||
GGML_OP_SUB,
|
||||
GGML_OP_MUL,
|
||||
GGML_OP_DIV,
|
||||
GGML_OP_SQR,
|
||||
GGML_OP_SQRT,
|
||||
GGML_OP_SUM,
|
||||
GGML_OP_MEAN,
|
||||
GGML_OP_REPEAT,
|
||||
GGML_OP_ABS,
|
||||
GGML_OP_SGN,
|
||||
GGML_OP_NEG,
|
||||
// Element-wise exponential function `e^x`.
|
||||
// Same as `torch.exp(x)` from PyTorch.
|
||||
GGML_OP_EXP,
|
||||
// Element-wise `1 - x`.
|
||||
GGML_OP_1_MINUS_X,
|
||||
|
||||
// Element-wise maximum of 2 values. Argument shapes must match.
|
||||
// Same as `torch.maximum(x)` from PyTorch.
|
||||
GGML_OP_MAX,
|
||||
|
||||
GGML_OP_STEP,
|
||||
GGML_OP_RELU,
|
||||
GGML_OP_GELU,
|
||||
// Element-wise sigmoid activation `1 / (1 + e^-x)`, also called logistic function.
|
||||
// Same as `torch.sigmoid(x)` from PyTorch.
|
||||
GGML_OP_SIGMOID,
|
||||
GGML_OP_SILU,
|
||||
GGML_OP_NORM, // normalize
|
||||
GGML_OP_RMS_NORM,
|
||||
|
||||
GGML_OP_MUL_MAT,
|
||||
|
||||
GGML_OP_SCALE,
|
||||
GGML_OP_CPY,
|
||||
GGML_OP_RESHAPE,
|
||||
GGML_OP_VIEW,
|
||||
GGML_OP_PERMUTE,
|
||||
GGML_OP_TRANSPOSE,
|
||||
GGML_OP_GET_ROWS,
|
||||
GGML_OP_DIAG_MASK_INF,
|
||||
GGML_OP_SOFT_MAX,
|
||||
GGML_OP_ROPE,
|
||||
GGML_OP_CONV_1D_1S,
|
||||
GGML_OP_CONV_1D_2S,
|
||||
|
||||
GGML_OP_FLASH_ATTN,
|
||||
GGML_OP_FLASH_FF,
|
||||
|
||||
GGML_OP_COUNT,
|
||||
};
|
||||
|
||||
// n-dimensional tensor
|
||||
struct ggml_tensor {
|
||||
enum ggml_type type;
|
||||
|
||||
int n_dims;
|
||||
int ne[GGML_MAX_DIMS]; // number of elements
|
||||
size_t nb[GGML_MAX_DIMS]; // stride in bytes:
|
||||
// nb[0] = sizeof(type)
|
||||
// nb[1] = nb[0] * ne[0] + padding
|
||||
// nb[i] = nb[i-1] * ne[i-1]
|
||||
|
||||
// compute data
|
||||
enum ggml_op op;
|
||||
|
||||
bool is_param;
|
||||
|
||||
struct ggml_tensor * grad;
|
||||
struct ggml_tensor * src0;
|
||||
struct ggml_tensor * src1;
|
||||
struct ggml_tensor * opt[GGML_MAX_OPT];
|
||||
|
||||
// thread scheduling
|
||||
int n_tasks;
|
||||
|
||||
// performance
|
||||
int perf_runs;
|
||||
int64_t perf_cycles;
|
||||
int64_t perf_time_us;
|
||||
|
||||
void * data;
|
||||
char padding[8];
|
||||
};
|
||||
|
||||
// computation graph
|
||||
struct ggml_cgraph {
|
||||
int n_nodes;
|
||||
int n_leafs;
|
||||
int n_threads;
|
||||
|
||||
size_t work_size;
|
||||
struct ggml_tensor * work;
|
||||
|
||||
struct ggml_tensor * nodes[GGML_MAX_NODES];
|
||||
struct ggml_tensor * grads[GGML_MAX_NODES];
|
||||
struct ggml_tensor * leafs[GGML_MAX_NODES];
|
||||
|
||||
// performance
|
||||
int perf_runs;
|
||||
int64_t perf_cycles;
|
||||
int64_t perf_time_us;
|
||||
};
|
||||
|
||||
// scratch buffer
|
||||
struct ggml_scratch {
|
||||
size_t offs;
|
||||
size_t size;
|
||||
void * data;
|
||||
};
|
||||
|
||||
struct ggml_init_params {
|
||||
// memory pool
|
||||
size_t mem_size; // bytes
|
||||
void * mem_buffer; // if NULL, memory will be allocated internally
|
||||
};
|
||||
|
||||
void ggml_time_init(void); // call this once at the beginning of the program
|
||||
int64_t ggml_time_ms(void);
|
||||
int64_t ggml_time_us(void);
|
||||
int64_t ggml_cycles(void);
|
||||
int64_t ggml_cycles_per_ms(void);
|
||||
|
||||
void ggml_print_object (const struct ggml_object * obj);
|
||||
void ggml_print_objects(const struct ggml_context * ctx);
|
||||
|
||||
int ggml_nelements(const struct ggml_tensor * tensor);
|
||||
size_t ggml_nbytes (const struct ggml_tensor * tensor);
|
||||
|
||||
int ggml_blck_size (enum ggml_type type);
|
||||
size_t ggml_type_size (enum ggml_type type); // size in bytes for all elements in a block
|
||||
float ggml_type_sizef(enum ggml_type type); // ggml_type_size()/ggml_blck_size() as float
|
||||
|
||||
size_t ggml_element_size(const struct ggml_tensor * tensor);
|
||||
|
||||
struct ggml_context * ggml_init(struct ggml_init_params params);
|
||||
void ggml_free(struct ggml_context * ctx);
|
||||
|
||||
size_t ggml_used_mem(const struct ggml_context * ctx);
|
||||
|
||||
size_t ggml_set_scratch(struct ggml_context * ctx, struct ggml_scratch scratch);
|
||||
|
||||
bool ggml_mlock_supported(void);
|
||||
bool ggml_mlock(struct ggml_context * ctx, char ** err_p);
|
||||
|
||||
struct ggml_tensor * ggml_new_tensor(
|
||||
struct ggml_context * ctx,
|
||||
enum ggml_type type,
|
||||
int n_dims,
|
||||
const int *ne);
|
||||
|
||||
struct ggml_tensor * ggml_new_tensor_1d(
|
||||
struct ggml_context * ctx,
|
||||
enum ggml_type type,
|
||||
int ne0);
|
||||
|
||||
struct ggml_tensor * ggml_new_tensor_2d(
|
||||
struct ggml_context * ctx,
|
||||
enum ggml_type type,
|
||||
int ne0,
|
||||
int ne1);
|
||||
|
||||
struct ggml_tensor * ggml_new_tensor_3d(
|
||||
struct ggml_context * ctx,
|
||||
enum ggml_type type,
|
||||
int ne0,
|
||||
int ne1,
|
||||
int ne2);
|
||||
|
||||
struct ggml_tensor * ggml_new_tensor_4d(
|
||||
struct ggml_context * ctx,
|
||||
enum ggml_type type,
|
||||
int ne0,
|
||||
int ne1,
|
||||
int ne2,
|
||||
int ne3);
|
||||
|
||||
struct ggml_tensor * ggml_new_i32(struct ggml_context * ctx, int32_t value);
|
||||
struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value);
|
||||
|
||||
struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src);
|
||||
struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src);
|
||||
|
||||
struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor);
|
||||
struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value);
|
||||
struct ggml_tensor * ggml_set_f32 (struct ggml_tensor * tensor, float value);
|
||||
|
||||
int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i);
|
||||
void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value);
|
||||
|
||||
float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i);
|
||||
void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value);
|
||||
|
||||
void * ggml_get_data (const struct ggml_tensor * tensor);
|
||||
float * ggml_get_data_f32(const struct ggml_tensor * tensor);
|
||||
|
||||
//
|
||||
// operations on tensors with backpropagation
|
||||
//
|
||||
|
||||
struct ggml_tensor * ggml_dup(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_add(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
struct ggml_tensor * ggml_sub(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
struct ggml_tensor * ggml_mul(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
struct ggml_tensor * ggml_div(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
struct ggml_tensor * ggml_sqr(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_sqrt(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// return scalar
|
||||
// TODO: compute sum along rows
|
||||
struct ggml_tensor * ggml_sum(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// mean along rows
|
||||
struct ggml_tensor * ggml_mean(
|
||||
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
|
||||
struct ggml_tensor * ggml_repeat(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
struct ggml_tensor * ggml_abs(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_sgn(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_neg(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_exp(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_1_minus_x(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_max(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
struct ggml_tensor * ggml_step(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_relu(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// TODO: double-check this computation is correct
|
||||
struct ggml_tensor * ggml_gelu(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_sigmoid(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_silu(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// normalize along rows
|
||||
// TODO: eps is hardcoded to 1e-5 for now
|
||||
struct ggml_tensor * ggml_norm(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_rms_norm(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// A: m rows, n columns
|
||||
// B: p rows, n columns (i.e. we transpose it internally)
|
||||
// result is m columns, p rows
|
||||
struct ggml_tensor * ggml_mul_mat(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
//
|
||||
// operations on tensors without backpropagation
|
||||
//
|
||||
|
||||
// in-place, returns view(a)
|
||||
struct ggml_tensor * ggml_scale(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// a -> b, return view(b)
|
||||
struct ggml_tensor * ggml_cpy(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// return view(a), b specifies the new shape
|
||||
// TODO: when we start computing gradient, make a copy instead of view
|
||||
struct ggml_tensor * ggml_reshape(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// return view(a)
|
||||
// TODO: when we start computing gradient, make a copy instead of view
|
||||
struct ggml_tensor * ggml_reshape_2d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int ne0,
|
||||
int ne1);
|
||||
|
||||
// return view(a)
|
||||
// TODO: when we start computing gradient, make a copy instead of view
|
||||
struct ggml_tensor * ggml_reshape_3d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int ne0,
|
||||
int ne1,
|
||||
int ne2);
|
||||
|
||||
// offset in bytes
|
||||
struct ggml_tensor * ggml_view_1d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int ne0,
|
||||
size_t offset);
|
||||
|
||||
struct ggml_tensor * ggml_view_2d(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int ne0,
|
||||
int ne1,
|
||||
size_t nb1, // row stride in bytes
|
||||
size_t offset);
|
||||
|
||||
struct ggml_tensor * ggml_permute(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int axis0,
|
||||
int axis1,
|
||||
int axis2,
|
||||
int axis3);
|
||||
|
||||
// alias for ggml_permute(ctx, a, 1, 0, 2, 3)
|
||||
struct ggml_tensor * ggml_transpose(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
struct ggml_tensor * ggml_get_rows(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
// set elements above the diagonal to -INF
|
||||
// in-place, returns view(a)
|
||||
struct ggml_tensor * ggml_diag_mask_inf(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_past);
|
||||
|
||||
// in-place, returns view(a)
|
||||
struct ggml_tensor * ggml_soft_max(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a);
|
||||
|
||||
// rotary position embedding
|
||||
// in-place, returns view(a)
|
||||
// if mode == 1, skip n_past elements
|
||||
// TODO: avoid creating a new tensor every time
|
||||
struct ggml_tensor * ggml_rope(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
int n_past,
|
||||
int n_dims,
|
||||
int mode);
|
||||
|
||||
// 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 ..
|
||||
struct ggml_tensor * ggml_conv_1d_1s(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
struct ggml_tensor * ggml_conv_1d_2s(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b);
|
||||
|
||||
struct ggml_tensor * ggml_flash_attn(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * q,
|
||||
struct ggml_tensor * k,
|
||||
struct ggml_tensor * v,
|
||||
bool masked);
|
||||
|
||||
struct ggml_tensor * ggml_flash_ff(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * a,
|
||||
struct ggml_tensor * b0,
|
||||
struct ggml_tensor * b1,
|
||||
struct ggml_tensor * c0,
|
||||
struct ggml_tensor * c1);
|
||||
|
||||
//
|
||||
// automatic differentiation
|
||||
//
|
||||
|
||||
void ggml_set_param(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_tensor * tensor);
|
||||
|
||||
void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor);
|
||||
|
||||
struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor);
|
||||
struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep);
|
||||
|
||||
void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph);
|
||||
void ggml_graph_reset (struct ggml_cgraph * cgraph);
|
||||
|
||||
// print info and performance information for the graph
|
||||
void ggml_graph_print(const struct ggml_cgraph * cgraph);
|
||||
|
||||
// dump the graph into a file using the dot format
|
||||
void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename);
|
||||
|
||||
//
|
||||
// optimization
|
||||
//
|
||||
|
||||
// optimization methods
|
||||
enum ggml_opt_type {
|
||||
GGML_OPT_ADAM,
|
||||
GGML_OPT_LBFGS,
|
||||
};
|
||||
|
||||
// linesearch methods
|
||||
enum ggml_linesearch {
|
||||
GGML_LINESEARCH_DEFAULT = 1,
|
||||
|
||||
GGML_LINESEARCH_BACKTRACKING_ARMIJO = 0,
|
||||
GGML_LINESEARCH_BACKTRACKING_WOLFE = 1,
|
||||
GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE = 2,
|
||||
};
|
||||
|
||||
// optimization return values
|
||||
enum ggml_opt_result {
|
||||
GGML_OPT_OK = 0,
|
||||
GGML_OPT_DID_NOT_CONVERGE,
|
||||
GGML_OPT_NO_CONTEXT,
|
||||
GGML_OPT_INVALID_WOLFE,
|
||||
GGML_OPT_FAIL,
|
||||
|
||||
GGML_LINESEARCH_FAIL = -128,
|
||||
GGML_LINESEARCH_MINIMUM_STEP,
|
||||
GGML_LINESEARCH_MAXIMUM_STEP,
|
||||
GGML_LINESEARCH_MAXIMUM_ITERATIONS,
|
||||
GGML_LINESEARCH_INVALID_PARAMETERS,
|
||||
};
|
||||
|
||||
// optimization parameters
|
||||
//
|
||||
// see ggml.c (ggml_opt_default_params) for default values
|
||||
//
|
||||
struct ggml_opt_params {
|
||||
enum ggml_opt_type type;
|
||||
|
||||
int n_threads;
|
||||
|
||||
// delta-based convergence test
|
||||
//
|
||||
// if past == 0 - disabled
|
||||
// if past > 0:
|
||||
// stop if |f(x) - f(x_past)| < delta * max(1, |f(x)|)
|
||||
//
|
||||
int past;
|
||||
float delta;
|
||||
|
||||
// maximum number of iterations without improvement
|
||||
//
|
||||
// if 0 - disabled
|
||||
// if > 0:
|
||||
// assume convergence if no cost improvement in this number of iterations
|
||||
//
|
||||
int max_no_improvement;
|
||||
|
||||
bool print_forward_graph;
|
||||
bool print_backward_graph;
|
||||
|
||||
// ADAM parameters
|
||||
struct {
|
||||
int n_iter;
|
||||
|
||||
float alpha; // learning rate
|
||||
float beta1;
|
||||
float beta2;
|
||||
float eps; // epsilon for numerical stability
|
||||
float eps_f; // epsilon for convergence test
|
||||
float eps_g; // epsilon for convergence test
|
||||
} adam;
|
||||
|
||||
// LBFGS parameters
|
||||
struct {
|
||||
int m; // number of corrections to approximate the inv. Hessian
|
||||
int n_iter;
|
||||
int max_linesearch;
|
||||
|
||||
float eps; // convergence tolerance
|
||||
float ftol; // line search tolerance
|
||||
float wolfe;
|
||||
float min_step;
|
||||
float max_step;
|
||||
|
||||
enum ggml_linesearch linesearch;
|
||||
} lbfgs;
|
||||
};
|
||||
|
||||
struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type);
|
||||
|
||||
// optimize the function defined by the tensor f
|
||||
enum ggml_opt_result ggml_opt(
|
||||
struct ggml_context * ctx,
|
||||
struct ggml_opt_params params,
|
||||
struct ggml_tensor * f);
|
||||
|
||||
//
|
||||
// quantization
|
||||
//
|
||||
|
||||
size_t ggml_quantize_q4_0(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
size_t ggml_quantize_q4_1_o(const float * src, void * dst, int n, int k, int64_t * hist);
|
||||
|
||||
//
|
||||
// system info
|
||||
//
|
||||
|
||||
int ggml_cpu_has_avx(void);
|
||||
int ggml_cpu_has_avx2(void);
|
||||
int ggml_cpu_has_avx512(void);
|
||||
int ggml_cpu_has_fma(void);
|
||||
int ggml_cpu_has_neon(void);
|
||||
int ggml_cpu_has_arm_fma(void);
|
||||
int ggml_cpu_has_f16c(void);
|
||||
int ggml_cpu_has_fp16_va(void);
|
||||
int ggml_cpu_has_wasm_simd(void);
|
||||
int ggml_cpu_has_blas(void);
|
||||
int ggml_cpu_has_sse3(void);
|
||||
int ggml_cpu_has_vsx(void);
|
||||
|
||||
// Run test suite for ggml.
|
||||
// Exits normally, if all tests pass.
|
||||
// Aborts the execution if any test did not pass.
|
||||
void ggml_run_test_suite();
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
79
rwkv.cpp
79
rwkv.cpp
|
|
@ -118,6 +118,46 @@ bool set_block_parameter(std::unordered_map<std::string, struct ggml_tensor *> *
|
|||
|
||||
// --- Operators ---
|
||||
|
||||
void rwkv_exp_impl(const int n_cols, float * dest, const float * src) {
|
||||
for (int i = 0; i < n_cols; i++) {
|
||||
dest[i] = expf(src[i]);
|
||||
}
|
||||
}
|
||||
|
||||
void rwkv_1_minus_x_impl(const int n_cols, float * dest, const float * src) {
|
||||
for (int i = 0; i < n_cols; i++) {
|
||||
dest[i] = 1.0F - src[i];
|
||||
}
|
||||
}
|
||||
|
||||
void rwkv_sigmoid_impl(const int n_cols, float * dest, const float * src) {
|
||||
for (int i = 0; i < n_cols; i++) {
|
||||
dest[i] = 1.0F / (1.0F + expf(-src[i]));
|
||||
}
|
||||
}
|
||||
|
||||
void rwkv_max_impl(const int n_cols, float * dest, const float * src0, const float * src1) {
|
||||
for (int i = 0; i < n_cols; i++) {
|
||||
dest[i] = fmaxf(src0[i], src1[i]);
|
||||
}
|
||||
}
|
||||
|
||||
struct ggml_tensor * rwkv_exp(ggml_context * ctx, struct ggml_tensor * x) {
|
||||
return ggml_map_unary_f32(ctx, x, rwkv_exp_impl);
|
||||
}
|
||||
|
||||
struct ggml_tensor * rwkv_1_minus_x(ggml_context * ctx, struct ggml_tensor * x) {
|
||||
return ggml_map_unary_f32(ctx, x, rwkv_1_minus_x_impl);
|
||||
}
|
||||
|
||||
struct ggml_tensor * rwkv_sigmoid(ggml_context * ctx, struct ggml_tensor * x) {
|
||||
return ggml_map_unary_f32(ctx, x, rwkv_sigmoid_impl);
|
||||
}
|
||||
|
||||
struct ggml_tensor * rwkv_max(ggml_context * ctx, struct ggml_tensor * x, struct ggml_tensor * y) {
|
||||
return ggml_map_binary_f32(ctx, x, y, rwkv_max_impl);
|
||||
}
|
||||
|
||||
struct ggml_tensor * rwkv_layer_norm(ggml_context * ctx, struct ggml_tensor * x, struct ggml_tensor * weight, struct ggml_tensor * bias) {
|
||||
// LayerNorm in RWKV is `x = (x - mean(x)) / sqrt(variance(x) + 1e-5) * weight + bias`
|
||||
// Looks like ggml_norm does the first part, we only need to apply weight & bias.
|
||||
|
|
@ -302,8 +342,8 @@ struct rwkv_context * rwkv_init_from_file(const char * file_path, uint32_t n_thr
|
|||
// Verify order of dimensions
|
||||
struct ggml_tensor * emb = model->emb;
|
||||
RWKV_ASSERT_NULL(emb->n_dims == 2, "Unexpected dimension count of embedding matrix %d", emb->n_dims);
|
||||
RWKV_ASSERT_NULL(emb->ne[0] == model->n_embed, "Unexpected dimension of embedding matrix %d", emb->ne[0]);
|
||||
RWKV_ASSERT_NULL(emb->ne[1] == model->n_vocab, "Unexpected dimension of embedding matrix %d", emb->ne[1]);
|
||||
RWKV_ASSERT_NULL(emb->ne[0] == model->n_embed, "Unexpected dimension of embedding matrix %lld", emb->ne[0]);
|
||||
RWKV_ASSERT_NULL(emb->ne[1] == model->n_vocab, "Unexpected dimension of embedding matrix %lld", emb->ne[1]);
|
||||
|
||||
int32_t n_embed = model->n_embed;
|
||||
int32_t n_layer = model->n_layer;
|
||||
|
|
@ -336,23 +376,23 @@ struct rwkv_context * rwkv_init_from_file(const char * file_path, uint32_t n_thr
|
|||
struct ggml_tensor * xk = ggml_add(
|
||||
ctx,
|
||||
ggml_mul(ctx, x0, layer.att_time_mix_k),
|
||||
ggml_mul(ctx, x_prev, ggml_1_minus_x(ctx, layer.att_time_mix_k))
|
||||
ggml_mul(ctx, x_prev, rwkv_1_minus_x(ctx, layer.att_time_mix_k))
|
||||
);
|
||||
struct ggml_tensor * xv = ggml_add(
|
||||
ctx,
|
||||
ggml_mul(ctx, x0, layer.att_time_mix_v),
|
||||
ggml_mul(ctx, x_prev, ggml_1_minus_x(ctx, layer.att_time_mix_v))
|
||||
ggml_mul(ctx, x_prev, rwkv_1_minus_x(ctx, layer.att_time_mix_v))
|
||||
);
|
||||
struct ggml_tensor * xr = ggml_add(
|
||||
ctx,
|
||||
ggml_mul(ctx, x0, layer.att_time_mix_r),
|
||||
ggml_mul(ctx, x_prev, ggml_1_minus_x(ctx, layer.att_time_mix_r))
|
||||
ggml_mul(ctx, x_prev, rwkv_1_minus_x(ctx, layer.att_time_mix_r))
|
||||
);
|
||||
// state[5 * i + 1] = x
|
||||
state_parts[5 * i + 1] = x0;
|
||||
|
||||
// r = torch.sigmoid(rw @ xr)
|
||||
struct ggml_tensor * r = ggml_sigmoid(
|
||||
struct ggml_tensor * r = rwkv_sigmoid(
|
||||
ctx,
|
||||
ggml_mul_mat(ctx, layer.att_receptance, xr)
|
||||
);
|
||||
|
|
@ -371,11 +411,11 @@ struct rwkv_context * rwkv_init_from_file(const char * file_path, uint32_t n_thr
|
|||
// ww = time_first + k
|
||||
struct ggml_tensor * ww = ggml_add(ctx, layer.att_time_first, k);
|
||||
// qq = torch.maximum(pp, ww)
|
||||
struct ggml_tensor * qq = ggml_max(ctx, pp, ww);
|
||||
struct ggml_tensor * qq = rwkv_max(ctx, pp, ww);
|
||||
// e1 = torch.exp(pp - qq)
|
||||
struct ggml_tensor * e1 = ggml_exp(ctx, ggml_sub(ctx, pp, qq));
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||||
struct ggml_tensor * e1 = rwkv_exp(ctx, ggml_sub(ctx, pp, qq));
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||||
// e2 = torch.exp(ww - qq)
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||||
struct ggml_tensor * e2 = ggml_exp(ctx, ggml_sub(ctx, ww, qq));
|
||||
struct ggml_tensor * e2 = rwkv_exp(ctx, ggml_sub(ctx, ww, qq));
|
||||
// a = e1 * aa + e2 * v
|
||||
struct ggml_tensor * a = ggml_add(
|
||||
ctx,
|
||||
|
|
@ -393,11 +433,11 @@ struct rwkv_context * rwkv_init_from_file(const char * file_path, uint32_t n_thr
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|||
// ww = pp + time_decay
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||||
ww = ggml_add(ctx, pp, layer.att_time_decay);
|
||||
// qq = torch.maximum(ww, k)
|
||||
qq = ggml_max(ctx, ww, k);
|
||||
qq = rwkv_max(ctx, ww, k);
|
||||
// e1 = torch.exp(ww - qq)
|
||||
e1 = ggml_exp(ctx, ggml_sub(ctx, ww, qq));
|
||||
e1 = rwkv_exp(ctx, ggml_sub(ctx, ww, qq));
|
||||
// e2 = torch.exp(k - qq)
|
||||
e2 = ggml_exp(ctx, ggml_sub(ctx, k, qq));
|
||||
e2 = rwkv_exp(ctx, ggml_sub(ctx, k, qq));
|
||||
// state[5 * i + 2] = e1 * aa + e2 * v
|
||||
state_parts[5 * i + 2] = ggml_add(
|
||||
ctx,
|
||||
|
|
@ -435,18 +475,18 @@ struct rwkv_context * rwkv_init_from_file(const char * file_path, uint32_t n_thr
|
|||
struct ggml_tensor * xk = ggml_add(
|
||||
ctx,
|
||||
ggml_mul(ctx, x0, layer.ffn_time_mix_k),
|
||||
ggml_mul(ctx, x_prev, ggml_1_minus_x(ctx, layer.ffn_time_mix_k))
|
||||
ggml_mul(ctx, x_prev, rwkv_1_minus_x(ctx, layer.ffn_time_mix_k))
|
||||
);
|
||||
struct ggml_tensor * xr = ggml_add(
|
||||
ctx,
|
||||
ggml_mul(ctx, x0, layer.ffn_time_mix_r),
|
||||
ggml_mul(ctx, x_prev, ggml_1_minus_x(ctx, layer.ffn_time_mix_r))
|
||||
ggml_mul(ctx, x_prev, rwkv_1_minus_x(ctx, layer.ffn_time_mix_r))
|
||||
);
|
||||
// state[5 * i + 0] = x
|
||||
state_parts[5 * i + 0] = x0;
|
||||
|
||||
// r = torch.sigmoid(rw @ xr)
|
||||
struct ggml_tensor * r = ggml_sigmoid(
|
||||
struct ggml_tensor * r = rwkv_sigmoid(
|
||||
ctx,
|
||||
ggml_mul_mat(ctx, layer.ffn_receptance, xr)
|
||||
);
|
||||
|
|
@ -557,6 +597,13 @@ void rwkv_free(struct rwkv_context * ctx) {
|
|||
bool rwkv_quantize_model_file(const char * model_file_path_in, const char * model_file_path_out, uint32_t q_type) {
|
||||
RWKV_ASSERT_FALSE(q_type == 2 || q_type == 3 || q_type == 4, "Unsupported quantization type %d", q_type);
|
||||
|
||||
// Needed to initialize FP16 lookup table
|
||||
{
|
||||
struct ggml_init_params params = { 0, NULL };
|
||||
struct ggml_context * ctx = ggml_init(params);
|
||||
ggml_free(ctx);
|
||||
}
|
||||
|
||||
ggml_type type = FORMAT_TYPE_TO_GGML_TYPE[q_type];
|
||||
|
||||
printf("Loading model from '%s'\n", model_file_path_in);
|
||||
|
|
@ -742,7 +789,7 @@ bool rwkv_quantize_model_file(const char * model_file_path_in, const char * mode
|
|||
|
||||
printf("original size = %8.2f MB\n", total_size_orig / 1024.0 / 1024.0);
|
||||
printf("quantized size = %8.2f MB\n", total_size_new / 1024.0 / 1024.0);
|
||||
printf("compression ratio = %8.2f%\n", 1.0 * total_size_orig / total_size_new);
|
||||
printf("compression ratio = %8.2f%%\n", 1.0 * total_size_orig / total_size_new);
|
||||
|
||||
{
|
||||
int64_t sum_all = 0;
|
||||
|
|
|
|||
Loading…
Reference in New Issue