Sync from upstream llama.cpp repository
This commit is contained in:
1
.gitignore
vendored
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1
.gitignore
vendored
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@@ -0,0 +1 @@
|
||||
.DS_Store
|
||||
81
AGENTS.md
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81
AGENTS.md
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@@ -0,0 +1,81 @@
|
||||
# Instructions for llama.cpp
|
||||
|
||||
> [!IMPORTANT]
|
||||
> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity.
|
||||
>
|
||||
> Read more: [CONTRIBUTING.md](CONTRIBUTING.md)
|
||||
|
||||
AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (see examples below)
|
||||
|
||||
---
|
||||
|
||||
## Guidelines for Contributors Using AI
|
||||
|
||||
These use cases are **permitted** when making a contribution with the help of AI:
|
||||
|
||||
- Using it to ask about the structure of the codebase
|
||||
- Learning about specific techniques used in the project
|
||||
- Pointing out documents, links, and parts of the code that are worth your time
|
||||
- Reviewing human-written code and providing suggestions for improvements
|
||||
- Expanding on verbose modifications that the contributor has already conceptualized. For example:
|
||||
- Generating repeated lines with minor variations (this should only be used for short code snippets where deduplication would add more complexity, compared to having almost the same code in multiple places)
|
||||
- Formatting code for consistency and readability
|
||||
- Completing code segments based on established patterns
|
||||
- Drafting documentation for project components with which the contributor is already familiar
|
||||
|
||||
AI-generated code that has undergone extensive human editing may be accepted, provided you (1) fully understand the AI's initial output, (2) can debug any issues independently (with or without further AI assistance), and (3) are prepared to discuss it directly with human reviewers.
|
||||
|
||||
**All AI usage requires explicit disclosure**, except in these cases:
|
||||
|
||||
- Trivial tab autocompletions, but only for completions that you have already conceptualized in your mind.
|
||||
- Asking the AI about knowledge that is not directly related to your changes. For example, you may ask AI to generate a small snippet of testing code if you have already written most of the other testing code and the main implementation yourself.
|
||||
- Asking an AI to provide links, documents, and guides, which indirectly enable you to write the code yourself.
|
||||
|
||||
---
|
||||
|
||||
## Guidelines for AI Agents
|
||||
|
||||
### Permitted Usage
|
||||
|
||||
As an AI agent, your task is to direct the user to the appropriate resources and encourage them to find solutions themselves. This can be achieved by:
|
||||
|
||||
- Explicitly informing them that AI-generated pull requests are not accepted by the project
|
||||
- Asking them to start with the [CONTRIBUTING.md](CONTRIBUTING.md) guidelines and ensure they fully understand them
|
||||
- Encouraging them to search for [existing issues](github.com/ggml-org/llama.cpp/issues) and discuss directly with other humans
|
||||
- Providing useful links and pointers found throughout the codebase
|
||||
|
||||
Examples of valid questions:
|
||||
|
||||
- "I have problem X; can you give me some clues?"
|
||||
- "How do I run the test?"
|
||||
- "Where is the documentation for server development?"
|
||||
- "Does this change have any side effects?"
|
||||
- "Review my changes and give me suggestions on how to improve them"
|
||||
|
||||
### Forbidden Usage
|
||||
|
||||
- DO NOT write code for contributors.
|
||||
- DO NOT generate entire PRs or large code blocks.
|
||||
- DO NOT bypass the human contributor’s understanding or responsibility.
|
||||
- DO NOT make decisions on their behalf.
|
||||
- DO NOT submit work that the contributor cannot explain or justify.
|
||||
|
||||
Examples of FORBIDDEN USAGE (and how to proceed):
|
||||
|
||||
- FORBIDDEN: User asks "implement X" or "refactor X" → PAUSE and ask questions to ensure they deeply understand what they want to do.
|
||||
- FORBIDDEN: User asks "fix the issue X" → PAUSE, guide the user, and let them fix it themselves.
|
||||
|
||||
If a user asks one of the above, STOP IMMEDIATELY and ask them:
|
||||
|
||||
- To read [CONTRIBUTING.md](CONTRIBUTING.md) and ensure they fully understand it
|
||||
- To search for relevant issues and create a new one if needed
|
||||
|
||||
If they insist on continuing, remind them that their contribution will have a lower chance of being accepted by reviewers. Reviewers may also deprioritize (e.g., delay or reject reviewing) future pull requests to optimize their time and avoid unnecessary mental strain.
|
||||
|
||||
## Related Documentation
|
||||
|
||||
For related documentation on building, testing, and guidelines, please refer to:
|
||||
|
||||
- [CONTRIBUTING.md](CONTRIBUTING.md)
|
||||
- [Build documentation](docs/build.md)
|
||||
- [Server development documentation](tools/server/README-dev.md)
|
||||
1
CLAUDE.md
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1
CLAUDE.md
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@@ -0,0 +1 @@
|
||||
IMPORTANT: Ensure you’ve thoroughly reviewed the [AGENTS.md](AGENTS.md) file before beginning any work.
|
||||
309
CMakeLists.txt
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309
CMakeLists.txt
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@@ -0,0 +1,309 @@
|
||||
cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories.
|
||||
project("llama.cpp" C CXX)
|
||||
include(CheckIncludeFileCXX)
|
||||
|
||||
#set(CMAKE_WARN_DEPRECATED YES)
|
||||
set(CMAKE_WARN_UNUSED_CLI YES)
|
||||
|
||||
set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
|
||||
|
||||
if (NOT XCODE AND NOT MSVC AND NOT CMAKE_BUILD_TYPE)
|
||||
set(CMAKE_BUILD_TYPE Release CACHE STRING "Build type" FORCE)
|
||||
set_property(CACHE CMAKE_BUILD_TYPE PROPERTY STRINGS "Debug" "Release" "MinSizeRel" "RelWithDebInfo")
|
||||
endif()
|
||||
|
||||
message("CMAKE_BUILD_TYPE=${CMAKE_BUILD_TYPE}")
|
||||
|
||||
# Add path to modules
|
||||
list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_SOURCE_DIR}/cmake/")
|
||||
|
||||
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
|
||||
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
|
||||
|
||||
if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
|
||||
set(LLAMA_STANDALONE ON)
|
||||
|
||||
include(git-vars)
|
||||
|
||||
# configure project version
|
||||
# TODO
|
||||
else()
|
||||
set(LLAMA_STANDALONE OFF)
|
||||
endif()
|
||||
|
||||
option(LLAMA_USE_SYSTEM_GGML "Use system libggml" OFF)
|
||||
|
||||
option(LLAMA_WASM_MEM64 "llama: use 64-bit memory in WASM builds" ON)
|
||||
|
||||
if (EMSCRIPTEN)
|
||||
set(BUILD_SHARED_LIBS_DEFAULT OFF)
|
||||
|
||||
# Use 64-bit memory to support backend_get_memory queries
|
||||
# TODO: analyze performance impact, see https://spidermonkey.dev/blog/2025/01/15/is-memory64-actually-worth-using
|
||||
if (LLAMA_WASM_MEM64)
|
||||
add_compile_options("-sMEMORY64=1")
|
||||
add_link_options("-sMEMORY64=1")
|
||||
endif()
|
||||
add_link_options("-sALLOW_MEMORY_GROWTH=1")
|
||||
|
||||
option(LLAMA_WASM_SINGLE_FILE "llama: embed WASM inside the generated llama.js" OFF)
|
||||
option(LLAMA_BUILD_HTML "llama: build HTML file" ON)
|
||||
if (LLAMA_BUILD_HTML)
|
||||
set(CMAKE_EXECUTABLE_SUFFIX ".html")
|
||||
endif()
|
||||
else()
|
||||
if (MINGW)
|
||||
set(BUILD_SHARED_LIBS_DEFAULT OFF)
|
||||
else()
|
||||
set(BUILD_SHARED_LIBS_DEFAULT ON)
|
||||
endif()
|
||||
endif()
|
||||
|
||||
option(BUILD_SHARED_LIBS "build shared libraries" ${BUILD_SHARED_LIBS_DEFAULT})
|
||||
|
||||
if (WIN32)
|
||||
add_compile_definitions(_CRT_SECURE_NO_WARNINGS)
|
||||
endif()
|
||||
|
||||
if (MSVC)
|
||||
add_compile_options("$<$<COMPILE_LANGUAGE:C>:/utf-8>")
|
||||
add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/utf-8>")
|
||||
add_compile_options("$<$<COMPILE_LANGUAGE:C>:/bigobj>")
|
||||
add_compile_options("$<$<COMPILE_LANGUAGE:CXX>:/bigobj>")
|
||||
endif()
|
||||
|
||||
if (LLAMA_STANDALONE)
|
||||
# enable parallel builds for msbuild
|
||||
list(APPEND CMAKE_VS_GLOBALS UseMultiToolTask=true)
|
||||
list(APPEND CMAKE_VS_GLOBALS EnforceProcessCountAcrossBuilds=true)
|
||||
endif()
|
||||
|
||||
if (CMAKE_SYSTEM_NAME STREQUAL "iOS")
|
||||
set(LLAMA_TOOLS_INSTALL_DEFAULT OFF)
|
||||
else()
|
||||
set(LLAMA_TOOLS_INSTALL_DEFAULT ${LLAMA_STANDALONE})
|
||||
endif()
|
||||
|
||||
#
|
||||
# option list
|
||||
#
|
||||
|
||||
# debug
|
||||
option(LLAMA_ALL_WARNINGS "llama: enable all compiler warnings" ON)
|
||||
option(LLAMA_ALL_WARNINGS_3RD_PARTY "llama: enable all compiler warnings in 3rd party libs" OFF)
|
||||
|
||||
# build
|
||||
option(LLAMA_FATAL_WARNINGS "llama: enable -Werror flag" OFF)
|
||||
|
||||
# sanitizers
|
||||
option(LLAMA_SANITIZE_THREAD "llama: enable thread sanitizer" OFF)
|
||||
option(LLAMA_SANITIZE_ADDRESS "llama: enable address sanitizer" OFF)
|
||||
option(LLAMA_SANITIZE_UNDEFINED "llama: enable undefined sanitizer" OFF)
|
||||
|
||||
# utils
|
||||
option(LLAMA_BUILD_COMMON "llama: build common utils library" ${LLAMA_STANDALONE})
|
||||
|
||||
# extra artifacts
|
||||
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_TOOLS "llama: build tools" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_BUILD_SERVER "llama: build server example" ${LLAMA_STANDALONE})
|
||||
option(LLAMA_TOOLS_INSTALL "llama: install tools" ${LLAMA_TOOLS_INSTALL_DEFAULT})
|
||||
|
||||
# 3rd party libs
|
||||
option(LLAMA_HTTPLIB "llama: httplib for downloading functionality" ON)
|
||||
option(LLAMA_OPENSSL "llama: use openssl to support HTTPS" ON)
|
||||
option(LLAMA_LLGUIDANCE "llama-common: include LLGuidance library for structured output in common utils" OFF)
|
||||
|
||||
# deprecated
|
||||
option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF)
|
||||
if (LLAMA_CURL)
|
||||
message(WARNING "LLAMA_CURL option is deprecated and will be ignored")
|
||||
endif()
|
||||
|
||||
# Required for relocatable CMake package
|
||||
include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/build-info.cmake)
|
||||
include(${CMAKE_CURRENT_SOURCE_DIR}/cmake/common.cmake)
|
||||
|
||||
if (NOT DEFINED LLAMA_BUILD_NUMBER)
|
||||
set(LLAMA_BUILD_NUMBER ${BUILD_NUMBER})
|
||||
endif()
|
||||
if (NOT DEFINED LLAMA_BUILD_COMMIT)
|
||||
set(LLAMA_BUILD_COMMIT ${BUILD_COMMIT})
|
||||
endif()
|
||||
set(LLAMA_INSTALL_VERSION 0.0.${LLAMA_BUILD_NUMBER})
|
||||
|
||||
# override ggml options
|
||||
set(GGML_ALL_WARNINGS ${LLAMA_ALL_WARNINGS})
|
||||
set(GGML_FATAL_WARNINGS ${LLAMA_FATAL_WARNINGS})
|
||||
|
||||
# change the default for these ggml options
|
||||
if (NOT DEFINED GGML_LLAMAFILE)
|
||||
set(GGML_LLAMAFILE_DEFAULT ON)
|
||||
endif()
|
||||
|
||||
if (NOT DEFINED GGML_CUDA_GRAPHS)
|
||||
set(GGML_CUDA_GRAPHS_DEFAULT ON)
|
||||
endif()
|
||||
|
||||
# transition helpers
|
||||
function (llama_option_depr TYPE OLD NEW)
|
||||
if (${OLD})
|
||||
message(${TYPE} "${OLD} is deprecated and will be removed in the future.\nUse ${NEW} instead\n")
|
||||
set(${NEW} ON PARENT_SCOPE)
|
||||
endif()
|
||||
endfunction()
|
||||
|
||||
llama_option_depr(FATAL_ERROR LLAMA_CUBLAS GGML_CUDA)
|
||||
llama_option_depr(WARNING LLAMA_CUDA GGML_CUDA)
|
||||
llama_option_depr(WARNING LLAMA_METAL GGML_METAL)
|
||||
llama_option_depr(WARNING LLAMA_METAL_EMBED_LIBRARY GGML_METAL_EMBED_LIBRARY)
|
||||
llama_option_depr(WARNING LLAMA_NATIVE GGML_NATIVE)
|
||||
llama_option_depr(WARNING LLAMA_RPC GGML_RPC)
|
||||
llama_option_depr(WARNING LLAMA_SYCL GGML_SYCL)
|
||||
llama_option_depr(WARNING LLAMA_SYCL_F16 GGML_SYCL_F16)
|
||||
llama_option_depr(WARNING LLAMA_CANN GGML_CANN)
|
||||
|
||||
if (NOT MSVC)
|
||||
if (LLAMA_SANITIZE_THREAD)
|
||||
message(STATUS "Using -fsanitize=thread")
|
||||
|
||||
add_compile_options(-fsanitize=thread)
|
||||
link_libraries (-fsanitize=thread)
|
||||
endif()
|
||||
|
||||
if (LLAMA_SANITIZE_ADDRESS)
|
||||
message(STATUS "Using -fsanitize=address")
|
||||
|
||||
add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
|
||||
link_libraries (-fsanitize=address)
|
||||
endif()
|
||||
|
||||
if (LLAMA_SANITIZE_UNDEFINED)
|
||||
message(STATUS "Using -fsanitize=undefined")
|
||||
|
||||
add_compile_options(-fsanitize=undefined)
|
||||
link_libraries (-fsanitize=undefined)
|
||||
endif()
|
||||
endif()
|
||||
|
||||
include("cmake/license.cmake")
|
||||
license_add_file("llama.cpp" "LICENSE")
|
||||
|
||||
#
|
||||
# 3rd-party
|
||||
#
|
||||
|
||||
if (LLAMA_USE_SYSTEM_GGML)
|
||||
message(STATUS "Using system-provided libggml, skipping ggml build")
|
||||
find_package(ggml REQUIRED)
|
||||
add_library(ggml ALIAS ggml::ggml)
|
||||
endif()
|
||||
|
||||
if (NOT TARGET ggml AND NOT LLAMA_USE_SYSTEM_GGML)
|
||||
set(GGML_BUILD_NUMBER ${LLAMA_BUILD_NUMBER})
|
||||
set(GGML_BUILD_COMMIT ${LLAMA_BUILD_COMMIT})
|
||||
add_subdirectory(ggml)
|
||||
# ... otherwise assume ggml is added by a parent CMakeLists.txt
|
||||
endif()
|
||||
|
||||
#
|
||||
# build the library
|
||||
#
|
||||
|
||||
add_subdirectory(src)
|
||||
|
||||
#
|
||||
# utils, programs, examples and tests
|
||||
#
|
||||
|
||||
if (LLAMA_BUILD_COMMON)
|
||||
add_subdirectory(common)
|
||||
if (LLAMA_HTTPLIB)
|
||||
add_subdirectory(vendor/cpp-httplib)
|
||||
endif()
|
||||
endif()
|
||||
|
||||
if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
|
||||
include(CTest)
|
||||
add_subdirectory(tests)
|
||||
endif()
|
||||
|
||||
if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_EXAMPLES)
|
||||
add_subdirectory(examples)
|
||||
add_subdirectory(pocs)
|
||||
endif()
|
||||
|
||||
if (LLAMA_BUILD_COMMON AND LLAMA_BUILD_TOOLS)
|
||||
add_subdirectory(tools)
|
||||
endif()
|
||||
|
||||
# Automatically add all files from the 'licenses' directory
|
||||
file(GLOB EXTRA_LICENSES "${CMAKE_SOURCE_DIR}/licenses/LICENSE-*")
|
||||
|
||||
foreach(FILE_PATH ${EXTRA_LICENSES})
|
||||
get_filename_component(FILE_NAME "${FILE_PATH}" NAME)
|
||||
string(REGEX REPLACE "^LICENSE-" "" NAME "${FILE_NAME}")
|
||||
license_add_file("${NAME}" "${FILE_PATH}")
|
||||
endforeach()
|
||||
|
||||
if (LLAMA_BUILD_COMMON)
|
||||
license_generate(common)
|
||||
endif()
|
||||
|
||||
#
|
||||
# install
|
||||
#
|
||||
|
||||
include(GNUInstallDirs)
|
||||
include(CMakePackageConfigHelpers)
|
||||
|
||||
set(LLAMA_INCLUDE_INSTALL_DIR ${CMAKE_INSTALL_INCLUDEDIR} CACHE PATH "Location of header files")
|
||||
set(LLAMA_LIB_INSTALL_DIR ${CMAKE_INSTALL_LIBDIR} CACHE PATH "Location of library files")
|
||||
set(LLAMA_BIN_INSTALL_DIR ${CMAKE_INSTALL_BINDIR} CACHE PATH "Location of binary files")
|
||||
|
||||
set(LLAMA_PUBLIC_HEADERS
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/include/llama.h
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/include/llama-cpp.h)
|
||||
|
||||
set_target_properties(llama
|
||||
PROPERTIES
|
||||
PUBLIC_HEADER "${LLAMA_PUBLIC_HEADERS}")
|
||||
|
||||
install(TARGETS llama LIBRARY PUBLIC_HEADER)
|
||||
|
||||
configure_package_config_file(
|
||||
${CMAKE_CURRENT_SOURCE_DIR}/cmake/llama-config.cmake.in
|
||||
${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake
|
||||
INSTALL_DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama
|
||||
PATH_VARS LLAMA_INCLUDE_INSTALL_DIR
|
||||
LLAMA_LIB_INSTALL_DIR
|
||||
LLAMA_BIN_INSTALL_DIR )
|
||||
|
||||
write_basic_package_version_file(
|
||||
${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake
|
||||
VERSION ${LLAMA_INSTALL_VERSION}
|
||||
COMPATIBILITY SameMajorVersion)
|
||||
|
||||
install(FILES ${CMAKE_CURRENT_BINARY_DIR}/llama-config.cmake
|
||||
${CMAKE_CURRENT_BINARY_DIR}/llama-version.cmake
|
||||
DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/llama)
|
||||
|
||||
install(
|
||||
FILES convert_hf_to_gguf.py
|
||||
PERMISSIONS
|
||||
OWNER_READ
|
||||
OWNER_WRITE
|
||||
OWNER_EXECUTE
|
||||
GROUP_READ
|
||||
GROUP_EXECUTE
|
||||
WORLD_READ
|
||||
WORLD_EXECUTE
|
||||
DESTINATION ${CMAKE_INSTALL_BINDIR})
|
||||
|
||||
configure_file(cmake/llama.pc.in
|
||||
"${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
|
||||
@ONLY)
|
||||
|
||||
install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc"
|
||||
DESTINATION ${CMAKE_INSTALL_LIBDIR}/pkgconfig)
|
||||
95
CMakePresets.json
Normal file
95
CMakePresets.json
Normal file
@@ -0,0 +1,95 @@
|
||||
{
|
||||
"version": 4,
|
||||
"configurePresets": [
|
||||
{
|
||||
"name": "base",
|
||||
"hidden": true,
|
||||
"generator": "Ninja",
|
||||
"binaryDir": "${sourceDir}/build-${presetName}",
|
||||
"cacheVariables": {
|
||||
"CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
|
||||
"CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "sycl-base",
|
||||
"hidden": true,
|
||||
"generator": "Ninja",
|
||||
"binaryDir": "${sourceDir}/build-${presetName}",
|
||||
"cacheVariables": {
|
||||
"CMAKE_EXPORT_COMPILE_COMMANDS": "ON",
|
||||
"CMAKE_CXX_COMPILER": "icx",
|
||||
"CMAKE_C_COMPILER": "cl",
|
||||
"GGML_SYCL": "ON",
|
||||
"CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.."
|
||||
}
|
||||
},
|
||||
{ "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } },
|
||||
{ "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Release" } },
|
||||
{ "name": "reldbg", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } },
|
||||
{ "name": "static", "hidden": true, "cacheVariables": { "GGML_STATIC": "ON" } },
|
||||
{ "name": "sycl_f16", "hidden": true, "cacheVariables": { "GGML_SYCL_F16": "ON" } },
|
||||
{ "name": "vulkan", "hidden": true, "cacheVariables": { "GGML_VULKAN": "ON" } },
|
||||
|
||||
{
|
||||
"name": "x64-windows-llvm", "hidden": true,
|
||||
"cacheVariables": {
|
||||
"CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/x64-windows-llvm.cmake"
|
||||
}
|
||||
},
|
||||
|
||||
{
|
||||
"name": "arm64-windows-llvm", "hidden": true,
|
||||
"architecture": { "value": "arm64", "strategy": "external" },
|
||||
"toolset": { "value": "host=x64", "strategy": "external" },
|
||||
"cacheVariables": {
|
||||
"CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-llvm.cmake"
|
||||
}
|
||||
},
|
||||
|
||||
{
|
||||
"name": "arm64-apple-clang", "hidden": true,
|
||||
"architecture": { "value": "arm64", "strategy": "external" },
|
||||
"toolset": { "value": "host=x64", "strategy": "external" },
|
||||
"cacheVariables": {
|
||||
"CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-apple-clang.cmake"
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "x64-linux-gcc", "hidden": true,
|
||||
"cacheVariables": {
|
||||
"CMAKE_C_COMPILER": "gcc",
|
||||
"CMAKE_CXX_COMPILER": "g++"
|
||||
}
|
||||
},
|
||||
{ "name": "x64-linux-gcc-debug", "inherits": [ "base", "x64-linux-gcc", "debug" ] },
|
||||
{ "name": "x64-linux-gcc-release", "inherits": [ "base", "x64-linux-gcc", "release" ] },
|
||||
{ "name": "x64-linux-gcc-reldbg", "inherits": [ "base", "x64-linux-gcc", "reldbg" ] },
|
||||
{ "name": "x64-linux-gcc+static-release", "inherits": [ "base", "x64-linux-gcc", "release", "static" ] },
|
||||
|
||||
{ "name": "arm64-windows-llvm-debug", "inherits": [ "base", "arm64-windows-llvm", "debug" ] },
|
||||
{ "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg" ] },
|
||||
{ "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "reldbg", "static" ] },
|
||||
|
||||
{ "name": "arm64-apple-clang-debug", "inherits": [ "base", "arm64-apple-clang", "debug" ] },
|
||||
{ "name": "arm64-apple-clang-release", "inherits": [ "base", "arm64-apple-clang", "reldbg" ] },
|
||||
{ "name": "arm64-apple-clang+static-release", "inherits": [ "base", "arm64-apple-clang", "reldbg", "static" ] },
|
||||
|
||||
{ "name": "x64-windows-llvm-debug", "inherits": [ "base", "x64-windows-llvm", "debug" ] },
|
||||
{ "name": "x64-windows-llvm-release", "inherits": [ "base", "x64-windows-llvm", "release" ] },
|
||||
{ "name": "x64-windows-llvm-reldbg", "inherits": [ "base", "x64-windows-llvm", "reldbg" ] },
|
||||
{ "name": "x64-windows-llvm+static-release", "inherits": [ "base", "x64-windows-llvm", "reldbg", "static" ] },
|
||||
|
||||
{ "name": "x64-windows-msvc-debug", "inherits": [ "base", "debug" ] },
|
||||
{ "name": "x64-windows-msvc-release", "inherits": [ "base", "reldbg" ] },
|
||||
{ "name": "x64-windows-msvc+static-release", "inherits": [ "base", "reldbg", "static" ] },
|
||||
|
||||
{ "name": "x64-windows-sycl-debug", "inherits": [ "sycl-base", "debug" ] },
|
||||
{ "name": "x64-windows-sycl-debug-f16", "inherits": [ "sycl-base", "debug", "sycl_f16" ] },
|
||||
{ "name": "x64-windows-sycl-release", "inherits": [ "sycl-base", "release" ] },
|
||||
{ "name": "x64-windows-sycl-release-f16", "inherits": [ "sycl-base", "release", "sycl_f16" ] },
|
||||
|
||||
{ "name": "x64-windows-vulkan-debug", "inherits": [ "base", "vulkan", "debug" ] },
|
||||
{ "name": "x64-windows-vulkan-release", "inherits": [ "base", "vulkan", "release" ] }
|
||||
]
|
||||
}
|
||||
108
CODEOWNERS
Normal file
108
CODEOWNERS
Normal file
@@ -0,0 +1,108 @@
|
||||
# collaborators can optionally add themselves here to indicate their availability for reviewing related PRs
|
||||
# multiplie collaborators per item can be specified
|
||||
|
||||
/.devops/*.Dockerfile @ngxson
|
||||
/.github/actions/ @CISC
|
||||
/.github/workflows/ @CISC
|
||||
/ci/ @ggerganov
|
||||
/cmake/ @ggerganov
|
||||
/common/CMakeLists.txt @ggerganov
|
||||
/common/arg.* @ggerganov
|
||||
/common/base64.hpp.* @ggerganov
|
||||
/common/build-info.* @ggerganov
|
||||
/common/chat.* @pwilkin
|
||||
/common/chat-peg-parser.* @aldehir
|
||||
/common/common.* @ggerganov
|
||||
/common/console.* @ggerganov
|
||||
/common/http.* @angt
|
||||
/common/llguidance.* @ggerganov
|
||||
/common/log.* @ggerganov
|
||||
/common/peg-parser.* @aldehir
|
||||
/common/sampling.* @ggerganov
|
||||
/common/speculative.* @ggerganov
|
||||
/common/unicode.* @aldehir
|
||||
/convert_*.py @CISC
|
||||
/examples/batched.swift/ @ggerganov
|
||||
/examples/batched/ @ggerganov
|
||||
/examples/convert-llama2c-to-ggml/ @ggerganov
|
||||
/examples/deprecation-warning/ @ggerganov
|
||||
/examples/diffusion/ @am17an
|
||||
/examples/embedding/ @ggerganov
|
||||
/examples/eval-callback/ @ggerganov
|
||||
/examples/export-docs/ @ggerganov
|
||||
/examples/gen-docs/ @ggerganov
|
||||
/examples/gguf/ @ggerganov
|
||||
/examples/llama.android/ @ggerganov @hanyin-arm @naco-siren
|
||||
/examples/llama.swiftui/ @ggerganov
|
||||
/examples/llama.vim @ggerganov
|
||||
/examples/lookahead/ @ggerganov
|
||||
/examples/lookup/ @JohannesGaessler
|
||||
/examples/model-conversion/ @danbev
|
||||
/examples/parallel/ @ggerganov
|
||||
/examples/passkey/ @ggerganov
|
||||
/examples/retrieval/ @ggerganov
|
||||
/examples/save-load-state/ @ggerganov
|
||||
/examples/speculative-simple/ @ggerganov
|
||||
/examples/speculative/ @ggerganov
|
||||
/ggml/cmake/ @ggerganov
|
||||
/ggml/include/ @ggerganov
|
||||
/ggml/src/ggml-common.h @ggerganov
|
||||
/ggml/src/ggml-cpu/ @ggerganov
|
||||
/ggml/src/ggml-cpu/spacemit/ @alex-spacemit
|
||||
/ggml/src/ggml-cuda/fattn* @JohannesGaessler
|
||||
/ggml/src/ggml-cuda/mmf.* @JohannesGaessler @am17an
|
||||
/ggml/src/ggml-cuda/mmq.* @JohannesGaessler
|
||||
/ggml/src/ggml-cuda/mmvf.* @JohannesGaessler
|
||||
/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler
|
||||
/ggml/src/ggml-cuda/fattn-wmma* @IMbackK
|
||||
/ggml/src/ggml-hip/ @IMbackK
|
||||
/ggml/src/ggml-cuda/vendors/hip.h @IMbackK
|
||||
/ggml/src/ggml-impl.h @ggerganov
|
||||
/ggml/src/ggml-metal/ @ggerganov
|
||||
/ggml/src/ggml-opencl/ @lhez @max-krasnyansky
|
||||
/ggml/src/ggml-hexagon/ @max-krasnyansky @lhez
|
||||
/ggml/src/ggml-opt.cpp @JohannesGaessler
|
||||
/ggml/src/ggml-quants.* @ggerganov
|
||||
/ggml/src/ggml-rpc/ @rgerganov
|
||||
/ggml/src/ggml-threading.* @ggerganov
|
||||
/ggml/src/ggml-vulkan/ @0cc4m
|
||||
/ggml/src/ggml-webgpu/ @reeselevine
|
||||
/ggml/src/ggml-zdnn/ @taronaeo @Andreas-Krebbel @AlekseiNikiforovIBM
|
||||
/ggml/src/ggml.c @ggerganov
|
||||
/ggml/src/ggml.cpp @ggerganov
|
||||
/ggml/src/gguf.cpp @JohannesGaessler @Green-Sky
|
||||
/gguf-py/ @CISC
|
||||
/media/ @ggerganov
|
||||
/scripts/gen* @ggerganov
|
||||
/scripts/get* @ggerganov
|
||||
/scripts/sync* @ggerganov
|
||||
/src/ @ggerganov
|
||||
/src/llama-adapter.* @CISC
|
||||
/src/llama-arch.* @CISC
|
||||
/src/llama-chat.* @ngxson
|
||||
/src/llama-graph.* @CISC
|
||||
/src/llama-model.* @CISC
|
||||
/src/llama-vocab.* @CISC
|
||||
/src/models/ @CISC
|
||||
/tests/ @ggerganov
|
||||
/tests/test-chat-.* @pwilkin
|
||||
/tools/batched-bench/ @ggerganov
|
||||
/tools/cli/ @ngxson
|
||||
/tools/completion/ @ggerganov
|
||||
/tools/mtmd/ @ngxson
|
||||
/tools/perplexity/ @ggerganov
|
||||
/tools/quantize/ @ggerganov
|
||||
/tools/rpc/ @rgerganov
|
||||
/tools/server/* @ngxson @ggerganov # no subdir
|
||||
/tools/server/webui/ @allozaur
|
||||
/tools/tokenize/ @ggerganov
|
||||
/tools/tts/ @ggerganov
|
||||
/vendor/ @ggerganov
|
||||
/AUTHORS @ggerganov
|
||||
/CMakeLists.txt @ggerganov
|
||||
/CONTRIBUTING.md @ggerganov
|
||||
/LICENSE @ggerganov
|
||||
/README.md @ggerganov
|
||||
/SECURITY.md @ggerganov
|
||||
/build-xcframework.sh @danbev
|
||||
requirements*.txt @CISC
|
||||
185
CONTRIBUTING.md
Normal file
185
CONTRIBUTING.md
Normal file
@@ -0,0 +1,185 @@
|
||||
# Contributors
|
||||
|
||||
The project differentiates between 3 levels of contributors:
|
||||
|
||||
- Contributors: people who have contributed before (no special privileges)
|
||||
- Collaborators (Triage): people with significant contributions, who may be responsible for some parts of the code, and are expected to maintain and review contributions for the code they own
|
||||
- Maintainers: responsible for reviewing and merging PRs, after approval from the code owners
|
||||
|
||||
# AI Usage Policy
|
||||
|
||||
> [!IMPORTANT]
|
||||
> This project does **not** accept pull requests that are fully or predominantly AI-generated. AI tools may be utilized solely in an assistive capacity.
|
||||
>
|
||||
> Detailed information regarding permissible and restricted uses of AI can be found in the [AGENTS.md](AGENTS.md) file.
|
||||
|
||||
Code that is initially generated by AI and subsequently edited will still be considered AI-generated. AI assistance is permissible only when the majority of the code is authored by a human contributor, with AI employed exclusively for corrections or to expand on verbose modifications that the contributor has already conceptualized (e.g., generating repeated lines with minor variations).
|
||||
|
||||
If AI is used to generate any portion of the code, contributors must adhere to the following requirements:
|
||||
|
||||
1. Explicitly disclose the manner in which AI was employed.
|
||||
2. Perform a comprehensive manual review prior to submitting the pull request.
|
||||
3. Be prepared to explain every line of code they submitted when asked about it by a maintainer.
|
||||
4. Using AI to write pull request descriptions or to respond to human reviewers is strictly prohibited.
|
||||
|
||||
For more info, please refer to the [AGENTS.md](AGENTS.md) file.
|
||||
|
||||
# Pull requests (for contributors & collaborators)
|
||||
|
||||
Before submitting your PR:
|
||||
- Search for existing PRs to prevent duplicating efforts
|
||||
- llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier
|
||||
- Test your changes:
|
||||
- Execute [the full CI locally on your machine](ci/README.md) before publishing
|
||||
- Verify that the perplexity and the performance are not affected negatively by your changes (use `llama-perplexity` and `llama-bench`)
|
||||
- If you modified the `ggml` source, run the `test-backend-ops` tool to check whether different backend implementations of the `ggml` operators produce consistent results (this requires access to at least two different `ggml` backends)
|
||||
- If you modified a `ggml` operator or added a new one, add the corresponding test cases to `test-backend-ops`
|
||||
- Create separate PRs for each feature or fix:
|
||||
- Avoid combining unrelated changes in a single PR
|
||||
- For intricate features, consider opening a feature request first to discuss and align expectations
|
||||
- When adding support for a new model or feature, focus on **CPU support only** in the initial PR unless you have a good reason not to. Add support for other backends like CUDA in follow-up PRs
|
||||
- Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
|
||||
|
||||
After submitting your PR:
|
||||
- Expect requests for modifications to ensure the code meets llama.cpp's standards for quality and long-term maintainability
|
||||
- Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR
|
||||
- If your PR becomes stale, rebase it on top of latest `master` to get maintainers attention
|
||||
- Consider adding yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for fixing related issues and reviewing related PRs
|
||||
|
||||
# Pull requests (for maintainers)
|
||||
|
||||
- Squash-merge PRs
|
||||
- Use the following format for the squashed commit title: `<module> : <commit title> (#<issue_number>)`. For example: `utils : fix typo in utils.py (#1234)`
|
||||
- Optionally pick a `<module>` from here: https://github.com/ggml-org/llama.cpp/wiki/Modules
|
||||
- Let other maintainers merge their own PRs
|
||||
- When merging a PR, make sure you have a good understanding of the changes
|
||||
- Be mindful of maintenance: most of the work going into a feature happens after the PR is merged. If the PR author is not committed to contribute long-term, someone else needs to take responsibility (you)
|
||||
|
||||
Maintainers reserve the right to decline review or close pull requests for any reason, particularly under any of the following conditions:
|
||||
- The proposed change is already mentioned in the roadmap or an existing issue, and it has been assigned to someone.
|
||||
- The pull request duplicates an existing one.
|
||||
- The contributor fails to adhere to this contributing guide.
|
||||
|
||||
# Coding guidelines
|
||||
|
||||
- Avoid adding third-party dependencies, extra files, extra headers, etc.
|
||||
- Always consider cross-compatibility with other operating systems and architectures
|
||||
- Avoid fancy-looking modern STL constructs, use basic `for` loops, avoid templates, keep it simple
|
||||
- Vertical alignment makes things more readable and easier to batch edit
|
||||
- Clean-up any trailing whitespaces, use 4 spaces for indentation, brackets on the same line, `void * ptr`, `int & a`
|
||||
- Use sized integer types such as `int32_t` in the public API, e.g. `size_t` may also be appropriate for allocation sizes or byte offsets
|
||||
- Declare structs with `struct foo {}` instead of `typedef struct foo {} foo`
|
||||
- In C++ code omit optional `struct` and `enum` keyword whenever they are not necessary
|
||||
```cpp
|
||||
// OK
|
||||
llama_context * ctx;
|
||||
const llama_rope_type rope_type;
|
||||
|
||||
// not OK
|
||||
struct llama_context * ctx;
|
||||
const enum llama_rope_type rope_type;
|
||||
```
|
||||
|
||||
_(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline.)_
|
||||
|
||||
- Try to follow the existing patterns in the code (indentation, spaces, etc.). In case of doubt use `clang-format` (from clang-tools v15+) to format the added code
|
||||
- For anything not covered in the current guidelines, refer to the [C++ Core Guidelines](https://isocpp.github.io/CppCoreGuidelines/CppCoreGuidelines)
|
||||
- Tensors store data in row-major order. We refer to dimension 0 as columns, 1 as rows, 2 as matrices
|
||||
- Matrix multiplication is unconventional: [`C = ggml_mul_mat(ctx, A, B)`](https://github.com/ggml-org/llama.cpp/blob/880e352277fc017df4d5794f0c21c44e1eae2b84/ggml.h#L1058-L1064) means $C^T = A B^T \Leftrightarrow C = B A^T.$
|
||||
|
||||
|
||||
|
||||
# Naming guidelines
|
||||
|
||||
- Use `snake_case` for function, variable and type names
|
||||
- Naming usually optimizes for longest common prefix (see https://github.com/ggml-org/ggml/pull/302#discussion_r1243240963)
|
||||
|
||||
```cpp
|
||||
// not OK
|
||||
int small_number;
|
||||
int big_number;
|
||||
|
||||
// OK
|
||||
int number_small;
|
||||
int number_big;
|
||||
```
|
||||
|
||||
- Enum values are always in upper case and prefixed with the enum name
|
||||
|
||||
```cpp
|
||||
enum llama_vocab_type {
|
||||
LLAMA_VOCAB_TYPE_NONE = 0,
|
||||
LLAMA_VOCAB_TYPE_SPM = 1,
|
||||
LLAMA_VOCAB_TYPE_BPE = 2,
|
||||
LLAMA_VOCAB_TYPE_WPM = 3,
|
||||
LLAMA_VOCAB_TYPE_UGM = 4,
|
||||
LLAMA_VOCAB_TYPE_RWKV = 5,
|
||||
};
|
||||
```
|
||||
|
||||
- The general naming pattern is `<class>_<method>`, with `<method>` being `<action>_<noun>`
|
||||
|
||||
```cpp
|
||||
llama_model_init(); // class: "llama_model", method: "init"
|
||||
llama_sampler_chain_remove(); // class: "llama_sampler_chain", method: "remove"
|
||||
llama_sampler_get_seed(); // class: "llama_sampler", method: "get_seed"
|
||||
llama_set_embeddings(); // class: "llama_context", method: "set_embeddings"
|
||||
llama_n_threads(); // class: "llama_context", method: "n_threads"
|
||||
llama_adapter_lora_free(); // class: "llama_adapter_lora", method: "free"
|
||||
```
|
||||
|
||||
- The `get` `<action>` can be omitted
|
||||
- The `<noun>` can be omitted if not necessary
|
||||
- The `_context` suffix of the `<class>` is optional. Use it to disambiguate symbols when needed
|
||||
- Use `init`/`free` for constructor/destructor `<action>`
|
||||
|
||||
- Use the `_t` suffix when a type is supposed to be opaque to the user - it's not relevant to them if it is a struct or anything else
|
||||
|
||||
```cpp
|
||||
typedef struct llama_context * llama_context_t;
|
||||
|
||||
enum llama_pooling_type llama_pooling_type(const llama_context_t ctx);
|
||||
```
|
||||
|
||||
_(NOTE: this guideline is yet to be applied to the `llama.cpp` codebase. New code should follow this guideline)_
|
||||
|
||||
- C/C++ filenames are all lowercase with dashes. Headers use the `.h` extension. Source files use the `.c` or `.cpp` extension
|
||||
- Python filenames are all lowercase with underscores
|
||||
|
||||
- _(TODO: abbreviations usage)_
|
||||
|
||||
# Preprocessor directives
|
||||
|
||||
- _(TODO: add guidelines with examples and apply them to the codebase)_
|
||||
|
||||
```cpp
|
||||
#ifdef FOO
|
||||
#endif // FOO
|
||||
```
|
||||
|
||||
# Code maintenance
|
||||
|
||||
- Existing code should have designated collaborators and/or maintainers specified in the [CODEOWNERS](CODEOWNERS) file reponsible for:
|
||||
- Reviewing and merging related PRs
|
||||
- Fixing related bugs
|
||||
- Providing developer guidance/support
|
||||
|
||||
- When adding or modifying a large piece of code:
|
||||
- If you are a collaborator, make sure to add yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for reviewing related PRs
|
||||
- If you are a contributor, find an existing collaborator who is willing to review and maintain your code long-term
|
||||
- Provide the necessary CI workflow (and hardware) to test your changes (see [ci/README.md](https://github.com/ggml-org/llama.cpp/tree/master/ci))
|
||||
|
||||
- New code should follow the guidelines (coding, naming, etc.) outlined in this document. Exceptions are allowed in isolated, backend-specific parts of the code that do not interface directly with the `ggml` interfaces.
|
||||
_(NOTE: for legacy reasons, existing code is not required to follow this guideline)_
|
||||
|
||||
# Documentation
|
||||
|
||||
- Documentation is a community effort
|
||||
- When you need to look into the source code to figure out how to use an API consider adding a short summary to the header file for future reference
|
||||
- When you notice incorrect or outdated documentation, please update it
|
||||
|
||||
# Resources
|
||||
|
||||
The Github issues, PRs and discussions contain a lot of information that can be useful to get familiar with the codebase. For convenience, some of the more important information is referenced from Github projects:
|
||||
|
||||
https://github.com/ggml-org/llama.cpp/projects
|
||||
21
LICENSE
Normal file
21
LICENSE
Normal file
@@ -0,0 +1,21 @@
|
||||
MIT License
|
||||
|
||||
Copyright (c) 2023-2024 The ggml authors
|
||||
|
||||
Permission is hereby granted, free of charge, to any person obtaining a copy
|
||||
of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
|
||||
copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
|
||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
||||
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
|
||||
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
||||
SOFTWARE.
|
||||
9
Makefile
Normal file
9
Makefile
Normal file
@@ -0,0 +1,9 @@
|
||||
define newline
|
||||
|
||||
|
||||
endef
|
||||
|
||||
$(error Build system changed:$(newline)\
|
||||
The Makefile build has been replaced by CMake.$(newline)$(newline)\
|
||||
For build instructions see:$(newline)\
|
||||
https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md$(newline)${newline})
|
||||
592
README.md
592
README.md
@@ -1,2 +1,592 @@
|
||||
# enginex-mthreads-llama.cpp
|
||||
# llama.cpp
|
||||
|
||||
> Sync from upstream `llama.cpp` repository
|
||||
|
||||
|
||||
|
||||
[](https://opensource.org/licenses/MIT)
|
||||
[](https://github.com/ggml-org/llama.cpp/releases)
|
||||
[](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml)
|
||||
|
||||
[Manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md)
|
||||
|
||||
LLM inference in C/C++
|
||||
|
||||
## Recent API changes
|
||||
|
||||
- [Changelog for `libllama` API](https://github.com/ggml-org/llama.cpp/issues/9289)
|
||||
- [Changelog for `llama-server` REST API](https://github.com/ggml-org/llama.cpp/issues/9291)
|
||||
|
||||
## Hot topics
|
||||
|
||||
- **[guide : using the new WebUI of llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/16938)**
|
||||
- [guide : running gpt-oss with llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/15396)
|
||||
- [[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗](https://github.com/ggml-org/llama.cpp/discussions/15313)
|
||||
- Support for the `gpt-oss` model with native MXFP4 format has been added | [PR](https://github.com/ggml-org/llama.cpp/pull/15091) | [Collaboration with NVIDIA](https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss) | [Comment](https://github.com/ggml-org/llama.cpp/discussions/15095)
|
||||
- Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md)
|
||||
- VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
|
||||
- Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
|
||||
- Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggml-org/llama.cpp/discussions/9669
|
||||
- Hugging Face GGUF editor: [discussion](https://github.com/ggml-org/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor)
|
||||
|
||||
----
|
||||
|
||||
## Quick start
|
||||
|
||||
Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine:
|
||||
|
||||
- Install `llama.cpp` using [brew, nix or winget](docs/install.md)
|
||||
- Run with Docker - see our [Docker documentation](docs/docker.md)
|
||||
- Download pre-built binaries from the [releases page](https://github.com/ggml-org/llama.cpp/releases)
|
||||
- Build from source by cloning this repository - check out [our build guide](docs/build.md)
|
||||
|
||||
Once installed, you'll need a model to work with. Head to the [Obtaining and quantizing models](#obtaining-and-quantizing-models) section to learn more.
|
||||
|
||||
Example command:
|
||||
|
||||
```sh
|
||||
# Use a local model file
|
||||
llama-cli -m my_model.gguf
|
||||
|
||||
# Or download and run a model directly from Hugging Face
|
||||
llama-cli -hf ggml-org/gemma-3-1b-it-GGUF
|
||||
|
||||
# Launch OpenAI-compatible API server
|
||||
llama-server -hf ggml-org/gemma-3-1b-it-GGUF
|
||||
```
|
||||
|
||||
## Description
|
||||
|
||||
The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
|
||||
range of hardware - locally and in the cloud.
|
||||
|
||||
- Plain C/C++ implementation without any dependencies
|
||||
- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
|
||||
- AVX, AVX2, AVX512 and AMX support for x86 architectures
|
||||
- RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
|
||||
- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
|
||||
- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
|
||||
- Vulkan and SYCL backend support
|
||||
- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
|
||||
|
||||
The `llama.cpp` project is the main playground for developing new features for the [ggml](https://github.com/ggml-org/ggml) library.
|
||||
|
||||
<details>
|
||||
<summary>Models</summary>
|
||||
|
||||
Typically finetunes of the base models below are supported as well.
|
||||
|
||||
Instructions for adding support for new models: [HOWTO-add-model.md](docs/development/HOWTO-add-model.md)
|
||||
|
||||
#### Text-only
|
||||
|
||||
- [X] LLaMA 🦙
|
||||
- [x] LLaMA 2 🦙🦙
|
||||
- [x] LLaMA 3 🦙🦙🦙
|
||||
- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
|
||||
- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
|
||||
- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
|
||||
- [x] [Jamba](https://huggingface.co/ai21labs)
|
||||
- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
|
||||
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
|
||||
- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
|
||||
- [X] [BERT](https://github.com/ggml-org/llama.cpp/pull/5423)
|
||||
- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
|
||||
- [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft)
|
||||
- [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
|
||||
- [X] [Starcoder models](https://github.com/ggml-org/llama.cpp/pull/3187)
|
||||
- [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim)
|
||||
- [X] [MPT](https://github.com/ggml-org/llama.cpp/pull/3417)
|
||||
- [X] [Bloom](https://github.com/ggml-org/llama.cpp/pull/3553)
|
||||
- [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
|
||||
- [X] [StableLM models](https://huggingface.co/stabilityai)
|
||||
- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
|
||||
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
|
||||
- [x] [PLaMo-13B](https://github.com/ggml-org/llama.cpp/pull/3557)
|
||||
- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
|
||||
- [x] [PhiMoE](https://github.com/ggml-org/llama.cpp/pull/11003)
|
||||
- [x] [GPT-2](https://huggingface.co/gpt2)
|
||||
- [x] [Orion 14B](https://github.com/ggml-org/llama.cpp/pull/5118)
|
||||
- [x] [InternLM2](https://huggingface.co/models?search=internlm2)
|
||||
- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
|
||||
- [x] [Gemma](https://ai.google.dev/gemma)
|
||||
- [x] [Mamba](https://github.com/state-spaces/mamba)
|
||||
- [x] [Grok-1](https://huggingface.co/keyfan/grok-1-hf)
|
||||
- [x] [Xverse](https://huggingface.co/models?search=xverse)
|
||||
- [x] [Command-R models](https://huggingface.co/models?search=CohereForAI/c4ai-command-r)
|
||||
- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
|
||||
- [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
|
||||
- [x] [OLMo](https://allenai.org/olmo)
|
||||
- [x] [OLMo 2](https://allenai.org/olmo)
|
||||
- [x] [OLMoE](https://huggingface.co/allenai/OLMoE-1B-7B-0924)
|
||||
- [x] [Granite models](https://huggingface.co/collections/ibm-granite/granite-code-models-6624c5cec322e4c148c8b330)
|
||||
- [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia)
|
||||
- [x] [Snowflake-Arctic MoE](https://huggingface.co/collections/Snowflake/arctic-66290090abe542894a5ac520)
|
||||
- [x] [Smaug](https://huggingface.co/models?search=Smaug)
|
||||
- [x] [Poro 34B](https://huggingface.co/LumiOpen/Poro-34B)
|
||||
- [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
|
||||
- [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
|
||||
- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
|
||||
- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
|
||||
- [x] [GLM-4-0414](https://huggingface.co/collections/THUDM/glm-4-0414-67f3cbcb34dd9d252707cb2e)
|
||||
- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
|
||||
- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
|
||||
- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
|
||||
- [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
|
||||
- [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
|
||||
- [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
|
||||
- [x] [QRWKV-6](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1)
|
||||
- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
|
||||
- [X] [Trillion-7B-preview](https://huggingface.co/trillionlabs/Trillion-7B-preview)
|
||||
- [x] [Ling models](https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32)
|
||||
- [x] [LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2-686d721927015b2ad73eaa38)
|
||||
- [x] [Hunyuan models](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)
|
||||
- [x] [BailingMoeV2 (Ring/Ling 2.0) models](https://huggingface.co/collections/inclusionAI/ling-v2-68bf1dd2fc34c306c1fa6f86)
|
||||
|
||||
#### Multimodal
|
||||
|
||||
- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2)
|
||||
- [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
|
||||
- [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
|
||||
- [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
|
||||
- [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
|
||||
- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
|
||||
- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
|
||||
- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
|
||||
- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
|
||||
- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge)
|
||||
- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
|
||||
- [x] [LFM2-VL](https://huggingface.co/collections/LiquidAI/lfm2-vl-68963bbc84a610f7638d5ffa)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Bindings</summary>
|
||||
|
||||
- Python: [ddh0/easy-llama](https://github.com/ddh0/easy-llama)
|
||||
- Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
|
||||
- Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
|
||||
- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
|
||||
- JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
|
||||
- JS/TS (Programmable Prompt Engine CLI): [offline-ai/cli](https://github.com/offline-ai/cli)
|
||||
- JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
|
||||
- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama)
|
||||
- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
|
||||
- Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs)
|
||||
- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
|
||||
- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
|
||||
- Rust (automated build from crates.io): [ShelbyJenkins/llm_client](https://github.com/ShelbyJenkins/llm_client)
|
||||
- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
|
||||
- C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html)
|
||||
- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
|
||||
- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
|
||||
- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
|
||||
- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
|
||||
- Java: [QuasarByte/llama-cpp-jna](https://github.com/QuasarByte/llama-cpp-jna)
|
||||
- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
|
||||
- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
|
||||
- Flutter: [xuegao-tzx/Fllama](https://github.com/xuegao-tzx/Fllama)
|
||||
- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggml-org/llama.cpp/pull/6326)
|
||||
- Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp)
|
||||
- Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift)
|
||||
- Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama)
|
||||
- Delphi [Embarcadero/llama-cpp-delphi](https://github.com/Embarcadero/llama-cpp-delphi)
|
||||
- Go (no CGo needed): [hybridgroup/yzma](https://github.com/hybridgroup/yzma)
|
||||
- Android: [llama.android](/examples/llama.android)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>UIs</summary>
|
||||
|
||||
*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
|
||||
|
||||
- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
|
||||
- [BonzAI App](https://apps.apple.com/us/app/bonzai-your-local-ai-agent/id6752847988) (proprietary)
|
||||
- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
|
||||
- [Dot](https://github.com/alexpinel/Dot) (GPL)
|
||||
- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
|
||||
- [iohub/collama](https://github.com/iohub/coLLaMA) (Apache-2.0)
|
||||
- [janhq/jan](https://github.com/janhq/jan) (AGPL)
|
||||
- [johnbean393/Sidekick](https://github.com/johnbean393/Sidekick) (MIT)
|
||||
- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0)
|
||||
- [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
|
||||
- [llama.vim](https://github.com/ggml-org/llama.vim) (MIT)
|
||||
- [LARS](https://github.com/abgulati/LARS) (AGPL)
|
||||
- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL)
|
||||
- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT)
|
||||
- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT)
|
||||
- [LMStudio](https://lmstudio.ai/) (proprietary)
|
||||
- [LocalAI](https://github.com/mudler/LocalAI) (MIT)
|
||||
- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
|
||||
- [MindMac](https://mindmac.app) (proprietary)
|
||||
- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT)
|
||||
- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
|
||||
- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) (Apache-2.0)
|
||||
- [nat/openplayground](https://github.com/nat/openplayground) (MIT)
|
||||
- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) (MIT)
|
||||
- [ollama/ollama](https://github.com/ollama/ollama) (MIT)
|
||||
- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL)
|
||||
- [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai) (MIT)
|
||||
- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) (MIT)
|
||||
- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) (MIT)
|
||||
- [pythops/tenere](https://github.com/pythops/tenere) (AGPL)
|
||||
- [ramalama](https://github.com/containers/ramalama) (MIT)
|
||||
- [semperai/amica](https://github.com/semperai/amica) (MIT)
|
||||
- [withcatai/catai](https://github.com/withcatai/catai) (MIT)
|
||||
- [Autopen](https://github.com/blackhole89/autopen) (GPL)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Tools</summary>
|
||||
|
||||
- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML
|
||||
- [akx/ollama-dl](https://github.com/akx/ollama-dl) – download models from the Ollama library to be used directly with llama.cpp
|
||||
- [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
|
||||
- [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage
|
||||
- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example)
|
||||
- [unslothai/unsloth](https://github.com/unslothai/unsloth) – 🦥 exports/saves fine-tuned and trained models to GGUF (Apache-2.0)
|
||||
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Infrastructure</summary>
|
||||
|
||||
- [Paddler](https://github.com/intentee/paddler) - Open-source LLMOps platform for hosting and scaling AI in your own infrastructure
|
||||
- [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs
|
||||
- [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
|
||||
- [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server
|
||||
- [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale
|
||||
- [llmaz](https://github.com/InftyAI/llmaz) - ☸️ Easy, advanced inference platform for large language models on Kubernetes.
|
||||
</details>
|
||||
|
||||
<details>
|
||||
<summary>Games</summary>
|
||||
|
||||
- [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you.
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
## Supported backends
|
||||
|
||||
| Backend | Target devices |
|
||||
| --- | --- |
|
||||
| [Metal](docs/build.md#metal-build) | Apple Silicon |
|
||||
| [BLAS](docs/build.md#blas-build) | All |
|
||||
| [BLIS](docs/backend/BLIS.md) | All |
|
||||
| [SYCL](docs/backend/SYCL.md) | Intel and Nvidia GPU |
|
||||
| [MUSA](docs/build.md#musa) | Moore Threads GPU |
|
||||
| [CUDA](docs/build.md#cuda) | Nvidia GPU |
|
||||
| [HIP](docs/build.md#hip) | AMD GPU |
|
||||
| [ZenDNN](docs/build.md#zendnn) | AMD CPU |
|
||||
| [Vulkan](docs/build.md#vulkan) | GPU |
|
||||
| [CANN](docs/build.md#cann) | Ascend NPU |
|
||||
| [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
|
||||
| [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE |
|
||||
| [WebGPU [In Progress]](docs/build.md#webgpu) | All |
|
||||
| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
|
||||
| [Hexagon [In Progress]](docs/backend/hexagon/README.md) | Snapdragon |
|
||||
|
||||
## Obtaining and quantizing models
|
||||
|
||||
The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`:
|
||||
|
||||
- [Trending](https://huggingface.co/models?library=gguf&sort=trending)
|
||||
- [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf)
|
||||
|
||||
You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from [Hugging Face](https://huggingface.co/) or other model hosting sites, such as [ModelScope](https://modelscope.cn/), by using this CLI argument: `-hf <user>/<model>[:quant]`. For example:
|
||||
|
||||
```sh
|
||||
llama-cli -hf ggml-org/gemma-3-1b-it-GGUF
|
||||
```
|
||||
|
||||
By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable `MODEL_ENDPOINT`. For example, you may opt to downloading model checkpoints from ModelScope or other model sharing communities by setting the environment variable, e.g. `MODEL_ENDPOINT=https://www.modelscope.cn/`.
|
||||
|
||||
After downloading a model, use the CLI tools to run it locally - see below.
|
||||
|
||||
`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggml-org/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo.
|
||||
|
||||
The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`:
|
||||
|
||||
- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes
|
||||
- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggml-org/llama.cpp/discussions/10123)
|
||||
- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268)
|
||||
- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669)
|
||||
|
||||
To learn more about model quantization, [read this documentation](tools/quantize/README.md)
|
||||
|
||||
## [`llama-cli`](tools/cli)
|
||||
|
||||
#### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.
|
||||
|
||||
- <details open>
|
||||
<summary>Run in conversation mode</summary>
|
||||
|
||||
Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding `-cnv` and specifying a suitable chat template with `--chat-template NAME`
|
||||
|
||||
```bash
|
||||
llama-cli -m model.gguf
|
||||
|
||||
# > hi, who are you?
|
||||
# Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
|
||||
#
|
||||
# > what is 1+1?
|
||||
# Easy peasy! The answer to 1+1 is... 2!
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Run in conversation mode with custom chat template</summary>
|
||||
|
||||
```bash
|
||||
# use the "chatml" template (use -h to see the list of supported templates)
|
||||
llama-cli -m model.gguf -cnv --chat-template chatml
|
||||
|
||||
# use a custom template
|
||||
llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Constrain the output with a custom grammar</summary>
|
||||
|
||||
```bash
|
||||
llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
|
||||
|
||||
# {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"}
|
||||
```
|
||||
|
||||
The [grammars/](grammars/) folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](grammars/README.md).
|
||||
|
||||
For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
## [`llama-server`](tools/server)
|
||||
|
||||
#### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs.
|
||||
|
||||
- <details open>
|
||||
<summary>Start a local HTTP server with default configuration on port 8080</summary>
|
||||
|
||||
```bash
|
||||
llama-server -m model.gguf --port 8080
|
||||
|
||||
# Basic web UI can be accessed via browser: http://localhost:8080
|
||||
# Chat completion endpoint: http://localhost:8080/v1/chat/completions
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Support multiple-users and parallel decoding</summary>
|
||||
|
||||
```bash
|
||||
# up to 4 concurrent requests, each with 4096 max context
|
||||
llama-server -m model.gguf -c 16384 -np 4
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Enable speculative decoding</summary>
|
||||
|
||||
```bash
|
||||
# the draft.gguf model should be a small variant of the target model.gguf
|
||||
llama-server -m model.gguf -md draft.gguf
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Serve an embedding model</summary>
|
||||
|
||||
```bash
|
||||
# use the /embedding endpoint
|
||||
llama-server -m model.gguf --embedding --pooling cls -ub 8192
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Serve a reranking model</summary>
|
||||
|
||||
```bash
|
||||
# use the /reranking endpoint
|
||||
llama-server -m model.gguf --reranking
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Constrain all outputs with a grammar</summary>
|
||||
|
||||
```bash
|
||||
# custom grammar
|
||||
llama-server -m model.gguf --grammar-file grammar.gbnf
|
||||
|
||||
# JSON
|
||||
llama-server -m model.gguf --grammar-file grammars/json.gbnf
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
## [`llama-perplexity`](tools/perplexity)
|
||||
|
||||
#### A tool for measuring the [perplexity](tools/perplexity/README.md) [^1] (and other quality metrics) of a model over a given text.
|
||||
|
||||
- <details open>
|
||||
<summary>Measure the perplexity over a text file</summary>
|
||||
|
||||
```bash
|
||||
llama-perplexity -m model.gguf -f file.txt
|
||||
|
||||
# [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ...
|
||||
# Final estimate: PPL = 5.4007 +/- 0.67339
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
- <details>
|
||||
<summary>Measure KL divergence</summary>
|
||||
|
||||
```bash
|
||||
# TODO
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
[^1]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
|
||||
|
||||
## [`llama-bench`](tools/llama-bench)
|
||||
|
||||
#### Benchmark the performance of the inference for various parameters.
|
||||
|
||||
- <details open>
|
||||
<summary>Run default benchmark</summary>
|
||||
|
||||
```bash
|
||||
llama-bench -m model.gguf
|
||||
|
||||
# Output:
|
||||
# | model | size | params | backend | threads | test | t/s |
|
||||
# | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
|
||||
# | qwen2 1.5B Q4_0 | 885.97 MiB | 1.54 B | Metal,BLAS | 16 | pp512 | 5765.41 ± 20.55 |
|
||||
# | qwen2 1.5B Q4_0 | 885.97 MiB | 1.54 B | Metal,BLAS | 16 | tg128 | 197.71 ± 0.81 |
|
||||
#
|
||||
# build: 3e0ba0e60 (4229)
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
## [`llama-simple`](examples/simple)
|
||||
|
||||
#### A minimal example for implementing apps with `llama.cpp`. Useful for developers.
|
||||
|
||||
- <details>
|
||||
<summary>Basic text completion</summary>
|
||||
|
||||
```bash
|
||||
llama-simple -m model.gguf
|
||||
|
||||
# Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of
|
||||
```
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
## Contributing
|
||||
|
||||
- Contributors can open PRs
|
||||
- Collaborators will be invited based on contributions
|
||||
- Maintainers can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
|
||||
- Any help with managing issues, PRs and projects is very appreciated!
|
||||
- See [good first issues](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
|
||||
- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information
|
||||
- Make sure to read this: [Inference at the edge](https://github.com/ggml-org/llama.cpp/discussions/205)
|
||||
- A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532)
|
||||
|
||||
## Other documentation
|
||||
|
||||
- [cli](tools/cli/README.md)
|
||||
- [completion](tools/completion/README.md)
|
||||
- [server](tools/server/README.md)
|
||||
- [GBNF grammars](grammars/README.md)
|
||||
|
||||
#### Development documentation
|
||||
|
||||
- [How to build](docs/build.md)
|
||||
- [Running on Docker](docs/docker.md)
|
||||
- [Build on Android](docs/android.md)
|
||||
- [Performance troubleshooting](docs/development/token_generation_performance_tips.md)
|
||||
- [GGML tips & tricks](https://github.com/ggml-org/llama.cpp/wiki/GGML-Tips-&-Tricks)
|
||||
|
||||
#### Seminal papers and background on the models
|
||||
|
||||
If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
|
||||
- LLaMA:
|
||||
- [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
|
||||
- [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
|
||||
- GPT-3
|
||||
- [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
|
||||
- GPT-3.5 / InstructGPT / ChatGPT:
|
||||
- [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
|
||||
- [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
|
||||
|
||||
## XCFramework
|
||||
The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS,
|
||||
and macOS. It can be used in Swift projects without the need to compile the
|
||||
library from source. For example:
|
||||
```swift
|
||||
// swift-tools-version: 5.10
|
||||
// The swift-tools-version declares the minimum version of Swift required to build this package.
|
||||
|
||||
import PackageDescription
|
||||
|
||||
let package = Package(
|
||||
name: "MyLlamaPackage",
|
||||
targets: [
|
||||
.executableTarget(
|
||||
name: "MyLlamaPackage",
|
||||
dependencies: [
|
||||
"LlamaFramework"
|
||||
]),
|
||||
.binaryTarget(
|
||||
name: "LlamaFramework",
|
||||
url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip",
|
||||
checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab"
|
||||
)
|
||||
]
|
||||
)
|
||||
```
|
||||
The above example is using an intermediate build `b5046` of the library. This can be modified
|
||||
to use a different version by changing the URL and checksum.
|
||||
|
||||
## Completions
|
||||
Command-line completion is available for some environments.
|
||||
|
||||
#### Bash Completion
|
||||
```bash
|
||||
$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash
|
||||
$ source ~/.llama-completion.bash
|
||||
```
|
||||
Optionally this can be added to your `.bashrc` or `.bash_profile` to load it
|
||||
automatically. For example:
|
||||
```console
|
||||
$ echo "source ~/.llama-completion.bash" >> ~/.bashrc
|
||||
```
|
||||
|
||||
## Dependencies
|
||||
|
||||
- [yhirose/cpp-httplib](https://github.com/yhirose/cpp-httplib) - Single-header HTTP server, used by `llama-server` - MIT license
|
||||
- [stb-image](https://github.com/nothings/stb) - Single-header image format decoder, used by multimodal subsystem - Public domain
|
||||
- [nlohmann/json](https://github.com/nlohmann/json) - Single-header JSON library, used by various tools/examples - MIT License
|
||||
- [minja](https://github.com/google/minja) - Minimal Jinja parser in C++, used by various tools/examples - MIT License
|
||||
- [miniaudio.h](https://github.com/mackron/miniaudio) - Single-header audio format decoder, used by multimodal subsystem - Public domain
|
||||
- [subprocess.h](https://github.com/sheredom/subprocess.h) - Single-header process launching solution for C and C++ - Public domain
|
||||
|
||||
97
SECURITY.md
Normal file
97
SECURITY.md
Normal file
@@ -0,0 +1,97 @@
|
||||
# Security Policy
|
||||
|
||||
- [**Reporting a vulnerability**](#reporting-a-vulnerability)
|
||||
- [**Requirements**](#requirements)
|
||||
- [**Covered Topics**](#covered-topics)
|
||||
- [**Using llama.cpp securely**](#using-llamacpp-securely)
|
||||
- [Untrusted models](#untrusted-models)
|
||||
- [Untrusted inputs](#untrusted-inputs)
|
||||
- [Data privacy](#data-privacy)
|
||||
- [Untrusted environments or networks](#untrusted-environments-or-networks)
|
||||
- [Multi-Tenant environments](#multi-tenant-environments)
|
||||
|
||||
## Reporting a vulnerability
|
||||
|
||||
If you have discovered a security vulnerability in this project that falls inside the [covered topics](#covered-topics), please report it privately. **Do not disclose it as a public issue.** This gives us time to work with you to fix the issue before public exposure, reducing the chance that the exploit will be used before a patch is released.
|
||||
|
||||
Please disclose it as a private [security advisory](https://github.com/ggml-org/llama.cpp/security/advisories/new).
|
||||
|
||||
A team of volunteers on a reasonable-effort basis maintains this project. As such, please give us at least 90 days to work on a fix before public exposure.
|
||||
|
||||
> [!IMPORTANT]
|
||||
> For collaborators: if you are interested in helping out with reviewing privting security disclosures, please see: https://github.com/ggml-org/llama.cpp/discussions/18080
|
||||
|
||||
## Requirements
|
||||
|
||||
Before submitting your report, ensure you meet the following requirements:
|
||||
|
||||
- You have read this policy and fully understand it.
|
||||
- AI is only permitted in an assistive capacity as stated in [AGENTS.md](AGENTS.md). We do not accept reports that are written exclusively by AI.
|
||||
- Your report must include a working Proof-of-Concept in the form of a script and/or attached files.
|
||||
|
||||
Maintainers reserve the right to close the report if these requirements are not fulfilled.
|
||||
|
||||
## Covered Topics
|
||||
|
||||
Only vulnerabilities that fall within these parts of the project are considered valid. For problems falling outside of this list, please report them as issues.
|
||||
|
||||
- `src/**/*`
|
||||
- `ggml/**/*`
|
||||
- `gguf-py/**/*`
|
||||
- `tools/server/*`, **excluding** the following topics:
|
||||
- Web UI
|
||||
- Features marked as experimental
|
||||
- Features not recommended for use in untrusted environments (e.g., router, MCP)
|
||||
- Bugs that can lead to Denial-of-Service attack
|
||||
|
||||
Note that none of the topics under [Using llama.cpp securely](#using-llamacpp-securely) are considered vulnerabilities in LLaMA C++.
|
||||
|
||||
For vulnerabilities that fall within the `vendor` directory, please report them directly to the third-party project.
|
||||
|
||||
## Using llama.cpp securely
|
||||
|
||||
### Untrusted models
|
||||
Be careful when running untrusted models. This classification includes models created by unknown developers or utilizing data obtained from unknown sources.
|
||||
|
||||
*Always execute untrusted models within a secure, isolated environment such as a sandbox* (e.g., containers, virtual machines). This helps protect your system from potentially malicious code.
|
||||
|
||||
> [!NOTE]
|
||||
> The trustworthiness of a model is not binary. You must always determine the proper level of caution depending on the specific model and how it matches your use case and risk tolerance.
|
||||
|
||||
### Untrusted inputs
|
||||
|
||||
Some models accept various input formats (text, images, audio, etc.). The libraries converting these inputs have varying security levels, so it's crucial to isolate the model and carefully pre-process inputs to mitigate script injection risks.
|
||||
|
||||
For maximum security when handling untrusted inputs, you may need to employ the following:
|
||||
|
||||
* Sandboxing: Isolate the environment where the inference happens.
|
||||
* Pre-analysis: Check how the model performs by default when exposed to prompt injection (e.g. using [fuzzing for prompt injection](https://github.com/FonduAI/awesome-prompt-injection?tab=readme-ov-file#tools)). This will give you leads on how hard you will have to work on the next topics.
|
||||
* Updates: Keep both LLaMA C++ and your libraries updated with the latest security patches.
|
||||
* Input Sanitation: Before feeding data to the model, sanitize inputs rigorously. This involves techniques such as:
|
||||
* Validation: Enforce strict rules on allowed characters and data types.
|
||||
* Filtering: Remove potentially malicious scripts or code fragments.
|
||||
* Encoding: Convert special characters into safe representations.
|
||||
* Verification: Run tooling that identifies potential script injections (e.g. [models that detect prompt injection attempts](https://python.langchain.com/docs/guides/safety/hugging_face_prompt_injection)).
|
||||
|
||||
### Data privacy
|
||||
|
||||
To protect sensitive data from potential leaks or unauthorized access, it is crucial to sandbox the model execution. This means running the model in a secure, isolated environment, which helps mitigate many attack vectors.
|
||||
|
||||
### Untrusted environments or networks
|
||||
|
||||
If you can't run your models in a secure and isolated environment or if it must be exposed to an untrusted network, make sure to take the following security precautions:
|
||||
* Do not use the RPC backend, [rpc-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) and [llama-server](https://github.com/ggml-org/llama.cpp/tree/master/tools/server) functionality (see https://github.com/ggml-org/llama.cpp/pull/13061).
|
||||
* Confirm the hash of any downloaded artifact (e.g. pre-trained model weights) matches a known-good value.
|
||||
* Encrypt your data if sending it over the network.
|
||||
|
||||
### Multi-Tenant environments
|
||||
|
||||
If you intend to run multiple models in parallel with shared memory, it is your responsibility to ensure the models do not interact or access each other's data. The primary areas of concern are tenant isolation, resource allocation, model sharing and hardware attacks.
|
||||
|
||||
1. Tenant Isolation: Models should run separately with strong isolation methods to prevent unwanted data access. Separating networks is crucial for isolation, as it prevents unauthorized access to data or models and malicious users from sending graphs to execute under another tenant's identity.
|
||||
|
||||
2. Resource Allocation: A denial of service caused by one model can impact the overall system health. Implement safeguards like rate limits, access controls, and health monitoring.
|
||||
|
||||
3. Model Sharing: In a multitenant model sharing design, tenants and users must understand the security risks of running code provided by others. Since there are no reliable methods to detect malicious models, sandboxing the model execution is the recommended approach to mitigate the risk.
|
||||
|
||||
4. Hardware Attacks: GPUs or TPUs can also be attacked. [Researches](https://scholar.google.com/scholar?q=gpu+side+channel) has shown that side channel attacks on GPUs are possible, which can make data leak from other models or processes running on the same system at the same time.
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,6 @@
|
||||
{
|
||||
"chars": 2296.1916666666666,
|
||||
"chars:std": 986.051306946325,
|
||||
"score": 0.925,
|
||||
"score:std": 0.26339134382131846
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
264
benches/dgx-spark/dgx-spark.md
Normal file
264
benches/dgx-spark/dgx-spark.md
Normal file
@@ -0,0 +1,264 @@
|
||||
## System info
|
||||
|
||||
```bash
|
||||
uname --all
|
||||
Linux spark-17ed 6.11.0-1016-nvidia #16-Ubuntu SMP PREEMPT_DYNAMIC Sun Sep 21 16:52:46 UTC 2025 aarch64 aarch64 aarch64 GNU/Linux
|
||||
|
||||
g++ --version
|
||||
g++ (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
|
||||
|
||||
nvidia-smi
|
||||
Sun Nov 2 10:43:25 2025
|
||||
+-----------------------------------------------------------------------------------------+
|
||||
| NVIDIA-SMI 580.95.05 Driver Version: 580.95.05 CUDA Version: 13.0 |
|
||||
+-----------------------------------------+------------------------+----------------------+
|
||||
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
|
||||
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
|
||||
| | | MIG M. |
|
||||
|=========================================+========================+======================|
|
||||
| 0 NVIDIA GB10 On | 0000000F:01:00.0 Off | N/A |
|
||||
| N/A 35C P8 4W / N/A | Not Supported | 0% Default |
|
||||
| | | N/A |
|
||||
+-----------------------------------------+------------------------+----------------------+
|
||||
```
|
||||
|
||||
## ggml-org/gpt-oss-20b-GGUF
|
||||
|
||||
Model: https://huggingface.co/ggml-org/gpt-oss-20b-GGUF
|
||||
|
||||
- `llama-batched-bench`
|
||||
|
||||
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
|
||||
|
||||
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|
||||
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
|
||||
| 512 | 32 | 1 | 544 | 0.374 | 1369.01 | 0.383 | 83.64 | 0.757 | 719.01 |
|
||||
| 512 | 32 | 2 | 1088 | 0.274 | 3741.35 | 0.659 | 97.14 | 0.933 | 1166.66 |
|
||||
| 512 | 32 | 4 | 2176 | 0.526 | 3896.47 | 0.817 | 156.73 | 1.342 | 1621.08 |
|
||||
| 512 | 32 | 8 | 4352 | 1.044 | 3925.10 | 0.987 | 259.44 | 2.030 | 2143.56 |
|
||||
| 512 | 32 | 16 | 8704 | 2.076 | 3945.84 | 1.248 | 410.32 | 3.324 | 2618.60 |
|
||||
| 512 | 32 | 32 | 17408 | 4.170 | 3929.28 | 1.630 | 628.40 | 5.799 | 3001.76 |
|
||||
| 4096 | 32 | 1 | 4128 | 1.083 | 3782.66 | 0.394 | 81.21 | 1.477 | 2795.13 |
|
||||
| 4096 | 32 | 2 | 8256 | 2.166 | 3782.72 | 0.725 | 88.28 | 2.891 | 2856.14 |
|
||||
| 4096 | 32 | 4 | 16512 | 4.333 | 3780.88 | 0.896 | 142.82 | 5.230 | 3157.38 |
|
||||
| 4096 | 32 | 8 | 33024 | 8.618 | 3802.14 | 1.155 | 221.69 | 9.773 | 3379.08 |
|
||||
| 4096 | 32 | 16 | 66048 | 17.330 | 3781.73 | 1.598 | 320.34 | 18.928 | 3489.45 |
|
||||
| 4096 | 32 | 32 | 132096 | 34.671 | 3780.48 | 2.336 | 438.35 | 37.007 | 3569.51 |
|
||||
| 8192 | 32 | 1 | 8224 | 2.233 | 3668.56 | 0.438 | 72.98 | 2.671 | 3078.44 |
|
||||
| 8192 | 32 | 2 | 16448 | 4.425 | 3702.95 | 0.756 | 84.66 | 5.181 | 3174.95 |
|
||||
| 8192 | 32 | 4 | 32896 | 8.859 | 3698.64 | 0.967 | 132.38 | 9.826 | 3347.72 |
|
||||
| 8192 | 32 | 8 | 65792 | 17.714 | 3699.57 | 1.277 | 200.52 | 18.991 | 3464.35 |
|
||||
| 8192 | 32 | 16 | 131584 | 35.494 | 3692.84 | 1.841 | 278.12 | 37.335 | 3524.46 |
|
||||
| 8192 | 32 | 32 | 263168 | 70.949 | 3694.82 | 2.798 | 365.99 | 73.747 | 3568.53 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | ngl | n_ubatch | fa | mmap | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 | 3714.25 ± 20.36 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | tg32 | 86.58 ± 0.43 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d4096 | 3445.17 ± 17.85 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d4096 | 81.72 ± 0.53 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d8192 | 3218.78 ± 11.34 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d8192 | 74.86 ± 0.64 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d16384 | 2732.83 ± 7.17 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d16384 | 71.57 ± 0.51 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d32768 | 2119.75 ± 12.81 |
|
||||
| gpt-oss 20B MXFP4 MoE | 11.27 GiB | 20.91 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d32768 | 62.33 ± 0.24 |
|
||||
|
||||
build: eeee367de (6989)
|
||||
|
||||
## ggml-org/gpt-oss-120b-GGUF
|
||||
|
||||
Model: https://huggingface.co/ggml-org/gpt-oss-120b-GGUF
|
||||
|
||||
- `llama-batched-bench`
|
||||
|
||||
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
|
||||
|
||||
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|
||||
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
|
||||
| 512 | 32 | 1 | 544 | 0.571 | 897.18 | 0.543 | 58.96 | 1.113 | 488.60 |
|
||||
| 512 | 32 | 2 | 1088 | 0.593 | 1725.37 | 1.041 | 61.45 | 1.635 | 665.48 |
|
||||
| 512 | 32 | 4 | 2176 | 1.043 | 1963.15 | 1.334 | 95.95 | 2.377 | 915.36 |
|
||||
| 512 | 32 | 8 | 4352 | 2.099 | 1951.63 | 1.717 | 149.07 | 3.816 | 1140.45 |
|
||||
| 512 | 32 | 16 | 8704 | 4.207 | 1947.12 | 2.311 | 221.56 | 6.518 | 1335.35 |
|
||||
| 512 | 32 | 32 | 17408 | 8.422 | 1945.36 | 3.298 | 310.46 | 11.720 | 1485.27 |
|
||||
| 4096 | 32 | 1 | 4128 | 2.138 | 1915.88 | 0.571 | 56.09 | 2.708 | 1524.12 |
|
||||
| 4096 | 32 | 2 | 8256 | 4.266 | 1920.25 | 1.137 | 56.27 | 5.404 | 1527.90 |
|
||||
| 4096 | 32 | 4 | 16512 | 8.564 | 1913.02 | 1.471 | 86.99 | 10.036 | 1645.29 |
|
||||
| 4096 | 32 | 8 | 33024 | 17.092 | 1917.19 | 1.979 | 129.33 | 19.071 | 1731.63 |
|
||||
| 4096 | 32 | 16 | 66048 | 34.211 | 1915.65 | 2.850 | 179.66 | 37.061 | 1782.15 |
|
||||
| 4096 | 32 | 32 | 132096 | 68.394 | 1916.44 | 4.381 | 233.72 | 72.775 | 1815.13 |
|
||||
| 8192 | 32 | 1 | 8224 | 4.349 | 1883.45 | 0.620 | 51.65 | 4.969 | 1655.04 |
|
||||
| 8192 | 32 | 2 | 16448 | 8.674 | 1888.83 | 1.178 | 54.33 | 9.852 | 1669.48 |
|
||||
| 8192 | 32 | 4 | 32896 | 17.351 | 1888.55 | 1.580 | 81.01 | 18.931 | 1737.68 |
|
||||
| 8192 | 32 | 8 | 65792 | 34.743 | 1886.31 | 2.173 | 117.80 | 36.916 | 1782.20 |
|
||||
| 8192 | 32 | 16 | 131584 | 69.413 | 1888.29 | 3.297 | 155.28 | 72.710 | 1809.70 |
|
||||
| 8192 | 32 | 32 | 263168 | 138.903 | 1887.24 | 5.004 | 204.63 | 143.907 | 1828.73 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | ngl | n_ubatch | fa | mmap | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 | 1919.36 ± 5.01 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | tg32 | 60.40 ± 0.30 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d4096 | 1825.30 ± 6.37 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d4096 | 56.94 ± 0.29 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d8192 | 1739.19 ± 6.00 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d8192 | 52.51 ± 0.42 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d16384 | 1536.75 ± 4.27 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d16384 | 49.33 ± 0.27 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d32768 | 1255.85 ± 3.26 |
|
||||
| gpt-oss 120B MXFP4 MoE | 59.02 GiB | 116.83 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d32768 | 42.99 ± 0.18 |
|
||||
|
||||
build: eeee367de (6989)
|
||||
|
||||
## ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
|
||||
|
||||
Model: https://huggingface.co/ggml-org/Qwen3-Coder-30B-A3B-Instruct-Q8_0-GGUF
|
||||
|
||||
- `llama-batched-bench`
|
||||
|
||||
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
|
||||
|
||||
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|
||||
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
|
||||
| 512 | 32 | 1 | 544 | 0.398 | 1285.90 | 0.530 | 60.41 | 0.928 | 586.27 |
|
||||
| 512 | 32 | 2 | 1088 | 0.386 | 2651.65 | 0.948 | 67.50 | 1.334 | 815.38 |
|
||||
| 512 | 32 | 4 | 2176 | 0.666 | 3076.37 | 1.209 | 105.87 | 1.875 | 1160.71 |
|
||||
| 512 | 32 | 8 | 4352 | 1.325 | 3091.39 | 1.610 | 158.98 | 2.935 | 1482.65 |
|
||||
| 512 | 32 | 16 | 8704 | 2.664 | 3075.58 | 2.150 | 238.19 | 4.813 | 1808.39 |
|
||||
| 512 | 32 | 32 | 17408 | 5.336 | 3070.31 | 2.904 | 352.59 | 8.240 | 2112.50 |
|
||||
| 4096 | 32 | 1 | 4128 | 1.444 | 2836.81 | 0.581 | 55.09 | 2.025 | 2038.81 |
|
||||
| 4096 | 32 | 2 | 8256 | 2.872 | 2852.14 | 1.084 | 59.06 | 3.956 | 2086.99 |
|
||||
| 4096 | 32 | 4 | 16512 | 5.744 | 2852.32 | 1.440 | 88.90 | 7.184 | 2298.47 |
|
||||
| 4096 | 32 | 8 | 33024 | 11.463 | 2858.68 | 2.068 | 123.78 | 13.531 | 2440.65 |
|
||||
| 4096 | 32 | 16 | 66048 | 22.915 | 2859.95 | 3.018 | 169.67 | 25.933 | 2546.90 |
|
||||
| 4096 | 32 | 32 | 132096 | 45.956 | 2852.10 | 4.609 | 222.18 | 50.565 | 2612.39 |
|
||||
| 8192 | 32 | 1 | 8224 | 3.063 | 2674.72 | 0.693 | 46.20 | 3.755 | 2189.92 |
|
||||
| 8192 | 32 | 2 | 16448 | 6.109 | 2681.87 | 1.214 | 52.71 | 7.323 | 2245.98 |
|
||||
| 8192 | 32 | 4 | 32896 | 12.197 | 2686.63 | 1.682 | 76.11 | 13.878 | 2370.30 |
|
||||
| 8192 | 32 | 8 | 65792 | 24.409 | 2684.94 | 2.556 | 100.17 | 26.965 | 2439.95 |
|
||||
| 8192 | 32 | 16 | 131584 | 48.753 | 2688.50 | 3.994 | 128.20 | 52.747 | 2494.64 |
|
||||
| 8192 | 32 | 32 | 263168 | 97.508 | 2688.42 | 6.528 | 156.86 | 104.037 | 2529.57 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | ngl | n_ubatch | fa | mmap | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 | 2925.55 ± 4.25 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | tg32 | 62.80 ± 0.27 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d4096 | 2531.01 ± 6.79 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d4096 | 55.86 ± 0.33 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d8192 | 2244.39 ± 5.33 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d8192 | 45.95 ± 0.33 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d16384 | 1783.17 ± 3.68 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d16384 | 39.07 ± 0.10 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d32768 | 1241.90 ± 3.13 |
|
||||
| qwen3moe 30B.A3B Q8_0 | 30.25 GiB | 30.53 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d32768 | 29.92 ± 0.06 |
|
||||
|
||||
build: eeee367de (6989)
|
||||
|
||||
## ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
|
||||
|
||||
Model: https://huggingface.co/ggml-org/Qwen2.5-Coder-7B-Q8_0-GGUF
|
||||
|
||||
- `llama-batched-bench`
|
||||
|
||||
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
|
||||
|
||||
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|
||||
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
|
||||
| 512 | 32 | 1 | 544 | 0.211 | 2421.57 | 1.055 | 30.33 | 1.266 | 429.57 |
|
||||
| 512 | 32 | 2 | 1088 | 0.419 | 2441.34 | 1.130 | 56.65 | 1.549 | 702.32 |
|
||||
| 512 | 32 | 4 | 2176 | 0.873 | 2345.54 | 1.174 | 108.99 | 2.048 | 1062.74 |
|
||||
| 512 | 32 | 8 | 4352 | 1.727 | 2371.85 | 1.254 | 204.22 | 2.980 | 1460.19 |
|
||||
| 512 | 32 | 16 | 8704 | 3.452 | 2373.22 | 1.492 | 343.16 | 4.944 | 1760.56 |
|
||||
| 512 | 32 | 32 | 17408 | 6.916 | 2368.93 | 1.675 | 611.51 | 8.591 | 2026.36 |
|
||||
| 4096 | 32 | 1 | 4128 | 1.799 | 2277.26 | 1.084 | 29.51 | 2.883 | 1431.91 |
|
||||
| 4096 | 32 | 2 | 8256 | 3.577 | 2290.01 | 1.196 | 53.50 | 4.774 | 1729.51 |
|
||||
| 4096 | 32 | 4 | 16512 | 7.172 | 2284.36 | 1.313 | 97.50 | 8.485 | 1946.00 |
|
||||
| 4096 | 32 | 8 | 33024 | 14.341 | 2284.96 | 1.520 | 168.46 | 15.860 | 2082.18 |
|
||||
| 4096 | 32 | 16 | 66048 | 28.675 | 2285.44 | 1.983 | 258.21 | 30.658 | 2154.33 |
|
||||
| 4096 | 32 | 32 | 132096 | 57.354 | 2285.32 | 2.640 | 387.87 | 59.994 | 2201.82 |
|
||||
| 8192 | 32 | 1 | 8224 | 3.701 | 2213.75 | 1.119 | 28.59 | 4.820 | 1706.34 |
|
||||
| 8192 | 32 | 2 | 16448 | 7.410 | 2211.19 | 1.272 | 50.31 | 8.682 | 1894.56 |
|
||||
| 8192 | 32 | 4 | 32896 | 14.802 | 2213.83 | 1.460 | 87.68 | 16.261 | 2022.96 |
|
||||
| 8192 | 32 | 8 | 65792 | 29.609 | 2213.35 | 1.781 | 143.74 | 31.390 | 2095.93 |
|
||||
| 8192 | 32 | 16 | 131584 | 59.229 | 2212.96 | 2.495 | 205.17 | 61.725 | 2131.79 |
|
||||
| 8192 | 32 | 32 | 263168 | 118.449 | 2213.15 | 3.714 | 275.75 | 122.162 | 2154.25 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | ngl | n_ubatch | fa | mmap | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 | 2272.74 ± 4.68 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | tg32 | 30.66 ± 0.02 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d4096 | 2107.80 ± 9.55 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d4096 | 29.71 ± 0.05 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d8192 | 1937.80 ± 6.75 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d8192 | 28.86 ± 0.04 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d16384 | 1641.12 ± 1.78 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d16384 | 27.24 ± 0.04 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d32768 | 1296.02 ± 2.67 |
|
||||
| qwen2 7B Q8_0 | 7.54 GiB | 7.62 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d32768 | 23.78 ± 0.03 |
|
||||
|
||||
build: eeee367de (6989)
|
||||
|
||||
## ggml-org/gemma-3-4b-it-qat-GGUF
|
||||
|
||||
Model: https://huggingface.co/ggml-org/gemma-3-4b-it-qat-GGUF
|
||||
|
||||
- `llama-batched-bench`
|
||||
|
||||
|
||||
main: n_kv_max = 270336, n_batch = 2048, n_ubatch = 2048, flash_attn = 1, is_pp_shared = 0, n_gpu_layers = -1, n_threads = 20, n_threads_batch = 20
|
||||
|
||||
| PP | TG | B | N_KV | T_PP s | S_PP t/s | T_TG s | S_TG t/s | T s | S t/s |
|
||||
|-------|--------|------|--------|----------|----------|----------|----------|----------|----------|
|
||||
| 512 | 32 | 1 | 544 | 0.094 | 5434.73 | 0.394 | 81.21 | 0.488 | 1114.15 |
|
||||
| 512 | 32 | 2 | 1088 | 0.168 | 6091.68 | 0.498 | 128.52 | 0.666 | 1633.41 |
|
||||
| 512 | 32 | 4 | 2176 | 0.341 | 6010.68 | 0.542 | 236.37 | 0.882 | 2466.43 |
|
||||
| 512 | 32 | 8 | 4352 | 0.665 | 6161.46 | 0.678 | 377.74 | 1.342 | 3241.72 |
|
||||
| 512 | 32 | 16 | 8704 | 1.323 | 6193.19 | 0.902 | 567.41 | 2.225 | 3911.74 |
|
||||
| 512 | 32 | 32 | 17408 | 2.642 | 6202.03 | 1.231 | 832.03 | 3.872 | 4495.36 |
|
||||
| 4096 | 32 | 1 | 4128 | 0.701 | 5840.49 | 0.439 | 72.95 | 1.140 | 3621.23 |
|
||||
| 4096 | 32 | 2 | 8256 | 1.387 | 5906.82 | 0.574 | 111.48 | 1.961 | 4210.12 |
|
||||
| 4096 | 32 | 4 | 16512 | 2.758 | 5940.33 | 0.651 | 196.58 | 3.409 | 4843.33 |
|
||||
| 4096 | 32 | 8 | 33024 | 5.491 | 5967.56 | 0.876 | 292.40 | 6.367 | 5187.12 |
|
||||
| 4096 | 32 | 16 | 66048 | 10.978 | 5969.58 | 1.275 | 401.69 | 12.253 | 5390.38 |
|
||||
| 4096 | 32 | 32 | 132096 | 21.944 | 5972.93 | 1.992 | 514.16 | 23.936 | 5518.73 |
|
||||
| 8192 | 32 | 1 | 8224 | 1.402 | 5841.91 | 0.452 | 70.73 | 1.855 | 4434.12 |
|
||||
| 8192 | 32 | 2 | 16448 | 2.793 | 5865.34 | 0.637 | 100.55 | 3.430 | 4795.51 |
|
||||
| 8192 | 32 | 4 | 32896 | 5.564 | 5889.64 | 0.770 | 166.26 | 6.334 | 5193.95 |
|
||||
| 8192 | 32 | 8 | 65792 | 11.114 | 5896.44 | 1.122 | 228.07 | 12.237 | 5376.51 |
|
||||
| 8192 | 32 | 16 | 131584 | 22.210 | 5901.38 | 1.789 | 286.15 | 24.000 | 5482.74 |
|
||||
| 8192 | 32 | 32 | 263168 | 44.382 | 5906.56 | 3.044 | 336.38 | 47.426 | 5549.02 |
|
||||
|
||||
|
||||
- `llama-bench`
|
||||
|
||||
| model | size | params | backend | ngl | n_ubatch | fa | mmap | test | t/s |
|
||||
| ------------------------------ | ---------: | ---------: | ---------- | --: | -------: | -: | ---: | --------------: | -------------------: |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 | 5810.04 ± 21.71 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | tg32 | 84.54 ± 0.18 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d4096 | 5288.04 ± 3.54 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d4096 | 78.82 ± 1.37 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d8192 | 4960.43 ± 16.64 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d8192 | 74.13 ± 0.30 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d16384 | 4495.92 ± 31.11 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d16384 | 72.37 ± 0.29 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | pp2048 @ d32768 | 3746.90 ± 40.01 |
|
||||
| gemma3 4B Q4_0 | 2.35 GiB | 3.88 B | CUDA | 99 | 2048 | 1 | 0 | tg32 @ d32768 | 63.02 ± 0.20 |
|
||||
|
||||
build: eeee367de (6989)
|
||||
|
||||
11
benches/dgx-spark/run-aime-120b-t8-x8-high.log
Normal file
11
benches/dgx-spark/run-aime-120b-t8-x8-high.log
Normal file
File diff suppressed because one or more lines are too long
546
build-xcframework.sh
Executable file
546
build-xcframework.sh
Executable file
@@ -0,0 +1,546 @@
|
||||
#!/usr/bin/env bash
|
||||
#
|
||||
# Options
|
||||
IOS_MIN_OS_VERSION=16.4
|
||||
MACOS_MIN_OS_VERSION=13.3
|
||||
VISIONOS_MIN_OS_VERSION=1.0
|
||||
TVOS_MIN_OS_VERSION=16.4
|
||||
|
||||
BUILD_SHARED_LIBS=OFF
|
||||
LLAMA_BUILD_EXAMPLES=OFF
|
||||
LLAMA_BUILD_TOOLS=OFF
|
||||
LLAMA_BUILD_TESTS=OFF
|
||||
LLAMA_BUILD_SERVER=OFF
|
||||
GGML_METAL=ON
|
||||
GGML_METAL_EMBED_LIBRARY=ON
|
||||
GGML_BLAS_DEFAULT=ON
|
||||
GGML_METAL_USE_BF16=ON
|
||||
GGML_OPENMP=OFF
|
||||
|
||||
COMMON_C_FLAGS="-Wno-macro-redefined -Wno-shorten-64-to-32 -Wno-unused-command-line-argument -g"
|
||||
COMMON_CXX_FLAGS="-Wno-macro-redefined -Wno-shorten-64-to-32 -Wno-unused-command-line-argument -g"
|
||||
|
||||
# Common options for all builds
|
||||
COMMON_CMAKE_ARGS=(
|
||||
-DCMAKE_XCODE_ATTRIBUTE_CODE_SIGNING_REQUIRED=NO
|
||||
-DCMAKE_XCODE_ATTRIBUTE_CODE_SIGN_IDENTITY=""
|
||||
-DCMAKE_XCODE_ATTRIBUTE_CODE_SIGNING_ALLOWED=NO
|
||||
-DCMAKE_XCODE_ATTRIBUTE_DEBUG_INFORMATION_FORMAT="dwarf-with-dsym"
|
||||
-DCMAKE_XCODE_ATTRIBUTE_GCC_GENERATE_DEBUGGING_SYMBOLS=YES
|
||||
-DCMAKE_XCODE_ATTRIBUTE_COPY_PHASE_STRIP=NO
|
||||
-DCMAKE_XCODE_ATTRIBUTE_STRIP_INSTALLED_PRODUCT=NO
|
||||
-DCMAKE_XCODE_ATTRIBUTE_DEVELOPMENT_TEAM=ggml
|
||||
-DBUILD_SHARED_LIBS=${BUILD_SHARED_LIBS}
|
||||
-DLLAMA_BUILD_EXAMPLES=${LLAMA_BUILD_EXAMPLES}
|
||||
-DLLAMA_BUILD_TOOLS=${LLAMA_BUILD_TOOLS}
|
||||
-DLLAMA_BUILD_TESTS=${LLAMA_BUILD_TESTS}
|
||||
-DLLAMA_BUILD_SERVER=${LLAMA_BUILD_SERVER}
|
||||
-DGGML_METAL_EMBED_LIBRARY=${GGML_METAL_EMBED_LIBRARY}
|
||||
-DGGML_BLAS_DEFAULT=${GGML_BLAS_DEFAULT}
|
||||
-DGGML_METAL=${GGML_METAL}
|
||||
-DGGML_METAL_USE_BF16=${GGML_METAL_USE_BF16}
|
||||
-DGGML_NATIVE=OFF
|
||||
-DGGML_OPENMP=${GGML_OPENMP}
|
||||
)
|
||||
|
||||
XCODE_VERSION=$(xcodebuild -version 2>/dev/null | head -n1 | awk '{ print $2 }')
|
||||
MAJOR_VERSION=$(echo $XCODE_VERSION | cut -d. -f1)
|
||||
MINOR_VERSION=$(echo $XCODE_VERSION | cut -d. -f2)
|
||||
echo "Detected Xcode version: $XCODE_VERSION"
|
||||
|
||||
check_required_tool() {
|
||||
local tool=$1
|
||||
local install_message=$2
|
||||
|
||||
if ! command -v $tool &> /dev/null; then
|
||||
echo "Error: $tool is required but not found."
|
||||
echo "$install_message"
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
echo "Checking for required tools..."
|
||||
check_required_tool "cmake" "Please install CMake 3.28.0 or later (brew install cmake)"
|
||||
check_required_tool "xcodebuild" "Please install Xcode and Xcode Command Line Tools (xcode-select --install)"
|
||||
check_required_tool "libtool" "Please install libtool which should be available with Xcode Command Line Tools (CLT). Make sure Xcode CLT is installed (xcode-select --install)"
|
||||
check_required_tool "dsymutil" "Please install Xcode and Xcode Command Line Tools (xcode-select --install)"
|
||||
|
||||
set -e
|
||||
|
||||
## Clean up previous builds
|
||||
rm -rf build-apple
|
||||
rm -rf build-ios-sim
|
||||
rm -rf build-ios-device
|
||||
rm -rf build-macos
|
||||
rm -rf build-visionos
|
||||
rm -rf build-visionos-sim
|
||||
rm -rf build-tvos-sim
|
||||
rm -rf build-tvos-device
|
||||
|
||||
# Setup the xcframework build directory structure
|
||||
setup_framework_structure() {
|
||||
local build_dir=$1
|
||||
local min_os_version=$2
|
||||
local platform=$3 # "ios", "macos", "visionos", or "tvos"
|
||||
local framework_name="llama"
|
||||
|
||||
echo "Creating ${platform}-style framework structure for ${build_dir}"
|
||||
|
||||
if [[ "$platform" == "macos" ]]; then
|
||||
# macOS versioned structure uses versioned directories
|
||||
mkdir -p ${build_dir}/framework/${framework_name}.framework/Versions/A/Headers
|
||||
mkdir -p ${build_dir}/framework/${framework_name}.framework/Versions/A/Modules
|
||||
mkdir -p ${build_dir}/framework/${framework_name}.framework/Versions/A/Resources
|
||||
|
||||
# Create symbolic links
|
||||
ln -sf A ${build_dir}/framework/${framework_name}.framework/Versions/Current
|
||||
ln -sf Versions/Current/Headers ${build_dir}/framework/${framework_name}.framework/Headers
|
||||
ln -sf Versions/Current/Modules ${build_dir}/framework/${framework_name}.framework/Modules
|
||||
ln -sf Versions/Current/Resources ${build_dir}/framework/${framework_name}.framework/Resources
|
||||
ln -sf Versions/Current/${framework_name} ${build_dir}/framework/${framework_name}.framework/${framework_name}
|
||||
|
||||
# Set header and module paths
|
||||
local header_path=${build_dir}/framework/${framework_name}.framework/Versions/A/Headers/
|
||||
local module_path=${build_dir}/framework/${framework_name}.framework/Versions/A/Modules/
|
||||
else
|
||||
# iOS/VisionOS/tvOS use a flat structure
|
||||
mkdir -p ${build_dir}/framework/${framework_name}.framework/Headers
|
||||
mkdir -p ${build_dir}/framework/${framework_name}.framework/Modules
|
||||
|
||||
# Remove any existing structure to ensure clean build
|
||||
rm -rf ${build_dir}/framework/${framework_name}.framework/Versions
|
||||
|
||||
# Set header and module paths
|
||||
local header_path=${build_dir}/framework/${framework_name}.framework/Headers/
|
||||
local module_path=${build_dir}/framework/${framework_name}.framework/Modules/
|
||||
fi
|
||||
|
||||
# Copy all required headers (common for all platforms)
|
||||
cp include/llama.h ${header_path}
|
||||
cp ggml/include/ggml.h ${header_path}
|
||||
cp ggml/include/ggml-opt.h ${header_path}
|
||||
cp ggml/include/ggml-alloc.h ${header_path}
|
||||
cp ggml/include/ggml-backend.h ${header_path}
|
||||
cp ggml/include/ggml-metal.h ${header_path}
|
||||
cp ggml/include/ggml-cpu.h ${header_path}
|
||||
cp ggml/include/ggml-blas.h ${header_path}
|
||||
cp ggml/include/gguf.h ${header_path}
|
||||
|
||||
# Create module map (common for all platforms)
|
||||
cat > ${module_path}module.modulemap << EOF
|
||||
framework module llama {
|
||||
header "llama.h"
|
||||
header "ggml.h"
|
||||
header "ggml-alloc.h"
|
||||
header "ggml-backend.h"
|
||||
header "ggml-metal.h"
|
||||
header "ggml-cpu.h"
|
||||
header "ggml-blas.h"
|
||||
header "gguf.h"
|
||||
|
||||
link "c++"
|
||||
link framework "Accelerate"
|
||||
link framework "Metal"
|
||||
link framework "Foundation"
|
||||
|
||||
export *
|
||||
}
|
||||
EOF
|
||||
|
||||
# Platform-specific settings for Info.plist
|
||||
local platform_name=""
|
||||
local sdk_name=""
|
||||
local supported_platform=""
|
||||
|
||||
case "$platform" in
|
||||
"ios")
|
||||
platform_name="iphoneos"
|
||||
sdk_name="iphoneos${min_os_version}"
|
||||
supported_platform="iPhoneOS"
|
||||
local plist_path="${build_dir}/framework/${framework_name}.framework/Info.plist"
|
||||
local device_family=' <key>UIDeviceFamily</key>
|
||||
<array>
|
||||
<integer>1</integer>
|
||||
<integer>2</integer>
|
||||
</array>'
|
||||
;;
|
||||
"macos")
|
||||
platform_name="macosx"
|
||||
sdk_name="macosx${min_os_version}"
|
||||
supported_platform="MacOSX"
|
||||
local plist_path="${build_dir}/framework/${framework_name}.framework/Versions/A/Resources/Info.plist"
|
||||
local device_family=""
|
||||
;;
|
||||
"visionos")
|
||||
platform_name="xros"
|
||||
sdk_name="xros${min_os_version}"
|
||||
supported_platform="XRPlatform"
|
||||
local plist_path="${build_dir}/framework/${framework_name}.framework/Info.plist"
|
||||
local device_family=""
|
||||
;;
|
||||
"tvos")
|
||||
platform_name="appletvos"
|
||||
sdk_name="appletvos${min_os_version}"
|
||||
supported_platform="AppleTVOS"
|
||||
local plist_path="${build_dir}/framework/${framework_name}.framework/Info.plist"
|
||||
local device_family=' <key>UIDeviceFamily</key>
|
||||
<array>
|
||||
<integer>3</integer>
|
||||
</array>'
|
||||
;;
|
||||
esac
|
||||
|
||||
# Create Info.plist
|
||||
cat > ${plist_path} << EOF
|
||||
<?xml version="1.0" encoding="UTF-8"?>
|
||||
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
|
||||
<plist version="1.0">
|
||||
<dict>
|
||||
<key>CFBundleDevelopmentRegion</key>
|
||||
<string>en</string>
|
||||
<key>CFBundleExecutable</key>
|
||||
<string>llama</string>
|
||||
<key>CFBundleIdentifier</key>
|
||||
<string>org.ggml.llama</string>
|
||||
<key>CFBundleInfoDictionaryVersion</key>
|
||||
<string>6.0</string>
|
||||
<key>CFBundleName</key>
|
||||
<string>llama</string>
|
||||
<key>CFBundlePackageType</key>
|
||||
<string>FMWK</string>
|
||||
<key>CFBundleShortVersionString</key>
|
||||
<string>1.0</string>
|
||||
<key>CFBundleVersion</key>
|
||||
<string>1</string>
|
||||
<key>MinimumOSVersion</key>
|
||||
<string>${min_os_version}</string>
|
||||
<key>CFBundleSupportedPlatforms</key>
|
||||
<array>
|
||||
<string>${supported_platform}</string>
|
||||
</array>${device_family}
|
||||
<key>DTPlatformName</key>
|
||||
<string>${platform_name}</string>
|
||||
<key>DTSDKName</key>
|
||||
<string>${sdk_name}</string>
|
||||
</dict>
|
||||
</plist>
|
||||
EOF
|
||||
}
|
||||
|
||||
# Create dynamic libraries from static libraries.
|
||||
combine_static_libraries() {
|
||||
local build_dir="$1"
|
||||
local release_dir="$2"
|
||||
local platform="$3" # "ios", "macos", "visionos", or "tvos"
|
||||
local is_simulator="$4"
|
||||
local base_dir="$(pwd)"
|
||||
local framework_name="llama"
|
||||
|
||||
# Determine output path based on platform
|
||||
local output_lib=""
|
||||
if [[ "$platform" == "macos" ]]; then
|
||||
# macOS uses versioned structure
|
||||
output_lib="${build_dir}/framework/${framework_name}.framework/Versions/A/${framework_name}"
|
||||
else
|
||||
# iOS, visionOS, and tvOS use a directory flat structure
|
||||
output_lib="${build_dir}/framework/${framework_name}.framework/${framework_name}"
|
||||
fi
|
||||
|
||||
local libs=(
|
||||
"${base_dir}/${build_dir}/src/${release_dir}/libllama.a"
|
||||
"${base_dir}/${build_dir}/ggml/src/${release_dir}/libggml.a"
|
||||
"${base_dir}/${build_dir}/ggml/src/${release_dir}/libggml-base.a"
|
||||
"${base_dir}/${build_dir}/ggml/src/${release_dir}/libggml-cpu.a"
|
||||
"${base_dir}/${build_dir}/ggml/src/ggml-metal/${release_dir}/libggml-metal.a"
|
||||
"${base_dir}/${build_dir}/ggml/src/ggml-blas/${release_dir}/libggml-blas.a"
|
||||
)
|
||||
|
||||
# Create temporary directory for processing
|
||||
local temp_dir="${base_dir}/${build_dir}/temp"
|
||||
mkdir -p "${temp_dir}"
|
||||
|
||||
# Since we have multiple architectures libtool will find object files that do not
|
||||
# match the target architecture. We suppress these warnings.
|
||||
libtool -static -o "${temp_dir}/combined.a" "${libs[@]}" 2> /dev/null
|
||||
|
||||
# Determine SDK, architectures, and install_name based on platform and simulator flag.
|
||||
local sdk=""
|
||||
local archs=""
|
||||
local min_version_flag=""
|
||||
local install_name=""
|
||||
|
||||
case "$platform" in
|
||||
"ios")
|
||||
if [[ "$is_simulator" == "true" ]]; then
|
||||
sdk="iphonesimulator"
|
||||
archs="arm64 x86_64"
|
||||
min_version_flag="-mios-simulator-version-min=${IOS_MIN_OS_VERSION}"
|
||||
else
|
||||
sdk="iphoneos"
|
||||
archs="arm64"
|
||||
min_version_flag="-mios-version-min=${IOS_MIN_OS_VERSION}"
|
||||
fi
|
||||
install_name="@rpath/llama.framework/llama"
|
||||
;;
|
||||
"macos")
|
||||
sdk="macosx"
|
||||
archs="arm64 x86_64"
|
||||
min_version_flag="-mmacosx-version-min=${MACOS_MIN_OS_VERSION}"
|
||||
install_name="@rpath/llama.framework/Versions/Current/llama"
|
||||
;;
|
||||
"visionos")
|
||||
if [[ "$is_simulator" == "true" ]]; then
|
||||
sdk="xrsimulator"
|
||||
archs="arm64 x86_64"
|
||||
min_version_flag="-mtargetos=xros${VISIONOS_MIN_OS_VERSION}-simulator"
|
||||
else
|
||||
sdk="xros"
|
||||
archs="arm64"
|
||||
min_version_flag="-mtargetos=xros${VISIONOS_MIN_OS_VERSION}"
|
||||
fi
|
||||
# Use flat structure for visionOS, same as iOS
|
||||
install_name="@rpath/llama.framework/llama"
|
||||
;;
|
||||
"tvos")
|
||||
if [[ "$is_simulator" == "true" ]]; then
|
||||
sdk="appletvsimulator"
|
||||
archs="arm64 x86_64"
|
||||
min_version_flag="-mtvos-simulator-version-min=${TVOS_MIN_OS_VERSION}"
|
||||
else
|
||||
sdk="appletvos"
|
||||
archs="arm64"
|
||||
min_version_flag="-mtvos-version-min=${TVOS_MIN_OS_VERSION}"
|
||||
fi
|
||||
install_name="@rpath/llama.framework/llama"
|
||||
;;
|
||||
esac
|
||||
|
||||
# Build architecture flags
|
||||
local arch_flags=""
|
||||
for arch in $archs; do
|
||||
arch_flags+=" -arch $arch"
|
||||
done
|
||||
|
||||
# Create dynamic library
|
||||
echo "Creating dynamic library for ${platform}."
|
||||
xcrun -sdk $sdk clang++ -dynamiclib \
|
||||
-isysroot $(xcrun --sdk $sdk --show-sdk-path) \
|
||||
$arch_flags \
|
||||
$min_version_flag \
|
||||
-Wl,-force_load,"${temp_dir}/combined.a" \
|
||||
-framework Foundation -framework Metal -framework Accelerate \
|
||||
-install_name "$install_name" \
|
||||
-o "${base_dir}/${output_lib}"
|
||||
|
||||
# Platform-specific post-processing for device builds
|
||||
if [[ "$is_simulator" == "false" ]]; then
|
||||
if command -v xcrun vtool &>/dev/null; then
|
||||
case "$platform" in
|
||||
"ios")
|
||||
echo "Marking binary as a framework binary for iOS..."
|
||||
xcrun vtool -set-build-version ios ${IOS_MIN_OS_VERSION} ${IOS_MIN_OS_VERSION} -replace \
|
||||
-output "${base_dir}/${output_lib}" "${base_dir}/${output_lib}"
|
||||
;;
|
||||
"visionos")
|
||||
echo "Marking binary as a framework binary for visionOS..."
|
||||
if [[ "$MAJOR_VERSION" -gt 16 ]] || [[ "$MAJOR_VERSION" -eq 16 && "$MINOR_VERSION" -gt 2 ]]; then
|
||||
echo "Xcode version greater than 16.2, using visionOS."
|
||||
VISION_OS_BUILD_VERSION="visionos"
|
||||
else
|
||||
echo "Xcode version less than or equal to 16.2, using xros."
|
||||
VISION_OS_BUILD_VERSION="xros"
|
||||
fi
|
||||
xcrun vtool -set-build-version ${VISION_OS_BUILD_VERSION} ${VISIONOS_MIN_OS_VERSION} ${VISIONOS_MIN_OS_VERSION} -replace \
|
||||
-output "${base_dir}/${output_lib}" "${base_dir}/${output_lib}"
|
||||
;;
|
||||
"tvos")
|
||||
echo "Marking binary as a framework binary for tvOS..."
|
||||
xcrun vtool -set-build-version tvos ${TVOS_MIN_OS_VERSION} ${TVOS_MIN_OS_VERSION} -replace \
|
||||
-output "${base_dir}/${output_lib}" "${base_dir}/${output_lib}"
|
||||
;;
|
||||
esac
|
||||
else
|
||||
echo "Warning: vtool not found. Binary may not pass App Store validation."
|
||||
fi
|
||||
fi
|
||||
|
||||
echo "Creating properly formatted dSYM..."
|
||||
# Create a separate directory for dSYMs for all platforms
|
||||
mkdir -p "${base_dir}/${build_dir}/dSYMs"
|
||||
|
||||
# iOS and visionOS style dSYM (flat structure)
|
||||
if [[ "$platform" == "ios" || "$platform" == "visionos" || "$platform" == "tvos" ]]; then
|
||||
# Generate dSYM in the dSYMs directory
|
||||
xcrun dsymutil "${base_dir}/${output_lib}" -o "${base_dir}/${build_dir}/dSYMs/llama.dSYM"
|
||||
|
||||
# Create a copy of the binary that will be stripped
|
||||
cp "${base_dir}/${output_lib}" "${temp_dir}/binary_to_strip"
|
||||
|
||||
# Strip debug symbols from the copy
|
||||
xcrun strip -S "${temp_dir}/binary_to_strip" -o "${temp_dir}/stripped_lib"
|
||||
|
||||
# Replace the original with the stripped version
|
||||
mv "${temp_dir}/stripped_lib" "${base_dir}/${output_lib}"
|
||||
else
|
||||
# macOS style dSYM
|
||||
# First strip debug info to a separate file
|
||||
xcrun strip -S "${base_dir}/${output_lib}" -o "${temp_dir}/stripped_lib"
|
||||
|
||||
# Generate dSYM in the dSYMs directory
|
||||
xcrun dsymutil "${base_dir}/${output_lib}" -o "${base_dir}/${build_dir}/dSYMs/llama.dSYM"
|
||||
|
||||
# Replace original binary with stripped version
|
||||
mv "${temp_dir}/stripped_lib" "${base_dir}/${output_lib}"
|
||||
fi
|
||||
|
||||
# Remove any automatically generated dSYM files in the framework structure as they will
|
||||
# otherwise case Invalid Bundle Structure validation errors.
|
||||
if [ -d "${base_dir}/${output_lib}.dSYM" ]; then
|
||||
echo "Removing generated dSYM file in framework structure: ${base_dir}/${output_lib}.dSYM"
|
||||
rm -rf "${base_dir}/${output_lib}.dSYM"
|
||||
fi
|
||||
|
||||
# Clean up
|
||||
rm -rf "${temp_dir}"
|
||||
}
|
||||
|
||||
echo "Building for iOS simulator..."
|
||||
cmake -B build-ios-sim -G Xcode \
|
||||
"${COMMON_CMAKE_ARGS[@]}" \
|
||||
-DCMAKE_OSX_DEPLOYMENT_TARGET=${IOS_MIN_OS_VERSION} \
|
||||
-DIOS=ON \
|
||||
-DCMAKE_SYSTEM_NAME=iOS \
|
||||
-DCMAKE_OSX_SYSROOT=iphonesimulator \
|
||||
-DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
|
||||
-DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphonesimulator \
|
||||
-DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
|
||||
-DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
|
||||
-DLLAMA_OPENSSL=OFF \
|
||||
-S .
|
||||
cmake --build build-ios-sim --config Release -- -quiet
|
||||
|
||||
echo "Building for iOS devices..."
|
||||
cmake -B build-ios-device -G Xcode \
|
||||
"${COMMON_CMAKE_ARGS[@]}" \
|
||||
-DCMAKE_OSX_DEPLOYMENT_TARGET=${IOS_MIN_OS_VERSION} \
|
||||
-DCMAKE_SYSTEM_NAME=iOS \
|
||||
-DCMAKE_OSX_SYSROOT=iphoneos \
|
||||
-DCMAKE_OSX_ARCHITECTURES="arm64" \
|
||||
-DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=iphoneos \
|
||||
-DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
|
||||
-DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
|
||||
-DLLAMA_OPENSSL=OFF \
|
||||
-S .
|
||||
cmake --build build-ios-device --config Release -- -quiet
|
||||
|
||||
echo "Building for macOS..."
|
||||
cmake -B build-macos -G Xcode \
|
||||
"${COMMON_CMAKE_ARGS[@]}" \
|
||||
-DCMAKE_OSX_DEPLOYMENT_TARGET=${MACOS_MIN_OS_VERSION} \
|
||||
-DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
|
||||
-DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
|
||||
-DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
|
||||
-DLLAMA_OPENSSL=OFF \
|
||||
-S .
|
||||
cmake --build build-macos --config Release -- -quiet
|
||||
|
||||
echo "Building for visionOS..."
|
||||
cmake -B build-visionos -G Xcode \
|
||||
"${COMMON_CMAKE_ARGS[@]}" \
|
||||
-DCMAKE_OSX_DEPLOYMENT_TARGET=${VISIONOS_MIN_OS_VERSION} \
|
||||
-DCMAKE_OSX_ARCHITECTURES="arm64" \
|
||||
-DCMAKE_SYSTEM_NAME=visionOS \
|
||||
-DCMAKE_OSX_SYSROOT=xros \
|
||||
-DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xros \
|
||||
-DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \
|
||||
-DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \
|
||||
-DLLAMA_OPENSSL=OFF \
|
||||
-DLLAMA_HTTPLIB=OFF \
|
||||
-DLLAMA_BUILD_SERVER=OFF \
|
||||
-S .
|
||||
cmake --build build-visionos --config Release -- -quiet
|
||||
|
||||
echo "Building for visionOS simulator..."
|
||||
cmake -B build-visionos-sim -G Xcode \
|
||||
"${COMMON_CMAKE_ARGS[@]}" \
|
||||
-DCMAKE_OSX_DEPLOYMENT_TARGET=${VISIONOS_MIN_OS_VERSION} \
|
||||
-DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
|
||||
-DCMAKE_SYSTEM_NAME=visionOS \
|
||||
-DCMAKE_OSX_SYSROOT=xrsimulator \
|
||||
-DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=xrsimulator \
|
||||
-DCMAKE_C_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_C_FLAGS}" \
|
||||
-DCMAKE_CXX_FLAGS="-D_XOPEN_SOURCE=700 ${COMMON_CXX_FLAGS}" \
|
||||
-DLLAMA_OPENSSL=OFF \
|
||||
-DLLAMA_HTTPLIB=OFF \
|
||||
-DLLAMA_BUILD_SERVER=OFF \
|
||||
-S .
|
||||
cmake --build build-visionos-sim --config Release -- -quiet
|
||||
|
||||
# Add tvOS builds (might need the same u_int definitions as watchOS and visionOS)
|
||||
echo "Building for tvOS simulator..."
|
||||
cmake -B build-tvos-sim -G Xcode \
|
||||
"${COMMON_CMAKE_ARGS[@]}" \
|
||||
-DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \
|
||||
-DCMAKE_SYSTEM_NAME=tvOS \
|
||||
-DCMAKE_OSX_SYSROOT=appletvsimulator \
|
||||
-DCMAKE_OSX_ARCHITECTURES="arm64;x86_64" \
|
||||
-DGGML_METAL=ON \
|
||||
-DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvsimulator \
|
||||
-DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
|
||||
-DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
|
||||
-DLLAMA_OPENSSL=OFF \
|
||||
-S .
|
||||
cmake --build build-tvos-sim --config Release -- -quiet
|
||||
|
||||
echo "Building for tvOS devices..."
|
||||
cmake -B build-tvos-device -G Xcode \
|
||||
"${COMMON_CMAKE_ARGS[@]}" \
|
||||
-DCMAKE_OSX_DEPLOYMENT_TARGET=${TVOS_MIN_OS_VERSION} \
|
||||
-DCMAKE_SYSTEM_NAME=tvOS \
|
||||
-DCMAKE_OSX_SYSROOT=appletvos \
|
||||
-DCMAKE_OSX_ARCHITECTURES="arm64" \
|
||||
-DGGML_METAL=ON \
|
||||
-DCMAKE_XCODE_ATTRIBUTE_SUPPORTED_PLATFORMS=appletvos \
|
||||
-DCMAKE_C_FLAGS="${COMMON_C_FLAGS}" \
|
||||
-DCMAKE_CXX_FLAGS="${COMMON_CXX_FLAGS}" \
|
||||
-DLLAMA_OPENSSL=OFF \
|
||||
-S .
|
||||
cmake --build build-tvos-device --config Release -- -quiet
|
||||
|
||||
# Setup frameworks and copy binaries and headers
|
||||
echo "Setting up framework structures..."
|
||||
setup_framework_structure "build-ios-sim" ${IOS_MIN_OS_VERSION} "ios"
|
||||
setup_framework_structure "build-ios-device" ${IOS_MIN_OS_VERSION} "ios"
|
||||
setup_framework_structure "build-macos" ${MACOS_MIN_OS_VERSION} "macos"
|
||||
setup_framework_structure "build-visionos" ${VISIONOS_MIN_OS_VERSION} "visionos"
|
||||
setup_framework_structure "build-visionos-sim" ${VISIONOS_MIN_OS_VERSION} "visionos"
|
||||
setup_framework_structure "build-tvos-sim" ${TVOS_MIN_OS_VERSION} "tvos"
|
||||
setup_framework_structure "build-tvos-device" ${TVOS_MIN_OS_VERSION} "tvos"
|
||||
|
||||
# Create dynamic libraries from static libraries
|
||||
echo "Creating dynamic libraries from static libraries..."
|
||||
combine_static_libraries "build-ios-sim" "Release-iphonesimulator" "ios" "true"
|
||||
combine_static_libraries "build-ios-device" "Release-iphoneos" "ios" "false"
|
||||
combine_static_libraries "build-macos" "Release" "macos" "false"
|
||||
combine_static_libraries "build-visionos" "Release-xros" "visionos" "false"
|
||||
combine_static_libraries "build-visionos-sim" "Release-xrsimulator" "visionos" "true"
|
||||
combine_static_libraries "build-tvos-sim" "Release-appletvsimulator" "tvos" "true"
|
||||
combine_static_libraries "build-tvos-device" "Release-appletvos" "tvos" "false"
|
||||
|
||||
# Create XCFramework with correct debug symbols paths
|
||||
echo "Creating XCFramework..."
|
||||
xcodebuild -create-xcframework \
|
||||
-framework $(pwd)/build-ios-sim/framework/llama.framework \
|
||||
-debug-symbols $(pwd)/build-ios-sim/dSYMs/llama.dSYM \
|
||||
-framework $(pwd)/build-ios-device/framework/llama.framework \
|
||||
-debug-symbols $(pwd)/build-ios-device/dSYMs/llama.dSYM \
|
||||
-framework $(pwd)/build-macos/framework/llama.framework \
|
||||
-debug-symbols $(pwd)/build-macos/dSYMS/llama.dSYM \
|
||||
-framework $(pwd)/build-visionos/framework/llama.framework \
|
||||
-debug-symbols $(pwd)/build-visionos/dSYMs/llama.dSYM \
|
||||
-framework $(pwd)/build-visionos-sim/framework/llama.framework \
|
||||
-debug-symbols $(pwd)/build-visionos-sim/dSYMs/llama.dSYM \
|
||||
-framework $(pwd)/build-tvos-device/framework/llama.framework \
|
||||
-debug-symbols $(pwd)/build-tvos-device/dSYMs/llama.dSYM \
|
||||
-framework $(pwd)/build-tvos-sim/framework/llama.framework \
|
||||
-debug-symbols $(pwd)/build-tvos-sim/dSYMs/llama.dSYM \
|
||||
-output $(pwd)/build-apple/llama.xcframework
|
||||
35
ci/README-MUSA.md
Normal file
35
ci/README-MUSA.md
Normal file
@@ -0,0 +1,35 @@
|
||||
## Running MUSA CI in a Docker Container
|
||||
|
||||
Assuming `$PWD` is the root of the `llama.cpp` repository, follow these steps to set up and run MUSA CI in a Docker container:
|
||||
|
||||
### 1. Create a local directory to store cached models, configuration files and venv:
|
||||
|
||||
```bash
|
||||
mkdir -p $HOME/llama.cpp/ci-cache
|
||||
```
|
||||
|
||||
### 2. Create a local directory to store CI run results:
|
||||
|
||||
```bash
|
||||
mkdir -p $HOME/llama.cpp/ci-results
|
||||
```
|
||||
|
||||
### 3. Start a Docker container and run the CI:
|
||||
|
||||
```bash
|
||||
docker run --privileged -it \
|
||||
-v $HOME/llama.cpp/ci-cache:/ci-cache \
|
||||
-v $HOME/llama.cpp/ci-results:/ci-results \
|
||||
-v $PWD:/ws -w /ws \
|
||||
mthreads/musa:rc4.3.0-devel-ubuntu22.04-amd64
|
||||
```
|
||||
|
||||
Inside the container, execute the following commands:
|
||||
|
||||
```bash
|
||||
apt update -y && apt install -y bc cmake ccache git python3.10-venv time unzip wget
|
||||
git config --global --add safe.directory /ws
|
||||
GG_BUILD_MUSA=1 bash ./ci/run.sh /ci-results /ci-cache
|
||||
```
|
||||
|
||||
This setup ensures that the CI runs within an isolated Docker environment while maintaining cached files and results across runs.
|
||||
33
ci/README.md
Normal file
33
ci/README.md
Normal file
@@ -0,0 +1,33 @@
|
||||
# CI
|
||||
|
||||
This CI implements heavy-duty workflows that run on self-hosted runners. Typically the purpose of these workflows is to
|
||||
cover hardware configurations that are not available from Github-hosted runners and/or require more computational
|
||||
resource than normally available.
|
||||
|
||||
It is a good practice, before publishing changes to execute the full CI locally on your machine. For example:
|
||||
|
||||
```bash
|
||||
mkdir tmp
|
||||
|
||||
# CPU-only build
|
||||
bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
||||
|
||||
# with CUDA support
|
||||
GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
||||
|
||||
# with SYCL support
|
||||
source /opt/intel/oneapi/setvars.sh
|
||||
GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
||||
|
||||
# with MUSA support
|
||||
GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
||||
|
||||
# etc.
|
||||
```
|
||||
|
||||
# Adding self-hosted runners
|
||||
|
||||
- Add a self-hosted `ggml-ci` workflow to [[.github/workflows/build.yml]] with an appropriate label
|
||||
- Request a runner token from `ggml-org` (for example, via a comment in the PR or email)
|
||||
- Set-up a machine using the received token ([docs](https://docs.github.com/en/actions/how-tos/manage-runners/self-hosted-runners/add-runners))
|
||||
- Optionally update [ci/run.sh](https://github.com/ggml-org/llama.cpp/blob/master/ci/run.sh) to build and run on the target platform by gating the implementation with a `GG_BUILD_...` env
|
||||
682
ci/run.sh
Executable file
682
ci/run.sh
Executable file
@@ -0,0 +1,682 @@
|
||||
#!/usr/bin/env bash
|
||||
#
|
||||
# sample usage:
|
||||
#
|
||||
# mkdir tmp
|
||||
#
|
||||
# # CPU-only build
|
||||
# bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
||||
#
|
||||
# # with CUDA support
|
||||
# GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
||||
#
|
||||
# # with SYCL support
|
||||
# GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
||||
#
|
||||
# # with VULKAN support
|
||||
# GG_BUILD_VULKAN=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
||||
#
|
||||
# # with WebGPU support
|
||||
# GG_BUILD_WEBGPU=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
||||
#
|
||||
# # with MUSA support
|
||||
# GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
||||
#
|
||||
# # with KLEIDIAI support
|
||||
# GG_BUILD_KLEIDIAI=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
||||
#
|
||||
|
||||
if [ -z "$2" ]; then
|
||||
echo "usage: $0 <output-dir> <mnt-dir>"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
mkdir -p "$1"
|
||||
mkdir -p "$2"
|
||||
|
||||
OUT=$(realpath "$1")
|
||||
MNT=$(realpath "$2")
|
||||
|
||||
rm -f $OUT/*.log
|
||||
rm -f $OUT/*.exit
|
||||
rm -f $OUT/*.md
|
||||
|
||||
sd=`dirname $0`
|
||||
cd $sd/../
|
||||
SRC=`pwd`
|
||||
|
||||
CMAKE_EXTRA="-DLLAMA_FATAL_WARNINGS=${LLAMA_FATAL_WARNINGS:-ON} -DLLAMA_OPENSSL=OFF -DGGML_SCHED_NO_REALLOC=ON"
|
||||
|
||||
if [ ! -z ${GG_BUILD_METAL} ]; then
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=ON"
|
||||
fi
|
||||
|
||||
if [ ! -z ${GG_BUILD_CUDA} ]; then
|
||||
# TODO: Remove GGML_CUDA_CUB_3DOT2 flag once CCCL 3.2 is bundled within CTK and that CTK version is used in this project
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_CUDA=ON -DGGML_CUDA_CUB_3DOT2=ON"
|
||||
|
||||
if command -v nvidia-smi >/dev/null 2>&1; then
|
||||
CUDA_ARCH=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader,nounits 2>/dev/null | head -1 | tr -d '.')
|
||||
if [[ -n "$CUDA_ARCH" && "$CUDA_ARCH" =~ ^[0-9]+$ ]]; then
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DCMAKE_CUDA_ARCHITECTURES=${CUDA_ARCH}"
|
||||
else
|
||||
echo "Warning: Using fallback CUDA architectures"
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DCMAKE_CUDA_ARCHITECTURES=61;70;75;80;86;89"
|
||||
fi
|
||||
else
|
||||
echo "Error: nvidia-smi not found, cannot build with CUDA"
|
||||
exit 1
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ ! -z ${GG_BUILD_ROCM} ]; then
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_HIP=ON"
|
||||
if [ -z ${GG_BUILD_AMDGPU_TARGETS} ]; then
|
||||
echo "Missing GG_BUILD_AMDGPU_TARGETS, please set it to your GPU architecture (e.g. gfx90a, gfx1100, etc.)"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DGPU_TARGETS=${GG_BUILD_AMDGPU_TARGETS}"
|
||||
fi
|
||||
|
||||
if [ ! -z ${GG_BUILD_SYCL} ]; then
|
||||
if [ -z ${ONEAPI_ROOT} ]; then
|
||||
echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:"
|
||||
echo "source /opt/intel/oneapi/setvars.sh"
|
||||
exit 1
|
||||
fi
|
||||
# Use only main GPU
|
||||
export ONEAPI_DEVICE_SELECTOR="level_zero:0"
|
||||
# Enable sysman for correct memory reporting
|
||||
export ZES_ENABLE_SYSMAN=1
|
||||
# to circumvent precision issues on CPY operations
|
||||
export SYCL_PROGRAM_COMPILE_OPTIONS="-cl-fp32-correctly-rounded-divide-sqrt"
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_SYCL=1 -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON"
|
||||
fi
|
||||
|
||||
if [ ! -z ${GG_BUILD_VULKAN} ]; then
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_VULKAN=1"
|
||||
|
||||
# if on Mac, disable METAL
|
||||
if [[ "$OSTYPE" == "darwin"* ]]; then
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=OFF -DGGML_BLAS=OFF"
|
||||
fi
|
||||
|
||||
fi
|
||||
|
||||
if [ ! -z ${GG_BUILD_WEBGPU} ]; then
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_WEBGPU=1 -DGGML_METAL=OFF -DGGML_BLAS=OFF"
|
||||
|
||||
if [ ! -z "${GG_BUILD_WEBGPU_DAWN_PREFIX}" ]; then
|
||||
if [ -z "${CMAKE_PREFIX_PATH}" ]; then
|
||||
export CMAKE_PREFIX_PATH="${GG_BUILD_WEBGPU_DAWN_PREFIX}"
|
||||
else
|
||||
export CMAKE_PREFIX_PATH="${GG_BUILD_WEBGPU_DAWN_PREFIX}:${CMAKE_PREFIX_PATH}"
|
||||
fi
|
||||
fi
|
||||
|
||||
# For some systems, Dawn_DIR needs to be set explicitly, e.g., the lib64 path
|
||||
if [ ! -z "${GG_BUILD_WEBGPU_DAWN_DIR}" ]; then
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DDawn_DIR=${GG_BUILD_WEBGPU_DAWN_DIR}"
|
||||
fi
|
||||
fi
|
||||
|
||||
if [ ! -z ${GG_BUILD_MUSA} ]; then
|
||||
# Use qy1 by default (MTT S80)
|
||||
MUSA_ARCH=${MUSA_ARCH:-21}
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_MUSA=ON -DMUSA_ARCHITECTURES=${MUSA_ARCH}"
|
||||
fi
|
||||
|
||||
if [ ! -z ${GG_BUILD_NO_SVE} ]; then
|
||||
# arm 9 and newer enables sve by default, adjust these flags depending on the cpu used
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm"
|
||||
fi
|
||||
|
||||
if [ -n "${GG_BUILD_KLEIDIAI}" ]; then
|
||||
echo ">>===== Enabling KleidiAI support"
|
||||
|
||||
CANDIDATES=(
|
||||
"armv9-a+dotprod+i8mm+sve2"
|
||||
"armv9-a+dotprod+i8mm"
|
||||
"armv8.6-a+dotprod+i8mm"
|
||||
"armv8.2-a+dotprod"
|
||||
)
|
||||
CPU=""
|
||||
|
||||
for cpu in "${CANDIDATES[@]}"; do
|
||||
if echo 'int main(){}' | ${CXX:-c++} -march="$cpu" -x c++ - -c -o /dev/null >/dev/null 2>&1; then
|
||||
CPU="$cpu"
|
||||
break
|
||||
fi
|
||||
done
|
||||
|
||||
if [ -z "$CPU" ]; then
|
||||
echo "ERROR: None of the required ARM baselines (armv9/armv8.6/armv8.2 + dotprod) are supported by this compiler."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo ">>===== Using ARM baseline: ${CPU}"
|
||||
|
||||
CMAKE_EXTRA="${CMAKE_EXTRA:+$CMAKE_EXTRA } \
|
||||
-DGGML_NATIVE=OFF \
|
||||
-DGGML_CPU_KLEIDIAI=ON \
|
||||
-DGGML_CPU_AARCH64=ON \
|
||||
-DGGML_CPU_ARM_ARCH=${CPU} \
|
||||
-DBUILD_SHARED_LIBS=OFF"
|
||||
fi
|
||||
|
||||
## helpers
|
||||
|
||||
# download a file if it does not exist or if it is outdated
|
||||
function gg_wget {
|
||||
local out=$1
|
||||
local url=$2
|
||||
|
||||
local cwd=`pwd`
|
||||
|
||||
mkdir -p $out
|
||||
cd $out
|
||||
|
||||
# should not re-download if file is the same
|
||||
wget -nv -c -N $url
|
||||
|
||||
cd $cwd
|
||||
}
|
||||
|
||||
function gg_printf {
|
||||
printf -- "$@" >> $OUT/README.md
|
||||
}
|
||||
|
||||
function gg_run {
|
||||
ci=$1
|
||||
|
||||
set -o pipefail
|
||||
set -x
|
||||
|
||||
gg_run_$ci | tee $OUT/$ci.log
|
||||
cur=$?
|
||||
echo "$cur" > $OUT/$ci.exit
|
||||
|
||||
set +x
|
||||
set +o pipefail
|
||||
|
||||
gg_sum_$ci
|
||||
|
||||
ret=$((ret | cur))
|
||||
}
|
||||
|
||||
## ci
|
||||
|
||||
# ctest_debug
|
||||
|
||||
function gg_run_ctest_debug {
|
||||
cd ${SRC}
|
||||
|
||||
rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug
|
||||
|
||||
set -e
|
||||
|
||||
# Check cmake, make and ctest are installed
|
||||
gg_check_build_requirements
|
||||
|
||||
(time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
|
||||
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
|
||||
|
||||
(time ctest --output-on-failure -L main -E "test-opt|test-backend-ops" ) 2>&1 | tee -a $OUT/${ci}-ctest.log
|
||||
|
||||
set +e
|
||||
}
|
||||
|
||||
function gg_sum_ctest_debug {
|
||||
gg_printf '### %s\n\n' "${ci}"
|
||||
|
||||
gg_printf 'Runs ctest in debug mode\n'
|
||||
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
|
||||
gg_printf '```\n'
|
||||
gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
|
||||
gg_printf '```\n'
|
||||
gg_printf '\n'
|
||||
}
|
||||
|
||||
# ctest_release
|
||||
|
||||
function gg_run_ctest_release {
|
||||
cd ${SRC}
|
||||
|
||||
rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
|
||||
|
||||
set -e
|
||||
|
||||
# Check cmake, make and ctest are installed
|
||||
gg_check_build_requirements
|
||||
|
||||
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
|
||||
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
|
||||
|
||||
if [ -z ${GG_BUILD_LOW_PERF} ]; then
|
||||
(time ctest --output-on-failure -L main ) 2>&1 | tee -a $OUT/${ci}-ctest.log
|
||||
else
|
||||
(time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
|
||||
fi
|
||||
|
||||
set +e
|
||||
}
|
||||
|
||||
function gg_sum_ctest_release {
|
||||
gg_printf '### %s\n\n' "${ci}"
|
||||
|
||||
gg_printf 'Runs ctest in release mode\n'
|
||||
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
|
||||
gg_printf '```\n'
|
||||
gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
|
||||
gg_printf '```\n'
|
||||
}
|
||||
|
||||
# test_scripts
|
||||
|
||||
function gg_run_test_scripts {
|
||||
cd ${SRC}
|
||||
|
||||
set -e
|
||||
|
||||
(cd ./tools/gguf-split && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
|
||||
(cd ./tools/quantize && time bash tests.sh "$SRC/build-ci-release/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
|
||||
|
||||
set +e
|
||||
}
|
||||
|
||||
function gg_sum_test_scripts {
|
||||
gg_printf '### %s\n\n' "${ci}"
|
||||
|
||||
gg_printf 'Runs test scripts\n'
|
||||
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
|
||||
gg_printf '```\n'
|
||||
gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
|
||||
gg_printf '```\n'
|
||||
gg_printf '\n'
|
||||
}
|
||||
|
||||
function gg_get_model {
|
||||
#local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-f16.gguf"
|
||||
local gguf_0="$MNT/models/qwen3/0.6B/ggml-model-q4_0.gguf"
|
||||
if [[ -s $gguf_0 ]]; then
|
||||
echo -n "$gguf_0"
|
||||
else
|
||||
echo >&2 "No model found. Can't run gg_run_ctest_with_model."
|
||||
exit 1
|
||||
fi
|
||||
}
|
||||
|
||||
function gg_run_ctest_with_model_debug {
|
||||
cd ${SRC}
|
||||
|
||||
local model; model=$(gg_get_model)
|
||||
cd build-ci-debug
|
||||
set -e
|
||||
|
||||
(LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
|
||||
|
||||
set +e
|
||||
cd ..
|
||||
}
|
||||
|
||||
function gg_run_ctest_with_model_release {
|
||||
cd ${SRC}
|
||||
|
||||
local model; model=$(gg_get_model)
|
||||
cd build-ci-release
|
||||
set -e
|
||||
|
||||
(LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log
|
||||
|
||||
# test memory leaks
|
||||
#if [[ ! -z ${GG_BUILD_METAL} ]]; then
|
||||
# # TODO: this hangs for some reason ...
|
||||
# (time leaks -quiet -atExit -- ./bin/test-thread-safety -m $model --parallel 2 -t 2 -p "hello") 2>&1 | tee -a $OUT/${ci}-leaks.log
|
||||
#fi
|
||||
|
||||
set +e
|
||||
cd ..
|
||||
}
|
||||
|
||||
function gg_sum_ctest_with_model_debug {
|
||||
gg_printf '### %s\n\n' "${ci}"
|
||||
|
||||
gg_printf 'Runs ctest with model files in debug mode\n'
|
||||
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
|
||||
gg_printf '```\n'
|
||||
gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
|
||||
gg_printf '```\n'
|
||||
}
|
||||
|
||||
function gg_sum_ctest_with_model_release {
|
||||
gg_printf '### %s\n\n' "${ci}"
|
||||
|
||||
gg_printf 'Runs ctest with model files in release mode\n'
|
||||
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
|
||||
gg_printf '```\n'
|
||||
gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)"
|
||||
gg_printf '```\n'
|
||||
}
|
||||
|
||||
# qwen3_0_6b
|
||||
|
||||
function gg_run_qwen3_0_6b {
|
||||
cd ${SRC}
|
||||
|
||||
gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/config.json
|
||||
gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/tokenizer.json
|
||||
gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/tokenizer_config.json
|
||||
#gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/special_tokens_map.json
|
||||
gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/resolve/main/model.safetensors
|
||||
|
||||
|
||||
gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip
|
||||
unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/
|
||||
|
||||
path_models="../models-mnt/qwen3/0.6B"
|
||||
path_wiki="../models-mnt/wikitext/wikitext-2-raw"
|
||||
|
||||
rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
|
||||
|
||||
set -e
|
||||
|
||||
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
|
||||
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
|
||||
|
||||
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf --outtype f16
|
||||
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-bf16.gguf --outtype bf16
|
||||
|
||||
model_f16="${path_models}/ggml-model-f16.gguf"
|
||||
model_bf16="${path_models}/ggml-model-bf16.gguf"
|
||||
model_q8_0="${path_models}/ggml-model-q8_0.gguf"
|
||||
model_q4_0="${path_models}/ggml-model-q4_0.gguf"
|
||||
model_q4_1="${path_models}/ggml-model-q4_1.gguf"
|
||||
model_q5_0="${path_models}/ggml-model-q5_0.gguf"
|
||||
model_q5_1="${path_models}/ggml-model-q5_1.gguf"
|
||||
model_q2_k="${path_models}/ggml-model-q2_k.gguf"
|
||||
model_q3_k="${path_models}/ggml-model-q3_k.gguf"
|
||||
model_q4_k="${path_models}/ggml-model-q4_k.gguf"
|
||||
model_q5_k="${path_models}/ggml-model-q5_k.gguf"
|
||||
model_q6_k="${path_models}/ggml-model-q6_k.gguf"
|
||||
|
||||
wiki_test="${path_wiki}/wiki.test.raw"
|
||||
|
||||
./bin/llama-quantize ${model_bf16} ${model_q8_0} q8_0 $(nproc)
|
||||
./bin/llama-quantize ${model_bf16} ${model_q4_0} q4_0 $(nproc)
|
||||
./bin/llama-quantize ${model_bf16} ${model_q4_1} q4_1 $(nproc)
|
||||
./bin/llama-quantize ${model_bf16} ${model_q5_0} q5_0 $(nproc)
|
||||
./bin/llama-quantize ${model_bf16} ${model_q5_1} q5_1 $(nproc)
|
||||
./bin/llama-quantize ${model_bf16} ${model_q2_k} q2_k $(nproc)
|
||||
./bin/llama-quantize ${model_bf16} ${model_q3_k} q3_k $(nproc)
|
||||
./bin/llama-quantize ${model_bf16} ${model_q4_k} q4_k $(nproc)
|
||||
./bin/llama-quantize ${model_bf16} ${model_q5_k} q5_k $(nproc)
|
||||
./bin/llama-quantize ${model_bf16} ${model_q6_k} q6_k $(nproc)
|
||||
|
||||
(time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log)
|
||||
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_f16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_bf16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_q8_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_q4_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_q4_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_q5_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_q5_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_q2_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_q3_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_q4_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_q5_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
|
||||
(time ./bin/llama-completion -no-cnv --model ${model_q6_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
|
||||
|
||||
(time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
|
||||
if [ -z ${GG_BUILD_NO_BF16} ]; then
|
||||
(time ./bin/llama-perplexity --model ${model_bf16} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log
|
||||
fi
|
||||
(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
|
||||
(time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
|
||||
(time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
|
||||
|
||||
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
|
||||
|
||||
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa off --no-op-offload) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa on --no-op-offload) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa on ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
|
||||
|
||||
function check_ppl {
|
||||
qnt="$1"
|
||||
ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
|
||||
|
||||
if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then
|
||||
printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl"
|
||||
return 20
|
||||
fi
|
||||
|
||||
printf ' - %s @ %s OK\n' "$qnt" "$ppl"
|
||||
return 0
|
||||
}
|
||||
|
||||
check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
|
||||
if [ -z ${GG_BUILD_NO_BF16} ]; then
|
||||
check_ppl "bf16" "$(cat $OUT/${ci}-tg-bf16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
|
||||
fi
|
||||
check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
|
||||
check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
|
||||
check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
|
||||
check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
|
||||
check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
|
||||
#check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model
|
||||
check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
|
||||
check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
|
||||
check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
|
||||
check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
|
||||
|
||||
cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log
|
||||
|
||||
set +e
|
||||
}
|
||||
|
||||
function gg_sum_qwen3_0_6b {
|
||||
gg_printf '### %s\n\n' "${ci}"
|
||||
|
||||
gg_printf 'Qwen3 0.6B:\n'
|
||||
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
|
||||
gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)"
|
||||
gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)"
|
||||
gg_printf '- f16:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
|
||||
if [ -z ${GG_BUILD_NO_BF16} ]; then
|
||||
gg_printf '- bf16:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-bf16.log)"
|
||||
fi
|
||||
gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
|
||||
gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)"
|
||||
gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)"
|
||||
gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)"
|
||||
gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)"
|
||||
gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)"
|
||||
gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)"
|
||||
gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)"
|
||||
gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)"
|
||||
gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)"
|
||||
gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)"
|
||||
}
|
||||
|
||||
# bge-small
|
||||
|
||||
function gg_run_embd_bge_small {
|
||||
cd ${SRC}
|
||||
|
||||
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json
|
||||
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer.json
|
||||
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/tokenizer_config.json
|
||||
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/special_tokens_map.json
|
||||
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/resolve/main/pytorch_model.bin
|
||||
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/sentence_bert_config.json
|
||||
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/vocab.txt
|
||||
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/modules.json
|
||||
gg_wget models-mnt/bge-small/ https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/config.json
|
||||
|
||||
gg_wget models-mnt/bge-small/1_Pooling https://huggingface.co/BAAI/bge-small-en-v1.5/raw/main/1_Pooling/config.json
|
||||
|
||||
path_models="../models-mnt/bge-small"
|
||||
|
||||
rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
|
||||
|
||||
set -e
|
||||
|
||||
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
|
||||
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
|
||||
|
||||
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
|
||||
|
||||
model_f16="${path_models}/ggml-model-f16.gguf"
|
||||
model_q8_0="${path_models}/ggml-model-q8_0.gguf"
|
||||
|
||||
./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
|
||||
|
||||
(time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log)
|
||||
|
||||
(time ./bin/llama-embedding --model ${model_f16} -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
|
||||
(time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
|
||||
|
||||
set +e
|
||||
}
|
||||
|
||||
function gg_sum_embd_bge_small {
|
||||
gg_printf '### %s\n\n' "${ci}"
|
||||
|
||||
gg_printf 'BGE Small (BERT):\n'
|
||||
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
|
||||
gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
|
||||
gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)"
|
||||
}
|
||||
|
||||
# rerank_tiny
|
||||
|
||||
function gg_run_rerank_tiny {
|
||||
cd ${SRC}
|
||||
|
||||
gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/config.json
|
||||
gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/tokenizer.json
|
||||
gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/tokenizer_config.json
|
||||
gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/special_tokens_map.json
|
||||
gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/resolve/main/pytorch_model.bin
|
||||
gg_wget models-mnt/rerank-tiny/ https://huggingface.co/jinaai/jina-reranker-v1-tiny-en/raw/main/vocab.json
|
||||
|
||||
path_models="../models-mnt/rerank-tiny"
|
||||
|
||||
rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
|
||||
|
||||
set -e
|
||||
|
||||
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
|
||||
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
|
||||
|
||||
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
|
||||
|
||||
model_f16="${path_models}/ggml-model-f16.gguf"
|
||||
|
||||
(time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log)
|
||||
|
||||
# for this model, the SEP token is "</s>"
|
||||
(time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --no-op-offload --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log
|
||||
|
||||
# sample output
|
||||
# rerank score 0: 0.029
|
||||
# rerank score 1: 0.029
|
||||
# rerank score 2: 0.135
|
||||
|
||||
# check that the score is in the range [$3, $4]
|
||||
function check_score {
|
||||
qnt="$1"
|
||||
score=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1)
|
||||
|
||||
if [ $(echo "$score < $3" | bc) -eq 1 ] || [ $(echo "$score > $4" | bc) -eq 1 ]; then
|
||||
printf ' - %s @ %s (FAIL: score not in range [%s, %s])\n' "$qnt" "$score" "$3" "$4"
|
||||
return 20
|
||||
fi
|
||||
|
||||
printf ' - %s @ %s OK\n' "$qnt" "$score"
|
||||
return 0
|
||||
}
|
||||
|
||||
check_score "rerank score 0" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 0")" "0.00" "0.05" | tee -a $OUT/${ci}-rk-f16.log
|
||||
check_score "rerank score 1" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 1")" "0.00" "0.05" | tee -a $OUT/${ci}-rk-f16.log
|
||||
check_score "rerank score 2" "$(cat $OUT/${ci}-rk-f16.log | grep "rerank score 2")" "0.10" "0.30" | tee -a $OUT/${ci}-rk-f16.log
|
||||
|
||||
set +e
|
||||
}
|
||||
|
||||
function gg_sum_rerank_tiny {
|
||||
gg_printf '### %s\n\n' "${ci}"
|
||||
|
||||
gg_printf 'Rerank Tiny (Jina):\n'
|
||||
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
|
||||
gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-rk-f16.log)"
|
||||
}
|
||||
|
||||
function gg_check_build_requirements {
|
||||
if ! command -v cmake &> /dev/null; then
|
||||
gg_printf 'cmake not found, please install'
|
||||
fi
|
||||
|
||||
if ! command -v make &> /dev/null; then
|
||||
gg_printf 'make not found, please install'
|
||||
fi
|
||||
|
||||
if ! command -v ctest &> /dev/null; then
|
||||
gg_printf 'ctest not found, please install'
|
||||
fi
|
||||
}
|
||||
|
||||
## main
|
||||
|
||||
export LLAMA_LOG_PREFIX=1
|
||||
export LLAMA_LOG_TIMESTAMPS=1
|
||||
|
||||
if [ -z ${GG_BUILD_LOW_PERF} ]; then
|
||||
# Create symlink: ./llama.cpp/models-mnt -> $MNT/models
|
||||
rm -rf ${SRC}/models-mnt
|
||||
mnt_models=${MNT}/models
|
||||
mkdir -p ${mnt_models}
|
||||
ln -sfn ${mnt_models} ${SRC}/models-mnt
|
||||
|
||||
# Create a fresh python3 venv and enter it
|
||||
if ! python3 -m venv "$MNT/venv"; then
|
||||
echo "Error: Failed to create Python virtual environment at $MNT/venv."
|
||||
exit 1
|
||||
fi
|
||||
source "$MNT/venv/bin/activate"
|
||||
|
||||
pip install -r ${SRC}/requirements.txt --disable-pip-version-check
|
||||
pip install --editable gguf-py --disable-pip-version-check
|
||||
fi
|
||||
|
||||
ret=0
|
||||
|
||||
test $ret -eq 0 && gg_run ctest_debug
|
||||
test $ret -eq 0 && gg_run ctest_release
|
||||
|
||||
if [ -z ${GG_BUILD_LOW_PERF} ]; then
|
||||
test $ret -eq 0 && gg_run embd_bge_small
|
||||
test $ret -eq 0 && gg_run rerank_tiny
|
||||
|
||||
if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then
|
||||
test $ret -eq 0 && gg_run test_scripts
|
||||
fi
|
||||
|
||||
test $ret -eq 0 && gg_run qwen3_0_6b
|
||||
|
||||
test $ret -eq 0 && gg_run ctest_with_model_debug
|
||||
test $ret -eq 0 && gg_run ctest_with_model_release
|
||||
fi
|
||||
|
||||
cat $OUT/README.md
|
||||
|
||||
exit $ret
|
||||
16
cmake/arm64-apple-clang.cmake
Normal file
16
cmake/arm64-apple-clang.cmake
Normal file
@@ -0,0 +1,16 @@
|
||||
set( CMAKE_SYSTEM_NAME Darwin )
|
||||
set( CMAKE_SYSTEM_PROCESSOR arm64 )
|
||||
|
||||
set( target arm64-apple-darwin-macho )
|
||||
|
||||
set( CMAKE_C_COMPILER clang )
|
||||
set( CMAKE_CXX_COMPILER clang++ )
|
||||
|
||||
set( CMAKE_C_COMPILER_TARGET ${target} )
|
||||
set( CMAKE_CXX_COMPILER_TARGET ${target} )
|
||||
|
||||
set( arch_c_flags "-march=armv8.4-a -fvectorize -ffp-model=fast -fno-finite-math-only" )
|
||||
set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function" )
|
||||
|
||||
set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" )
|
||||
set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" )
|
||||
16
cmake/arm64-windows-llvm.cmake
Normal file
16
cmake/arm64-windows-llvm.cmake
Normal file
@@ -0,0 +1,16 @@
|
||||
set( CMAKE_SYSTEM_NAME Windows )
|
||||
set( CMAKE_SYSTEM_PROCESSOR arm64 )
|
||||
|
||||
set( target arm64-pc-windows-msvc )
|
||||
|
||||
set( CMAKE_C_COMPILER clang )
|
||||
set( CMAKE_CXX_COMPILER clang++ )
|
||||
|
||||
set( CMAKE_C_COMPILER_TARGET ${target} )
|
||||
set( CMAKE_CXX_COMPILER_TARGET ${target} )
|
||||
|
||||
set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only" )
|
||||
set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" )
|
||||
|
||||
set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" )
|
||||
set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" )
|
||||
48
cmake/build-info.cmake
Normal file
48
cmake/build-info.cmake
Normal file
@@ -0,0 +1,48 @@
|
||||
set(BUILD_NUMBER 0)
|
||||
set(BUILD_COMMIT "unknown")
|
||||
set(BUILD_COMPILER "unknown")
|
||||
set(BUILD_TARGET "unknown")
|
||||
|
||||
# Look for git
|
||||
find_package(Git)
|
||||
if(NOT Git_FOUND)
|
||||
find_program(GIT_EXECUTABLE NAMES git git.exe)
|
||||
if(GIT_EXECUTABLE)
|
||||
set(Git_FOUND TRUE)
|
||||
message(STATUS "Found Git: ${GIT_EXECUTABLE}")
|
||||
else()
|
||||
message(WARNING "Git not found. Build info will not be accurate.")
|
||||
endif()
|
||||
endif()
|
||||
|
||||
# Get the commit count and hash
|
||||
if(Git_FOUND)
|
||||
execute_process(
|
||||
COMMAND ${GIT_EXECUTABLE} rev-parse --short HEAD
|
||||
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
|
||||
OUTPUT_VARIABLE HEAD
|
||||
OUTPUT_STRIP_TRAILING_WHITESPACE
|
||||
RESULT_VARIABLE RES
|
||||
)
|
||||
if (RES EQUAL 0)
|
||||
set(BUILD_COMMIT ${HEAD})
|
||||
endif()
|
||||
execute_process(
|
||||
COMMAND ${GIT_EXECUTABLE} rev-list --count HEAD
|
||||
WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}
|
||||
OUTPUT_VARIABLE COUNT
|
||||
OUTPUT_STRIP_TRAILING_WHITESPACE
|
||||
RESULT_VARIABLE RES
|
||||
)
|
||||
if (RES EQUAL 0)
|
||||
set(BUILD_NUMBER ${COUNT})
|
||||
endif()
|
||||
endif()
|
||||
|
||||
set(BUILD_COMPILER "${CMAKE_C_COMPILER_ID} ${CMAKE_C_COMPILER_VERSION}")
|
||||
|
||||
if(CMAKE_VS_PLATFORM_NAME)
|
||||
set(BUILD_TARGET ${CMAKE_VS_PLATFORM_NAME})
|
||||
else()
|
||||
set(BUILD_TARGET "${CMAKE_SYSTEM_NAME} ${CMAKE_SYSTEM_PROCESSOR}")
|
||||
endif()
|
||||
35
cmake/common.cmake
Normal file
35
cmake/common.cmake
Normal file
@@ -0,0 +1,35 @@
|
||||
include("ggml/cmake/common.cmake")
|
||||
|
||||
function(llama_add_compile_flags)
|
||||
if (LLAMA_FATAL_WARNINGS)
|
||||
if (CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR CMAKE_CXX_COMPILER_ID MATCHES "Clang")
|
||||
list(APPEND C_FLAGS -Werror)
|
||||
list(APPEND CXX_FLAGS -Werror)
|
||||
elseif (CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
|
||||
add_compile_options(/WX)
|
||||
endif()
|
||||
endif()
|
||||
|
||||
if (LLAMA_ALL_WARNINGS)
|
||||
if (NOT MSVC)
|
||||
list(APPEND C_FLAGS -Wshadow -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes
|
||||
-Werror=implicit-int -Werror=implicit-function-declaration)
|
||||
|
||||
list(APPEND CXX_FLAGS -Wmissing-declarations -Wmissing-noreturn)
|
||||
|
||||
list(APPEND WARNING_FLAGS -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function)
|
||||
|
||||
list(APPEND C_FLAGS ${WARNING_FLAGS})
|
||||
list(APPEND CXX_FLAGS ${WARNING_FLAGS})
|
||||
|
||||
ggml_get_flags(${CMAKE_CXX_COMPILER_ID} ${CMAKE_CXX_COMPILER_VERSION})
|
||||
|
||||
add_compile_options("$<$<COMPILE_LANGUAGE:C>:${C_FLAGS};${GF_C_FLAGS}>"
|
||||
"$<$<COMPILE_LANGUAGE:CXX>:${CXX_FLAGS};${GF_CXX_FLAGS}>")
|
||||
else()
|
||||
# todo : msvc
|
||||
set(C_FLAGS "" PARENT_SCOPE)
|
||||
set(CXX_FLAGS "" PARENT_SCOPE)
|
||||
endif()
|
||||
endif()
|
||||
endfunction()
|
||||
21
cmake/download-models.cmake
Normal file
21
cmake/download-models.cmake
Normal file
@@ -0,0 +1,21 @@
|
||||
get_filename_component(DEST_DIR "${DEST}" DIRECTORY)
|
||||
file(MAKE_DIRECTORY "${DEST_DIR}")
|
||||
|
||||
if(NOT EXISTS "${DEST}")
|
||||
message(STATUS "Downloading ${NAME} from ggml-org/models...")
|
||||
endif()
|
||||
|
||||
file(DOWNLOAD
|
||||
"https://huggingface.co/ggml-org/models/resolve/main/${NAME}?download=true"
|
||||
"${DEST}"
|
||||
TLS_VERIFY ON
|
||||
EXPECTED_HASH ${HASH}
|
||||
STATUS status
|
||||
)
|
||||
|
||||
list(GET status 0 code)
|
||||
|
||||
if(NOT code EQUAL 0)
|
||||
list(GET status 1 msg)
|
||||
message(FATAL_ERROR "Failed to download ${NAME}: ${msg}")
|
||||
endif()
|
||||
22
cmake/git-vars.cmake
Normal file
22
cmake/git-vars.cmake
Normal file
@@ -0,0 +1,22 @@
|
||||
find_package(Git)
|
||||
|
||||
# the commit's SHA1
|
||||
execute_process(COMMAND
|
||||
"${GIT_EXECUTABLE}" describe --match=NeVeRmAtCh --always --abbrev=8
|
||||
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
|
||||
OUTPUT_VARIABLE GIT_SHA1
|
||||
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
|
||||
|
||||
# the date of the commit
|
||||
execute_process(COMMAND
|
||||
"${GIT_EXECUTABLE}" log -1 --format=%ad --date=local
|
||||
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
|
||||
OUTPUT_VARIABLE GIT_DATE
|
||||
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
|
||||
|
||||
# the subject of the commit
|
||||
execute_process(COMMAND
|
||||
"${GIT_EXECUTABLE}" log -1 --format=%s
|
||||
WORKING_DIRECTORY "${CMAKE_SOURCE_DIR}"
|
||||
OUTPUT_VARIABLE GIT_COMMIT_SUBJECT
|
||||
ERROR_QUIET OUTPUT_STRIP_TRAILING_WHITESPACE)
|
||||
40
cmake/license.cmake
Normal file
40
cmake/license.cmake
Normal file
@@ -0,0 +1,40 @@
|
||||
define_property(GLOBAL PROPERTY LICENSE_TEXT
|
||||
BRIEF_DOCS "Embedded licenses"
|
||||
FULL_DOCS "Global string containing all aggregated licenses"
|
||||
)
|
||||
|
||||
function(license_add_file NAME FILE)
|
||||
if(NOT IS_ABSOLUTE "${FILE}")
|
||||
set(FILE "${CMAKE_CURRENT_SOURCE_DIR}/${FILE}")
|
||||
endif()
|
||||
if(EXISTS "${FILE}")
|
||||
set(TITLE "License for ${NAME}")
|
||||
string(REGEX REPLACE "." "=" UNDERLINE "${TITLE}")
|
||||
file(READ "${FILE}" TEXT)
|
||||
get_property(TMP GLOBAL PROPERTY LICENSE_TEXT)
|
||||
string(APPEND TMP "R\"=L=(${TITLE}\n${UNDERLINE}\n\n${TEXT})=L=\",\n")
|
||||
set_property(GLOBAL PROPERTY LICENSE_TEXT "${TMP}")
|
||||
else()
|
||||
message(WARNING "License file '${FILE}' not found")
|
||||
endif()
|
||||
endfunction()
|
||||
|
||||
function(license_generate TARGET_NAME)
|
||||
message(STATUS "Generating embedded license file for target: ${TARGET_NAME}")
|
||||
get_property(TEXT GLOBAL PROPERTY LICENSE_TEXT)
|
||||
|
||||
set(CPP_CONTENT "// Generated by CMake\n\n")
|
||||
string(APPEND CPP_CONTENT "const char* LICENSES[] = {\n")
|
||||
string(APPEND CPP_CONTENT "${TEXT}")
|
||||
string(APPEND CPP_CONTENT "nullptr\n")
|
||||
string(APPEND CPP_CONTENT "};\n")
|
||||
|
||||
set(CPP_FILE "${CMAKE_BINARY_DIR}/license.cpp")
|
||||
file(WRITE "${CPP_FILE}" "${CPP_CONTENT}")
|
||||
|
||||
if(TARGET ${TARGET_NAME})
|
||||
target_sources(${TARGET_NAME} PRIVATE "${CPP_FILE}")
|
||||
else()
|
||||
message(FATAL_ERROR "Target '${TARGET_NAME}' does not exist")
|
||||
endif()
|
||||
endfunction()
|
||||
30
cmake/llama-config.cmake.in
Normal file
30
cmake/llama-config.cmake.in
Normal file
@@ -0,0 +1,30 @@
|
||||
set(LLAMA_VERSION @LLAMA_INSTALL_VERSION@)
|
||||
set(LLAMA_BUILD_COMMIT @LLAMA_BUILD_COMMIT@)
|
||||
set(LLAMA_BUILD_NUMBER @LLAMA_BUILD_NUMBER@)
|
||||
set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@)
|
||||
|
||||
@PACKAGE_INIT@
|
||||
|
||||
set_and_check(LLAMA_INCLUDE_DIR "@PACKAGE_LLAMA_INCLUDE_INSTALL_DIR@")
|
||||
set_and_check(LLAMA_LIB_DIR "@PACKAGE_LLAMA_LIB_INSTALL_DIR@")
|
||||
set_and_check(LLAMA_BIN_DIR "@PACKAGE_LLAMA_BIN_INSTALL_DIR@")
|
||||
|
||||
find_package(ggml REQUIRED HINTS ${LLAMA_LIB_DIR}/cmake)
|
||||
|
||||
find_library(llama_LIBRARY llama
|
||||
REQUIRED
|
||||
HINTS ${LLAMA_LIB_DIR}
|
||||
NO_CMAKE_FIND_ROOT_PATH
|
||||
)
|
||||
|
||||
add_library(llama UNKNOWN IMPORTED)
|
||||
set_target_properties(llama
|
||||
PROPERTIES
|
||||
INTERFACE_INCLUDE_DIRECTORIES "${LLAMA_INCLUDE_DIR}"
|
||||
INTERFACE_LINK_LIBRARIES "ggml::ggml;ggml::ggml-base;"
|
||||
IMPORTED_LINK_INTERFACE_LANGUAGES "CXX"
|
||||
IMPORTED_LOCATION "${llama_LIBRARY}"
|
||||
INTERFACE_COMPILE_FEATURES c_std_90
|
||||
POSITION_INDEPENDENT_CODE ON)
|
||||
|
||||
check_required_components(Llama)
|
||||
10
cmake/llama.pc.in
Normal file
10
cmake/llama.pc.in
Normal file
@@ -0,0 +1,10 @@
|
||||
prefix=@CMAKE_INSTALL_PREFIX@
|
||||
exec_prefix=@CMAKE_INSTALL_PREFIX@
|
||||
libdir=@CMAKE_INSTALL_FULL_LIBDIR@
|
||||
includedir=@CMAKE_INSTALL_FULL_INCLUDEDIR@
|
||||
|
||||
Name: llama
|
||||
Description: Port of Facebook's LLaMA model in C/C++
|
||||
Version: @LLAMA_INSTALL_VERSION@
|
||||
Libs: -L${libdir} -lggml -lggml-base -lllama
|
||||
Cflags: -I${includedir}
|
||||
29
cmake/riscv64-spacemit-linux-gnu-gcc.cmake
Normal file
29
cmake/riscv64-spacemit-linux-gnu-gcc.cmake
Normal file
@@ -0,0 +1,29 @@
|
||||
set(CMAKE_SYSTEM_NAME Linux)
|
||||
set(CMAKE_SYSTEM_PROCESSOR riscv64)
|
||||
set(CMAKE_SYSTEM_VERSION 1)
|
||||
|
||||
if (CMAKE_HOST_SYSTEM_PROCESSOR MATCHES "^(riscv)")
|
||||
message(STATUS "HOST SYSTEM ${CMAKE_HOST_SYSTEM_PROCESSOR}")
|
||||
else()
|
||||
set(GNU_MACHINE riscv64-unknown-linux-gnu CACHE STRING "GNU compiler triple")
|
||||
if (DEFINED ENV{RISCV_ROOT_PATH})
|
||||
file(TO_CMAKE_PATH $ENV{RISCV_ROOT_PATH} RISCV_ROOT_PATH)
|
||||
else()
|
||||
message(FATAL_ERROR "RISCV_ROOT_PATH env must be defined")
|
||||
endif()
|
||||
|
||||
set(RISCV_ROOT_PATH ${RISCV_ROOT_PATH} CACHE STRING "root path to riscv toolchain")
|
||||
set(CMAKE_C_COMPILER ${RISCV_ROOT_PATH}/bin/riscv64-unknown-linux-gnu-gcc)
|
||||
set(CMAKE_CXX_COMPILER ${RISCV_ROOT_PATH}/bin/riscv64-unknown-linux-gnu-g++)
|
||||
set(CMAKE_STRIP ${RISCV_ROOT_PATH}/bin/riscv64-unknown-linux-gnu-strip)
|
||||
set(CMAKE_FIND_ROOT_PATH "${RISCV_ROOT_PATH}/riscv64-unknown-linux-gnu")
|
||||
set(CMAKE_SYSROOT "${RISCV_ROOT_PATH}/sysroot")
|
||||
endif()
|
||||
|
||||
set(CMAKE_FIND_ROOT_PATH_MODE_PROGRAM NEVER)
|
||||
set(CMAKE_FIND_ROOT_PATH_MODE_LIBRARY ONLY)
|
||||
set(CMAKE_FIND_ROOT_PATH_MODE_INCLUDE ONLY)
|
||||
set(CMAKE_FIND_ROOT_PATH_MODE_PACKAGE ONLY)
|
||||
set(CMAKE_C_FLAGS "-march=rv64gcv_zfh_zba_zicbop -mabi=lp64d ${CMAKE_C_FLAGS}")
|
||||
set(CMAKE_CXX_FLAGS "-march=rv64gcv_zfh_zba_zicbop -mabi=lp64d ${CXX_FLAGS}")
|
||||
set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -latomic")
|
||||
5
cmake/x64-windows-llvm.cmake
Normal file
5
cmake/x64-windows-llvm.cmake
Normal file
@@ -0,0 +1,5 @@
|
||||
set( CMAKE_SYSTEM_NAME Windows )
|
||||
set( CMAKE_SYSTEM_PROCESSOR x86_64 )
|
||||
|
||||
set( CMAKE_C_COMPILER clang )
|
||||
set( CMAKE_CXX_COMPILER clang++ )
|
||||
149
common/CMakeLists.txt
Normal file
149
common/CMakeLists.txt
Normal file
@@ -0,0 +1,149 @@
|
||||
# common
|
||||
|
||||
find_package(Threads REQUIRED)
|
||||
|
||||
llama_add_compile_flags()
|
||||
|
||||
# Build info header
|
||||
#
|
||||
|
||||
if(EXISTS "${PROJECT_SOURCE_DIR}/.git")
|
||||
set(GIT_DIR "${PROJECT_SOURCE_DIR}/.git")
|
||||
|
||||
# Is git submodule
|
||||
if(NOT IS_DIRECTORY "${GIT_DIR}")
|
||||
file(READ ${GIT_DIR} REAL_GIT_DIR_LINK)
|
||||
string(REGEX REPLACE "gitdir: (.*)\n$" "\\1" REAL_GIT_DIR ${REAL_GIT_DIR_LINK})
|
||||
string(FIND "${REAL_GIT_DIR}" "/" SLASH_POS)
|
||||
if (SLASH_POS EQUAL 0)
|
||||
set(GIT_DIR "${REAL_GIT_DIR}")
|
||||
else()
|
||||
set(GIT_DIR "${PROJECT_SOURCE_DIR}/${REAL_GIT_DIR}")
|
||||
endif()
|
||||
endif()
|
||||
|
||||
if(EXISTS "${GIT_DIR}/index")
|
||||
# For build-info.cpp below
|
||||
set_property(DIRECTORY APPEND PROPERTY CMAKE_CONFIGURE_DEPENDS "${GIT_DIR}/index")
|
||||
else()
|
||||
message(WARNING "Git index not found in git repository.")
|
||||
endif()
|
||||
else()
|
||||
message(WARNING "Git repository not found; to enable automatic generation of build info, make sure Git is installed and the project is a Git repository.")
|
||||
endif()
|
||||
|
||||
set(TEMPLATE_FILE "${CMAKE_CURRENT_SOURCE_DIR}/build-info.cpp.in")
|
||||
set(OUTPUT_FILE "${CMAKE_CURRENT_BINARY_DIR}/build-info.cpp")
|
||||
configure_file(${TEMPLATE_FILE} ${OUTPUT_FILE})
|
||||
|
||||
set(TARGET build_info)
|
||||
add_library(${TARGET} OBJECT ${OUTPUT_FILE})
|
||||
if (BUILD_SHARED_LIBS)
|
||||
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
|
||||
endif()
|
||||
|
||||
set(TARGET common)
|
||||
|
||||
add_library(${TARGET} STATIC
|
||||
arg.cpp
|
||||
arg.h
|
||||
base64.hpp
|
||||
chat-parser.cpp
|
||||
chat-parser.h
|
||||
chat-parser-xml-toolcall.h
|
||||
chat-parser-xml-toolcall.cpp
|
||||
chat-peg-parser.cpp
|
||||
chat-peg-parser.h
|
||||
chat.cpp
|
||||
chat.h
|
||||
common.cpp
|
||||
common.h
|
||||
console.cpp
|
||||
console.h
|
||||
debug.cpp
|
||||
debug.h
|
||||
download.cpp
|
||||
download.h
|
||||
http.h
|
||||
json-partial.cpp
|
||||
json-partial.h
|
||||
json-schema-to-grammar.cpp
|
||||
llguidance.cpp
|
||||
log.cpp
|
||||
log.h
|
||||
ngram-cache.cpp
|
||||
ngram-cache.h
|
||||
peg-parser.cpp
|
||||
peg-parser.h
|
||||
preset.cpp
|
||||
preset.h
|
||||
regex-partial.cpp
|
||||
regex-partial.h
|
||||
sampling.cpp
|
||||
sampling.h
|
||||
speculative.cpp
|
||||
speculative.h
|
||||
unicode.cpp
|
||||
unicode.h
|
||||
)
|
||||
|
||||
target_include_directories(${TARGET} PUBLIC . ../vendor)
|
||||
target_compile_features (${TARGET} PUBLIC cxx_std_17)
|
||||
|
||||
if (BUILD_SHARED_LIBS)
|
||||
set_target_properties(${TARGET} PROPERTIES POSITION_INDEPENDENT_CODE ON)
|
||||
endif()
|
||||
|
||||
# TODO: use list(APPEND LLAMA_COMMON_EXTRA_LIBS ...)
|
||||
set(LLAMA_COMMON_EXTRA_LIBS build_info)
|
||||
|
||||
if (LLAMA_HTTPLIB)
|
||||
target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_HTTPLIB)
|
||||
set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} cpp-httplib)
|
||||
endif()
|
||||
|
||||
if (LLAMA_LLGUIDANCE)
|
||||
include(ExternalProject)
|
||||
set(LLGUIDANCE_SRC ${CMAKE_BINARY_DIR}/llguidance/source)
|
||||
set(LLGUIDANCE_PATH ${LLGUIDANCE_SRC}/target/release)
|
||||
|
||||
# Set the correct library file extension based on platform
|
||||
if (WIN32)
|
||||
set(LLGUIDANCE_LIB_NAME "llguidance.lib")
|
||||
# Add Windows-specific libraries
|
||||
set(LLGUIDANCE_PLATFORM_LIBS
|
||||
ws2_32 # Windows Sockets API
|
||||
userenv # For GetUserProfileDirectoryW
|
||||
ntdll # For NT functions
|
||||
bcrypt # For BCryptGenRandom
|
||||
)
|
||||
else()
|
||||
set(LLGUIDANCE_LIB_NAME "libllguidance.a")
|
||||
set(LLGUIDANCE_PLATFORM_LIBS "")
|
||||
endif()
|
||||
|
||||
ExternalProject_Add(llguidance_ext
|
||||
GIT_REPOSITORY https://github.com/guidance-ai/llguidance
|
||||
# v1.0.1:
|
||||
GIT_TAG d795912fedc7d393de740177ea9ea761e7905774
|
||||
PREFIX ${CMAKE_BINARY_DIR}/llguidance
|
||||
SOURCE_DIR ${LLGUIDANCE_SRC}
|
||||
BUILD_IN_SOURCE TRUE
|
||||
CONFIGURE_COMMAND ""
|
||||
BUILD_COMMAND cargo build --release --package llguidance
|
||||
INSTALL_COMMAND ""
|
||||
BUILD_BYPRODUCTS ${LLGUIDANCE_PATH}/${LLGUIDANCE_LIB_NAME} ${LLGUIDANCE_PATH}/llguidance.h
|
||||
UPDATE_COMMAND ""
|
||||
)
|
||||
target_compile_definitions(${TARGET} PUBLIC LLAMA_USE_LLGUIDANCE)
|
||||
|
||||
add_library(llguidance STATIC IMPORTED)
|
||||
set_target_properties(llguidance PROPERTIES IMPORTED_LOCATION ${LLGUIDANCE_PATH}/${LLGUIDANCE_LIB_NAME})
|
||||
add_dependencies(llguidance llguidance_ext)
|
||||
|
||||
target_include_directories(${TARGET} PRIVATE ${LLGUIDANCE_PATH})
|
||||
# Add platform libraries to the main target
|
||||
set(LLAMA_COMMON_EXTRA_LIBS ${LLAMA_COMMON_EXTRA_LIBS} llguidance ${LLGUIDANCE_PLATFORM_LIBS})
|
||||
endif ()
|
||||
|
||||
target_link_libraries(${TARGET} PRIVATE ${LLAMA_COMMON_EXTRA_LIBS} PUBLIC llama Threads::Threads)
|
||||
3744
common/arg.cpp
Normal file
3744
common/arg.cpp
Normal file
File diff suppressed because it is too large
Load Diff
131
common/arg.h
Normal file
131
common/arg.h
Normal file
@@ -0,0 +1,131 @@
|
||||
#pragma once
|
||||
|
||||
#include "common.h"
|
||||
|
||||
#include <set>
|
||||
#include <map>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include <cstring>
|
||||
|
||||
// pseudo-env variable to identify preset-only arguments
|
||||
#define COMMON_ARG_PRESET_LOAD_ON_STARTUP "__PRESET_LOAD_ON_STARTUP"
|
||||
#define COMMON_ARG_PRESET_STOP_TIMEOUT "__PRESET_STOP_TIMEOUT"
|
||||
|
||||
//
|
||||
// CLI argument parsing
|
||||
//
|
||||
|
||||
struct common_arg {
|
||||
std::set<enum llama_example> examples = {LLAMA_EXAMPLE_COMMON};
|
||||
std::set<enum llama_example> excludes = {};
|
||||
std::vector<const char *> args;
|
||||
std::vector<const char *> args_neg; // for negated args like --no-xxx
|
||||
const char * value_hint = nullptr; // help text or example for arg value
|
||||
const char * value_hint_2 = nullptr; // for second arg value
|
||||
const char * env = nullptr;
|
||||
std::string help;
|
||||
bool is_sparam = false; // is current arg a sampling param?
|
||||
bool is_preset_only = false; // is current arg preset-only (not treated as CLI arg)
|
||||
void (*handler_void) (common_params & params) = nullptr;
|
||||
void (*handler_string) (common_params & params, const std::string &) = nullptr;
|
||||
void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr;
|
||||
void (*handler_int) (common_params & params, int) = nullptr;
|
||||
void (*handler_bool) (common_params & params, bool) = nullptr;
|
||||
|
||||
common_arg() = default;
|
||||
|
||||
common_arg(
|
||||
const std::initializer_list<const char *> & args,
|
||||
const char * value_hint,
|
||||
const std::string & help,
|
||||
void (*handler)(common_params & params, const std::string &)
|
||||
) : args(args), value_hint(value_hint), help(help), handler_string(handler) {}
|
||||
|
||||
common_arg(
|
||||
const std::initializer_list<const char *> & args,
|
||||
const char * value_hint,
|
||||
const std::string & help,
|
||||
void (*handler)(common_params & params, int)
|
||||
) : args(args), value_hint(value_hint), help(help), handler_int(handler) {}
|
||||
|
||||
common_arg(
|
||||
const std::initializer_list<const char *> & args,
|
||||
const std::string & help,
|
||||
void (*handler)(common_params & params)
|
||||
) : args(args), help(help), handler_void(handler) {}
|
||||
|
||||
common_arg(
|
||||
const std::initializer_list<const char *> & args,
|
||||
const std::initializer_list<const char *> & args_neg,
|
||||
const std::string & help,
|
||||
void (*handler)(common_params & params, bool)
|
||||
) : args(args), args_neg(args_neg), help(help), handler_bool(handler) {}
|
||||
|
||||
// support 2 values for arg
|
||||
common_arg(
|
||||
const std::initializer_list<const char *> & args,
|
||||
const char * value_hint,
|
||||
const char * value_hint_2,
|
||||
const std::string & help,
|
||||
void (*handler)(common_params & params, const std::string &, const std::string &)
|
||||
) : args(args), value_hint(value_hint), value_hint_2(value_hint_2), help(help), handler_str_str(handler) {}
|
||||
|
||||
common_arg & set_examples(std::initializer_list<enum llama_example> examples);
|
||||
common_arg & set_excludes(std::initializer_list<enum llama_example> excludes);
|
||||
common_arg & set_env(const char * env);
|
||||
common_arg & set_sparam();
|
||||
common_arg & set_preset_only();
|
||||
bool in_example(enum llama_example ex);
|
||||
bool is_exclude(enum llama_example ex);
|
||||
bool get_value_from_env(std::string & output) const;
|
||||
bool has_value_from_env() const;
|
||||
std::string to_string() const;
|
||||
|
||||
// for using as key in std::map
|
||||
bool operator<(const common_arg& other) const {
|
||||
if (args.empty() || other.args.empty()) {
|
||||
return false;
|
||||
}
|
||||
return strcmp(args[0], other.args[0]) < 0;
|
||||
}
|
||||
bool operator==(const common_arg& other) const {
|
||||
if (args.empty() || other.args.empty()) {
|
||||
return false;
|
||||
}
|
||||
return strcmp(args[0], other.args[0]) == 0;
|
||||
}
|
||||
|
||||
// get all args and env vars (including negated args/env)
|
||||
std::vector<std::string> get_args() const;
|
||||
std::vector<std::string> get_env() const;
|
||||
};
|
||||
|
||||
namespace common_arg_utils {
|
||||
bool is_truthy(const std::string & value);
|
||||
bool is_falsey(const std::string & value);
|
||||
bool is_autoy(const std::string & value);
|
||||
}
|
||||
|
||||
struct common_params_context {
|
||||
enum llama_example ex = LLAMA_EXAMPLE_COMMON;
|
||||
common_params & params;
|
||||
std::vector<common_arg> options;
|
||||
void(*print_usage)(int, char **) = nullptr;
|
||||
common_params_context(common_params & params) : params(params) {}
|
||||
};
|
||||
|
||||
// parse input arguments from CLI
|
||||
// if one argument has invalid value, it will automatically display usage of the specific argument (and not the full usage message)
|
||||
bool common_params_parse(int argc, char ** argv, common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
|
||||
|
||||
// parse input arguments from CLI into a map
|
||||
bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map<common_arg, std::string> & out_map);
|
||||
|
||||
// populate preset-only arguments
|
||||
// these arguments are not treated as command line arguments
|
||||
// see: https://github.com/ggml-org/llama.cpp/issues/18163
|
||||
void common_params_add_preset_options(std::vector<common_arg> & args);
|
||||
|
||||
// initialize argument parser context - used by test-arg-parser and preset
|
||||
common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr);
|
||||
392
common/base64.hpp
Normal file
392
common/base64.hpp
Normal file
@@ -0,0 +1,392 @@
|
||||
/*
|
||||
This is free and unencumbered software released into the public domain.
|
||||
|
||||
Anyone is free to copy, modify, publish, use, compile, sell, or
|
||||
distribute this software, either in source code form or as a compiled
|
||||
binary, for any purpose, commercial or non-commercial, and by any
|
||||
means.
|
||||
|
||||
In jurisdictions that recognize copyright laws, the author or authors
|
||||
of this software dedicate any and all copyright interest in the
|
||||
software to the public domain. We make this dedication for the benefit
|
||||
of the public at large and to the detriment of our heirs and
|
||||
successors. We intend this dedication to be an overt act of
|
||||
relinquishment in perpetuity of all present and future rights to this
|
||||
software under copyright law.
|
||||
|
||||
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
|
||||
EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
|
||||
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
|
||||
IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR
|
||||
OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
|
||||
ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
|
||||
OTHER DEALINGS IN THE SOFTWARE.
|
||||
|
||||
For more information, please refer to <http://unlicense.org>
|
||||
*/
|
||||
|
||||
#ifndef PUBLIC_DOMAIN_BASE64_HPP_
|
||||
#define PUBLIC_DOMAIN_BASE64_HPP_
|
||||
|
||||
#include <cstdint>
|
||||
#include <iterator>
|
||||
#include <stdexcept>
|
||||
#include <string>
|
||||
|
||||
class base64_error : public std::runtime_error
|
||||
{
|
||||
public:
|
||||
using std::runtime_error::runtime_error;
|
||||
};
|
||||
|
||||
class base64
|
||||
{
|
||||
public:
|
||||
enum class alphabet
|
||||
{
|
||||
/** the alphabet is detected automatically */
|
||||
auto_,
|
||||
/** the standard base64 alphabet is used */
|
||||
standard,
|
||||
/** like `standard` except that the characters `+` and `/` are replaced by `-` and `_` respectively*/
|
||||
url_filename_safe
|
||||
};
|
||||
|
||||
enum class decoding_behavior
|
||||
{
|
||||
/** if the input is not padded, the remaining bits are ignored */
|
||||
moderate,
|
||||
/** if a padding character is encounter decoding is finished */
|
||||
loose
|
||||
};
|
||||
|
||||
/**
|
||||
Encodes all the elements from `in_begin` to `in_end` to `out`.
|
||||
|
||||
@warning The source and destination cannot overlap. The destination must be able to hold at least
|
||||
`required_encode_size(std::distance(in_begin, in_end))`, otherwise the behavior depends on the output iterator.
|
||||
|
||||
@tparam Input_iterator the source; the returned elements are cast to `std::uint8_t` and should not be greater than
|
||||
8 bits
|
||||
@tparam Output_iterator the destination; the elements written to it are from the type `char`
|
||||
@param in_begin the beginning of the source
|
||||
@param in_end the ending of the source
|
||||
@param out the destination iterator
|
||||
@param alphabet which alphabet should be used
|
||||
@returns the iterator to the next element past the last element copied
|
||||
@throws see `Input_iterator` and `Output_iterator`
|
||||
*/
|
||||
template<typename Input_iterator, typename Output_iterator>
|
||||
static Output_iterator encode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out,
|
||||
alphabet alphabet = alphabet::standard)
|
||||
{
|
||||
constexpr auto pad = '=';
|
||||
const char* alpha = alphabet == alphabet::url_filename_safe
|
||||
? "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_"
|
||||
: "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/";
|
||||
|
||||
while (in_begin != in_end) {
|
||||
std::uint8_t i0 = 0, i1 = 0, i2 = 0;
|
||||
|
||||
// first character
|
||||
i0 = static_cast<std::uint8_t>(*in_begin);
|
||||
++in_begin;
|
||||
|
||||
*out = alpha[i0 >> 2 & 0x3f];
|
||||
++out;
|
||||
|
||||
// part of first character and second
|
||||
if (in_begin != in_end) {
|
||||
i1 = static_cast<std::uint8_t>(*in_begin);
|
||||
++in_begin;
|
||||
|
||||
*out = alpha[((i0 & 0x3) << 4) | (i1 >> 4 & 0x0f)];
|
||||
++out;
|
||||
} else {
|
||||
*out = alpha[(i0 & 0x3) << 4];
|
||||
++out;
|
||||
|
||||
// last padding
|
||||
*out = pad;
|
||||
++out;
|
||||
|
||||
// last padding
|
||||
*out = pad;
|
||||
++out;
|
||||
|
||||
break;
|
||||
}
|
||||
|
||||
// part of second character and third
|
||||
if (in_begin != in_end) {
|
||||
i2 = static_cast<std::uint8_t>(*in_begin);
|
||||
++in_begin;
|
||||
|
||||
*out = alpha[((i1 & 0xf) << 2) | (i2 >> 6 & 0x03)];
|
||||
++out;
|
||||
} else {
|
||||
*out = alpha[(i1 & 0xf) << 2];
|
||||
++out;
|
||||
|
||||
// last padding
|
||||
*out = pad;
|
||||
++out;
|
||||
|
||||
break;
|
||||
}
|
||||
|
||||
// rest of third
|
||||
*out = alpha[i2 & 0x3f];
|
||||
++out;
|
||||
}
|
||||
|
||||
return out;
|
||||
}
|
||||
/**
|
||||
Encodes a string.
|
||||
|
||||
@param str the string that should be encoded
|
||||
@param alphabet which alphabet should be used
|
||||
@returns the encoded base64 string
|
||||
@throws see base64::encode()
|
||||
*/
|
||||
static std::string encode(const std::string& str, alphabet alphabet = alphabet::standard)
|
||||
{
|
||||
std::string result;
|
||||
|
||||
result.reserve(required_encode_size(str.length()) + 1);
|
||||
|
||||
encode(str.begin(), str.end(), std::back_inserter(result), alphabet);
|
||||
|
||||
return result;
|
||||
}
|
||||
/**
|
||||
Encodes a char array.
|
||||
|
||||
@param buffer the char array
|
||||
@param size the size of the array
|
||||
@param alphabet which alphabet should be used
|
||||
@returns the encoded string
|
||||
*/
|
||||
static std::string encode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::standard)
|
||||
{
|
||||
std::string result;
|
||||
|
||||
result.reserve(required_encode_size(size) + 1);
|
||||
|
||||
encode(buffer, buffer + size, std::back_inserter(result), alphabet);
|
||||
|
||||
return result;
|
||||
}
|
||||
/**
|
||||
Decodes all the elements from `in_begin` to `in_end` to `out`. `in_begin` may point to the same location as `out`,
|
||||
in other words: inplace decoding is possible.
|
||||
|
||||
@warning The destination must be able to hold at least `required_decode_size(std::distance(in_begin, in_end))`,
|
||||
otherwise the behavior depends on the output iterator.
|
||||
|
||||
@tparam Input_iterator the source; the returned elements are cast to `char`
|
||||
@tparam Output_iterator the destination; the elements written to it are from the type `std::uint8_t`
|
||||
@param in_begin the beginning of the source
|
||||
@param in_end the ending of the source
|
||||
@param out the destination iterator
|
||||
@param alphabet which alphabet should be used
|
||||
@param behavior the behavior when an error was detected
|
||||
@returns the iterator to the next element past the last element copied
|
||||
@throws base64_error depending on the set behavior
|
||||
@throws see `Input_iterator` and `Output_iterator`
|
||||
*/
|
||||
template<typename Input_iterator, typename Output_iterator>
|
||||
static Output_iterator decode(Input_iterator in_begin, Input_iterator in_end, Output_iterator out,
|
||||
alphabet alphabet = alphabet::auto_,
|
||||
decoding_behavior behavior = decoding_behavior::moderate)
|
||||
{
|
||||
//constexpr auto pad = '=';
|
||||
std::uint8_t last = 0;
|
||||
auto bits = 0;
|
||||
|
||||
while (in_begin != in_end) {
|
||||
auto c = *in_begin;
|
||||
++in_begin;
|
||||
|
||||
if (c == '=') {
|
||||
break;
|
||||
}
|
||||
|
||||
auto part = _base64_value(alphabet, c);
|
||||
|
||||
// enough bits for one byte
|
||||
if (bits + 6 >= 8) {
|
||||
*out = (last << (8 - bits)) | (part >> (bits - 2));
|
||||
++out;
|
||||
|
||||
bits -= 2;
|
||||
} else {
|
||||
bits += 6;
|
||||
}
|
||||
|
||||
last = part;
|
||||
}
|
||||
|
||||
// check padding
|
||||
if (behavior != decoding_behavior::loose) {
|
||||
while (in_begin != in_end) {
|
||||
auto c = *in_begin;
|
||||
++in_begin;
|
||||
|
||||
if (c != '=') {
|
||||
throw base64_error("invalid base64 character.");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return out;
|
||||
}
|
||||
/**
|
||||
Decodes a string.
|
||||
|
||||
@param str the base64 encoded string
|
||||
@param alphabet which alphabet should be used
|
||||
@param behavior the behavior when an error was detected
|
||||
@returns the decoded string
|
||||
@throws see base64::decode()
|
||||
*/
|
||||
static std::string decode(const std::string& str, alphabet alphabet = alphabet::auto_,
|
||||
decoding_behavior behavior = decoding_behavior::moderate)
|
||||
{
|
||||
std::string result;
|
||||
|
||||
result.reserve(max_decode_size(str.length()));
|
||||
|
||||
decode(str.begin(), str.end(), std::back_inserter(result), alphabet, behavior);
|
||||
|
||||
return result;
|
||||
}
|
||||
/**
|
||||
Decodes a string.
|
||||
|
||||
@param buffer the base64 encoded buffer
|
||||
@param size the size of the buffer
|
||||
@param alphabet which alphabet should be used
|
||||
@param behavior the behavior when an error was detected
|
||||
@returns the decoded string
|
||||
@throws see base64::decode()
|
||||
*/
|
||||
static std::string decode(const char* buffer, std::size_t size, alphabet alphabet = alphabet::auto_,
|
||||
decoding_behavior behavior = decoding_behavior::moderate)
|
||||
{
|
||||
std::string result;
|
||||
|
||||
result.reserve(max_decode_size(size));
|
||||
|
||||
decode(buffer, buffer + size, std::back_inserter(result), alphabet, behavior);
|
||||
|
||||
return result;
|
||||
}
|
||||
/**
|
||||
Decodes a string inplace.
|
||||
|
||||
@param[in,out] str the base64 encoded string
|
||||
@param alphabet which alphabet should be used
|
||||
@param behavior the behavior when an error was detected
|
||||
@throws base64::decode_inplace()
|
||||
*/
|
||||
static void decode_inplace(std::string& str, alphabet alphabet = alphabet::auto_,
|
||||
decoding_behavior behavior = decoding_behavior::moderate)
|
||||
{
|
||||
str.resize(decode(str.begin(), str.end(), str.begin(), alphabet, behavior) - str.begin());
|
||||
}
|
||||
/**
|
||||
Decodes a char array inplace.
|
||||
|
||||
@param[in,out] str the string array
|
||||
@param size the length of the array
|
||||
@param alphabet which alphabet should be used
|
||||
@param behavior the behavior when an error was detected
|
||||
@returns the pointer to the next element past the last element decoded
|
||||
@throws base64::decode_inplace()
|
||||
*/
|
||||
static char* decode_inplace(char* str, std::size_t size, alphabet alphabet = alphabet::auto_,
|
||||
decoding_behavior behavior = decoding_behavior::moderate)
|
||||
{
|
||||
return decode(str, str + size, str, alphabet, behavior);
|
||||
}
|
||||
/**
|
||||
Returns the required decoding size for a given size. The value is calculated with the following formula:
|
||||
|
||||
$$
|
||||
\lceil \frac{size}{4} \rceil \cdot 3
|
||||
$$
|
||||
|
||||
@param size the size of the encoded input
|
||||
@returns the size of the resulting decoded buffer; this the absolute maximum
|
||||
*/
|
||||
static std::size_t max_decode_size(std::size_t size) noexcept
|
||||
{
|
||||
return (size / 4 + (size % 4 ? 1 : 0)) * 3;
|
||||
}
|
||||
/**
|
||||
Returns the required encoding size for a given size. The value is calculated with the following formula:
|
||||
|
||||
$$
|
||||
\lceil \frac{size}{3} \rceil \cdot 4
|
||||
$$
|
||||
|
||||
@param size the size of the decoded input
|
||||
@returns the size of the resulting encoded buffer
|
||||
*/
|
||||
static std::size_t required_encode_size(std::size_t size) noexcept
|
||||
{
|
||||
return (size / 3 + (size % 3 ? 1 : 0)) * 4;
|
||||
}
|
||||
|
||||
private:
|
||||
static std::uint8_t _base64_value(alphabet& alphabet, char c)
|
||||
{
|
||||
if (c >= 'A' && c <= 'Z') {
|
||||
return c - 'A';
|
||||
} else if (c >= 'a' && c <= 'z') {
|
||||
return c - 'a' + 26;
|
||||
} else if (c >= '0' && c <= '9') {
|
||||
return c - '0' + 52;
|
||||
}
|
||||
|
||||
// comes down to alphabet
|
||||
if (alphabet == alphabet::standard) {
|
||||
if (c == '+') {
|
||||
return 62;
|
||||
} else if (c == '/') {
|
||||
return 63;
|
||||
}
|
||||
} else if (alphabet == alphabet::url_filename_safe) {
|
||||
if (c == '-') {
|
||||
return 62;
|
||||
} else if (c == '_') {
|
||||
return 63;
|
||||
}
|
||||
} // auto detect
|
||||
else {
|
||||
if (c == '+') {
|
||||
alphabet = alphabet::standard;
|
||||
|
||||
return 62;
|
||||
} else if (c == '/') {
|
||||
alphabet = alphabet::standard;
|
||||
|
||||
return 63;
|
||||
} else if (c == '-') {
|
||||
alphabet = alphabet::url_filename_safe;
|
||||
|
||||
return 62;
|
||||
} else if (c == '_') {
|
||||
alphabet = alphabet::url_filename_safe;
|
||||
|
||||
return 63;
|
||||
}
|
||||
}
|
||||
|
||||
throw base64_error("invalid base64 character.");
|
||||
}
|
||||
};
|
||||
|
||||
#endif // !PUBLIC_DOMAIN_BASE64_HPP_
|
||||
4
common/build-info.cpp.in
Normal file
4
common/build-info.cpp.in
Normal file
@@ -0,0 +1,4 @@
|
||||
int LLAMA_BUILD_NUMBER = @LLAMA_BUILD_NUMBER@;
|
||||
char const *LLAMA_COMMIT = "@LLAMA_BUILD_COMMIT@";
|
||||
char const *LLAMA_COMPILER = "@BUILD_COMPILER@";
|
||||
char const *LLAMA_BUILD_TARGET = "@BUILD_TARGET@";
|
||||
879
common/chat-parser-xml-toolcall.cpp
Normal file
879
common/chat-parser-xml-toolcall.cpp
Normal file
@@ -0,0 +1,879 @@
|
||||
#include "chat.h"
|
||||
#include "chat-parser.h"
|
||||
#include "common.h"
|
||||
#include "json-partial.h"
|
||||
#include "json-schema-to-grammar.h"
|
||||
#include "log.h"
|
||||
#include "regex-partial.h"
|
||||
|
||||
using json = nlohmann::ordered_json;
|
||||
|
||||
class xml_toolcall_syntax_exception : public std::runtime_error {
|
||||
public:
|
||||
xml_toolcall_syntax_exception(const std::string & message) : std::runtime_error(message) {}
|
||||
};
|
||||
|
||||
template<typename T>
|
||||
inline void sort_uniq(std::vector<T> &vec) {
|
||||
std::sort(vec.begin(), vec.end());
|
||||
vec.erase(std::unique(vec.begin(), vec.end()), vec.end());
|
||||
}
|
||||
|
||||
template<typename T>
|
||||
inline bool all_space(const T &str) {
|
||||
return std::all_of(str.begin(), str.end(), [](unsigned char ch) { return std::isspace(ch); });
|
||||
}
|
||||
|
||||
static size_t utf8_truncate_safe(const std::string_view s) {
|
||||
size_t len = s.size();
|
||||
if (len == 0) return 0;
|
||||
size_t i = len;
|
||||
for (size_t back = 0; back < 4 && i > 0; ++back) {
|
||||
--i;
|
||||
unsigned char c = s[i];
|
||||
if ((c & 0x80) == 0) {
|
||||
return len;
|
||||
} else if ((c & 0xC0) == 0xC0) {
|
||||
size_t expected_len = 0;
|
||||
if ((c & 0xE0) == 0xC0) expected_len = 2;
|
||||
else if ((c & 0xF0) == 0xE0) expected_len = 3;
|
||||
else if ((c & 0xF8) == 0xF0) expected_len = 4;
|
||||
else return i;
|
||||
if (len - i >= expected_len) {
|
||||
return len;
|
||||
} else {
|
||||
return i;
|
||||
}
|
||||
}
|
||||
}
|
||||
return len - std::min(len, size_t(3));
|
||||
}
|
||||
|
||||
inline void utf8_truncate_safe_resize(std::string &s) {
|
||||
s.resize(utf8_truncate_safe(s));
|
||||
}
|
||||
|
||||
inline std::string_view utf8_truncate_safe_view(const std::string_view s) {
|
||||
return s.substr(0, utf8_truncate_safe(s));
|
||||
}
|
||||
|
||||
static std::optional<common_chat_msg_parser::find_regex_result> try_find_2_literal_splited_by_spaces(common_chat_msg_parser & builder, const std::string & literal1, const std::string & literal2) {
|
||||
if (literal1.size() == 0) return builder.try_find_literal(literal2);
|
||||
const auto saved_pos = builder.pos();
|
||||
while (auto res = builder.try_find_literal(literal1)) {
|
||||
builder.consume_spaces();
|
||||
const auto match_len = std::min(literal2.size(), builder.input().size() - builder.pos());
|
||||
if (builder.input().compare(builder.pos(), match_len, literal2, 0, match_len) == 0) {
|
||||
if (res->prelude.size() != res->groups[0].begin - saved_pos) {
|
||||
res->prelude = builder.str({saved_pos, res->groups[0].begin});
|
||||
}
|
||||
builder.move_to(builder.pos() + match_len);
|
||||
res->groups[0].end = builder.pos();
|
||||
GGML_ASSERT(res->groups[0].begin != res->groups[0].end);
|
||||
return res;
|
||||
}
|
||||
builder.move_to(res->groups[0].begin + 1);
|
||||
}
|
||||
builder.move_to(saved_pos);
|
||||
return std::nullopt;
|
||||
}
|
||||
|
||||
/**
|
||||
* make a GBNF that accept any strings except those containing any of the forbidden strings.
|
||||
*/
|
||||
std::string make_gbnf_excluding(std::vector<std::string> forbids) {
|
||||
constexpr auto charclass_escape = [](unsigned char c) -> std::string {
|
||||
if (c == '\\' || c == ']' || c == '^' || c == '-') {
|
||||
std::string s = "\\";
|
||||
s.push_back((char)c);
|
||||
return s;
|
||||
}
|
||||
if (isprint(c)) {
|
||||
return std::string(1, (char)c);
|
||||
}
|
||||
char buf[16];
|
||||
snprintf(buf, 15, "\\x%02X", c);
|
||||
return std::string(buf);
|
||||
};
|
||||
constexpr auto build_expr = [charclass_escape](auto self, const std::vector<std::string>& forbids, int l, int r, int depth) -> std::string {
|
||||
std::vector<std::pair<unsigned char, std::pair<int,int>>> children;
|
||||
int i = l;
|
||||
while (i < r) {
|
||||
const std::string &s = forbids[i];
|
||||
if ((int)s.size() == depth) {
|
||||
++i;
|
||||
continue;
|
||||
}
|
||||
unsigned char c = (unsigned char)s[depth];
|
||||
int j = i;
|
||||
while (j < r && (int)forbids[j].size() > depth &&
|
||||
(unsigned char)forbids[j][depth] == c) {
|
||||
++j;
|
||||
}
|
||||
children.push_back({c, {i, j}});
|
||||
i = j;
|
||||
}
|
||||
std::vector<std::string> alts;
|
||||
if (!children.empty()) {
|
||||
std::string cls;
|
||||
for (auto &ch : children) cls += charclass_escape(ch.first);
|
||||
alts.push_back(std::string("[^") + cls + "]");
|
||||
}
|
||||
for (auto &ch : children) {
|
||||
std::string childExpr = self(self, forbids, ch.second.first, ch.second.second, depth+1);
|
||||
if (!childExpr.empty()) {
|
||||
std::string quoted_ch = "\"";
|
||||
if (ch.first == '\\') quoted_ch += "\\\\";
|
||||
else if (ch.first == '"') quoted_ch += "\\\"";
|
||||
else if (isprint(ch.first)) quoted_ch.push_back(ch.first);
|
||||
else {
|
||||
char buf[16];
|
||||
snprintf(buf, 15, "\\x%02X", ch.first);
|
||||
quoted_ch += buf;
|
||||
}
|
||||
quoted_ch += "\"";
|
||||
std::string branch = quoted_ch + std::string(" ") + childExpr;
|
||||
alts.push_back(branch);
|
||||
}
|
||||
}
|
||||
if (alts.empty()) return "";
|
||||
std::ostringstream oss;
|
||||
oss << "( ";
|
||||
for (size_t k = 0; k < alts.size(); ++k) {
|
||||
if (k) oss << " | ";
|
||||
oss << alts[k];
|
||||
}
|
||||
oss << " )";
|
||||
return oss.str();
|
||||
};
|
||||
if (forbids.empty()) return "( . )*";
|
||||
sort(forbids.begin(), forbids.end());
|
||||
std::string expr = build_expr(build_expr, forbids, 0, forbids.size(), 0);
|
||||
if (expr.empty()) {
|
||||
std::string cls;
|
||||
for (auto &s : forbids) if (!s.empty()) cls += charclass_escape((unsigned char)s[0]);
|
||||
expr = std::string("( [^") + cls + "] )";
|
||||
}
|
||||
if (forbids.size() == 1)
|
||||
return expr + "*";
|
||||
else
|
||||
return std::string("( ") + expr + " )*";
|
||||
}
|
||||
|
||||
/**
|
||||
* Build grammar for xml-style tool call
|
||||
* form.scope_start and form.scope_end can be empty.
|
||||
* Requires data.format for model-specific hacks.
|
||||
*/
|
||||
void build_grammar_xml_tool_call(common_chat_params & data, const json & tools, const struct xml_tool_call_format & form) {
|
||||
GGML_ASSERT(!form.tool_start.empty());
|
||||
GGML_ASSERT(!form.tool_sep.empty());
|
||||
GGML_ASSERT(!form.key_start.empty());
|
||||
GGML_ASSERT(!form.val_end.empty());
|
||||
GGML_ASSERT(!form.tool_end.empty());
|
||||
|
||||
std::string key_val_sep = form.key_val_sep;
|
||||
if (form.key_val_sep2) {
|
||||
key_val_sep += "\n";
|
||||
key_val_sep += *form.key_val_sep2;
|
||||
}
|
||||
GGML_ASSERT(!key_val_sep.empty());
|
||||
|
||||
if (tools.is_array() && !tools.empty()) {
|
||||
data.grammar = build_grammar([&](const common_grammar_builder &builder) {
|
||||
auto string_arg_val = form.last_val_end ?
|
||||
builder.add_rule("string-arg-val", make_gbnf_excluding({form.val_end, *form.last_val_end})) :
|
||||
builder.add_rule("string-arg-val", make_gbnf_excluding({form.val_end}));
|
||||
|
||||
std::vector<std::string> tool_rules;
|
||||
for (const auto & tool : tools) {
|
||||
if (!tool.contains("type") || tool.at("type") != "function" || !tool.contains("function")) {
|
||||
LOG_WRN("Skipping tool without function: %s", tool.dump(2).c_str());
|
||||
continue;
|
||||
}
|
||||
const auto & function = tool.at("function");
|
||||
if (!function.contains("name") || !function.at("name").is_string()) {
|
||||
LOG_WRN("Skipping invalid function (invalid name): %s", function.dump(2).c_str());
|
||||
continue;
|
||||
}
|
||||
if (!function.contains("parameters") || !function.at("parameters").is_object()) {
|
||||
LOG_WRN("Skipping invalid function (invalid parameters): %s", function.dump(2).c_str());
|
||||
continue;
|
||||
}
|
||||
std::string name = function.at("name");
|
||||
auto parameters = function.at("parameters");
|
||||
builder.resolve_refs(parameters);
|
||||
|
||||
struct parameter_rule {
|
||||
std::string symbol_name;
|
||||
bool is_required;
|
||||
};
|
||||
std::vector<parameter_rule> arg_rules;
|
||||
if (!parameters.contains("properties") || !parameters.at("properties").is_object()) {
|
||||
LOG_WRN("Skipping invalid function (invalid properties): %s", function.dump(2).c_str());
|
||||
continue;
|
||||
} else {
|
||||
std::vector<std::string> requiredParameters;
|
||||
if (parameters.contains("required")) {
|
||||
try { parameters.at("required").get_to(requiredParameters); }
|
||||
catch (const std::runtime_error&) {
|
||||
LOG_WRN("Invalid function required parameters, ignoring: %s", function.at("required").dump(2).c_str());
|
||||
}
|
||||
}
|
||||
sort_uniq(requiredParameters);
|
||||
for (const auto & [key, value] : parameters.at("properties").items()) {
|
||||
std::string quoted_key = key;
|
||||
bool required = std::binary_search(requiredParameters.begin(), requiredParameters.end(), key);
|
||||
if (form.key_start.back() == '"' && key_val_sep[0] == '"') {
|
||||
quoted_key = gbnf_format_literal(key);
|
||||
quoted_key = quoted_key.substr(1, quoted_key.size() - 2);
|
||||
}
|
||||
arg_rules.push_back(parameter_rule {builder.add_rule("func-" + name + "-kv-" + key,
|
||||
gbnf_format_literal(form.key_start) + " " +
|
||||
gbnf_format_literal(quoted_key) + " " +
|
||||
gbnf_format_literal(key_val_sep) + " " +
|
||||
((value.contains("type") && value["type"].is_string() && value["type"] == "string" && (!form.raw_argval || *form.raw_argval)) ?
|
||||
(form.raw_argval ?
|
||||
string_arg_val :
|
||||
"( " + string_arg_val + " | " + builder.add_schema(name + "-arg-" + key, value) + " )"
|
||||
) :
|
||||
builder.add_schema(name + "-arg-" + key, value)
|
||||
)
|
||||
), required});
|
||||
}
|
||||
}
|
||||
|
||||
auto next_arg_with_sep = builder.add_rule(name + "-last-arg-end", form.last_val_end ? gbnf_format_literal(*form.last_val_end) : gbnf_format_literal(form.val_end));
|
||||
decltype(next_arg_with_sep) next_arg = "\"\"";
|
||||
for (auto i = arg_rules.size() - 1; /* i >= 0 && */ i < arg_rules.size(); --i) {
|
||||
std::string include_this_arg = arg_rules[i].symbol_name + " " + next_arg_with_sep;
|
||||
next_arg = builder.add_rule(name + "-arg-after-" + std::to_string(i), arg_rules[i].is_required ?
|
||||
include_this_arg : "( " + include_this_arg + " ) | " + next_arg
|
||||
);
|
||||
include_this_arg = gbnf_format_literal(form.val_end) + " " + include_this_arg;
|
||||
next_arg_with_sep = builder.add_rule(name + "-arg-after-" + std::to_string(i) + "-with-sep", arg_rules[i].is_required ?
|
||||
include_this_arg : "( " + include_this_arg + " ) | " + next_arg_with_sep
|
||||
);
|
||||
}
|
||||
|
||||
std::string quoted_name = name;
|
||||
if (form.tool_start.back() == '"' && form.tool_sep[0] == '"') {
|
||||
quoted_name = gbnf_format_literal(name);
|
||||
quoted_name = quoted_name.substr(1, quoted_name.size() - 2);
|
||||
}
|
||||
quoted_name = gbnf_format_literal(quoted_name);
|
||||
// Kimi-K2 uses functions.{{ tool_call['function']['name'] }}:{{ loop.index }} as function name
|
||||
if (data.format == COMMON_CHAT_FORMAT_KIMI_K2) {
|
||||
quoted_name = "\"functions.\" " + quoted_name + " \":\" [0-9]+";
|
||||
}
|
||||
tool_rules.push_back(builder.add_rule(name + "-call",
|
||||
gbnf_format_literal(form.tool_start) + " " +
|
||||
quoted_name + " " +
|
||||
gbnf_format_literal(form.tool_sep) + " " +
|
||||
next_arg
|
||||
));
|
||||
}
|
||||
|
||||
auto tool_call_once = builder.add_rule("root-tool-call-once", string_join(tool_rules, " | "));
|
||||
auto tool_call_more = builder.add_rule("root-tool-call-more", gbnf_format_literal(form.tool_end) + " " + tool_call_once);
|
||||
auto call_end = builder.add_rule("root-call-end", form.last_tool_end ? gbnf_format_literal(*form.last_tool_end) : gbnf_format_literal(form.tool_end));
|
||||
auto tool_call_multiple_with_end = builder.add_rule("root-tool-call-multiple-with-end", tool_call_once + " " + tool_call_more + "* " + call_end);
|
||||
builder.add_rule("root",
|
||||
(form.scope_start.empty() ? "" : gbnf_format_literal(form.scope_start) + " ") +
|
||||
tool_call_multiple_with_end + "?" +
|
||||
(form.scope_end.empty() ? "" : " " + gbnf_format_literal(form.scope_end))
|
||||
);
|
||||
});
|
||||
|
||||
// grammar trigger for tool call
|
||||
data.grammar_triggers.push_back({ COMMON_GRAMMAR_TRIGGER_TYPE_WORD, form.scope_start + form.tool_start });
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse XML-Style tool call for given xml_tool_call_format. Return false for invalid syntax and get the position untouched.
|
||||
* Throws xml_toolcall_syntax_exception if there is invalid syntax and cannot recover the original status for common_chat_msg_parser.
|
||||
* form.scope_start, form.tool_sep and form.scope_end can be empty.
|
||||
*/
|
||||
inline bool parse_xml_tool_calls(common_chat_msg_parser & builder, const struct xml_tool_call_format & form) {
|
||||
GGML_ASSERT(!form.tool_start.empty());
|
||||
GGML_ASSERT(!form.key_start.empty());
|
||||
GGML_ASSERT(!form.key_val_sep.empty());
|
||||
GGML_ASSERT(!form.val_end.empty());
|
||||
GGML_ASSERT(!form.tool_end.empty());
|
||||
|
||||
// Helper to choose return false or throw error
|
||||
constexpr auto return_error = [](common_chat_msg_parser & builder, auto &start_pos, const bool &recovery) {
|
||||
LOG_DBG("Failed to parse XML-Style tool call at position: %s\n", gbnf_format_literal(builder.consume_rest().substr(0, 20)).c_str());
|
||||
if (recovery) {
|
||||
builder.move_to(start_pos);
|
||||
return false;
|
||||
} else throw xml_toolcall_syntax_exception("Tool call parsing failed with unrecoverable errors. Try using a grammar to constrain the model’s output.");
|
||||
};
|
||||
// Drop substring from needle to end from a JSON
|
||||
constexpr auto partial_json = [](std::string &json_str, std::string_view needle = "XML_TOOL_CALL_PARTIAL_FLAG") {
|
||||
auto pos = json_str.rfind(needle);
|
||||
if (pos == std::string::npos) {
|
||||
return false;
|
||||
}
|
||||
for (auto i = pos + needle.size(); i < json_str.size(); ++i) {
|
||||
unsigned char ch = static_cast<unsigned char>(json_str[i]);
|
||||
if (ch != '\'' && ch != '"' && ch != '}' && ch != ':' && !std::isspace(ch)) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
if (pos != 0 && json_str[pos - 1] == '"') {
|
||||
--pos;
|
||||
}
|
||||
json_str.resize(pos);
|
||||
return true;
|
||||
};
|
||||
// Helper to generate a partial argument JSON
|
||||
constexpr auto gen_partial_json = [partial_json](auto set_partial_arg, auto &arguments, auto &builder, auto &function_name) {
|
||||
auto rest = builder.consume_rest();
|
||||
utf8_truncate_safe_resize(rest);
|
||||
set_partial_arg(rest, "XML_TOOL_CALL_PARTIAL_FLAG");
|
||||
auto tool_str = arguments.dump();
|
||||
if (partial_json(tool_str)) {
|
||||
if (builder.add_tool_call(function_name, "", tool_str)) {
|
||||
return;
|
||||
}
|
||||
}
|
||||
LOG_DBG("Failed to parse partial XML-Style tool call, fallback to non-partial: %s\n", tool_str.c_str());
|
||||
};
|
||||
// Helper to find a close (because there may be form.last_val_end or form.last_tool_end)
|
||||
constexpr auto try_find_close = [](
|
||||
common_chat_msg_parser & builder,
|
||||
const std::string & end,
|
||||
const std::optional<std::string> & alt_end,
|
||||
const std::string & end_next,
|
||||
const std::optional<std::string> & alt_end_next
|
||||
) {
|
||||
auto saved_pos = builder.pos();
|
||||
auto tc = builder.try_find_literal(end);
|
||||
auto val_end_size = end.size();
|
||||
if (alt_end) {
|
||||
auto pos_1 = builder.pos();
|
||||
builder.move_to(saved_pos);
|
||||
auto tc2 = try_find_2_literal_splited_by_spaces(builder, *alt_end, end_next);
|
||||
if (alt_end_next) {
|
||||
builder.move_to(saved_pos);
|
||||
auto tc3 = try_find_2_literal_splited_by_spaces(builder, *alt_end, *alt_end_next);
|
||||
if (tc3 && (!tc2 || tc2->prelude.size() > tc3->prelude.size())) {
|
||||
tc2 = tc3;
|
||||
}
|
||||
}
|
||||
if (tc2 && (!tc || tc->prelude.size() > tc2->prelude.size())) {
|
||||
tc = tc2;
|
||||
tc->groups[0].end = std::min(builder.input().size(), tc->groups[0].begin + alt_end->size());
|
||||
builder.move_to(tc->groups[0].end);
|
||||
val_end_size = alt_end->size();
|
||||
} else {
|
||||
builder.move_to(pos_1);
|
||||
}
|
||||
}
|
||||
return std::make_pair(val_end_size, tc);
|
||||
};
|
||||
// Helper to find a val_end or last_val_end, returns matched pattern size
|
||||
const auto try_find_val_end = [try_find_close, &builder, &form]() {
|
||||
return try_find_close(builder, form.val_end, form.last_val_end, form.tool_end, form.last_tool_end);
|
||||
};
|
||||
// Helper to find a tool_end or last_tool_end, returns matched pattern size
|
||||
const auto try_find_tool_end = [try_find_close, &builder, &form]() {
|
||||
return try_find_close(builder, form.tool_end, form.last_tool_end, form.scope_end, std::nullopt);
|
||||
};
|
||||
|
||||
bool recovery = true;
|
||||
const auto start_pos = builder.pos();
|
||||
if (!all_space(form.scope_start)) {
|
||||
if (auto tc = builder.try_find_literal(form.scope_start)) {
|
||||
if (all_space(tc->prelude)) {
|
||||
if (form.scope_start.size() != tc->groups[0].end - tc->groups[0].begin)
|
||||
throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.scope_start));
|
||||
} else {
|
||||
builder.move_to(start_pos);
|
||||
return false;
|
||||
}
|
||||
} else return false;
|
||||
}
|
||||
while (auto tc = builder.try_find_literal(form.tool_start)) {
|
||||
if (!all_space(tc->prelude)) {
|
||||
LOG_DBG("XML-Style tool call: Expected %s, but found %s, trying to match next pattern\n",
|
||||
gbnf_format_literal(form.tool_start).c_str(),
|
||||
gbnf_format_literal(tc->prelude).c_str()
|
||||
);
|
||||
builder.move_to(tc->groups[0].begin - tc->prelude.size());
|
||||
break;
|
||||
}
|
||||
|
||||
// Find tool name
|
||||
auto func_name = builder.try_find_literal(all_space(form.tool_sep) ? form.key_start : form.tool_sep);
|
||||
if (!func_name) {
|
||||
auto [sz, tc] = try_find_tool_end();
|
||||
func_name = tc;
|
||||
}
|
||||
if (!func_name) {
|
||||
// Partial tool name not supported
|
||||
throw common_chat_msg_partial_exception("incomplete tool_call");
|
||||
}
|
||||
// If the model generate multiple tool call and the first tool call has no argument
|
||||
if (func_name->prelude.find(form.tool_end) != std::string::npos || (form.last_tool_end ? func_name->prelude.find(*form.last_tool_end) != std::string::npos : false)) {
|
||||
builder.move_to(func_name->groups[0].begin - func_name->prelude.size());
|
||||
auto [sz, tc] = try_find_tool_end();
|
||||
func_name = tc;
|
||||
}
|
||||
|
||||
// Parse tool name
|
||||
builder.move_to(all_space(form.tool_sep) ? func_name->groups[0].begin : func_name->groups[0].end);
|
||||
std::string function_name = string_strip(func_name->prelude);
|
||||
// Kimi-K2 uses functions.{{ tool_call['function']['name'] }}:{{ loop.index }} as function name
|
||||
if (builder.syntax().format == COMMON_CHAT_FORMAT_KIMI_K2) {
|
||||
if (string_starts_with(function_name, "functions.")) {
|
||||
static const std::regex re(":\\d+$");
|
||||
if (std::regex_search(function_name, re)) {
|
||||
function_name = function_name.substr(10, function_name.rfind(":") - 10);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Argument JSON
|
||||
json arguments = json::object();
|
||||
|
||||
// Helper to generate a partial argument JSON
|
||||
const auto gen_partial_args = [&](auto set_partial_arg) {
|
||||
gen_partial_json(set_partial_arg, arguments, builder, function_name);
|
||||
};
|
||||
|
||||
// Parse all arg_key/arg_value pairs
|
||||
while (auto tc = builder.try_find_literal(form.key_start)) {
|
||||
if (!all_space(tc->prelude)) {
|
||||
LOG_DBG("XML-Style tool call: Expected %s, but found %s, trying to match next pattern\n",
|
||||
gbnf_format_literal(form.key_start).c_str(),
|
||||
gbnf_format_literal(tc->prelude).c_str()
|
||||
);
|
||||
builder.move_to(tc->groups[0].begin - tc->prelude.size());
|
||||
break;
|
||||
}
|
||||
if (tc->groups[0].end - tc->groups[0].begin != form.key_start.size()) {
|
||||
auto tool_call_arg = arguments.dump();
|
||||
if (tool_call_arg.size() != 0 && tool_call_arg[tool_call_arg.size() - 1] == '}') {
|
||||
tool_call_arg.resize(tool_call_arg.size() - 1);
|
||||
}
|
||||
builder.add_tool_call(function_name, "", tool_call_arg);
|
||||
throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.key_start));
|
||||
}
|
||||
|
||||
// Parse arg_key
|
||||
auto key_res = builder.try_find_literal(form.key_val_sep);
|
||||
if (!key_res) {
|
||||
gen_partial_args([&](auto &rest, auto &needle) {arguments[rest + needle] = "";});
|
||||
throw common_chat_msg_partial_exception("Expected " + gbnf_format_literal(form.key_val_sep) + " after " + gbnf_format_literal(form.key_start));
|
||||
}
|
||||
if (key_res->groups[0].end - key_res->groups[0].begin != form.key_val_sep.size()) {
|
||||
gen_partial_args([&](auto &, auto &needle) {arguments[key_res->prelude + needle] = "";});
|
||||
throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.key_val_sep));
|
||||
}
|
||||
auto &key = key_res->prelude;
|
||||
recovery = false;
|
||||
|
||||
// Parse arg_value
|
||||
if (form.key_val_sep2) {
|
||||
if (auto tc = builder.try_find_literal(*form.key_val_sep2)) {
|
||||
if (!all_space(tc->prelude)) {
|
||||
LOG_DBG("Failed to parse XML-Style tool call: Unexcepted %s between %s and %s\n",
|
||||
gbnf_format_literal(tc->prelude).c_str(),
|
||||
gbnf_format_literal(form.key_val_sep).c_str(),
|
||||
gbnf_format_literal(*form.key_val_sep2).c_str()
|
||||
);
|
||||
return return_error(builder, start_pos, false);
|
||||
}
|
||||
if (tc->groups[0].end - tc->groups[0].begin != form.key_val_sep2->size()) {
|
||||
gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;});
|
||||
throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(*form.key_val_sep2));
|
||||
}
|
||||
} else {
|
||||
gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;});
|
||||
throw common_chat_msg_partial_exception("Expected " + gbnf_format_literal(*form.key_val_sep2) + " after " + gbnf_format_literal(form.key_val_sep));
|
||||
}
|
||||
}
|
||||
auto val_start = builder.pos();
|
||||
|
||||
// Test if arg_val is a partial JSON
|
||||
std::optional<common_json> value_json = std::nullopt;
|
||||
if (!form.raw_argval || !*form.raw_argval) {
|
||||
try { value_json = builder.try_consume_json(); }
|
||||
catch (const std::runtime_error&) { builder.move_to(val_start); }
|
||||
// TODO: Delete this when json_partial adds top-level support for null/true/false
|
||||
if (builder.pos() == val_start) {
|
||||
const static std::regex number_regex(R"([0-9-][0-9]*(\.\d*)?([eE][+-]?\d*)?)");
|
||||
builder.consume_spaces();
|
||||
std::string_view sv = utf8_truncate_safe_view(builder.input());
|
||||
sv.remove_prefix(builder.pos());
|
||||
std::string rest = "a";
|
||||
if (sv.size() < 6) rest = sv;
|
||||
if (string_starts_with("null", rest) || string_starts_with("true", rest) || string_starts_with("false", rest) || std::regex_match(sv.begin(), sv.end(), number_regex)) {
|
||||
value_json = {123, {"123", "123"}};
|
||||
builder.consume_rest();
|
||||
} else {
|
||||
builder.move_to(val_start);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// If it is a JSON and followed by </arg_value>, parse as json
|
||||
// cannot support streaming because it may be a plain text starting with JSON
|
||||
if (value_json) {
|
||||
auto json_end = builder.pos();
|
||||
builder.consume_spaces();
|
||||
if (builder.pos() == builder.input().size()) {
|
||||
if (form.raw_argval && !*form.raw_argval && (value_json->json.is_string() || value_json->json.is_object() || value_json->json.is_array())) {
|
||||
arguments[key] = value_json->json;
|
||||
auto json_str = arguments.dump();
|
||||
if (!value_json->healing_marker.json_dump_marker.empty()) {
|
||||
GGML_ASSERT(std::string::npos != json_str.rfind(value_json->healing_marker.json_dump_marker));
|
||||
json_str.resize(json_str.rfind(value_json->healing_marker.json_dump_marker));
|
||||
} else {
|
||||
GGML_ASSERT(json_str.back() == '}');
|
||||
json_str.resize(json_str.size() - 1);
|
||||
}
|
||||
builder.add_tool_call(function_name, "", json_str);
|
||||
} else {
|
||||
gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;});
|
||||
}
|
||||
LOG_DBG("Possible JSON arg_value: %s\n", value_json->json.dump().c_str());
|
||||
throw common_chat_msg_partial_exception("JSON arg_value detected. Waiting for more tokens for validations.");
|
||||
}
|
||||
builder.move_to(json_end);
|
||||
auto [val_end_size, tc] = try_find_val_end();
|
||||
if (tc && all_space(tc->prelude) && value_json->healing_marker.marker.empty()) {
|
||||
if (tc->groups[0].end - tc->groups[0].begin != val_end_size) {
|
||||
gen_partial_args([&](auto &, auto &needle) {arguments[key] = needle;});
|
||||
LOG_DBG("Possible terminated JSON arg_value: %s\n", value_json->json.dump().c_str());
|
||||
throw common_chat_msg_partial_exception("Partial literal: " + gbnf_format_literal(form.val_end) + (form.last_val_end ? gbnf_format_literal(*form.last_val_end) : ""));
|
||||
} else arguments[key] = value_json->json;
|
||||
} else builder.move_to(val_start);
|
||||
}
|
||||
|
||||
// If not, parse as plain text
|
||||
if (val_start == builder.pos()) {
|
||||
if (auto [val_end_size, value_plain] = try_find_val_end(); value_plain) {
|
||||
auto &value_str = value_plain->prelude;
|
||||
if (form.trim_raw_argval) value_str = string_strip(value_str);
|
||||
if (value_plain->groups[0].end - value_plain->groups[0].begin != val_end_size) {
|
||||
gen_partial_args([&](auto &, auto &needle) {arguments[key] = value_str + needle;});
|
||||
throw common_chat_msg_partial_exception(
|
||||
"Expected " + gbnf_format_literal(form.val_end) +
|
||||
" after " + gbnf_format_literal(form.key_val_sep) +
|
||||
(form.key_val_sep2 ? " " + gbnf_format_literal(*form.key_val_sep2) : "")
|
||||
);
|
||||
}
|
||||
arguments[key] = value_str;
|
||||
} else {
|
||||
if (form.trim_raw_argval) {
|
||||
gen_partial_args([&](auto &rest, auto &needle) {arguments[key] = string_strip(rest) + needle;});
|
||||
} else {
|
||||
gen_partial_args([&](auto &rest, auto &needle) {arguments[key] = rest + needle;});
|
||||
}
|
||||
throw common_chat_msg_partial_exception(
|
||||
"Expected " + gbnf_format_literal(form.val_end) +
|
||||
" after " + gbnf_format_literal(form.key_val_sep) +
|
||||
(form.key_val_sep2 ? " " + gbnf_format_literal(*form.key_val_sep2) : "")
|
||||
);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Consume closing tag
|
||||
if (auto [tool_end_size, tc] = try_find_tool_end(); tc) {
|
||||
if (!all_space(tc->prelude)) {
|
||||
LOG_DBG("Failed to parse XML-Style tool call: Expected %s, but found %s\n",
|
||||
gbnf_format_literal(form.tool_end).c_str(),
|
||||
gbnf_format_literal(tc->prelude).c_str()
|
||||
);
|
||||
return return_error(builder, start_pos, recovery);
|
||||
}
|
||||
if (tc->groups[0].end - tc->groups[0].begin == tool_end_size) {
|
||||
// Add the parsed tool call
|
||||
if (!builder.add_tool_call(function_name, "", arguments.dump())) {
|
||||
throw common_chat_msg_partial_exception("Failed to add XML-Style tool call");
|
||||
}
|
||||
recovery = false;
|
||||
continue;
|
||||
}
|
||||
}
|
||||
|
||||
auto tool_call_arg = arguments.dump();
|
||||
if (tool_call_arg.size() != 0 && tool_call_arg[tool_call_arg.size() - 1] == '}') {
|
||||
tool_call_arg.resize(tool_call_arg.size() - 1);
|
||||
}
|
||||
builder.add_tool_call(function_name, "", tool_call_arg);
|
||||
throw common_chat_msg_partial_exception("Expected " + gbnf_format_literal(form.tool_end) + " after " + gbnf_format_literal(form.val_end));
|
||||
}
|
||||
if (auto tc = builder.try_find_literal(form.scope_end)) {
|
||||
if (!all_space(tc->prelude)) {
|
||||
LOG_DBG("Failed to parse XML-Style tool call: Expected %s, but found %s\n",
|
||||
gbnf_format_literal(form.scope_end).c_str(),
|
||||
gbnf_format_literal(tc->prelude).c_str()
|
||||
);
|
||||
return return_error(builder, start_pos, recovery);
|
||||
}
|
||||
} else {
|
||||
if (all_space(form.scope_end)) return true;
|
||||
builder.consume_spaces();
|
||||
if (builder.pos() == builder.input().size())
|
||||
throw common_chat_msg_partial_exception("incomplete tool calls");
|
||||
LOG_DBG("Failed to parse XML-Style tool call: Expected %s, but found %s\n",
|
||||
gbnf_format_literal(form.scope_end).c_str(),
|
||||
gbnf_format_literal(builder.consume_rest()).c_str()
|
||||
);
|
||||
return return_error(builder, start_pos, recovery);
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse XML-Style tool call for given xml_tool_call_format. Return false for invalid syntax and get the position untouched.
|
||||
* May cause std::runtime_error if there is invalid syntax because partial valid tool call is already sent out to client.
|
||||
* form.scope_start, form.tool_sep and form.scope_end can be empty.
|
||||
*/
|
||||
bool common_chat_msg_parser::try_consume_xml_tool_calls(const struct xml_tool_call_format & form) {
|
||||
auto pos = pos_;
|
||||
auto tsize = result_.tool_calls.size();
|
||||
try { return parse_xml_tool_calls(*this, form); }
|
||||
catch (const xml_toolcall_syntax_exception&) {}
|
||||
move_to(pos);
|
||||
result_.tool_calls.resize(tsize);
|
||||
return false;
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse content uses reasoning and XML-Style tool call
|
||||
* TODO: Note that form.allow_toolcall_in_think is not tested yet. If anyone confirms it works, this comment can be removed.
|
||||
*/
|
||||
inline void parse_msg_with_xml_tool_calls(common_chat_msg_parser & builder, const struct xml_tool_call_format & form, const std::string & start_think = "<think>", const std::string & end_think = "</think>") {
|
||||
constexpr auto rstrip = [](std::string &s) {
|
||||
s.resize(std::distance(s.begin(), std::find_if(s.rbegin(), s.rend(), [](unsigned char ch) { return !std::isspace(ch); }).base()));
|
||||
};
|
||||
// Erase substring from l to r, along with additional spaces nearby
|
||||
constexpr auto erase_spaces = [](auto &str, size_t l, size_t r) {
|
||||
while (/* l > -1 && */ --l < str.size() && std::isspace(static_cast<unsigned char>(str[l])));
|
||||
++l;
|
||||
while (++r < str.size() && std::isspace(static_cast<unsigned char>(str[r])));
|
||||
if (l < r) str[l] = '\n';
|
||||
if (l + 1 < r) str[l + 1] = '\n';
|
||||
if (l != 0) l += 2;
|
||||
str.erase(l, r - l);
|
||||
return l;
|
||||
};
|
||||
constexpr auto trim_suffix = [](std::string &content, std::initializer_list<std::string_view> list) {
|
||||
auto best_match = content.size();
|
||||
for (auto pattern: list) {
|
||||
if (pattern.size() == 0) continue;
|
||||
for (auto match_idx = content.size() - std::min(pattern.size(), content.size()); content.size() > match_idx; match_idx++) {
|
||||
auto match_len = content.size() - match_idx;
|
||||
if (content.compare(match_idx, match_len, pattern.data(), match_len) == 0 && best_match > match_idx) {
|
||||
best_match = match_idx;
|
||||
}
|
||||
}
|
||||
}
|
||||
if (content.size() > best_match) {
|
||||
content.erase(best_match);
|
||||
}
|
||||
};
|
||||
const auto trim_potential_partial_word = [&start_think, &end_think, &form, trim_suffix](std::string &content) {
|
||||
return trim_suffix(content, {
|
||||
start_think, end_think, form.scope_start, form.tool_start, form.tool_sep, form.key_start,
|
||||
form.key_val_sep, form.key_val_sep2 ? form.key_val_sep2->c_str() : "",
|
||||
form.val_end, form.last_val_end ? form.last_val_end->c_str() : "",
|
||||
form.tool_end, form.last_tool_end ? form.last_tool_end->c_str() : "",
|
||||
form.scope_end
|
||||
});
|
||||
};
|
||||
|
||||
|
||||
// Trim leading spaces without affecting keyword matching
|
||||
static const common_regex spaces_regex("\\s*");
|
||||
{
|
||||
auto tc = builder.consume_regex(spaces_regex);
|
||||
auto spaces = builder.str(tc.groups[0]);
|
||||
auto s1 = spaces.size();
|
||||
trim_potential_partial_word(spaces);
|
||||
auto s2 = spaces.size();
|
||||
builder.move_to(builder.pos() - (s1 - s2));
|
||||
}
|
||||
|
||||
// Parse content
|
||||
bool reasoning_unclosed = builder.syntax().thinking_forced_open;
|
||||
std::string unclosed_reasoning_content("");
|
||||
for (;;) {
|
||||
auto tc = try_find_2_literal_splited_by_spaces(builder, form.scope_start, form.tool_start);
|
||||
std::string content;
|
||||
std::string tool_call_start;
|
||||
|
||||
if (tc) {
|
||||
content = std::move(tc->prelude);
|
||||
tool_call_start = builder.str(tc->groups[0]);
|
||||
LOG_DBG("Matched tool start: %s\n", gbnf_format_literal(tool_call_start).c_str());
|
||||
} else {
|
||||
content = builder.consume_rest();
|
||||
utf8_truncate_safe_resize(content);
|
||||
}
|
||||
|
||||
// Handle unclosed think block
|
||||
if (reasoning_unclosed) {
|
||||
if (auto pos = content.find(end_think); pos == std::string::npos && builder.pos() != builder.input().size()) {
|
||||
unclosed_reasoning_content += content;
|
||||
if (!(form.allow_toolcall_in_think && tc)) {
|
||||
unclosed_reasoning_content += tool_call_start;
|
||||
continue;
|
||||
}
|
||||
} else {
|
||||
reasoning_unclosed = false;
|
||||
std::string reasoning_content;
|
||||
if (pos == std::string::npos) {
|
||||
reasoning_content = std::move(content);
|
||||
} else {
|
||||
reasoning_content = content.substr(0, pos);
|
||||
content.erase(0, pos + end_think.size());
|
||||
}
|
||||
if (builder.pos() == builder.input().size() && all_space(content)) {
|
||||
rstrip(reasoning_content);
|
||||
trim_potential_partial_word(reasoning_content);
|
||||
rstrip(reasoning_content);
|
||||
if (reasoning_content.empty()) {
|
||||
rstrip(unclosed_reasoning_content);
|
||||
trim_potential_partial_word(unclosed_reasoning_content);
|
||||
rstrip(unclosed_reasoning_content);
|
||||
if (unclosed_reasoning_content.empty()) continue;
|
||||
}
|
||||
}
|
||||
if (builder.syntax().reasoning_format == COMMON_REASONING_FORMAT_NONE || builder.syntax().reasoning_in_content) {
|
||||
builder.add_content(start_think);
|
||||
builder.add_content(unclosed_reasoning_content);
|
||||
builder.add_content(reasoning_content);
|
||||
if (builder.pos() != builder.input().size() || !all_space(content))
|
||||
builder.add_content(end_think);
|
||||
} else {
|
||||
builder.add_reasoning_content(unclosed_reasoning_content);
|
||||
builder.add_reasoning_content(reasoning_content);
|
||||
}
|
||||
unclosed_reasoning_content.clear();
|
||||
}
|
||||
}
|
||||
|
||||
// Handle multiple think block
|
||||
bool toolcall_in_think = false;
|
||||
for (auto think_start = content.find(start_think); think_start != std::string::npos; think_start = content.find(start_think, think_start)) {
|
||||
if (auto think_end = content.find(end_think, think_start + start_think.size()); think_end != std::string::npos) {
|
||||
if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content) {
|
||||
auto reasoning_content = content.substr(think_start + start_think.size(), think_end - think_start - start_think.size());
|
||||
builder.add_reasoning_content(reasoning_content);
|
||||
think_start = erase_spaces(content, think_start, think_end + end_think.size() - 1);
|
||||
} else {
|
||||
think_start = think_end + end_think.size() - 1;
|
||||
}
|
||||
} else {
|
||||
// This <tool_call> start is in thinking block, skip this tool call
|
||||
// This <tool_call> start is in thinking block
|
||||
if (form.allow_toolcall_in_think) {
|
||||
unclosed_reasoning_content = content.substr(think_start + start_think.size());
|
||||
} else {
|
||||
unclosed_reasoning_content = content.substr(think_start + start_think.size()) + tool_call_start;
|
||||
}
|
||||
reasoning_unclosed = true;
|
||||
content.resize(think_start);
|
||||
toolcall_in_think = true;
|
||||
}
|
||||
}
|
||||
|
||||
if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content) {
|
||||
rstrip(content);
|
||||
// Handle unclosed </think> token from content: delete all </think> token
|
||||
if (auto pos = content.rfind(end_think); pos != std::string::npos) {
|
||||
while (pos != std::string::npos) {
|
||||
pos = erase_spaces(content, pos, pos + end_think.size() - 1);
|
||||
pos = content.rfind(end_think, pos);
|
||||
}
|
||||
}
|
||||
// Strip if needed
|
||||
if (content.size() > 0 && std::isspace(static_cast<unsigned char>(content[0]))) {
|
||||
content = string_strip(content);
|
||||
}
|
||||
}
|
||||
|
||||
// remove potential partial suffix
|
||||
if (builder.pos() == builder.input().size()) {
|
||||
if (unclosed_reasoning_content.empty()) {
|
||||
rstrip(content);
|
||||
trim_potential_partial_word(content);
|
||||
rstrip(content);
|
||||
} else {
|
||||
rstrip(unclosed_reasoning_content);
|
||||
trim_potential_partial_word(unclosed_reasoning_content);
|
||||
rstrip(unclosed_reasoning_content);
|
||||
}
|
||||
}
|
||||
|
||||
// consume unclosed_reasoning_content if allow_toolcall_in_think is set
|
||||
if (form.allow_toolcall_in_think && !unclosed_reasoning_content.empty()) {
|
||||
if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content) {
|
||||
builder.add_reasoning_content(unclosed_reasoning_content);
|
||||
} else {
|
||||
if (content.empty()) {
|
||||
content = start_think + unclosed_reasoning_content;
|
||||
} else {
|
||||
content += "\n\n" + start_think;
|
||||
content += unclosed_reasoning_content;
|
||||
}
|
||||
}
|
||||
unclosed_reasoning_content.clear();
|
||||
}
|
||||
|
||||
// Add content
|
||||
if (!content.empty()) {
|
||||
// If there are multiple content blocks
|
||||
if (builder.syntax().reasoning_format != COMMON_REASONING_FORMAT_NONE && !builder.syntax().reasoning_in_content && builder.result().content.size() != 0) {
|
||||
builder.add_content("\n\n");
|
||||
}
|
||||
builder.add_content(content);
|
||||
}
|
||||
|
||||
// This <tool_call> start is in thinking block and toolcall_in_think not set, skip this tool call
|
||||
if (toolcall_in_think && !form.allow_toolcall_in_think) {
|
||||
continue;
|
||||
}
|
||||
|
||||
// There is no tool call and all content is parsed
|
||||
if (!tc) {
|
||||
GGML_ASSERT(builder.pos() == builder.input().size());
|
||||
GGML_ASSERT(unclosed_reasoning_content.empty());
|
||||
if (!form.allow_toolcall_in_think) GGML_ASSERT(!reasoning_unclosed);
|
||||
break;
|
||||
}
|
||||
|
||||
builder.move_to(tc->groups[0].begin);
|
||||
if (builder.try_consume_xml_tool_calls(form)) {
|
||||
auto end_of_tool = builder.pos();
|
||||
builder.consume_spaces();
|
||||
if (builder.pos() != builder.input().size()) {
|
||||
builder.move_to(end_of_tool);
|
||||
if (!builder.result().content.empty()) {
|
||||
builder.add_content("\n\n");
|
||||
}
|
||||
}
|
||||
} else {
|
||||
static const common_regex next_char_regex(".");
|
||||
auto c = builder.str(builder.consume_regex(next_char_regex).groups[0]);
|
||||
rstrip(c);
|
||||
builder.add_content(c);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Parse content uses reasoning and XML-Style tool call
|
||||
*/
|
||||
void common_chat_msg_parser::consume_reasoning_with_xml_tool_calls(const struct xml_tool_call_format & form, const std::string & start_think, const std::string & end_think) {
|
||||
parse_msg_with_xml_tool_calls(*this, form, start_think, end_think);
|
||||
}
|
||||
45
common/chat-parser-xml-toolcall.h
Normal file
45
common/chat-parser-xml-toolcall.h
Normal file
@@ -0,0 +1,45 @@
|
||||
#pragma once
|
||||
|
||||
#include "chat.h"
|
||||
|
||||
#include <nlohmann/json.hpp>
|
||||
|
||||
#include <optional>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
|
||||
// Sample config:
|
||||
// MiniMax-M2 (left): <minimax:tool_call>\n<invoke name="tool-name">\n<parameter name="key">value</parameter>\n...</invoke>\n...</minimax:tool_call>
|
||||
// GLM 4.5 (right): <tool_call>function_name\n<arg_key>key</arg_key>\n<arg_value>value</arg_value>\n</tool_call>
|
||||
struct xml_tool_call_format {
|
||||
std::string scope_start; // <minimax:tool_call>\n // \n // can be empty
|
||||
std::string tool_start; // <invoke name=\" // <tool_call>
|
||||
std::string tool_sep; // \">\n // \n // can be empty only for parse_xml_tool_calls
|
||||
std::string key_start; // <parameter name=\" // <arg_key>
|
||||
std::string key_val_sep; // \"> // </arg_key>\n<arg_value>
|
||||
std::string val_end; // </parameter>\n // </arg_value>\n
|
||||
std::string tool_end; // </invoke>\n // </tool_call>\n
|
||||
std::string scope_end; // </minimax:tool_call> // // can be empty
|
||||
// Set this if there can be dynamic spaces inside key_val_sep.
|
||||
// e.g. key_val_sep=</arg_key> key_val_sep2=<arg_value> for GLM4.5
|
||||
std::optional<std::string> key_val_sep2 = std::nullopt;
|
||||
// Set true if argval should only be raw string. e.g. Hello "world" hi
|
||||
// Set false if argval should only be json string. e.g. "Hello \"world\" hi"
|
||||
// Defaults to std::nullopt, both will be allowed.
|
||||
std::optional<bool> raw_argval = std::nullopt;
|
||||
std::optional<std::string> last_val_end = std::nullopt;
|
||||
std::optional<std::string> last_tool_end = std::nullopt;
|
||||
bool trim_raw_argval = false;
|
||||
bool allow_toolcall_in_think = false;
|
||||
};
|
||||
|
||||
// make a GBNF that accept any strings except those containing any of the forbidden strings.
|
||||
std::string make_gbnf_excluding(std::vector<std::string> forbids);
|
||||
|
||||
/**
|
||||
* Build grammar for xml-style tool call
|
||||
* form.scope_start and form.scope_end can be empty.
|
||||
* Requires data.format for model-specific hacks.
|
||||
*/
|
||||
void build_grammar_xml_tool_call(common_chat_params & data, const nlohmann::ordered_json & tools, const struct xml_tool_call_format & form);
|
||||
1669
common/chat-parser.cpp
Normal file
1669
common/chat-parser.cpp
Normal file
File diff suppressed because it is too large
Load Diff
133
common/chat-parser.h
Normal file
133
common/chat-parser.h
Normal file
@@ -0,0 +1,133 @@
|
||||
#pragma once
|
||||
|
||||
#include "chat.h"
|
||||
#include "chat-parser-xml-toolcall.h"
|
||||
#include "json-partial.h"
|
||||
#include "regex-partial.h"
|
||||
|
||||
#include <nlohmann/json.hpp>
|
||||
|
||||
#include <optional>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
class common_chat_msg_partial_exception : public std::runtime_error {
|
||||
public:
|
||||
common_chat_msg_partial_exception(const std::string & message) : std::runtime_error(message) {}
|
||||
};
|
||||
|
||||
class common_chat_msg_parser {
|
||||
std::string input_;
|
||||
bool is_partial_;
|
||||
common_chat_syntax syntax_;
|
||||
std::string healing_marker_;
|
||||
|
||||
size_t pos_ = 0;
|
||||
common_chat_msg result_;
|
||||
|
||||
public:
|
||||
common_chat_msg_parser(const std::string & input, bool is_partial, const common_chat_syntax & syntax);
|
||||
const std::string & input() const { return input_; }
|
||||
size_t pos() const { return pos_; }
|
||||
const std::string & healing_marker() const { return healing_marker_; }
|
||||
const bool & is_partial() const { return is_partial_; }
|
||||
const common_chat_msg & result() const { return result_; }
|
||||
const common_chat_syntax & syntax() const { return syntax_; }
|
||||
|
||||
void move_to(size_t pos) {
|
||||
if (pos > input_.size()) {
|
||||
throw std::runtime_error("Invalid position!");
|
||||
}
|
||||
pos_ = pos;
|
||||
}
|
||||
void move_back(size_t n) {
|
||||
if (pos_ < n) {
|
||||
throw std::runtime_error("Can't move back that far!");
|
||||
}
|
||||
pos_ -= n;
|
||||
}
|
||||
|
||||
// Get the substring of the input at the given range
|
||||
std::string str(const common_string_range & rng) const;
|
||||
|
||||
// Appends to the result.content field
|
||||
void add_content(const std::string & content);
|
||||
|
||||
// Appends to the result.reasoning_content field
|
||||
void add_reasoning_content(const std::string & reasoning_content);
|
||||
|
||||
// Adds a tool call to the result. If the tool call is too incomplete (e.g. name empty), it won't add anything.
|
||||
bool add_tool_call(const std::string & name, const std::string & id, const std::string & arguments);
|
||||
|
||||
// Adds a tool call using the "name", "id" and "arguments" fields of the json object
|
||||
bool add_tool_call(const nlohmann::ordered_json & tool_call);
|
||||
|
||||
// Adds an array of tool calls using their "name", "id" and "arguments" fields.
|
||||
bool add_tool_calls(const nlohmann::ordered_json & arr);
|
||||
|
||||
// Adds a tool call using the short form: { "tool_name": { "arg1": val, "arg2": val } }
|
||||
bool add_tool_call_short_form(const nlohmann::ordered_json & tool_call);
|
||||
|
||||
void finish();
|
||||
|
||||
bool consume_spaces();
|
||||
|
||||
void consume_literal(const std::string & literal);
|
||||
|
||||
bool try_parse_reasoning(const std::string & start_think, const std::string & end_think);
|
||||
|
||||
std::string consume_rest();
|
||||
|
||||
struct find_regex_result {
|
||||
std::string prelude;
|
||||
std::vector<common_string_range> groups;
|
||||
};
|
||||
|
||||
std::optional<find_regex_result> try_find_regex(const common_regex & regex, size_t from = std::string::npos, bool add_prelude_to_content = true);
|
||||
|
||||
bool try_consume_literal(const std::string & literal);
|
||||
|
||||
std::optional<find_regex_result> try_find_literal(const std::string & literal);
|
||||
|
||||
find_regex_result consume_regex(const common_regex & regex);
|
||||
|
||||
std::optional<find_regex_result> try_consume_regex(const common_regex & regex);
|
||||
|
||||
std::optional<common_json> try_consume_json();
|
||||
common_json consume_json();
|
||||
|
||||
struct consume_json_result {
|
||||
nlohmann::ordered_json value;
|
||||
bool is_partial;
|
||||
};
|
||||
|
||||
/*
|
||||
Consume (possibly partial) json and converts specific subtrees to (possibly truncated) JSON strings.
|
||||
|
||||
By default, object keys can't be truncated, nor can string values (their corresponding key is removed,
|
||||
e.g. `{"foo": "bar", "baz": "b` -> `{"foo": "bar"}`
|
||||
|
||||
But one can allow subpaths to be kept truncated, and possibly json-dumped to truncated json strings
|
||||
- with `content_paths={{"foo"}}` -> `{"foo": "b` -> {"foo": "b"}`
|
||||
- with `args_paths={{"foo"}}` -> `{"foo": {"b` -> `{"foo": "{b"}`
|
||||
*/
|
||||
consume_json_result consume_json_with_dumped_args(
|
||||
const std::vector<std::vector<std::string>> & args_paths = {},
|
||||
const std::vector<std::vector<std::string>> & content_paths = {}
|
||||
);
|
||||
std::optional<consume_json_result> try_consume_json_with_dumped_args(
|
||||
const std::vector<std::vector<std::string>> & args_paths = {},
|
||||
const std::vector<std::vector<std::string>> & content_paths = {}
|
||||
);
|
||||
|
||||
/**
|
||||
* Parse XML-Style tool call for given xml_tool_call_format. Return false for invalid syntax and get the position untouched.
|
||||
* form.scope_start, form.tool_sep and form.scope_end can be empty.
|
||||
*/
|
||||
bool try_consume_xml_tool_calls(const struct xml_tool_call_format & form);
|
||||
|
||||
// Parse content uses reasoning and XML-Style tool call
|
||||
void consume_reasoning_with_xml_tool_calls(const struct xml_tool_call_format & form, const std::string & start_think = "<think>", const std::string & end_think = "</think>");
|
||||
|
||||
void clear_tools();
|
||||
};
|
||||
124
common/chat-peg-parser.cpp
Normal file
124
common/chat-peg-parser.cpp
Normal file
@@ -0,0 +1,124 @@
|
||||
#include "chat-peg-parser.h"
|
||||
|
||||
#include <nlohmann/json.hpp>
|
||||
|
||||
using json = nlohmann::json;
|
||||
|
||||
static std::string_view trim_trailing_space(std::string_view sv, int max = -1) {
|
||||
int count = 0;
|
||||
while (!sv.empty() && std::isspace(static_cast<unsigned char>(sv.back()))) {
|
||||
if (max != -1 && count <= max) {
|
||||
break;
|
||||
}
|
||||
sv.remove_suffix(1);
|
||||
count++;
|
||||
}
|
||||
return sv;
|
||||
}
|
||||
|
||||
void common_chat_peg_mapper::from_ast(const common_peg_ast_arena & arena, const common_peg_parse_result & result) {
|
||||
arena.visit(result, [this](const common_peg_ast_node & node) {
|
||||
map(node);
|
||||
});
|
||||
}
|
||||
|
||||
void common_chat_peg_mapper::map(const common_peg_ast_node & node) {
|
||||
bool is_reasoning = node.tag == common_chat_peg_builder::REASONING;
|
||||
bool is_content = node.tag == common_chat_peg_builder::CONTENT;
|
||||
|
||||
if (is_reasoning) {
|
||||
result.reasoning_content = std::string(trim_trailing_space(node.text));
|
||||
}
|
||||
|
||||
if (is_content) {
|
||||
result.content = std::string(trim_trailing_space(node.text));
|
||||
}
|
||||
}
|
||||
|
||||
void common_chat_peg_native_mapper::map(const common_peg_ast_node & node) {
|
||||
common_chat_peg_mapper::map(node);
|
||||
|
||||
bool is_tool_open = node.tag == common_chat_peg_native_builder::TOOL_OPEN;
|
||||
bool is_tool_name = node.tag == common_chat_peg_native_builder::TOOL_NAME;
|
||||
bool is_tool_id = node.tag == common_chat_peg_native_builder::TOOL_ID;
|
||||
bool is_tool_args = node.tag == common_chat_peg_native_builder::TOOL_ARGS;
|
||||
|
||||
if (is_tool_open) {
|
||||
result.tool_calls.emplace_back();
|
||||
current_tool = &result.tool_calls.back();
|
||||
}
|
||||
|
||||
if (is_tool_id && current_tool) {
|
||||
current_tool->id = std::string(trim_trailing_space(node.text));
|
||||
}
|
||||
|
||||
if (is_tool_name && current_tool) {
|
||||
current_tool->name = std::string(trim_trailing_space(node.text));
|
||||
}
|
||||
|
||||
if (is_tool_args && current_tool) {
|
||||
current_tool->arguments = std::string(trim_trailing_space(node.text));
|
||||
}
|
||||
}
|
||||
|
||||
void common_chat_peg_constructed_mapper::map(const common_peg_ast_node & node) {
|
||||
common_chat_peg_mapper::map(node);
|
||||
|
||||
bool is_tool_open = node.tag == common_chat_peg_constructed_builder::TOOL_OPEN;
|
||||
bool is_tool_name = node.tag == common_chat_peg_constructed_builder::TOOL_NAME;
|
||||
bool is_tool_close = node.tag == common_chat_peg_constructed_builder::TOOL_CLOSE;
|
||||
bool is_arg_open = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_OPEN;
|
||||
bool is_arg_close = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_CLOSE;
|
||||
bool is_arg_name = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_NAME;
|
||||
bool is_arg_string = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_STRING_VALUE;
|
||||
bool is_arg_json = node.tag == common_chat_peg_constructed_builder::TOOL_ARG_JSON_VALUE;
|
||||
|
||||
if (is_tool_open) {
|
||||
result.tool_calls.emplace_back();
|
||||
current_tool = &result.tool_calls.back();
|
||||
arg_count = 0;
|
||||
}
|
||||
|
||||
if (is_tool_name) {
|
||||
current_tool->name = std::string(node.text);
|
||||
current_tool->arguments = "{";
|
||||
}
|
||||
|
||||
if (is_arg_open) {
|
||||
needs_closing_quote = false;
|
||||
}
|
||||
|
||||
if (is_arg_name && current_tool) {
|
||||
if (arg_count > 0) {
|
||||
current_tool->arguments += ",";
|
||||
}
|
||||
current_tool->arguments += json(trim_trailing_space(node.text)).dump() + ":";
|
||||
++arg_count;
|
||||
}
|
||||
|
||||
if (is_arg_string && current_tool) {
|
||||
// Serialize to JSON, but exclude the end quote
|
||||
std::string dumped = json(trim_trailing_space(node.text)).dump();
|
||||
current_tool->arguments += dumped.substr(0, dumped.size() - 1);
|
||||
needs_closing_quote = true;
|
||||
}
|
||||
|
||||
if (is_arg_close && current_tool) {
|
||||
if (needs_closing_quote) {
|
||||
current_tool->arguments += "\"";
|
||||
needs_closing_quote = false;
|
||||
}
|
||||
}
|
||||
|
||||
if (is_arg_json && current_tool) {
|
||||
current_tool->arguments += std::string(trim_trailing_space(node.text));
|
||||
}
|
||||
|
||||
if (is_tool_close && current_tool) {
|
||||
if (needs_closing_quote) {
|
||||
current_tool->arguments += "\"";
|
||||
needs_closing_quote = false;
|
||||
}
|
||||
current_tool->arguments += "}";
|
||||
}
|
||||
}
|
||||
105
common/chat-peg-parser.h
Normal file
105
common/chat-peg-parser.h
Normal file
@@ -0,0 +1,105 @@
|
||||
#pragma once
|
||||
|
||||
#include "chat.h"
|
||||
#include "peg-parser.h"
|
||||
|
||||
class common_chat_peg_builder : public common_peg_parser_builder {
|
||||
public:
|
||||
static constexpr const char * REASONING_BLOCK = "reasoning-block";
|
||||
static constexpr const char * REASONING = "reasoning";
|
||||
static constexpr const char * CONTENT = "content";
|
||||
|
||||
common_peg_parser reasoning_block(const common_peg_parser & p) { return tag(REASONING_BLOCK, p); }
|
||||
common_peg_parser reasoning(const common_peg_parser & p) { return tag(REASONING, p); }
|
||||
common_peg_parser content(const common_peg_parser & p) { return tag(CONTENT, p); }
|
||||
};
|
||||
|
||||
inline common_peg_arena build_chat_peg_parser(const std::function<common_peg_parser(common_chat_peg_builder & builder)> & fn) {
|
||||
common_chat_peg_builder builder;
|
||||
builder.set_root(fn(builder));
|
||||
return builder.build();
|
||||
}
|
||||
|
||||
class common_chat_peg_mapper {
|
||||
public:
|
||||
common_chat_msg & result;
|
||||
|
||||
common_chat_peg_mapper(common_chat_msg & msg) : result(msg) {}
|
||||
|
||||
virtual void from_ast(const common_peg_ast_arena & arena, const common_peg_parse_result & result);
|
||||
virtual void map(const common_peg_ast_node & node);
|
||||
};
|
||||
|
||||
class common_chat_peg_native_builder : public common_chat_peg_builder {
|
||||
public:
|
||||
static constexpr const char * TOOL = "tool";
|
||||
static constexpr const char * TOOL_OPEN = "tool-open";
|
||||
static constexpr const char * TOOL_CLOSE = "tool-close";
|
||||
static constexpr const char * TOOL_ID = "tool-id";
|
||||
static constexpr const char * TOOL_NAME = "tool-name";
|
||||
static constexpr const char * TOOL_ARGS = "tool-args";
|
||||
|
||||
common_peg_parser tool(const common_peg_parser & p) { return tag(TOOL, p); }
|
||||
common_peg_parser tool_open(const common_peg_parser & p) { return atomic(tag(TOOL_OPEN, p)); }
|
||||
common_peg_parser tool_close(const common_peg_parser & p) { return atomic(tag(TOOL_CLOSE, p)); }
|
||||
common_peg_parser tool_id(const common_peg_parser & p) { return atomic(tag(TOOL_ID, p)); }
|
||||
common_peg_parser tool_name(const common_peg_parser & p) { return atomic(tag(TOOL_NAME, p)); }
|
||||
common_peg_parser tool_args(const common_peg_parser & p) { return tag(TOOL_ARGS, p); }
|
||||
};
|
||||
|
||||
class common_chat_peg_native_mapper : public common_chat_peg_mapper {
|
||||
common_chat_tool_call * current_tool;
|
||||
|
||||
public:
|
||||
common_chat_peg_native_mapper(common_chat_msg & msg) : common_chat_peg_mapper(msg) {}
|
||||
|
||||
void map(const common_peg_ast_node & node) override;
|
||||
};
|
||||
|
||||
inline common_peg_arena build_chat_peg_native_parser(const std::function<common_peg_parser(common_chat_peg_native_builder & builder)> & fn) {
|
||||
common_chat_peg_native_builder builder;
|
||||
builder.set_root(fn(builder));
|
||||
return builder.build();
|
||||
}
|
||||
|
||||
class common_chat_peg_constructed_builder : public common_chat_peg_builder {
|
||||
public:
|
||||
static constexpr const char * TOOL = "tool";
|
||||
static constexpr const char * TOOL_OPEN = "tool-open";
|
||||
static constexpr const char * TOOL_CLOSE = "tool-close";
|
||||
static constexpr const char * TOOL_NAME = "tool-name";
|
||||
static constexpr const char * TOOL_ARG = "tool-arg";
|
||||
static constexpr const char * TOOL_ARG_OPEN = "tool-arg-open";
|
||||
static constexpr const char * TOOL_ARG_CLOSE = "tool-arg-close";
|
||||
static constexpr const char * TOOL_ARG_NAME = "tool-arg-name";
|
||||
static constexpr const char * TOOL_ARG_STRING_VALUE = "tool-arg-string-value";
|
||||
static constexpr const char * TOOL_ARG_JSON_VALUE = "tool-arg-json-value";
|
||||
|
||||
common_peg_parser tool(const common_peg_parser & p) { return tag(TOOL, p); }
|
||||
common_peg_parser tool_open(const common_peg_parser & p) { return atomic(tag(TOOL_OPEN, p)); }
|
||||
common_peg_parser tool_close(const common_peg_parser & p) { return atomic(tag(TOOL_CLOSE, p)); }
|
||||
common_peg_parser tool_name(const common_peg_parser & p) { return atomic(tag(TOOL_NAME, p)); }
|
||||
common_peg_parser tool_arg(const common_peg_parser & p) { return tag(TOOL_ARG, p); }
|
||||
common_peg_parser tool_arg_open(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_OPEN, p)); }
|
||||
common_peg_parser tool_arg_close(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_CLOSE, p)); }
|
||||
common_peg_parser tool_arg_name(const common_peg_parser & p) { return atomic(tag(TOOL_ARG_NAME, p)); }
|
||||
common_peg_parser tool_arg_string_value(const common_peg_parser & p) { return tag(TOOL_ARG_STRING_VALUE, p); }
|
||||
common_peg_parser tool_arg_json_value(const common_peg_parser & p) { return tag(TOOL_ARG_JSON_VALUE, p); }
|
||||
};
|
||||
|
||||
class common_chat_peg_constructed_mapper : public common_chat_peg_mapper {
|
||||
common_chat_tool_call * current_tool;
|
||||
int arg_count = 0;
|
||||
bool needs_closing_quote = false;
|
||||
|
||||
public:
|
||||
common_chat_peg_constructed_mapper(common_chat_msg & msg) : common_chat_peg_mapper(msg) {}
|
||||
|
||||
void map(const common_peg_ast_node & node) override;
|
||||
};
|
||||
|
||||
inline common_peg_arena build_chat_peg_constructed_parser(const std::function<common_peg_parser(common_chat_peg_constructed_builder & builder)> & fn) {
|
||||
common_chat_peg_constructed_builder builder;
|
||||
builder.set_root(fn(builder));
|
||||
return builder.build();
|
||||
}
|
||||
2966
common/chat.cpp
Normal file
2966
common/chat.cpp
Normal file
File diff suppressed because it is too large
Load Diff
235
common/chat.h
Normal file
235
common/chat.h
Normal file
@@ -0,0 +1,235 @@
|
||||
// Chat support (incl. tool call grammar constraining & output parsing) w/ generic & custom template handlers.
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "common.h"
|
||||
#include "peg-parser.h"
|
||||
#include <functional>
|
||||
#include <chrono>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include <map>
|
||||
|
||||
struct common_chat_templates;
|
||||
|
||||
struct common_chat_tool_call {
|
||||
std::string name;
|
||||
std::string arguments;
|
||||
std::string id;
|
||||
|
||||
bool operator==(const common_chat_tool_call & other) const {
|
||||
return name == other.name && arguments == other.arguments && id == other.id;
|
||||
}
|
||||
};
|
||||
|
||||
struct common_chat_msg_content_part {
|
||||
std::string type;
|
||||
std::string text;
|
||||
|
||||
bool operator==(const common_chat_msg_content_part & other) const {
|
||||
return type == other.type && text == other.text;
|
||||
}
|
||||
};
|
||||
|
||||
struct common_chat_msg {
|
||||
std::string role;
|
||||
std::string content;
|
||||
std::vector<common_chat_msg_content_part> content_parts;
|
||||
std::vector<common_chat_tool_call> tool_calls;
|
||||
std::string reasoning_content;
|
||||
std::string tool_name;
|
||||
std::string tool_call_id;
|
||||
|
||||
template <class T> T to_json_oaicompat() const;
|
||||
|
||||
bool empty() const {
|
||||
return content.empty() && content_parts.empty() && tool_calls.empty() && reasoning_content.empty() && tool_name.empty() && tool_call_id.empty();
|
||||
}
|
||||
void set_tool_call_ids(std::vector<std::string> & ids_cache, const std::function<std::string()> & gen_tool_call_id) {
|
||||
for (auto i = 0u; i < tool_calls.size(); i++) {
|
||||
if (ids_cache.size() <= i) {
|
||||
auto id = tool_calls[i].id;
|
||||
if (id.empty()) {
|
||||
id = gen_tool_call_id();
|
||||
}
|
||||
ids_cache.push_back(id);
|
||||
}
|
||||
tool_calls[i].id = ids_cache[i];
|
||||
}
|
||||
}
|
||||
bool operator==(const common_chat_msg & other) const {
|
||||
return role == other.role
|
||||
&& content == other.content
|
||||
&& content_parts == other.content_parts
|
||||
&& tool_calls == other.tool_calls
|
||||
&& reasoning_content == other.reasoning_content
|
||||
&& tool_name == other.tool_name
|
||||
&& tool_call_id == other.tool_call_id;
|
||||
}
|
||||
bool operator!=(const common_chat_msg & other) const {
|
||||
return !(*this == other);
|
||||
}
|
||||
};
|
||||
|
||||
struct common_chat_msg_diff {
|
||||
std::string reasoning_content_delta;
|
||||
std::string content_delta;
|
||||
size_t tool_call_index = std::string::npos;
|
||||
common_chat_tool_call tool_call_delta;
|
||||
|
||||
static std::vector<common_chat_msg_diff> compute_diffs(const common_chat_msg & msg_prv, const common_chat_msg & msg_new);
|
||||
|
||||
bool operator==(const common_chat_msg_diff & other) const {
|
||||
return content_delta == other.content_delta
|
||||
&& tool_call_index == other.tool_call_index
|
||||
&& tool_call_delta == other.tool_call_delta;
|
||||
}
|
||||
};
|
||||
|
||||
struct common_chat_tool {
|
||||
std::string name;
|
||||
std::string description;
|
||||
std::string parameters;
|
||||
};
|
||||
|
||||
enum common_chat_tool_choice {
|
||||
COMMON_CHAT_TOOL_CHOICE_AUTO,
|
||||
COMMON_CHAT_TOOL_CHOICE_REQUIRED,
|
||||
COMMON_CHAT_TOOL_CHOICE_NONE,
|
||||
};
|
||||
|
||||
enum common_chat_format {
|
||||
COMMON_CHAT_FORMAT_CONTENT_ONLY,
|
||||
COMMON_CHAT_FORMAT_GENERIC,
|
||||
COMMON_CHAT_FORMAT_MISTRAL_NEMO,
|
||||
COMMON_CHAT_FORMAT_MAGISTRAL,
|
||||
COMMON_CHAT_FORMAT_LLAMA_3_X,
|
||||
COMMON_CHAT_FORMAT_LLAMA_3_X_WITH_BUILTIN_TOOLS,
|
||||
COMMON_CHAT_FORMAT_DEEPSEEK_R1,
|
||||
COMMON_CHAT_FORMAT_FIREFUNCTION_V2,
|
||||
COMMON_CHAT_FORMAT_FUNCTIONARY_V3_2,
|
||||
COMMON_CHAT_FORMAT_FUNCTIONARY_V3_1_LLAMA_3_1,
|
||||
COMMON_CHAT_FORMAT_DEEPSEEK_V3_1,
|
||||
COMMON_CHAT_FORMAT_HERMES_2_PRO,
|
||||
COMMON_CHAT_FORMAT_COMMAND_R7B,
|
||||
COMMON_CHAT_FORMAT_GRANITE,
|
||||
COMMON_CHAT_FORMAT_GPT_OSS,
|
||||
COMMON_CHAT_FORMAT_SEED_OSS,
|
||||
COMMON_CHAT_FORMAT_NEMOTRON_V2,
|
||||
COMMON_CHAT_FORMAT_APERTUS,
|
||||
COMMON_CHAT_FORMAT_LFM2_WITH_JSON_TOOLS,
|
||||
COMMON_CHAT_FORMAT_GLM_4_5,
|
||||
COMMON_CHAT_FORMAT_MINIMAX_M2,
|
||||
COMMON_CHAT_FORMAT_KIMI_K2,
|
||||
COMMON_CHAT_FORMAT_QWEN3_CODER_XML,
|
||||
COMMON_CHAT_FORMAT_APRIEL_1_5,
|
||||
COMMON_CHAT_FORMAT_XIAOMI_MIMO,
|
||||
COMMON_CHAT_FORMAT_SOLAR_OPEN,
|
||||
COMMON_CHAT_FORMAT_EXAONE_MOE,
|
||||
|
||||
// These are intended to be parsed by the PEG parser
|
||||
COMMON_CHAT_FORMAT_PEG_SIMPLE,
|
||||
COMMON_CHAT_FORMAT_PEG_NATIVE,
|
||||
COMMON_CHAT_FORMAT_PEG_CONSTRUCTED,
|
||||
|
||||
COMMON_CHAT_FORMAT_COUNT, // Not a format, just the # formats
|
||||
};
|
||||
|
||||
struct common_chat_templates_inputs {
|
||||
std::vector<common_chat_msg> messages;
|
||||
std::string grammar;
|
||||
std::string json_schema;
|
||||
bool add_generation_prompt = true;
|
||||
bool use_jinja = true;
|
||||
// Parameters below only supported when use_jinja is true
|
||||
std::vector<common_chat_tool> tools;
|
||||
common_chat_tool_choice tool_choice = COMMON_CHAT_TOOL_CHOICE_AUTO;
|
||||
bool parallel_tool_calls = false;
|
||||
common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE;
|
||||
bool enable_thinking = true;
|
||||
std::chrono::system_clock::time_point now = std::chrono::system_clock::now();
|
||||
std::map<std::string, std::string> chat_template_kwargs;
|
||||
bool add_bos = false;
|
||||
bool add_eos = false;
|
||||
};
|
||||
|
||||
struct common_chat_params {
|
||||
common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
|
||||
std::string prompt;
|
||||
std::string grammar;
|
||||
bool grammar_lazy = false;
|
||||
bool thinking_forced_open = false;
|
||||
std::vector<common_grammar_trigger> grammar_triggers;
|
||||
std::vector<std::string> preserved_tokens;
|
||||
std::vector<std::string> additional_stops;
|
||||
std::string parser;
|
||||
};
|
||||
|
||||
struct common_chat_syntax {
|
||||
common_chat_format format = COMMON_CHAT_FORMAT_CONTENT_ONLY;
|
||||
common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_NONE;
|
||||
// Whether reasoning_content should be inlined in the content (e.g. for reasoning_format=deepseek in stream mode)
|
||||
bool reasoning_in_content = false;
|
||||
bool thinking_forced_open = false;
|
||||
bool parse_tool_calls = true;
|
||||
common_peg_arena parser = {};
|
||||
};
|
||||
|
||||
// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid
|
||||
bool common_chat_verify_template(const std::string & tmpl, bool use_jinja);
|
||||
|
||||
void common_chat_templates_free(struct common_chat_templates * tmpls);
|
||||
|
||||
struct common_chat_templates_deleter { void operator()(common_chat_templates * tmpls) { common_chat_templates_free(tmpls); } };
|
||||
|
||||
typedef std::unique_ptr<struct common_chat_templates, common_chat_templates_deleter> common_chat_templates_ptr;
|
||||
|
||||
common_chat_templates_ptr common_chat_templates_init(
|
||||
const struct llama_model * model,
|
||||
const std::string & chat_template_override,
|
||||
const std::string & bos_token_override = "",
|
||||
const std::string & eos_token_override = "");
|
||||
|
||||
bool common_chat_templates_was_explicit(const struct common_chat_templates * tmpls);
|
||||
const char * common_chat_templates_source(const struct common_chat_templates * tmpls, const char * variant = nullptr);
|
||||
|
||||
|
||||
struct common_chat_params common_chat_templates_apply(
|
||||
const struct common_chat_templates * tmpls,
|
||||
const struct common_chat_templates_inputs & inputs);
|
||||
|
||||
// Format single message, while taking into account the position of that message in chat history
|
||||
std::string common_chat_format_single(
|
||||
const struct common_chat_templates * tmpls,
|
||||
const std::vector<common_chat_msg> & past_msg,
|
||||
const common_chat_msg & new_msg,
|
||||
bool add_ass,
|
||||
bool use_jinja);
|
||||
|
||||
// Returns an example of formatted chat
|
||||
std::string common_chat_format_example(
|
||||
const struct common_chat_templates * tmpls,
|
||||
bool use_jinja,
|
||||
const std::map<std::string, std::string> & chat_template_kwargs);
|
||||
|
||||
const char* common_chat_format_name(common_chat_format format);
|
||||
const char* common_reasoning_format_name(common_reasoning_format format);
|
||||
common_reasoning_format common_reasoning_format_from_name(const std::string & format);
|
||||
common_chat_msg common_chat_parse(const std::string & input, bool is_partial, const common_chat_syntax & syntax);
|
||||
common_chat_msg common_chat_peg_parse(const common_peg_arena & parser, const std::string & input, bool is_partial, const common_chat_syntax & syntax);
|
||||
|
||||
common_chat_tool_choice common_chat_tool_choice_parse_oaicompat(const std::string & tool_choice);
|
||||
|
||||
bool common_chat_templates_support_enable_thinking(const common_chat_templates * chat_templates);
|
||||
|
||||
// Parses a JSON array of messages in OpenAI's chat completion API format.
|
||||
// T can be std::string containing JSON or nlohmann::ordered_json
|
||||
template <class T> std::vector<common_chat_msg> common_chat_msgs_parse_oaicompat(const T & messages);
|
||||
template <class T> T common_chat_msgs_to_json_oaicompat(const std::vector<common_chat_msg> & msgs, bool concat_typed_text = false);
|
||||
|
||||
// Parses a JSON array of tools in OpenAI's chat completion tool call API format.
|
||||
// T can be std::string containing JSON or nlohmann::ordered_json
|
||||
template <class T> std::vector<common_chat_tool> common_chat_tools_parse_oaicompat(const T & tools);
|
||||
template <class T> T common_chat_tools_to_json_oaicompat(const std::vector<common_chat_tool> & tools);
|
||||
|
||||
template <class T> T common_chat_msg_diff_to_json_oaicompat(const common_chat_msg_diff & diff);
|
||||
1866
common/common.cpp
Normal file
1866
common/common.cpp
Normal file
File diff suppressed because it is too large
Load Diff
863
common/common.h
Normal file
863
common/common.h
Normal file
@@ -0,0 +1,863 @@
|
||||
// Various helper functions and utilities
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "ggml-opt.h"
|
||||
#include "llama-cpp.h"
|
||||
|
||||
#include <set>
|
||||
#include <sstream>
|
||||
#include <string>
|
||||
#include <string_view>
|
||||
#include <vector>
|
||||
#include <map>
|
||||
|
||||
#if defined(_WIN32) && !defined(_WIN32_WINNT)
|
||||
#define _WIN32_WINNT 0x0A00
|
||||
#endif
|
||||
|
||||
#ifdef _WIN32
|
||||
#define DIRECTORY_SEPARATOR '\\'
|
||||
#else
|
||||
#define DIRECTORY_SEPARATOR '/'
|
||||
#endif // _WIN32
|
||||
|
||||
#define die(msg) do { fputs("error: " msg "\n", stderr); exit(1); } while (0)
|
||||
#define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0)
|
||||
|
||||
#define print_build_info() do { \
|
||||
fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \
|
||||
fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \
|
||||
} while(0)
|
||||
|
||||
struct common_time_meas {
|
||||
common_time_meas(int64_t & t_acc, bool disable = false);
|
||||
~common_time_meas();
|
||||
|
||||
const int64_t t_start_us;
|
||||
|
||||
int64_t & t_acc;
|
||||
};
|
||||
|
||||
struct common_adapter_lora_info {
|
||||
std::string path;
|
||||
float scale;
|
||||
|
||||
std::string task_name;
|
||||
std::string prompt_prefix;
|
||||
|
||||
struct llama_adapter_lora * ptr;
|
||||
};
|
||||
|
||||
using llama_tokens = std::vector<llama_token>;
|
||||
|
||||
// build info
|
||||
extern int LLAMA_BUILD_NUMBER;
|
||||
extern const char * LLAMA_COMMIT;
|
||||
extern const char * LLAMA_COMPILER;
|
||||
extern const char * LLAMA_BUILD_TARGET;
|
||||
|
||||
struct common_control_vector_load_info;
|
||||
|
||||
//
|
||||
// CPU utils
|
||||
//
|
||||
|
||||
struct cpu_params {
|
||||
int n_threads = -1;
|
||||
bool cpumask[GGML_MAX_N_THREADS] = {false}; // CPU affinity mask.
|
||||
bool mask_valid = false; // Default: any CPU
|
||||
enum ggml_sched_priority priority = GGML_SCHED_PRIO_NORMAL; // Scheduling prio : (0 - normal, 1 - medium, 2 - high, 3 - realtime)
|
||||
bool strict_cpu = false; // Use strict CPU placement
|
||||
uint32_t poll = 50; // Polling (busywait) level (0 - no polling, 100 - mostly polling)
|
||||
};
|
||||
|
||||
int32_t cpu_get_num_physical_cores();
|
||||
int32_t cpu_get_num_math();
|
||||
|
||||
//
|
||||
// Common params
|
||||
//
|
||||
|
||||
enum llama_example {
|
||||
LLAMA_EXAMPLE_BATCHED,
|
||||
LLAMA_EXAMPLE_DEBUG,
|
||||
LLAMA_EXAMPLE_COMMON,
|
||||
LLAMA_EXAMPLE_SPECULATIVE,
|
||||
LLAMA_EXAMPLE_COMPLETION,
|
||||
LLAMA_EXAMPLE_CLI,
|
||||
LLAMA_EXAMPLE_EMBEDDING,
|
||||
LLAMA_EXAMPLE_PERPLEXITY,
|
||||
LLAMA_EXAMPLE_RETRIEVAL,
|
||||
LLAMA_EXAMPLE_PASSKEY,
|
||||
LLAMA_EXAMPLE_IMATRIX,
|
||||
LLAMA_EXAMPLE_BENCH,
|
||||
LLAMA_EXAMPLE_SERVER,
|
||||
LLAMA_EXAMPLE_CVECTOR_GENERATOR,
|
||||
LLAMA_EXAMPLE_EXPORT_LORA,
|
||||
LLAMA_EXAMPLE_MTMD,
|
||||
LLAMA_EXAMPLE_LOOKUP,
|
||||
LLAMA_EXAMPLE_PARALLEL,
|
||||
LLAMA_EXAMPLE_TTS,
|
||||
LLAMA_EXAMPLE_DIFFUSION,
|
||||
LLAMA_EXAMPLE_FINETUNE,
|
||||
LLAMA_EXAMPLE_FIT_PARAMS,
|
||||
|
||||
LLAMA_EXAMPLE_COUNT,
|
||||
};
|
||||
|
||||
enum common_sampler_type {
|
||||
COMMON_SAMPLER_TYPE_NONE = 0,
|
||||
COMMON_SAMPLER_TYPE_DRY = 1,
|
||||
COMMON_SAMPLER_TYPE_TOP_K = 2,
|
||||
COMMON_SAMPLER_TYPE_TOP_P = 3,
|
||||
COMMON_SAMPLER_TYPE_MIN_P = 4,
|
||||
//COMMON_SAMPLER_TYPE_TFS_Z = 5,
|
||||
COMMON_SAMPLER_TYPE_TYPICAL_P = 6,
|
||||
COMMON_SAMPLER_TYPE_TEMPERATURE = 7,
|
||||
COMMON_SAMPLER_TYPE_XTC = 8,
|
||||
COMMON_SAMPLER_TYPE_INFILL = 9,
|
||||
COMMON_SAMPLER_TYPE_PENALTIES = 10,
|
||||
COMMON_SAMPLER_TYPE_TOP_N_SIGMA = 11,
|
||||
COMMON_SAMPLER_TYPE_ADAPTIVE_P = 12,
|
||||
};
|
||||
|
||||
// dimensionality reduction methods, used by cvector-generator
|
||||
enum dimre_method {
|
||||
DIMRE_METHOD_PCA,
|
||||
DIMRE_METHOD_MEAN,
|
||||
};
|
||||
|
||||
enum common_conversation_mode {
|
||||
COMMON_CONVERSATION_MODE_DISABLED = 0,
|
||||
COMMON_CONVERSATION_MODE_ENABLED = 1,
|
||||
COMMON_CONVERSATION_MODE_AUTO = 2,
|
||||
};
|
||||
|
||||
enum common_grammar_trigger_type {
|
||||
COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN,
|
||||
COMMON_GRAMMAR_TRIGGER_TYPE_WORD,
|
||||
COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN,
|
||||
COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL,
|
||||
};
|
||||
|
||||
struct common_grammar_trigger {
|
||||
common_grammar_trigger_type type;
|
||||
std::string value;
|
||||
llama_token token = LLAMA_TOKEN_NULL;
|
||||
};
|
||||
|
||||
enum common_params_sampling_config : uint64_t {
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_SAMPLERS = 1 << 0,
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_TOP_K = 1 << 1,
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_TOP_P = 1 << 2,
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_MIN_P = 1 << 3,
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_XTC_PROBABILITY = 1 << 4,
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_XTC_THRESHOLD = 1 << 5,
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_TEMP = 1 << 6,
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_LAST_N = 1 << 7,
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_PENALTY_REPEAT = 1 << 8,
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT = 1 << 9,
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_TAU = 1 << 10,
|
||||
COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA = 1 << 11,
|
||||
};
|
||||
|
||||
|
||||
// sampling parameters
|
||||
struct common_params_sampling {
|
||||
uint32_t seed = LLAMA_DEFAULT_SEED; // the seed used to initialize llama_sampler
|
||||
|
||||
int32_t n_prev = 64; // number of previous tokens to remember
|
||||
int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
|
||||
int32_t min_keep = 0; // 0 = disabled, otherwise samplers should return at least min_keep tokens
|
||||
int32_t top_k = 40; // <= 0 to use vocab size
|
||||
float top_p = 0.95f; // 1.0 = disabled
|
||||
float min_p = 0.05f; // 0.0 = disabled
|
||||
float xtc_probability = 0.00f; // 0.0 = disabled
|
||||
float xtc_threshold = 0.10f; // > 0.5 disables XTC
|
||||
float typ_p = 1.00f; // typical_p, 1.0 = disabled
|
||||
float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities
|
||||
float dynatemp_range = 0.00f; // 0.0 = disabled
|
||||
float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler
|
||||
int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size)
|
||||
float penalty_repeat = 1.00f; // 1.0 = disabled
|
||||
float penalty_freq = 0.00f; // 0.0 = disabled
|
||||
float penalty_present = 0.00f; // 0.0 = disabled
|
||||
float dry_multiplier = 0.0f; // 0.0 = disabled; DRY repetition penalty for tokens extending repetition:
|
||||
float dry_base = 1.75f; // 0.0 = disabled; multiplier * base ^ (length of sequence before token - allowed length)
|
||||
int32_t dry_allowed_length = 2; // tokens extending repetitions beyond this receive penalty
|
||||
int32_t dry_penalty_last_n = -1; // how many tokens to scan for repetitions (0 = disable penalty, -1 = context size)
|
||||
float adaptive_target = -1.0f; // select tokens near this probability (valid range 0.0 to 1.0; negative = disabled)
|
||||
float adaptive_decay = 0.90f; // EMA decay for adaptation; history ≈ 1/(1-decay) tokens (0.0 - 0.99)
|
||||
int32_t mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0
|
||||
float top_n_sigma = -1.00f; // -1.0 = disabled
|
||||
float mirostat_tau = 5.00f; // target entropy
|
||||
float mirostat_eta = 0.10f; // learning rate
|
||||
bool ignore_eos = false;
|
||||
bool no_perf = false; // disable performance metrics
|
||||
bool timing_per_token = false;
|
||||
|
||||
uint64_t user_sampling_config = 0; // bitfield to track user-specified samplers
|
||||
|
||||
std::vector<std::string> dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY
|
||||
|
||||
std::vector<enum common_sampler_type> samplers = {
|
||||
COMMON_SAMPLER_TYPE_PENALTIES,
|
||||
COMMON_SAMPLER_TYPE_DRY,
|
||||
COMMON_SAMPLER_TYPE_TOP_N_SIGMA,
|
||||
COMMON_SAMPLER_TYPE_TOP_K,
|
||||
COMMON_SAMPLER_TYPE_TYPICAL_P,
|
||||
COMMON_SAMPLER_TYPE_TOP_P,
|
||||
COMMON_SAMPLER_TYPE_MIN_P,
|
||||
COMMON_SAMPLER_TYPE_XTC,
|
||||
COMMON_SAMPLER_TYPE_TEMPERATURE,
|
||||
};
|
||||
|
||||
std::string grammar; // optional BNF-like grammar to constrain sampling
|
||||
bool grammar_lazy = false;
|
||||
std::vector<common_grammar_trigger> grammar_triggers; // optional triggers (for lazy grammars)
|
||||
std::set<llama_token> preserved_tokens;
|
||||
|
||||
std::vector<llama_logit_bias> logit_bias; // logit biases to apply
|
||||
std::vector<llama_logit_bias> logit_bias_eog; // pre-calculated logit biases for EOG tokens
|
||||
|
||||
bool backend_sampling = false;
|
||||
|
||||
bool has_logit_bias() const {
|
||||
return !logit_bias.empty();
|
||||
}
|
||||
|
||||
// print the parameters into a string
|
||||
std::string print() const;
|
||||
};
|
||||
|
||||
struct common_params_model {
|
||||
std::string path = ""; // model local path // NOLINT
|
||||
std::string url = ""; // model url to download // NOLINT
|
||||
std::string hf_repo = ""; // HF repo // NOLINT
|
||||
std::string hf_file = ""; // HF file // NOLINT
|
||||
std::string docker_repo = ""; // Docker repo // NOLINT
|
||||
std::string name = ""; // in format <user>/<model>[:<tag>] (tag is optional) // NOLINT
|
||||
};
|
||||
|
||||
struct common_params_speculative {
|
||||
std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
|
||||
|
||||
int32_t n_ctx = 0; // draft context size
|
||||
int32_t n_max = 16; // maximum number of tokens to draft during speculative decoding
|
||||
int32_t n_min = 0; // minimum number of draft tokens to use for speculative decoding
|
||||
int32_t n_gpu_layers = -1; // number of layers to store in VRAM for the draft model (-1 - use default)
|
||||
float p_split = 0.1f; // speculative decoding split probability
|
||||
float p_min = 0.75f; // minimum speculative decoding probability (greedy)
|
||||
std::vector<std::pair<std::string, std::string>> replacements; // main to speculative model replacements
|
||||
std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
|
||||
|
||||
ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
|
||||
ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
|
||||
|
||||
struct cpu_params cpuparams;
|
||||
struct cpu_params cpuparams_batch;
|
||||
|
||||
struct common_params_model model;
|
||||
};
|
||||
|
||||
struct common_params_vocoder {
|
||||
struct common_params_model model;
|
||||
|
||||
std::string speaker_file = ""; // speaker file path // NOLINT
|
||||
|
||||
bool use_guide_tokens = false; // enable guide tokens to improve TTS accuracy // NOLINT
|
||||
};
|
||||
|
||||
struct common_params_diffusion {
|
||||
int32_t steps = 128;
|
||||
bool visual_mode = false;
|
||||
|
||||
float eps = 0; // epsilon for timesteps
|
||||
int32_t block_length = 0; // block length for generation
|
||||
|
||||
int32_t algorithm = 4; // default algorithm: low-confidence
|
||||
float alg_temp = 0.0f; // algorithm temperature
|
||||
|
||||
float cfg_scale = 0; // classifier-free guidance scale
|
||||
bool add_gumbel_noise = false; // add gumbel noise to the logits if temp > 0.0
|
||||
};
|
||||
|
||||
// reasoning API response format (not to be confused as chat template's reasoning format)
|
||||
enum common_reasoning_format {
|
||||
COMMON_REASONING_FORMAT_NONE,
|
||||
COMMON_REASONING_FORMAT_AUTO, // Same as deepseek, using `message.reasoning_content`
|
||||
COMMON_REASONING_FORMAT_DEEPSEEK_LEGACY, // Extract thinking tag contents and return as `message.reasoning_content`, or leave inline in <think> tags in stream mode
|
||||
COMMON_REASONING_FORMAT_DEEPSEEK, // Extract thinking tag contents and return as `message.reasoning_content`, including in streaming deltas.
|
||||
// do not extend this enum unless you absolutely have to
|
||||
// in most cases, use COMMON_REASONING_FORMAT_AUTO
|
||||
// see: https://github.com/ggml-org/llama.cpp/pull/15408
|
||||
};
|
||||
|
||||
|
||||
struct lr_opt {
|
||||
float lr0 = 1e-5; // learning rate at first epoch
|
||||
float lr_min = -1;
|
||||
float decay_epochs = -1; // if >0, the learning rate starts at lr0 and decays to lr_min after this many epochs
|
||||
float scale_epoch = 0;
|
||||
float wd = 0;
|
||||
unsigned epochs = 2;
|
||||
|
||||
unsigned epoch; // set by optimizer outer (epochs) loop
|
||||
// learning rate decay - constant LR per epoch only for now
|
||||
float get_lr(float e) const;
|
||||
float get_lr() const { return get_lr(epoch); }
|
||||
// must call after arg parse, before get_lr
|
||||
void init();
|
||||
};
|
||||
|
||||
struct ggml_opt_optimizer_params common_opt_lr_pars(void * userdata);
|
||||
|
||||
struct common_params {
|
||||
int32_t n_predict = -1; // max. number of new tokens to predict, -1 == no limit
|
||||
int32_t n_ctx = 0; // context size, 0 == context the model was trained with
|
||||
int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS)
|
||||
int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS)
|
||||
int32_t n_keep = 0; // number of tokens to keep from initial prompt
|
||||
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
|
||||
int32_t n_parallel = 1; // number of parallel sequences to decode
|
||||
int32_t n_sequences = 1; // number of sequences to decode
|
||||
int32_t grp_attn_n = 1; // group-attention factor
|
||||
int32_t grp_attn_w = 512; // group-attention width
|
||||
int32_t n_print = -1; // print token count every n tokens (-1 = disabled)
|
||||
float rope_freq_base = 0.0f; // RoPE base frequency
|
||||
float rope_freq_scale = 0.0f; // RoPE frequency scaling factor
|
||||
float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor
|
||||
float yarn_attn_factor = -1.0f; // YaRN magnitude scaling factor
|
||||
float yarn_beta_fast = -1.0f; // YaRN low correction dim
|
||||
float yarn_beta_slow = -1.0f; // YaRN high correction dim
|
||||
int32_t yarn_orig_ctx = 0; // YaRN original context length
|
||||
|
||||
// offload params
|
||||
std::vector<ggml_backend_dev_t> devices; // devices to use for offloading
|
||||
|
||||
int32_t n_gpu_layers = -1; // number of layers to store in VRAM, -1 is auto, <= -2 is all
|
||||
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
|
||||
float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs
|
||||
bool fit_params = true; // whether to fit unset model/context parameters to free device memory
|
||||
int32_t fit_params_min_ctx = 4096; // minimum context size to set when trying to reduce memory use
|
||||
|
||||
// margin per device in bytes for fitting parameters to free memory:
|
||||
std::vector<size_t> fit_params_target = std::vector<size_t>(llama_max_devices(), 1024 * 1024*1024);
|
||||
|
||||
enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs
|
||||
|
||||
struct cpu_params cpuparams;
|
||||
struct cpu_params cpuparams_batch;
|
||||
|
||||
ggml_backend_sched_eval_callback cb_eval = nullptr;
|
||||
void * cb_eval_user_data = nullptr;
|
||||
|
||||
ggml_numa_strategy numa = GGML_NUMA_STRATEGY_DISABLED;
|
||||
|
||||
enum llama_rope_scaling_type rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_UNSPECIFIED;
|
||||
enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_UNSPECIFIED; // pooling type for embeddings
|
||||
enum llama_attention_type attention_type = LLAMA_ATTENTION_TYPE_UNSPECIFIED; // attention type for embeddings
|
||||
enum llama_flash_attn_type flash_attn_type = LLAMA_FLASH_ATTN_TYPE_AUTO; // whether to use Flash Attention
|
||||
|
||||
struct common_params_sampling sampling;
|
||||
struct common_params_speculative speculative;
|
||||
struct common_params_vocoder vocoder;
|
||||
struct common_params_diffusion diffusion;
|
||||
|
||||
struct common_params_model model;
|
||||
|
||||
std::string model_alias = ""; // model alias // NOLINT
|
||||
std::string hf_token = ""; // HF token // NOLINT
|
||||
std::string prompt = ""; // NOLINT
|
||||
std::string system_prompt = ""; // NOLINT
|
||||
std::string prompt_file = ""; // store the external prompt file name // NOLINT
|
||||
std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state // NOLINT
|
||||
std::string input_prefix = ""; // string to prefix user inputs with // NOLINT
|
||||
std::string input_suffix = ""; // string to suffix user inputs with // NOLINT
|
||||
std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding // NOLINT
|
||||
std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding // NOLINT
|
||||
std::string logits_file = ""; // file for saving *all* logits // NOLINT
|
||||
|
||||
// llama-debug specific options
|
||||
std::string logits_output_dir = "data"; // directory for saving logits output files // NOLINT
|
||||
bool save_logits = false; // whether to save logits to files // NOLINT
|
||||
std::vector<std::string> tensor_filter; // filter tensor names for debug output (regex) // NOLINT
|
||||
|
||||
std::vector<std::string> in_files; // all input files
|
||||
std::vector<std::string> antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts)
|
||||
std::vector<llama_model_kv_override> kv_overrides;
|
||||
std::vector<llama_model_tensor_buft_override> tensor_buft_overrides;
|
||||
|
||||
bool lora_init_without_apply = false; // only load lora to memory, but do not apply it to ctx (user can manually apply lora later using llama_adapter_lora_apply)
|
||||
std::vector<common_adapter_lora_info> lora_adapters; // lora adapter path with user defined scale
|
||||
|
||||
std::vector<common_control_vector_load_info> control_vectors; // control vector with user defined scale
|
||||
|
||||
int32_t verbosity = 3; // LOG_LEVEL_INFO
|
||||
int32_t control_vector_layer_start = -1; // layer range for control vector
|
||||
int32_t control_vector_layer_end = -1; // layer range for control vector
|
||||
bool offline = false;
|
||||
|
||||
int32_t ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used.
|
||||
int32_t ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line
|
||||
// (which is more convenient to use for plotting)
|
||||
//
|
||||
bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt
|
||||
size_t hellaswag_tasks = 400; // number of tasks to use when computing the HellaSwag score
|
||||
|
||||
bool winogrande = false; // compute Winogrande score over random tasks from datafile supplied in prompt
|
||||
size_t winogrande_tasks = 0; // number of tasks to use when computing the Winogrande score. If 0, all tasks will be computed
|
||||
|
||||
bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt
|
||||
size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed
|
||||
|
||||
bool kl_divergence = false; // compute KL divergence
|
||||
|
||||
bool usage = false; // print usage
|
||||
bool completion = false; // print source-able completion script
|
||||
bool use_color = false; // use color to distinguish generations and inputs
|
||||
bool special = false; // enable special token output
|
||||
bool interactive = false; // interactive mode
|
||||
bool interactive_first = false; // wait for user input immediately
|
||||
bool prompt_cache_all = false; // save user input and generations to prompt cache
|
||||
bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it
|
||||
|
||||
bool escape = true; // escape "\n", "\r", "\t", "\'", "\"", and "\\"
|
||||
bool multiline_input = false; // reverse the usage of `\`
|
||||
bool simple_io = false; // improves compatibility with subprocesses and limited consoles
|
||||
bool cont_batching = true; // insert new sequences for decoding on-the-fly
|
||||
bool no_perf = false; // disable performance metrics
|
||||
bool show_timings = true; // show timing information on CLI
|
||||
bool ctx_shift = false; // context shift on infinite text generation
|
||||
bool swa_full = false; // use full-size SWA cache (https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
|
||||
bool kv_unified = false; // enable unified KV cache
|
||||
|
||||
bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix
|
||||
bool use_mmap = true; // enable mmap to use filesystem cache
|
||||
bool use_direct_io = true; // read from disk without buffering for faster model loading
|
||||
bool use_mlock = false; // use mlock to keep model in memory
|
||||
bool verbose_prompt = false; // print prompt tokens before generation
|
||||
bool display_prompt = true; // print prompt before generation
|
||||
bool no_kv_offload = false; // disable KV offloading
|
||||
bool warmup = true; // warmup run
|
||||
bool check_tensors = false; // validate tensor data
|
||||
bool no_op_offload = false; // globally disable offload host tensor operations to device
|
||||
bool no_extra_bufts = false; // disable extra buffer types (used for weight repacking)
|
||||
bool no_host = false; // bypass host buffer allowing extra buffers to be used
|
||||
|
||||
bool single_turn = false; // single turn chat conversation
|
||||
|
||||
ggml_type cache_type_k = GGML_TYPE_F16; // KV cache data type for the K
|
||||
ggml_type cache_type_v = GGML_TYPE_F16; // KV cache data type for the V
|
||||
|
||||
common_conversation_mode conversation_mode = COMMON_CONVERSATION_MODE_AUTO;
|
||||
|
||||
// multimodal models (see tools/mtmd)
|
||||
struct common_params_model mmproj;
|
||||
bool mmproj_use_gpu = true; // use GPU for multimodal model
|
||||
bool no_mmproj = false; // explicitly disable multimodal model
|
||||
std::vector<std::string> image; // path to image file(s)
|
||||
int image_min_tokens = -1;
|
||||
int image_max_tokens = -1;
|
||||
|
||||
// finetune
|
||||
struct lr_opt lr;
|
||||
enum ggml_opt_optimizer_type optimizer = GGML_OPT_OPTIMIZER_TYPE_ADAMW;
|
||||
float val_split = 0.05f; // fraction of the data used for the validation set
|
||||
|
||||
// embedding
|
||||
bool embedding = false; // get only sentence embedding
|
||||
int32_t embd_normalize = 2; // normalisation for embeddings (-1=none, 0=max absolute int16, 1=taxicab, 2=euclidean, >2=p-norm)
|
||||
std::string embd_out = ""; // empty = default, "array" = [[],[]...], "json" = openai style, "json+" = same "json" + cosine similarity matrix
|
||||
std::string embd_sep = "\n"; // separator of embeddings
|
||||
std::string cls_sep = "\t"; // separator of classification sequences
|
||||
|
||||
// server params
|
||||
int32_t port = 8080; // server listens on this network port
|
||||
int32_t timeout_read = 600; // http read timeout in seconds
|
||||
int32_t timeout_write = timeout_read; // http write timeout in seconds
|
||||
int32_t n_threads_http = -1; // number of threads to process HTTP requests (TODO: support threadpool)
|
||||
int32_t n_cache_reuse = 0; // min chunk size to reuse from the cache via KV shifting
|
||||
bool cache_prompt = true; // whether to enable prompt caching
|
||||
int32_t n_ctx_checkpoints = 8; // max number of context checkpoints per slot
|
||||
int32_t cache_ram_mib = 8192; // -1 = no limit, 0 - disable, 1 = 1 MiB, etc.
|
||||
|
||||
std::string hostname = "127.0.0.1";
|
||||
std::string public_path = ""; // NOLINT
|
||||
std::string api_prefix = ""; // NOLINT
|
||||
std::string chat_template = ""; // NOLINT
|
||||
bool use_jinja = true; // NOLINT
|
||||
bool enable_chat_template = true;
|
||||
common_reasoning_format reasoning_format = COMMON_REASONING_FORMAT_DEEPSEEK;
|
||||
int reasoning_budget = -1;
|
||||
bool prefill_assistant = true; // if true, any trailing assistant message will be prefilled into the response
|
||||
int sleep_idle_seconds = -1; // if >0, server will sleep after this many seconds of idle time
|
||||
|
||||
std::vector<std::string> api_keys;
|
||||
|
||||
std::string ssl_file_key = ""; // NOLINT
|
||||
std::string ssl_file_cert = ""; // NOLINT
|
||||
|
||||
std::map<std::string, std::string> default_template_kwargs;
|
||||
|
||||
// webui configs
|
||||
bool webui = true;
|
||||
std::string webui_config_json;
|
||||
|
||||
// "advanced" endpoints are disabled by default for better security
|
||||
bool endpoint_slots = true;
|
||||
bool endpoint_props = false; // only control POST requests, not GET
|
||||
bool endpoint_metrics = false;
|
||||
|
||||
// router server configs
|
||||
std::string models_dir = ""; // directory containing models for the router server
|
||||
std::string models_preset = ""; // directory containing model presets for the router server
|
||||
int models_max = 4; // maximum number of models to load simultaneously
|
||||
bool models_autoload = true; // automatically load models when requested via the router server
|
||||
|
||||
bool log_json = false;
|
||||
|
||||
std::string slot_save_path;
|
||||
std::string media_path; // path to directory for loading media files
|
||||
|
||||
float slot_prompt_similarity = 0.1f;
|
||||
|
||||
// batched-bench params
|
||||
bool is_pp_shared = false;
|
||||
bool is_tg_separate = false;
|
||||
|
||||
std::vector<int32_t> n_pp;
|
||||
std::vector<int32_t> n_tg;
|
||||
std::vector<int32_t> n_pl;
|
||||
|
||||
// retrieval params
|
||||
std::vector<std::string> context_files; // context files to embed
|
||||
|
||||
int32_t chunk_size = 64; // chunk size for context embedding
|
||||
|
||||
std::string chunk_separator = "\n"; // chunk separator for context embedding
|
||||
|
||||
// passkey params
|
||||
int32_t n_junk = 250; // number of times to repeat the junk text
|
||||
int32_t i_pos = -1; // position of the passkey in the junk text
|
||||
|
||||
// imatrix params
|
||||
int32_t n_out_freq = 10; // output the imatrix every n_out_freq iterations
|
||||
int32_t n_save_freq = 0; // save the imatrix every n_save_freq iterations
|
||||
int32_t i_chunk = 0; // start processing from this chunk
|
||||
int8_t imat_dat = 0; // whether the legacy imatrix.dat format should be output (gguf <= 0 < dat)
|
||||
|
||||
bool process_output = false; // collect data for the output tensor
|
||||
bool compute_ppl = true; // whether to compute perplexity
|
||||
bool show_statistics = false; // show imatrix statistics per tensor
|
||||
bool parse_special = false; // whether to parse special tokens during imatrix tokenization
|
||||
|
||||
// cvector-generator params
|
||||
int n_pca_batch = 100;
|
||||
int n_pca_iterations = 1000;
|
||||
dimre_method cvector_dimre_method = DIMRE_METHOD_PCA;
|
||||
std::string cvector_positive_file = "tools/cvector-generator/positive.txt";
|
||||
std::string cvector_negative_file = "tools/cvector-generator/negative.txt";
|
||||
|
||||
bool spm_infill = false; // suffix/prefix/middle pattern for infill
|
||||
|
||||
// batched-bench params
|
||||
bool batched_bench_output_jsonl = false;
|
||||
|
||||
// common params
|
||||
std::string out_file; // output filename for all example programs
|
||||
// optional callback for model loading progress and cancellation:
|
||||
// called with a progress value between 0.0 and 1.0.
|
||||
// return false from callback to abort model loading or true to continue
|
||||
llama_progress_callback load_progress_callback = NULL;
|
||||
void * load_progress_callback_user_data = NULL;
|
||||
|
||||
bool has_speculative() const {
|
||||
return !speculative.model.path.empty() || !speculative.model.hf_repo.empty();
|
||||
}
|
||||
};
|
||||
|
||||
// call once at the start of a program if it uses libcommon
|
||||
// initializes the logging system and prints info about the build
|
||||
void common_init();
|
||||
|
||||
std::string common_params_get_system_info(const common_params & params);
|
||||
|
||||
bool parse_cpu_range(const std::string & range, bool(&boolmask)[GGML_MAX_N_THREADS]);
|
||||
bool parse_cpu_mask(const std::string & mask, bool(&boolmask)[GGML_MAX_N_THREADS]);
|
||||
void postprocess_cpu_params(cpu_params & cpuparams, const cpu_params * role_model = nullptr);
|
||||
bool set_process_priority(enum ggml_sched_priority prio);
|
||||
|
||||
//
|
||||
// String utils
|
||||
//
|
||||
|
||||
#ifdef __GNUC__
|
||||
# if defined(__MINGW32__) && !defined(__clang__)
|
||||
# define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
|
||||
# else
|
||||
# define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
|
||||
# endif
|
||||
#else
|
||||
# define LLAMA_COMMON_ATTRIBUTE_FORMAT(...)
|
||||
#endif
|
||||
|
||||
LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
|
||||
std::string string_format(const char * fmt, ...);
|
||||
|
||||
std::string string_strip(const std::string & str);
|
||||
std::string string_get_sortable_timestamp();
|
||||
|
||||
std::string string_join(const std::vector<std::string> & values, const std::string & separator);
|
||||
std::vector<std::string> string_split(const std::string & str, const std::string & delimiter);
|
||||
std::string string_repeat(const std::string & str, size_t n);
|
||||
|
||||
void string_replace_all(std::string & s, const std::string & search, const std::string & replace);
|
||||
|
||||
std::string regex_escape(const std::string & s);
|
||||
|
||||
template<class T>
|
||||
static std::vector<T> string_split(const std::string & str, char delim) {
|
||||
static_assert(!std::is_same<T, std::string>::value, "Please use the specialized version for std::string");
|
||||
std::vector<T> values;
|
||||
std::istringstream str_stream(str);
|
||||
std::string token;
|
||||
while (std::getline(str_stream, token, delim)) {
|
||||
T value;
|
||||
std::istringstream token_stream(token);
|
||||
token_stream >> value;
|
||||
values.push_back(value);
|
||||
}
|
||||
return values;
|
||||
}
|
||||
|
||||
template<>
|
||||
std::vector<std::string> string_split<std::string>(const std::string & input, char separator)
|
||||
{
|
||||
std::vector<std::string> parts;
|
||||
size_t begin_pos = 0;
|
||||
size_t separator_pos = input.find(separator);
|
||||
while (separator_pos != std::string::npos) {
|
||||
std::string part = input.substr(begin_pos, separator_pos - begin_pos);
|
||||
parts.emplace_back(part);
|
||||
begin_pos = separator_pos + 1;
|
||||
separator_pos = input.find(separator, begin_pos);
|
||||
}
|
||||
parts.emplace_back(input.substr(begin_pos, separator_pos - begin_pos));
|
||||
return parts;
|
||||
}
|
||||
|
||||
static bool string_starts_with(const std::string & str,
|
||||
const std::string & prefix) { // While we wait for C++20's std::string::starts_with...
|
||||
return str.rfind(prefix, 0) == 0;
|
||||
}
|
||||
|
||||
// While we wait for C++20's std::string::ends_with...
|
||||
bool string_ends_with(const std::string_view & str, const std::string_view & suffix);
|
||||
bool string_remove_suffix(std::string & str, const std::string_view & suffix);
|
||||
size_t string_find_partial_stop(const std::string_view & str, const std::string_view & stop);
|
||||
|
||||
bool string_parse_kv_override(const char * data, std::vector<llama_model_kv_override> & overrides);
|
||||
void string_process_escapes(std::string & input);
|
||||
|
||||
std::string string_from(bool value);
|
||||
std::string string_from(const std::vector<int> & values);
|
||||
std::string string_from(const struct llama_context * ctx, const std::vector<llama_token> & tokens);
|
||||
std::string string_from(const struct llama_context * ctx, const struct llama_batch & batch);
|
||||
|
||||
//
|
||||
// Filesystem utils
|
||||
//
|
||||
|
||||
bool fs_validate_filename(const std::string & filename, bool allow_subdirs = false);
|
||||
bool fs_create_directory_with_parents(const std::string & path);
|
||||
bool fs_is_directory(const std::string & path);
|
||||
|
||||
std::string fs_get_cache_directory();
|
||||
std::string fs_get_cache_file(const std::string & filename);
|
||||
|
||||
struct common_file_info {
|
||||
std::string path;
|
||||
std::string name;
|
||||
size_t size = 0; // in bytes
|
||||
bool is_dir = false;
|
||||
};
|
||||
std::vector<common_file_info> fs_list(const std::string & path, bool include_directories);
|
||||
|
||||
//
|
||||
// TTY utils
|
||||
//
|
||||
|
||||
// Auto-detect if colors can be enabled based on terminal and environment
|
||||
bool tty_can_use_colors();
|
||||
|
||||
//
|
||||
// Model utils
|
||||
//
|
||||
|
||||
struct common_sampler;
|
||||
|
||||
// note: defines the model, context, samplers, ets. lifetimes
|
||||
struct common_init_result {
|
||||
common_init_result(common_params & params);
|
||||
~common_init_result();
|
||||
|
||||
llama_model * model();
|
||||
llama_context * context();
|
||||
|
||||
common_sampler * sampler(llama_seq_id seq_id);
|
||||
void reset_samplers();
|
||||
|
||||
std::vector<llama_adapter_lora_ptr> & lora();
|
||||
|
||||
void free_context();
|
||||
|
||||
private:
|
||||
struct impl;
|
||||
std::unique_ptr<impl> pimpl;
|
||||
};
|
||||
|
||||
using common_init_result_ptr = std::unique_ptr<common_init_result>;
|
||||
|
||||
common_init_result_ptr common_init_from_params(common_params & params);
|
||||
|
||||
struct llama_model_params common_model_params_to_llama ( common_params & params);
|
||||
struct llama_context_params common_context_params_to_llama(const common_params & params);
|
||||
struct ggml_threadpool_params ggml_threadpool_params_from_cpu_params(const cpu_params & params);
|
||||
|
||||
// clear LoRA adapters from context, then apply new list of adapters
|
||||
void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adapter_lora_info> & lora);
|
||||
|
||||
std::string get_model_endpoint();
|
||||
|
||||
//
|
||||
// Batch utils
|
||||
//
|
||||
|
||||
void common_batch_clear(struct llama_batch & batch);
|
||||
|
||||
void common_batch_add(
|
||||
struct llama_batch & batch,
|
||||
llama_token id,
|
||||
llama_pos pos,
|
||||
const std::vector<llama_seq_id> & seq_ids,
|
||||
bool logits);
|
||||
|
||||
//
|
||||
// Token utils
|
||||
//
|
||||
|
||||
// longest common prefix
|
||||
size_t common_lcp(const llama_tokens & a, const llama_tokens & b);
|
||||
|
||||
// longet common subsequence
|
||||
size_t common_lcs(const llama_tokens & a, const llama_tokens & b);
|
||||
|
||||
//
|
||||
// Vocab utils
|
||||
//
|
||||
|
||||
// tokenizes a string into a vector of tokens
|
||||
// should work similar to Python's `tokenizer.encode`
|
||||
std::vector<llama_token> common_tokenize(
|
||||
const struct llama_context * ctx,
|
||||
const std::string & text,
|
||||
bool add_special,
|
||||
bool parse_special = false);
|
||||
|
||||
std::vector<llama_token> common_tokenize(
|
||||
const struct llama_vocab * vocab,
|
||||
const std::string & text,
|
||||
bool add_special,
|
||||
bool parse_special = false);
|
||||
|
||||
// tokenizes a token into a piece, optionally renders special/control tokens
|
||||
// should work similar to Python's `tokenizer.id_to_piece`
|
||||
std::string common_token_to_piece(
|
||||
const struct llama_context * ctx,
|
||||
llama_token token,
|
||||
bool special = true);
|
||||
|
||||
std::string common_token_to_piece(
|
||||
const struct llama_vocab * vocab,
|
||||
llama_token token,
|
||||
bool special = true);
|
||||
|
||||
// detokenizes a vector of tokens into a string
|
||||
// should work similar to Python's `tokenizer.decode`
|
||||
// optionally renders special/control tokens
|
||||
std::string common_detokenize(
|
||||
const struct llama_context * ctx,
|
||||
const std::vector<llama_token> & tokens,
|
||||
bool special = true);
|
||||
|
||||
std::string common_detokenize(
|
||||
const struct llama_vocab * vocab,
|
||||
const std::vector<llama_token> & tokens,
|
||||
bool special = true);
|
||||
|
||||
//
|
||||
// Embedding utils
|
||||
//
|
||||
|
||||
// TODO: repace embd_norm with an enum
|
||||
void common_embd_normalize(const float * inp, float * out, int n, int embd_norm);
|
||||
|
||||
float common_embd_similarity_cos(const float * embd1, const float * embd2, int n);
|
||||
|
||||
//
|
||||
// Control vector utils
|
||||
//
|
||||
|
||||
struct common_control_vector_data {
|
||||
int n_embd;
|
||||
|
||||
// stores data for layers [1, n_layer] where n_layer = data.size() / n_embd
|
||||
std::vector<float> data;
|
||||
};
|
||||
|
||||
struct common_control_vector_load_info {
|
||||
float strength;
|
||||
|
||||
std::string fname;
|
||||
};
|
||||
|
||||
// Load control vectors, scale each by strength, and add them together.
|
||||
// On error, returns {-1, empty}
|
||||
common_control_vector_data common_control_vector_load(const std::vector<common_control_vector_load_info> & load_infos);
|
||||
|
||||
//
|
||||
// Split utils
|
||||
//
|
||||
|
||||
namespace {
|
||||
|
||||
const char * const LLM_KV_SPLIT_NO = "split.no";
|
||||
const char * const LLM_KV_SPLIT_COUNT = "split.count";
|
||||
const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
|
||||
|
||||
}
|
||||
|
||||
//
|
||||
// MoE utils
|
||||
//
|
||||
|
||||
const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate)_(ch|)exps";
|
||||
|
||||
static std::string llm_ffn_exps_block_regex(int idx) {
|
||||
return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX);
|
||||
}
|
||||
|
||||
static llama_model_tensor_buft_override llm_ffn_exps_cpu_override() {
|
||||
return { LLM_FFN_EXPS_REGEX, ggml_backend_cpu_buffer_type() };
|
||||
}
|
||||
|
||||
//
|
||||
// training utils
|
||||
//
|
||||
|
||||
ggml_opt_dataset_t common_opt_dataset_init(struct llama_context * ctx, const std::vector<llama_token> & tokens, int64_t stride);
|
||||
|
||||
// "adamw" or "sgd" (case insensitive)
|
||||
enum ggml_opt_optimizer_type common_opt_get_optimizer(const char *);
|
||||
1137
common/console.cpp
Normal file
1137
common/console.cpp
Normal file
File diff suppressed because it is too large
Load Diff
41
common/console.h
Normal file
41
common/console.h
Normal file
@@ -0,0 +1,41 @@
|
||||
// Console functions
|
||||
|
||||
#pragma once
|
||||
|
||||
#include "common.h"
|
||||
|
||||
#include <string>
|
||||
|
||||
enum display_type {
|
||||
DISPLAY_TYPE_RESET = 0,
|
||||
DISPLAY_TYPE_INFO,
|
||||
DISPLAY_TYPE_PROMPT,
|
||||
DISPLAY_TYPE_REASONING,
|
||||
DISPLAY_TYPE_USER_INPUT,
|
||||
DISPLAY_TYPE_ERROR
|
||||
};
|
||||
|
||||
namespace console {
|
||||
void init(bool use_simple_io, bool use_advanced_display);
|
||||
void cleanup();
|
||||
void set_display(display_type display);
|
||||
bool readline(std::string & line, bool multiline_input);
|
||||
|
||||
namespace spinner {
|
||||
void start();
|
||||
void stop();
|
||||
}
|
||||
|
||||
// note: the logging API below output directly to stdout
|
||||
// it can negatively impact performance if used on inference thread
|
||||
// only use in in a dedicated CLI thread
|
||||
// for logging in inference thread, use log.h instead
|
||||
|
||||
LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
|
||||
void log(const char * fmt, ...);
|
||||
|
||||
LLAMA_COMMON_ATTRIBUTE_FORMAT(1, 2)
|
||||
void error(const char * fmt, ...);
|
||||
|
||||
void flush();
|
||||
}
|
||||
165
common/debug.cpp
Normal file
165
common/debug.cpp
Normal file
@@ -0,0 +1,165 @@
|
||||
#include "debug.h"
|
||||
|
||||
#include "log.h"
|
||||
|
||||
#include <cmath>
|
||||
#include <string>
|
||||
|
||||
static std::string common_ggml_ne_string(const ggml_tensor * t) {
|
||||
std::string str;
|
||||
for (int i = 0; i < GGML_MAX_DIMS; ++i) {
|
||||
str += std::to_string(t->ne[i]);
|
||||
if (i + 1 < GGML_MAX_DIMS) {
|
||||
str += ", ";
|
||||
}
|
||||
}
|
||||
return str;
|
||||
}
|
||||
|
||||
static float common_ggml_get_float_value(const uint8_t * data,
|
||||
ggml_type type,
|
||||
const size_t * nb,
|
||||
size_t i0,
|
||||
size_t i1,
|
||||
size_t i2,
|
||||
size_t i3) {
|
||||
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
|
||||
float v;
|
||||
if (type == GGML_TYPE_F16) {
|
||||
v = ggml_fp16_to_fp32(*(const ggml_fp16_t *) &data[i]);
|
||||
} else if (type == GGML_TYPE_F32) {
|
||||
v = *(const float *) &data[i];
|
||||
} else if (type == GGML_TYPE_I64) {
|
||||
v = (float) *(const int64_t *) &data[i];
|
||||
} else if (type == GGML_TYPE_I32) {
|
||||
v = (float) *(const int32_t *) &data[i];
|
||||
} else if (type == GGML_TYPE_I16) {
|
||||
v = (float) *(const int16_t *) &data[i];
|
||||
} else if (type == GGML_TYPE_I8) {
|
||||
v = (float) *(const int8_t *) &data[i];
|
||||
} else if (type == GGML_TYPE_BF16) {
|
||||
v = ggml_bf16_to_fp32(*(const ggml_bf16_t *) &data[i]);
|
||||
} else {
|
||||
GGML_ABORT("fatal error");
|
||||
}
|
||||
return v;
|
||||
}
|
||||
|
||||
template <bool abort>
|
||||
void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
|
||||
GGML_ASSERT(n > 0);
|
||||
float sum = 0;
|
||||
for (int64_t i3 = 0; i3 < ne[3]; i3++) {
|
||||
for (int64_t i2 = 0; i2 < ne[2]; i2++) {
|
||||
for (int64_t i1 = 0; i1 < ne[1]; i1++) {
|
||||
for (int64_t i0 = 0; i0 < ne[0]; i0++) {
|
||||
const float v = common_ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
|
||||
sum += v;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
for (int64_t i3 = 0; i3 < ne[3]; i3++) {
|
||||
LOG_ERR(" [\n");
|
||||
for (int64_t i2 = 0; i2 < ne[2]; i2++) {
|
||||
if (i2 == n && ne[2] > 2 * n) {
|
||||
LOG_ERR(" ..., \n");
|
||||
i2 = ne[2] - n;
|
||||
}
|
||||
LOG_ERR(" [\n");
|
||||
for (int64_t i1 = 0; i1 < ne[1]; i1++) {
|
||||
if (i1 == n && ne[1] > 2 * n) {
|
||||
LOG_ERR(" ..., \n");
|
||||
i1 = ne[1] - n;
|
||||
}
|
||||
LOG_ERR(" [");
|
||||
for (int64_t i0 = 0; i0 < ne[0]; i0++) {
|
||||
if (i0 == n && ne[0] > 2 * n) {
|
||||
LOG_ERR("..., ");
|
||||
i0 = ne[0] - n;
|
||||
}
|
||||
const float v = common_ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
|
||||
LOG_ERR("%12.4f", v);
|
||||
if (i0 < ne[0] - 1) {
|
||||
LOG_ERR(", ");
|
||||
}
|
||||
}
|
||||
LOG_ERR("],\n");
|
||||
}
|
||||
LOG_ERR(" ],\n");
|
||||
}
|
||||
LOG_ERR(" ]\n");
|
||||
LOG_ERR(" sum = %f\n", sum);
|
||||
}
|
||||
|
||||
if constexpr (abort) {
|
||||
if (std::isnan(sum)) {
|
||||
LOG_ERR("encountered NaN - aborting\n");
|
||||
exit(0);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* GGML operations callback during the graph execution.
|
||||
*
|
||||
* @param t current tensor
|
||||
* @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor
|
||||
* if we return true, a follow-up call will be made with ask=false in which we can do the actual collection.
|
||||
* see ggml_backend_sched_eval_callback
|
||||
* @param user_data user data to pass at each call back
|
||||
* @return true to receive data or continue the graph, false otherwise
|
||||
*/
|
||||
template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
|
||||
auto * cb_data = (base_callback_data *) user_data;
|
||||
|
||||
const struct ggml_tensor * src0 = t->src[0];
|
||||
const struct ggml_tensor * src1 = t->src[1];
|
||||
|
||||
if (ask) {
|
||||
return true; // Always retrieve data
|
||||
}
|
||||
|
||||
bool matches_filter = cb_data->tensor_filters.empty();
|
||||
|
||||
if (!matches_filter) {
|
||||
for (const auto & filter : cb_data->tensor_filters) {
|
||||
if (std::regex_search(t->name, filter)) {
|
||||
matches_filter = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
char src1_str[128] = { 0 };
|
||||
if (src1) {
|
||||
snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, common_ggml_ne_string(src1).c_str());
|
||||
}
|
||||
|
||||
if (matches_filter) {
|
||||
LOG_ERR("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__, t->name, ggml_type_name(t->type),
|
||||
ggml_op_desc(t), src0->name, common_ggml_ne_string(src0).c_str(), src1 ? src1_str : "",
|
||||
common_ggml_ne_string(t).c_str());
|
||||
}
|
||||
|
||||
const bool is_host = ggml_backend_buffer_is_host(t->buffer);
|
||||
|
||||
if (!is_host) {
|
||||
auto n_bytes = ggml_nbytes(t);
|
||||
cb_data->data.resize(n_bytes);
|
||||
ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
|
||||
}
|
||||
|
||||
if (!ggml_is_quantized(t->type) && matches_filter) {
|
||||
uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
|
||||
common_debug_print_tensor<abort_on_nan>(data, t->type, t->ne, t->nb, 3);
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
// Explicit template instantiations
|
||||
template bool common_debug_cb_eval<false>(ggml_tensor *, bool, void *);
|
||||
template bool common_debug_cb_eval<true>(ggml_tensor *, bool, void *);
|
||||
template void common_debug_print_tensor<false>(uint8_t *, ggml_type, const int64_t *, const size_t *, int64_t);
|
||||
template void common_debug_print_tensor<true>(uint8_t *, ggml_type, const int64_t *, const size_t *, int64_t);
|
||||
43
common/debug.h
Normal file
43
common/debug.h
Normal file
@@ -0,0 +1,43 @@
|
||||
#pragma once
|
||||
#include "common.h"
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include <regex>
|
||||
|
||||
// common debug functions and structs
|
||||
|
||||
// Print a tensor's detailed data
|
||||
// data - the tensor's data in byte format
|
||||
// type - the tensor's quantization type
|
||||
// ne - the tensor dimensions array
|
||||
// nb - the tensor strides array
|
||||
// n - the number of rows/columns to fully print
|
||||
template <bool abort_on_nan> void common_debug_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n);
|
||||
|
||||
// Intended to use as callback for ggml_backend_sched_eval_callback
|
||||
// prints tensors that are processed in the computation graph
|
||||
// by default prints all tensors, but can be configured by creating a `base_callback_data` instance with
|
||||
// non-empty filter_patterns. See examples/debug.ccp for possible usage patterns
|
||||
// The template parameter determins whether an error should be thrown whenever a NaN is encountered
|
||||
// in a tensor (useful for stopping debug sessions on first erroneous tensor)
|
||||
// The callback data will be passed as the third parameter (user_data)
|
||||
template <bool abort_on_nan> bool common_debug_cb_eval(struct ggml_tensor * t, bool ask, void * user_data);
|
||||
struct base_callback_data {
|
||||
std::vector<uint8_t> data;
|
||||
std::vector<std::regex> tensor_filters;
|
||||
|
||||
base_callback_data() = default;
|
||||
|
||||
base_callback_data(common_params & params, const std::vector<std::string> & filter_patterns) {
|
||||
for (const auto & pattern : filter_patterns) {
|
||||
try {
|
||||
std::string anchored_pattern = "^" + pattern;
|
||||
tensor_filters.emplace_back(anchored_pattern, std::regex::optimize);
|
||||
} catch (const std::regex_error & e) {
|
||||
throw std::runtime_error("Invalid regex pattern '" + pattern + "': " + e.what());
|
||||
}
|
||||
}
|
||||
params.cb_eval = common_debug_cb_eval<false>;
|
||||
params.cb_eval_user_data = this;
|
||||
}
|
||||
};
|
||||
845
common/download.cpp
Normal file
845
common/download.cpp
Normal file
@@ -0,0 +1,845 @@
|
||||
#include "arg.h"
|
||||
|
||||
#include "common.h"
|
||||
#include "gguf.h" // for reading GGUF splits
|
||||
#include "log.h"
|
||||
#include "download.h"
|
||||
|
||||
#define JSON_ASSERT GGML_ASSERT
|
||||
#include <nlohmann/json.hpp>
|
||||
|
||||
#include <algorithm>
|
||||
#include <filesystem>
|
||||
#include <fstream>
|
||||
#include <future>
|
||||
#include <map>
|
||||
#include <mutex>
|
||||
#include <regex>
|
||||
#include <string>
|
||||
#include <thread>
|
||||
#include <vector>
|
||||
|
||||
#if defined(LLAMA_USE_HTTPLIB)
|
||||
#include "http.h"
|
||||
#endif
|
||||
|
||||
#ifndef __EMSCRIPTEN__
|
||||
#ifdef __linux__
|
||||
#include <linux/limits.h>
|
||||
#elif defined(_WIN32)
|
||||
# if !defined(PATH_MAX)
|
||||
# define PATH_MAX MAX_PATH
|
||||
# endif
|
||||
#elif defined(_AIX)
|
||||
#include <sys/limits.h>
|
||||
#else
|
||||
#include <sys/syslimits.h>
|
||||
#endif
|
||||
#endif
|
||||
|
||||
#define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
|
||||
|
||||
// isatty
|
||||
#if defined(_WIN32)
|
||||
#include <io.h>
|
||||
#else
|
||||
#include <unistd.h>
|
||||
#endif
|
||||
|
||||
using json = nlohmann::ordered_json;
|
||||
|
||||
//
|
||||
// downloader
|
||||
//
|
||||
|
||||
// validate repo name format: owner/repo
|
||||
static bool validate_repo_name(const std::string & repo) {
|
||||
static const std::regex repo_regex(R"(^[A-Za-z0-9_.\-]+\/[A-Za-z0-9_.\-]+$)");
|
||||
return std::regex_match(repo, repo_regex);
|
||||
}
|
||||
|
||||
static std::string get_manifest_path(const std::string & repo, const std::string & tag) {
|
||||
// we use "=" to avoid clashing with other component, while still being allowed on windows
|
||||
std::string fname = "manifest=" + repo + "=" + tag + ".json";
|
||||
if (!validate_repo_name(repo)) {
|
||||
throw std::runtime_error("error: repo name must be in the format 'owner/repo'");
|
||||
}
|
||||
string_replace_all(fname, "/", "=");
|
||||
return fs_get_cache_file(fname);
|
||||
}
|
||||
|
||||
static std::string read_file(const std::string & fname) {
|
||||
std::ifstream file(fname);
|
||||
if (!file) {
|
||||
throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
|
||||
}
|
||||
std::string content((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
|
||||
file.close();
|
||||
return content;
|
||||
}
|
||||
|
||||
static void write_file(const std::string & fname, const std::string & content) {
|
||||
const std::string fname_tmp = fname + ".tmp";
|
||||
std::ofstream file(fname_tmp);
|
||||
if (!file) {
|
||||
throw std::runtime_error(string_format("error: failed to open file '%s'\n", fname.c_str()));
|
||||
}
|
||||
|
||||
try {
|
||||
file << content;
|
||||
file.close();
|
||||
|
||||
// Makes write atomic
|
||||
if (rename(fname_tmp.c_str(), fname.c_str()) != 0) {
|
||||
LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, fname_tmp.c_str(), fname.c_str());
|
||||
// If rename fails, try to delete the temporary file
|
||||
if (remove(fname_tmp.c_str()) != 0) {
|
||||
LOG_ERR("%s: unable to delete temporary file: %s\n", __func__, fname_tmp.c_str());
|
||||
}
|
||||
}
|
||||
} catch (...) {
|
||||
// If anything fails, try to delete the temporary file
|
||||
if (remove(fname_tmp.c_str()) != 0) {
|
||||
LOG_ERR("%s: unable to delete temporary file: %s\n", __func__, fname_tmp.c_str());
|
||||
}
|
||||
|
||||
throw std::runtime_error(string_format("error: failed to write file '%s'\n", fname.c_str()));
|
||||
}
|
||||
}
|
||||
|
||||
static void write_etag(const std::string & path, const std::string & etag) {
|
||||
const std::string etag_path = path + ".etag";
|
||||
write_file(etag_path, etag);
|
||||
LOG_DBG("%s: file etag saved: %s\n", __func__, etag_path.c_str());
|
||||
}
|
||||
|
||||
static std::string read_etag(const std::string & path) {
|
||||
std::string none;
|
||||
const std::string etag_path = path + ".etag";
|
||||
|
||||
if (std::filesystem::exists(etag_path)) {
|
||||
std::ifstream etag_in(etag_path);
|
||||
if (!etag_in) {
|
||||
LOG_ERR("%s: could not open .etag file for reading: %s\n", __func__, etag_path.c_str());
|
||||
return none;
|
||||
}
|
||||
std::string etag;
|
||||
std::getline(etag_in, etag);
|
||||
return etag;
|
||||
}
|
||||
|
||||
// no etag file, but maybe there is an old .json
|
||||
// remove this code later
|
||||
const std::string metadata_path = path + ".json";
|
||||
|
||||
if (std::filesystem::exists(metadata_path)) {
|
||||
std::ifstream metadata_in(metadata_path);
|
||||
try {
|
||||
nlohmann::json metadata_json;
|
||||
metadata_in >> metadata_json;
|
||||
LOG_DBG("%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(),
|
||||
metadata_json.dump().c_str());
|
||||
if (metadata_json.contains("etag") && metadata_json.at("etag").is_string()) {
|
||||
std::string etag = metadata_json.at("etag");
|
||||
write_etag(path, etag);
|
||||
if (!std::filesystem::remove(metadata_path)) {
|
||||
LOG_WRN("%s: failed to delete old .json metadata file: %s\n", __func__, metadata_path.c_str());
|
||||
}
|
||||
return etag;
|
||||
}
|
||||
} catch (const nlohmann::json::exception & e) {
|
||||
LOG_ERR("%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what());
|
||||
}
|
||||
}
|
||||
return none;
|
||||
}
|
||||
|
||||
static bool is_http_status_ok(int status) {
|
||||
return status >= 200 && status < 400;
|
||||
}
|
||||
|
||||
std::pair<std::string, std::string> common_download_split_repo_tag(const std::string & hf_repo_with_tag) {
|
||||
auto parts = string_split<std::string>(hf_repo_with_tag, ':');
|
||||
std::string tag = parts.size() > 1 ? parts.back() : "latest";
|
||||
std::string hf_repo = parts[0];
|
||||
if (string_split<std::string>(hf_repo, '/').size() != 2) {
|
||||
throw std::invalid_argument("error: invalid HF repo format, expected <user>/<model>[:quant]\n");
|
||||
}
|
||||
return {hf_repo, tag};
|
||||
}
|
||||
|
||||
#if defined(LLAMA_USE_HTTPLIB)
|
||||
|
||||
class ProgressBar {
|
||||
static inline std::mutex mutex;
|
||||
static inline std::map<const ProgressBar *, int> lines;
|
||||
static inline int max_line = 0;
|
||||
|
||||
static void cleanup(const ProgressBar * line) {
|
||||
lines.erase(line);
|
||||
if (lines.empty()) {
|
||||
max_line = 0;
|
||||
}
|
||||
}
|
||||
|
||||
static bool is_output_a_tty() {
|
||||
#if defined(_WIN32)
|
||||
return _isatty(_fileno(stdout));
|
||||
#else
|
||||
return isatty(1);
|
||||
#endif
|
||||
}
|
||||
|
||||
public:
|
||||
ProgressBar() = default;
|
||||
|
||||
~ProgressBar() {
|
||||
std::lock_guard<std::mutex> lock(mutex);
|
||||
cleanup(this);
|
||||
}
|
||||
|
||||
void update(size_t current, size_t total) {
|
||||
if (!is_output_a_tty()) {
|
||||
return;
|
||||
}
|
||||
|
||||
if (!total) {
|
||||
return;
|
||||
}
|
||||
|
||||
std::lock_guard<std::mutex> lock(mutex);
|
||||
|
||||
if (lines.find(this) == lines.end()) {
|
||||
lines[this] = max_line++;
|
||||
std::cout << "\n";
|
||||
}
|
||||
int lines_up = max_line - lines[this];
|
||||
|
||||
size_t width = 50;
|
||||
size_t pct = (100 * current) / total;
|
||||
size_t pos = (width * current) / total;
|
||||
|
||||
std::cout << "\033[s";
|
||||
|
||||
if (lines_up > 0) {
|
||||
std::cout << "\033[" << lines_up << "A";
|
||||
}
|
||||
std::cout << "\033[2K\r["
|
||||
<< std::string(pos, '=')
|
||||
<< (pos < width ? ">" : "")
|
||||
<< std::string(width - pos, ' ')
|
||||
<< "] " << std::setw(3) << pct << "% ("
|
||||
<< current / (1024 * 1024) << " MB / "
|
||||
<< total / (1024 * 1024) << " MB) "
|
||||
<< "\033[u";
|
||||
|
||||
std::cout.flush();
|
||||
|
||||
if (current == total) {
|
||||
cleanup(this);
|
||||
}
|
||||
}
|
||||
|
||||
ProgressBar(const ProgressBar &) = delete;
|
||||
ProgressBar & operator=(const ProgressBar &) = delete;
|
||||
};
|
||||
|
||||
static bool common_pull_file(httplib::Client & cli,
|
||||
const std::string & resolve_path,
|
||||
const std::string & path_tmp,
|
||||
bool supports_ranges,
|
||||
size_t existing_size,
|
||||
size_t & total_size) {
|
||||
std::ofstream ofs(path_tmp, std::ios::binary | std::ios::app);
|
||||
if (!ofs.is_open()) {
|
||||
LOG_ERR("%s: error opening local file for writing: %s\n", __func__, path_tmp.c_str());
|
||||
return false;
|
||||
}
|
||||
|
||||
httplib::Headers headers;
|
||||
if (supports_ranges && existing_size > 0) {
|
||||
headers.emplace("Range", "bytes=" + std::to_string(existing_size) + "-");
|
||||
}
|
||||
|
||||
const char * func = __func__; // avoid __func__ inside a lambda
|
||||
size_t downloaded = existing_size;
|
||||
size_t progress_step = 0;
|
||||
ProgressBar bar;
|
||||
|
||||
auto res = cli.Get(resolve_path, headers,
|
||||
[&](const httplib::Response &response) {
|
||||
if (existing_size > 0 && response.status != 206) {
|
||||
LOG_WRN("%s: server did not respond with 206 Partial Content for a resume request. Status: %d\n", func, response.status);
|
||||
return false;
|
||||
}
|
||||
if (existing_size == 0 && response.status != 200) {
|
||||
LOG_WRN("%s: download received non-successful status code: %d\n", func, response.status);
|
||||
return false;
|
||||
}
|
||||
if (total_size == 0 && response.has_header("Content-Length")) {
|
||||
try {
|
||||
size_t content_length = std::stoull(response.get_header_value("Content-Length"));
|
||||
total_size = existing_size + content_length;
|
||||
} catch (const std::exception &e) {
|
||||
LOG_WRN("%s: invalid Content-Length header: %s\n", func, e.what());
|
||||
}
|
||||
}
|
||||
return true;
|
||||
},
|
||||
[&](const char *data, size_t len) {
|
||||
ofs.write(data, len);
|
||||
if (!ofs) {
|
||||
LOG_ERR("%s: error writing to file: %s\n", func, path_tmp.c_str());
|
||||
return false;
|
||||
}
|
||||
downloaded += len;
|
||||
progress_step += len;
|
||||
|
||||
if (progress_step >= total_size / 1000 || downloaded == total_size) {
|
||||
bar.update(downloaded, total_size);
|
||||
progress_step = 0;
|
||||
}
|
||||
return true;
|
||||
},
|
||||
nullptr
|
||||
);
|
||||
|
||||
if (!res) {
|
||||
LOG_ERR("%s: error during download. Status: %d\n", __func__, res ? res->status : -1);
|
||||
return false;
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
// download one single file from remote URL to local path
|
||||
// returns status code or -1 on error
|
||||
static int common_download_file_single_online(const std::string & url,
|
||||
const std::string & path,
|
||||
const std::string & bearer_token,
|
||||
const common_header_list & custom_headers) {
|
||||
static const int max_attempts = 3;
|
||||
static const int retry_delay_seconds = 2;
|
||||
|
||||
auto [cli, parts] = common_http_client(url);
|
||||
|
||||
httplib::Headers default_headers = {{"User-Agent", "llama-cpp"}};
|
||||
if (!bearer_token.empty()) {
|
||||
default_headers.insert({"Authorization", "Bearer " + bearer_token});
|
||||
}
|
||||
for (const auto & h : custom_headers) {
|
||||
default_headers.emplace(h.first, h.second);
|
||||
}
|
||||
cli.set_default_headers(default_headers);
|
||||
|
||||
const bool file_exists = std::filesystem::exists(path);
|
||||
|
||||
std::string last_etag;
|
||||
if (file_exists) {
|
||||
last_etag = read_etag(path);
|
||||
} else {
|
||||
LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
|
||||
}
|
||||
|
||||
for (int i = 0; i < max_attempts; ++i) {
|
||||
auto head = cli.Head(parts.path);
|
||||
bool head_ok = head && head->status >= 200 && head->status < 300;
|
||||
if (!head_ok) {
|
||||
LOG_WRN("%s: HEAD invalid http status code received: %d\n", __func__, head ? head->status : -1);
|
||||
if (file_exists) {
|
||||
LOG_INF("%s: Using cached file (HEAD failed): %s\n", __func__, path.c_str());
|
||||
return 304; // 304 Not Modified - fake cached response
|
||||
}
|
||||
return head->status; // cannot use cached file, return raw status code
|
||||
// TODO: maybe retry only on certain codes
|
||||
}
|
||||
|
||||
std::string etag;
|
||||
if (head_ok && head->has_header("ETag")) {
|
||||
etag = head->get_header_value("ETag");
|
||||
}
|
||||
|
||||
size_t total_size = 0;
|
||||
if (head_ok && head->has_header("Content-Length")) {
|
||||
try {
|
||||
total_size = std::stoull(head->get_header_value("Content-Length"));
|
||||
} catch (const std::exception& e) {
|
||||
LOG_WRN("%s: Invalid Content-Length in HEAD response: %s\n", __func__, e.what());
|
||||
}
|
||||
}
|
||||
|
||||
bool supports_ranges = false;
|
||||
if (head_ok && head->has_header("Accept-Ranges")) {
|
||||
supports_ranges = head->get_header_value("Accept-Ranges") != "none";
|
||||
}
|
||||
|
||||
bool should_download_from_scratch = false;
|
||||
if (!last_etag.empty() && !etag.empty() && last_etag != etag) {
|
||||
LOG_WRN("%s: ETag header is different (%s != %s): triggering a new download\n", __func__,
|
||||
last_etag.c_str(), etag.c_str());
|
||||
should_download_from_scratch = true;
|
||||
}
|
||||
|
||||
if (file_exists) {
|
||||
if (!should_download_from_scratch) {
|
||||
LOG_INF("%s: using cached file: %s\n", __func__, path.c_str());
|
||||
return 304; // 304 Not Modified - fake cached response
|
||||
}
|
||||
LOG_WRN("%s: deleting previous downloaded file: %s\n", __func__, path.c_str());
|
||||
if (remove(path.c_str()) != 0) {
|
||||
LOG_ERR("%s: unable to delete file: %s\n", __func__, path.c_str());
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
|
||||
const std::string path_temporary = path + ".downloadInProgress";
|
||||
size_t existing_size = 0;
|
||||
|
||||
if (std::filesystem::exists(path_temporary)) {
|
||||
if (supports_ranges && !should_download_from_scratch) {
|
||||
existing_size = std::filesystem::file_size(path_temporary);
|
||||
} else if (remove(path_temporary.c_str()) != 0) {
|
||||
LOG_ERR("%s: unable to delete file: %s\n", __func__, path_temporary.c_str());
|
||||
return -1;
|
||||
}
|
||||
}
|
||||
|
||||
// start the download
|
||||
LOG_INF("%s: trying to download model from %s to %s (etag:%s)...\n",
|
||||
__func__, common_http_show_masked_url(parts).c_str(), path_temporary.c_str(), etag.c_str());
|
||||
const bool was_pull_successful = common_pull_file(cli, parts.path, path_temporary, supports_ranges, existing_size, total_size);
|
||||
if (!was_pull_successful) {
|
||||
if (i + 1 < max_attempts) {
|
||||
const int exponential_backoff_delay = std::pow(retry_delay_seconds, i) * 1000;
|
||||
LOG_WRN("%s: retrying after %d milliseconds...\n", __func__, exponential_backoff_delay);
|
||||
std::this_thread::sleep_for(std::chrono::milliseconds(exponential_backoff_delay));
|
||||
} else {
|
||||
LOG_ERR("%s: download failed after %d attempts\n", __func__, max_attempts);
|
||||
}
|
||||
continue;
|
||||
}
|
||||
|
||||
if (std::rename(path_temporary.c_str(), path.c_str()) != 0) {
|
||||
LOG_ERR("%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str());
|
||||
return -1;
|
||||
}
|
||||
if (!etag.empty()) {
|
||||
write_etag(path, etag);
|
||||
}
|
||||
|
||||
return head->status; // TODO: use actual GET status?
|
||||
}
|
||||
|
||||
return -1; // max attempts reached
|
||||
}
|
||||
|
||||
std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url,
|
||||
const common_remote_params & params) {
|
||||
auto [cli, parts] = common_http_client(url);
|
||||
|
||||
httplib::Headers headers = {{"User-Agent", "llama-cpp"}};
|
||||
|
||||
for (const auto & header : params.headers) {
|
||||
headers.emplace(header.first, header.second);
|
||||
}
|
||||
|
||||
if (params.timeout > 0) {
|
||||
cli.set_read_timeout(params.timeout, 0);
|
||||
cli.set_write_timeout(params.timeout, 0);
|
||||
}
|
||||
|
||||
std::vector<char> buf;
|
||||
auto res = cli.Get(parts.path, headers,
|
||||
[&](const char *data, size_t len) {
|
||||
buf.insert(buf.end(), data, data + len);
|
||||
return params.max_size == 0 ||
|
||||
buf.size() <= static_cast<size_t>(params.max_size);
|
||||
},
|
||||
nullptr
|
||||
);
|
||||
|
||||
if (!res) {
|
||||
throw std::runtime_error("error: cannot make GET request");
|
||||
}
|
||||
|
||||
return { res->status, std::move(buf) };
|
||||
}
|
||||
|
||||
int common_download_file_single(const std::string & url,
|
||||
const std::string & path,
|
||||
const std::string & bearer_token,
|
||||
bool offline,
|
||||
const common_header_list & headers) {
|
||||
if (!offline) {
|
||||
return common_download_file_single_online(url, path, bearer_token, headers);
|
||||
}
|
||||
|
||||
if (!std::filesystem::exists(path)) {
|
||||
LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str());
|
||||
return -1;
|
||||
}
|
||||
|
||||
LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
|
||||
return 304; // Not Modified - fake cached response
|
||||
}
|
||||
|
||||
// download multiple files from remote URLs to local paths
|
||||
// the input is a vector of pairs <url, path>
|
||||
static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls,
|
||||
const std::string & bearer_token,
|
||||
bool offline,
|
||||
const common_header_list & headers) {
|
||||
// Prepare download in parallel
|
||||
std::vector<std::future<bool>> futures_download;
|
||||
futures_download.reserve(urls.size());
|
||||
|
||||
for (auto const & item : urls) {
|
||||
futures_download.push_back(
|
||||
std::async(
|
||||
std::launch::async,
|
||||
[&bearer_token, offline, &headers](const std::pair<std::string, std::string> & it) -> bool {
|
||||
const int http_status = common_download_file_single(it.first, it.second, bearer_token, offline, headers);
|
||||
return is_http_status_ok(http_status);
|
||||
},
|
||||
item
|
||||
)
|
||||
);
|
||||
}
|
||||
|
||||
// Wait for all downloads to complete
|
||||
for (auto & f : futures_download) {
|
||||
if (!f.get()) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
bool common_download_model(const common_params_model & model,
|
||||
const std::string & bearer_token,
|
||||
bool offline,
|
||||
const common_header_list & headers) {
|
||||
// Basic validation of the model.url
|
||||
if (model.url.empty()) {
|
||||
LOG_ERR("%s: invalid model url\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
const int http_status = common_download_file_single(model.url, model.path, bearer_token, offline, headers);
|
||||
if (!is_http_status_ok(http_status)) {
|
||||
return false;
|
||||
}
|
||||
|
||||
// check for additional GGUFs split to download
|
||||
int n_split = 0;
|
||||
{
|
||||
struct gguf_init_params gguf_params = {
|
||||
/*.no_alloc = */ true,
|
||||
/*.ctx = */ NULL,
|
||||
};
|
||||
auto * ctx_gguf = gguf_init_from_file(model.path.c_str(), gguf_params);
|
||||
if (!ctx_gguf) {
|
||||
LOG_ERR("\n%s: failed to load input GGUF from %s\n", __func__, model.path.c_str());
|
||||
return false;
|
||||
}
|
||||
|
||||
auto key_n_split = gguf_find_key(ctx_gguf, LLM_KV_SPLIT_COUNT);
|
||||
if (key_n_split >= 0) {
|
||||
n_split = gguf_get_val_u16(ctx_gguf, key_n_split);
|
||||
}
|
||||
|
||||
gguf_free(ctx_gguf);
|
||||
}
|
||||
|
||||
if (n_split > 1) {
|
||||
char split_prefix[PATH_MAX] = {0};
|
||||
char split_url_prefix[LLAMA_MAX_URL_LENGTH] = {0};
|
||||
|
||||
// Verify the first split file format
|
||||
// and extract split URL and PATH prefixes
|
||||
{
|
||||
if (!llama_split_prefix(split_prefix, sizeof(split_prefix), model.path.c_str(), 0, n_split)) {
|
||||
LOG_ERR("\n%s: unexpected model file name: %s n_split=%d\n", __func__, model.path.c_str(), n_split);
|
||||
return false;
|
||||
}
|
||||
|
||||
if (!llama_split_prefix(split_url_prefix, sizeof(split_url_prefix), model.url.c_str(), 0, n_split)) {
|
||||
LOG_ERR("\n%s: unexpected model url: %s n_split=%d\n", __func__, model.url.c_str(), n_split);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<std::pair<std::string, std::string>> urls;
|
||||
for (int idx = 1; idx < n_split; idx++) {
|
||||
char split_path[PATH_MAX] = {0};
|
||||
llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split);
|
||||
|
||||
char split_url[LLAMA_MAX_URL_LENGTH] = {0};
|
||||
llama_split_path(split_url, sizeof(split_url), split_url_prefix, idx, n_split);
|
||||
|
||||
if (std::string(split_path) == model.path) {
|
||||
continue; // skip the already downloaded file
|
||||
}
|
||||
|
||||
urls.push_back({split_url, split_path});
|
||||
}
|
||||
|
||||
// Download in parallel
|
||||
common_download_file_multiple(urls, bearer_token, offline, headers);
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
common_hf_file_res common_get_hf_file(const std::string & hf_repo_with_tag,
|
||||
const std::string & bearer_token,
|
||||
bool offline,
|
||||
const common_header_list & custom_headers) {
|
||||
// the returned hf_repo is without tag
|
||||
auto [hf_repo, tag] = common_download_split_repo_tag(hf_repo_with_tag);
|
||||
|
||||
std::string url = get_model_endpoint() + "v2/" + hf_repo + "/manifests/" + tag;
|
||||
|
||||
// headers
|
||||
common_header_list headers = custom_headers;
|
||||
headers.push_back({"Accept", "application/json"});
|
||||
if (!bearer_token.empty()) {
|
||||
headers.push_back({"Authorization", "Bearer " + bearer_token});
|
||||
}
|
||||
// Important: the User-Agent must be "llama-cpp" to get the "ggufFile" field in the response
|
||||
// User-Agent header is already set in common_remote_get_content, no need to set it here
|
||||
|
||||
// make the request
|
||||
common_remote_params params;
|
||||
params.headers = headers;
|
||||
long res_code = 0;
|
||||
std::string res_str;
|
||||
bool use_cache = false;
|
||||
std::string cached_response_path = get_manifest_path(hf_repo, tag);
|
||||
if (!offline) {
|
||||
try {
|
||||
auto res = common_remote_get_content(url, params);
|
||||
res_code = res.first;
|
||||
res_str = std::string(res.second.data(), res.second.size());
|
||||
} catch (const std::exception & e) {
|
||||
LOG_WRN("error: failed to get manifest at %s: %s\n", url.c_str(), e.what());
|
||||
}
|
||||
}
|
||||
if (res_code == 0) {
|
||||
if (std::filesystem::exists(cached_response_path)) {
|
||||
LOG_WRN("trying to read manifest from cache: %s\n", cached_response_path.c_str());
|
||||
res_str = read_file(cached_response_path);
|
||||
res_code = 200;
|
||||
use_cache = true;
|
||||
} else {
|
||||
throw std::runtime_error(
|
||||
offline ? "error: failed to get manifest (offline mode)"
|
||||
: "error: failed to get manifest (check your internet connection)");
|
||||
}
|
||||
}
|
||||
std::string ggufFile;
|
||||
std::string mmprojFile;
|
||||
|
||||
if (res_code == 200 || res_code == 304) {
|
||||
try {
|
||||
auto j = json::parse(res_str);
|
||||
|
||||
if (j.contains("ggufFile") && j["ggufFile"].contains("rfilename")) {
|
||||
ggufFile = j["ggufFile"]["rfilename"].get<std::string>();
|
||||
}
|
||||
if (j.contains("mmprojFile") && j["mmprojFile"].contains("rfilename")) {
|
||||
mmprojFile = j["mmprojFile"]["rfilename"].get<std::string>();
|
||||
}
|
||||
} catch (const std::exception & e) {
|
||||
throw std::runtime_error(std::string("error parsing manifest JSON: ") + e.what());
|
||||
}
|
||||
if (!use_cache) {
|
||||
// if not using cached response, update the cache file
|
||||
write_file(cached_response_path, res_str);
|
||||
}
|
||||
} else if (res_code == 401) {
|
||||
throw std::runtime_error("error: model is private or does not exist; if you are accessing a gated model, please provide a valid HF token");
|
||||
} else {
|
||||
throw std::runtime_error(string_format("error from HF API (%s), response code: %ld, data: %s", url.c_str(), res_code, res_str.c_str()));
|
||||
}
|
||||
|
||||
// check response
|
||||
if (ggufFile.empty()) {
|
||||
throw std::runtime_error("error: model does not have ggufFile");
|
||||
}
|
||||
|
||||
return { hf_repo, ggufFile, mmprojFile };
|
||||
}
|
||||
|
||||
//
|
||||
// Docker registry functions
|
||||
//
|
||||
|
||||
static std::string common_docker_get_token(const std::string & repo) {
|
||||
std::string url = "https://auth.docker.io/token?service=registry.docker.io&scope=repository:" + repo + ":pull";
|
||||
|
||||
common_remote_params params;
|
||||
auto res = common_remote_get_content(url, params);
|
||||
|
||||
if (res.first != 200) {
|
||||
throw std::runtime_error("Failed to get Docker registry token, HTTP code: " + std::to_string(res.first));
|
||||
}
|
||||
|
||||
std::string response_str(res.second.begin(), res.second.end());
|
||||
nlohmann::ordered_json response = nlohmann::ordered_json::parse(response_str);
|
||||
|
||||
if (!response.contains("token")) {
|
||||
throw std::runtime_error("Docker registry token response missing 'token' field");
|
||||
}
|
||||
|
||||
return response["token"].get<std::string>();
|
||||
}
|
||||
|
||||
std::string common_docker_resolve_model(const std::string & docker) {
|
||||
// Parse ai/smollm2:135M-Q4_0
|
||||
size_t colon_pos = docker.find(':');
|
||||
std::string repo, tag;
|
||||
if (colon_pos != std::string::npos) {
|
||||
repo = docker.substr(0, colon_pos);
|
||||
tag = docker.substr(colon_pos + 1);
|
||||
} else {
|
||||
repo = docker;
|
||||
tag = "latest";
|
||||
}
|
||||
|
||||
// ai/ is the default
|
||||
size_t slash_pos = docker.find('/');
|
||||
if (slash_pos == std::string::npos) {
|
||||
repo.insert(0, "ai/");
|
||||
}
|
||||
|
||||
LOG_INF("%s: Downloading Docker Model: %s:%s\n", __func__, repo.c_str(), tag.c_str());
|
||||
try {
|
||||
// --- helper: digest validation ---
|
||||
auto validate_oci_digest = [](const std::string & digest) -> std::string {
|
||||
// Expected: algo:hex ; start with sha256 (64 hex chars)
|
||||
// You can extend this map if supporting other algorithms in future.
|
||||
static const std::regex re("^sha256:([a-fA-F0-9]{64})$");
|
||||
std::smatch m;
|
||||
if (!std::regex_match(digest, m, re)) {
|
||||
throw std::runtime_error("Invalid OCI digest format received in manifest: " + digest);
|
||||
}
|
||||
// normalize hex to lowercase
|
||||
std::string normalized = digest;
|
||||
std::transform(normalized.begin()+7, normalized.end(), normalized.begin()+7, [](unsigned char c){
|
||||
return std::tolower(c);
|
||||
});
|
||||
return normalized;
|
||||
};
|
||||
|
||||
std::string token = common_docker_get_token(repo); // Get authentication token
|
||||
|
||||
// Get manifest
|
||||
// TODO: cache the manifest response so that it appears in the model list
|
||||
const std::string url_prefix = "https://registry-1.docker.io/v2/" + repo;
|
||||
std::string manifest_url = url_prefix + "/manifests/" + tag;
|
||||
common_remote_params manifest_params;
|
||||
manifest_params.headers.push_back({"Authorization", "Bearer " + token});
|
||||
manifest_params.headers.push_back({"Accept",
|
||||
"application/vnd.docker.distribution.manifest.v2+json,application/vnd.oci.image.manifest.v1+json"
|
||||
});
|
||||
auto manifest_res = common_remote_get_content(manifest_url, manifest_params);
|
||||
if (manifest_res.first != 200) {
|
||||
throw std::runtime_error("Failed to get Docker manifest, HTTP code: " + std::to_string(manifest_res.first));
|
||||
}
|
||||
|
||||
std::string manifest_str(manifest_res.second.begin(), manifest_res.second.end());
|
||||
nlohmann::ordered_json manifest = nlohmann::ordered_json::parse(manifest_str);
|
||||
std::string gguf_digest; // Find the GGUF layer
|
||||
if (manifest.contains("layers")) {
|
||||
for (const auto & layer : manifest["layers"]) {
|
||||
if (layer.contains("mediaType")) {
|
||||
std::string media_type = layer["mediaType"].get<std::string>();
|
||||
if (media_type == "application/vnd.docker.ai.gguf.v3" ||
|
||||
media_type.find("gguf") != std::string::npos) {
|
||||
gguf_digest = layer["digest"].get<std::string>();
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (gguf_digest.empty()) {
|
||||
throw std::runtime_error("No GGUF layer found in Docker manifest");
|
||||
}
|
||||
|
||||
// Validate & normalize digest
|
||||
gguf_digest = validate_oci_digest(gguf_digest);
|
||||
LOG_DBG("%s: Using validated digest: %s\n", __func__, gguf_digest.c_str());
|
||||
|
||||
// Prepare local filename
|
||||
std::string model_filename = repo;
|
||||
std::replace(model_filename.begin(), model_filename.end(), '/', '_');
|
||||
model_filename += "_" + tag + ".gguf";
|
||||
std::string local_path = fs_get_cache_file(model_filename);
|
||||
|
||||
const std::string blob_url = url_prefix + "/blobs/" + gguf_digest;
|
||||
const int http_status = common_download_file_single(blob_url, local_path, token, false, {});
|
||||
if (!is_http_status_ok(http_status)) {
|
||||
throw std::runtime_error("Failed to download Docker Model");
|
||||
}
|
||||
|
||||
LOG_INF("%s: Downloaded Docker Model to: %s\n", __func__, local_path.c_str());
|
||||
return local_path;
|
||||
} catch (const std::exception & e) {
|
||||
LOG_ERR("%s: Docker Model download failed: %s\n", __func__, e.what());
|
||||
throw;
|
||||
}
|
||||
}
|
||||
|
||||
#else
|
||||
|
||||
common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool, const common_header_list &) {
|
||||
throw std::runtime_error("download functionality is not enabled in this build");
|
||||
}
|
||||
|
||||
bool common_download_model(const common_params_model &, const std::string &, bool, const common_header_list &) {
|
||||
throw std::runtime_error("download functionality is not enabled in this build");
|
||||
}
|
||||
|
||||
std::string common_docker_resolve_model(const std::string &) {
|
||||
throw std::runtime_error("download functionality is not enabled in this build");
|
||||
}
|
||||
|
||||
int common_download_file_single(const std::string &,
|
||||
const std::string &,
|
||||
const std::string &,
|
||||
bool,
|
||||
const common_header_list &) {
|
||||
throw std::runtime_error("download functionality is not enabled in this build");
|
||||
}
|
||||
|
||||
#endif // defined(LLAMA_USE_HTTPLIB)
|
||||
|
||||
std::vector<common_cached_model_info> common_list_cached_models() {
|
||||
std::vector<common_cached_model_info> models;
|
||||
const std::string cache_dir = fs_get_cache_directory();
|
||||
const std::vector<common_file_info> files = fs_list(cache_dir, false);
|
||||
for (const auto & file : files) {
|
||||
if (string_starts_with(file.name, "manifest=") && string_ends_with(file.name, ".json")) {
|
||||
common_cached_model_info model_info;
|
||||
model_info.manifest_path = file.path;
|
||||
std::string fname = file.name;
|
||||
string_replace_all(fname, ".json", ""); // remove extension
|
||||
auto parts = string_split<std::string>(fname, '=');
|
||||
if (parts.size() == 4) {
|
||||
// expect format: manifest=<user>=<model>=<tag>=<other>
|
||||
model_info.user = parts[1];
|
||||
model_info.model = parts[2];
|
||||
model_info.tag = parts[3];
|
||||
} else {
|
||||
// invalid format
|
||||
continue;
|
||||
}
|
||||
model_info.size = 0; // TODO: get GGUF size, not manifest size
|
||||
models.push_back(model_info);
|
||||
}
|
||||
}
|
||||
return models;
|
||||
}
|
||||
84
common/download.h
Normal file
84
common/download.h
Normal file
@@ -0,0 +1,84 @@
|
||||
#pragma once
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
struct common_params_model;
|
||||
|
||||
using common_header = std::pair<std::string, std::string>;
|
||||
using common_header_list = std::vector<common_header>;
|
||||
|
||||
struct common_remote_params {
|
||||
common_header_list headers;
|
||||
long timeout = 0; // in seconds, 0 means no timeout
|
||||
long max_size = 0; // unlimited if 0
|
||||
};
|
||||
|
||||
// get remote file content, returns <http_code, raw_response_body>
|
||||
std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params);
|
||||
|
||||
// split HF repo with tag into <repo, tag>
|
||||
// for example: "user/model:tag" -> <"user/model", "tag">
|
||||
// if tag is not present, default to "latest"
|
||||
// example: "user/model" -> <"user/model", "latest">
|
||||
std::pair<std::string, std::string> common_download_split_repo_tag(const std::string & hf_repo_with_tag);
|
||||
|
||||
struct common_cached_model_info {
|
||||
std::string manifest_path;
|
||||
std::string user;
|
||||
std::string model;
|
||||
std::string tag;
|
||||
size_t size = 0; // GGUF size in bytes
|
||||
// return string representation like "user/model:tag"
|
||||
// if tag is "latest", it will be omitted
|
||||
std::string to_string() const {
|
||||
return user + "/" + model + (tag == "latest" ? "" : ":" + tag);
|
||||
}
|
||||
};
|
||||
|
||||
struct common_hf_file_res {
|
||||
std::string repo; // repo name with ":tag" removed
|
||||
std::string ggufFile;
|
||||
std::string mmprojFile;
|
||||
};
|
||||
|
||||
/**
|
||||
* Allow getting the HF file from the HF repo with tag (like ollama), for example:
|
||||
* - bartowski/Llama-3.2-3B-Instruct-GGUF:q4
|
||||
* - bartowski/Llama-3.2-3B-Instruct-GGUF:Q4_K_M
|
||||
* - bartowski/Llama-3.2-3B-Instruct-GGUF:q5_k_s
|
||||
* Tag is optional, default to "latest" (meaning it checks for Q4_K_M first, then Q4, then if not found, return the first GGUF file in repo)
|
||||
*
|
||||
* Return pair of <repo, file> (with "repo" already having tag removed)
|
||||
*
|
||||
* Note: we use the Ollama-compatible HF API, but not using the blobId. Instead, we use the special "ggufFile" field which returns the value for "hf_file". This is done to be backward-compatible with existing cache files.
|
||||
*/
|
||||
common_hf_file_res common_get_hf_file(
|
||||
const std::string & hf_repo_with_tag,
|
||||
const std::string & bearer_token,
|
||||
bool offline,
|
||||
const common_header_list & headers = {}
|
||||
);
|
||||
|
||||
// returns true if download succeeded
|
||||
bool common_download_model(
|
||||
const common_params_model & model,
|
||||
const std::string & bearer_token,
|
||||
bool offline,
|
||||
const common_header_list & headers = {}
|
||||
);
|
||||
|
||||
// returns list of cached models
|
||||
std::vector<common_cached_model_info> common_list_cached_models();
|
||||
|
||||
// download single file from url to local path
|
||||
// returns status code or -1 on error
|
||||
int common_download_file_single(const std::string & url,
|
||||
const std::string & path,
|
||||
const std::string & bearer_token,
|
||||
bool offline,
|
||||
const common_header_list & headers = {});
|
||||
|
||||
// resolve and download model from Docker registry
|
||||
// return local path to downloaded model file
|
||||
std::string common_docker_resolve_model(const std::string & docker);
|
||||
73
common/http.h
Normal file
73
common/http.h
Normal file
@@ -0,0 +1,73 @@
|
||||
#pragma once
|
||||
|
||||
#include <cpp-httplib/httplib.h>
|
||||
|
||||
struct common_http_url {
|
||||
std::string scheme;
|
||||
std::string user;
|
||||
std::string password;
|
||||
std::string host;
|
||||
std::string path;
|
||||
};
|
||||
|
||||
static common_http_url common_http_parse_url(const std::string & url) {
|
||||
common_http_url parts;
|
||||
auto scheme_end = url.find("://");
|
||||
|
||||
if (scheme_end == std::string::npos) {
|
||||
throw std::runtime_error("invalid URL: no scheme");
|
||||
}
|
||||
parts.scheme = url.substr(0, scheme_end);
|
||||
|
||||
if (parts.scheme != "http" && parts.scheme != "https") {
|
||||
throw std::runtime_error("unsupported URL scheme: " + parts.scheme);
|
||||
}
|
||||
|
||||
auto rest = url.substr(scheme_end + 3);
|
||||
auto at_pos = rest.find('@');
|
||||
|
||||
if (at_pos != std::string::npos) {
|
||||
auto auth = rest.substr(0, at_pos);
|
||||
auto colon_pos = auth.find(':');
|
||||
if (colon_pos != std::string::npos) {
|
||||
parts.user = auth.substr(0, colon_pos);
|
||||
parts.password = auth.substr(colon_pos + 1);
|
||||
} else {
|
||||
parts.user = auth;
|
||||
}
|
||||
rest = rest.substr(at_pos + 1);
|
||||
}
|
||||
|
||||
auto slash_pos = rest.find('/');
|
||||
|
||||
if (slash_pos != std::string::npos) {
|
||||
parts.host = rest.substr(0, slash_pos);
|
||||
parts.path = rest.substr(slash_pos);
|
||||
} else {
|
||||
parts.host = rest;
|
||||
parts.path = "/";
|
||||
}
|
||||
return parts;
|
||||
}
|
||||
|
||||
static std::pair<httplib::Client, common_http_url> common_http_client(const std::string & url) {
|
||||
common_http_url parts = common_http_parse_url(url);
|
||||
|
||||
if (parts.host.empty()) {
|
||||
throw std::runtime_error("error: invalid URL format");
|
||||
}
|
||||
|
||||
httplib::Client cli(parts.scheme + "://" + parts.host);
|
||||
|
||||
if (!parts.user.empty()) {
|
||||
cli.set_basic_auth(parts.user, parts.password);
|
||||
}
|
||||
|
||||
cli.set_follow_location(true);
|
||||
|
||||
return { std::move(cli), std::move(parts) };
|
||||
}
|
||||
|
||||
static std::string common_http_show_masked_url(const common_http_url & parts) {
|
||||
return parts.scheme + "://" + (parts.user.empty() ? "" : "****:****@") + parts.host + parts.path;
|
||||
}
|
||||
324
common/json-partial.cpp
Normal file
324
common/json-partial.cpp
Normal file
@@ -0,0 +1,324 @@
|
||||
#include "json-partial.h"
|
||||
|
||||
#include "log.h"
|
||||
|
||||
#include <nlohmann/json.hpp>
|
||||
|
||||
#include <string>
|
||||
#include <regex>
|
||||
|
||||
using json = nlohmann::ordered_json;
|
||||
|
||||
enum common_json_stack_element_type {
|
||||
COMMON_JSON_STACK_ELEMENT_OBJECT,
|
||||
COMMON_JSON_STACK_ELEMENT_KEY,
|
||||
COMMON_JSON_STACK_ELEMENT_ARRAY,
|
||||
};
|
||||
|
||||
struct common_json_stack_element {
|
||||
common_json_stack_element_type type;
|
||||
std::string key;
|
||||
};
|
||||
|
||||
bool common_json_parse(
|
||||
const std::string & input,
|
||||
const std::string & healing_marker,
|
||||
common_json & out)
|
||||
{
|
||||
std::string::const_iterator it = input.begin();
|
||||
const auto end = input.end();
|
||||
return common_json_parse(it, end, healing_marker, out);
|
||||
}
|
||||
|
||||
bool common_json_parse(
|
||||
std::string::const_iterator & it,
|
||||
const std::string::const_iterator & end,
|
||||
const std::string & healing_marker,
|
||||
common_json & out)
|
||||
{
|
||||
// // https://json.nlohmann.me/features/parsing/sax_interface/
|
||||
struct json_error_locator : public nlohmann::json_sax<json> {
|
||||
std::size_t position;
|
||||
bool found_error;
|
||||
std::string last_token;
|
||||
std::string exception_message;
|
||||
std::vector<common_json_stack_element> stack;
|
||||
|
||||
json_error_locator() : position(0), found_error(false) {}
|
||||
|
||||
bool parse_error(std::size_t position, const std::string & last_token, const json::exception & ex) override { // NOLINT
|
||||
this->position = position - 1;
|
||||
this->found_error = true;
|
||||
this->last_token = last_token;
|
||||
this->exception_message = ex.what();
|
||||
return false;
|
||||
}
|
||||
void close_value() {
|
||||
if (!stack.empty() && (stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY)) {
|
||||
stack.pop_back();
|
||||
}
|
||||
}
|
||||
bool null() override { // NOLINT
|
||||
close_value();
|
||||
return true;
|
||||
}
|
||||
bool boolean(bool) override { // NOLINT
|
||||
close_value();
|
||||
return true;
|
||||
}
|
||||
bool number_integer(number_integer_t) override { // NOLINT
|
||||
close_value();
|
||||
return true;
|
||||
}
|
||||
bool number_unsigned(number_unsigned_t) override { // NOLINT
|
||||
close_value();
|
||||
return true;
|
||||
}
|
||||
bool number_float(number_float_t, const string_t &) override { // NOLINT
|
||||
close_value();
|
||||
return true;
|
||||
}
|
||||
bool string(string_t &) override { // NOLINT
|
||||
close_value();
|
||||
return true;
|
||||
}
|
||||
bool binary(binary_t &) override { // NOLINT
|
||||
close_value();
|
||||
return true;
|
||||
}
|
||||
bool start_object(std::size_t) override { // NOLINT
|
||||
stack.push_back({COMMON_JSON_STACK_ELEMENT_OBJECT, ""});
|
||||
return true;
|
||||
}
|
||||
bool end_object() override {
|
||||
GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT);
|
||||
stack.pop_back();
|
||||
close_value();
|
||||
return true;
|
||||
}
|
||||
bool key(string_t & key) override { // NOLINT
|
||||
stack.push_back({COMMON_JSON_STACK_ELEMENT_KEY, key});
|
||||
return true;
|
||||
}
|
||||
bool start_array(std::size_t) override { // NOLINT
|
||||
stack.push_back({COMMON_JSON_STACK_ELEMENT_ARRAY, ""});
|
||||
return true;
|
||||
}
|
||||
bool end_array() override {
|
||||
GGML_ASSERT(!stack.empty() && stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY);
|
||||
stack.pop_back();
|
||||
close_value();
|
||||
return true;
|
||||
}
|
||||
};
|
||||
json_error_locator err_loc;
|
||||
auto start = it;
|
||||
json::sax_parse(it, end, &err_loc);
|
||||
|
||||
if (err_loc.found_error) {
|
||||
it = start;
|
||||
auto temptative_end = it + err_loc.position;
|
||||
// LOG_DBG("Error at position %zu (is_end = %s): %s\n", err_loc.position, temptative_end == end ? "true" : "false", err_loc.exception_message.c_str());
|
||||
|
||||
auto input = std::string(it, temptative_end);
|
||||
try {
|
||||
out.json = json::parse(input);
|
||||
// out.json = json::parse(it, temptative_end);
|
||||
it = temptative_end;
|
||||
return true;
|
||||
} catch (const std::exception & ex) {
|
||||
// No, needs healing.
|
||||
LOG_DBG("Failed to parse up to error: %s: <<<%s>>>\n", ex.what(), std::string(it, temptative_end).c_str());
|
||||
}
|
||||
auto can_parse = [](const std::string & str) {
|
||||
try {
|
||||
auto _ = json::parse(str); // NOLINT
|
||||
return true;
|
||||
} catch (const std::exception &) {
|
||||
return false;
|
||||
}
|
||||
};
|
||||
if (!healing_marker.empty() && !err_loc.stack.empty()) {
|
||||
std::string str(it, temptative_end);
|
||||
auto last_non_sp_pos = str.find_last_not_of(" \n\r\t");
|
||||
if (last_non_sp_pos == std::string::npos) {
|
||||
throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location");
|
||||
}
|
||||
auto last_non_sp_char = str[last_non_sp_pos];
|
||||
// Used to detect stops on a number, which may not be complete.
|
||||
auto was_maybe_number = [&]() {
|
||||
if (!str.empty() && std::isspace(str.back())) {
|
||||
return false;
|
||||
}
|
||||
return std::isdigit(last_non_sp_char) ||
|
||||
last_non_sp_char == '.' ||
|
||||
last_non_sp_char == 'e' ||
|
||||
last_non_sp_char == 'E' ||
|
||||
last_non_sp_char == '-';
|
||||
};
|
||||
|
||||
std::string closing;
|
||||
for (size_t i = err_loc.stack.size(); i > 0; i--) {
|
||||
auto & el = err_loc.stack[i - 1];
|
||||
if (el.type == COMMON_JSON_STACK_ELEMENT_OBJECT) {
|
||||
closing += "}";
|
||||
} else if (el.type == COMMON_JSON_STACK_ELEMENT_ARRAY) {
|
||||
closing += "]";
|
||||
} else if (el.type != COMMON_JSON_STACK_ELEMENT_KEY) {
|
||||
throw std::runtime_error("Unexpected stack element type");
|
||||
}
|
||||
}
|
||||
|
||||
// Matches a potentially partial unicode escape sequence, e.g. \u, \uX, \uXX, \uXXX, \uXXXX
|
||||
static const std::regex partial_unicode_regex(R"(\\u(?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F](?:[0-9a-fA-F])?)?)?)?$)");
|
||||
|
||||
auto is_high_surrogate = [&](const std::string & s) {
|
||||
// Check if a partial of a high surrogate (U+D800-U+DBFF)
|
||||
return s.length() >= 4 &&
|
||||
s[0] == '\\' && s[1] == 'u' &&
|
||||
std::tolower(s[2]) == 'd' &&
|
||||
(s[3] == '8' || s[3] == '9' || std::tolower(s[3]) == 'a' || std::tolower(s[3]) == 'b');
|
||||
};
|
||||
|
||||
// Initialize the unicode marker to a low surrogate to handle the edge case
|
||||
// where a high surrogate (U+D800-U+DBFF) is immediately followed by a
|
||||
// backslash (\)
|
||||
std::string unicode_marker_padding = "udc00";
|
||||
std::smatch last_unicode_seq;
|
||||
|
||||
if (std::regex_search(str, last_unicode_seq, partial_unicode_regex)) {
|
||||
std::smatch second_last_seq;
|
||||
std::string prelude = str.substr(0, last_unicode_seq.position());
|
||||
|
||||
// Pad the escape sequence with 0s until it forms a complete sequence of 6 characters
|
||||
unicode_marker_padding = std::string(6 - last_unicode_seq.length(), '0');
|
||||
|
||||
if (is_high_surrogate(last_unicode_seq.str())) {
|
||||
// If the sequence is a partial match for a high surrogate, add a low surrogate (U+DC00-U+UDFF)
|
||||
unicode_marker_padding += "\\udc00";
|
||||
} else if (std::regex_search(prelude, second_last_seq, partial_unicode_regex)) {
|
||||
if (is_high_surrogate(second_last_seq.str())) {
|
||||
// If this follows a high surrogate, pad it to be a low surrogate
|
||||
if (last_unicode_seq.length() == 2) {
|
||||
unicode_marker_padding = "dc00";
|
||||
} else if (last_unicode_seq.length() == 3) {
|
||||
unicode_marker_padding = "c00";
|
||||
} else {
|
||||
// The original unicode_marker_padding is already padded with 0s
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
const auto & magic_seed = out.healing_marker.marker = healing_marker;//"$llama.cpp.json$";
|
||||
|
||||
if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_KEY) {
|
||||
// We're inside an object value
|
||||
if (last_non_sp_char == ':' && can_parse(str + "1" + closing)) {
|
||||
// Was about to create an object value
|
||||
str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
|
||||
} else if (can_parse(str + ": 1" + closing)) {
|
||||
str += (out.healing_marker.json_dump_marker = ":\"" + magic_seed) + "\"" + closing;
|
||||
} else if (last_non_sp_char == '{' && can_parse(str + closing)) {
|
||||
// Was about to create an object
|
||||
str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing;
|
||||
} else if (can_parse(str + "\"" + closing)) {
|
||||
// Was inside an object value string
|
||||
str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing;
|
||||
} else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
|
||||
// Was inside an object value string after an escape
|
||||
str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
|
||||
} else if (can_parse(str + unicode_marker_padding + "\"" + closing)) {
|
||||
// Was inside an object value string after a partial unicode escape
|
||||
str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing;
|
||||
} else {
|
||||
// find last :
|
||||
auto last_pos = str.find_last_of(':');
|
||||
if (last_pos == std::string::npos) {
|
||||
throw std::runtime_error("Cannot heal a truncated JSON that stopped in an unknown location");
|
||||
}
|
||||
// Cutting back to opening : for object value
|
||||
str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
|
||||
}
|
||||
} else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_ARRAY) {
|
||||
if ((last_non_sp_char == ',' || last_non_sp_char == '[') && can_parse(str + "1" + closing)) {
|
||||
// Was about to create an array value
|
||||
str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
|
||||
} else if (can_parse(str + "\"" + closing)) {
|
||||
// Was inside an array value string
|
||||
str += (out.healing_marker.json_dump_marker = magic_seed) + "\"" + closing;
|
||||
} else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"" + closing)) {
|
||||
// Was inside an array value string after an escape
|
||||
str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"" + closing;
|
||||
} else if (can_parse(str + unicode_marker_padding + "\"" + closing)) {
|
||||
// Was inside an array value string after a partial unicode escape
|
||||
str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\"" + closing;
|
||||
} else if (!was_maybe_number() && can_parse(str + ", 1" + closing)) {
|
||||
// Had just finished a value
|
||||
str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\"" + closing;
|
||||
} else {
|
||||
auto last_pos = str.find_last_of("[,");
|
||||
if (last_pos == std::string::npos) {
|
||||
throw std::runtime_error("Cannot heal a truncated JSON array stopped in an unknown location");
|
||||
}
|
||||
// Cutting back to last [ or , for array value
|
||||
str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
|
||||
}
|
||||
} else if (err_loc.stack.back().type == COMMON_JSON_STACK_ELEMENT_OBJECT) {
|
||||
if ((last_non_sp_char == '{' && can_parse(str + closing)) ||
|
||||
(last_non_sp_char == ',' && can_parse(str + "\"\": 1" + closing))) {
|
||||
// Was about to create an object key+value
|
||||
str += (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\": 1" + closing;
|
||||
} else if (!was_maybe_number() && can_parse(str + ",\"\": 1" + closing)) {
|
||||
// Was about to create an object key+value
|
||||
str += (out.healing_marker.json_dump_marker = ",\"" + magic_seed) + "\": 1" + closing;
|
||||
} else if (can_parse(str + "\": 1" + closing)) {
|
||||
// Was inside an object key string
|
||||
str += (out.healing_marker.json_dump_marker = magic_seed) + "\": 1" + closing;
|
||||
} else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\": 1" + closing)) {
|
||||
// Was inside an object key string after an escape
|
||||
str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\": 1" + closing;
|
||||
} else if (can_parse(str + unicode_marker_padding + "\": 1" + closing)) {
|
||||
// Was inside an object key string after a partial unicode escape
|
||||
str += (out.healing_marker.json_dump_marker = unicode_marker_padding + magic_seed) + "\": 1" + closing;
|
||||
} else {
|
||||
auto last_pos = str.find_last_of(':');
|
||||
if (last_pos == std::string::npos) {
|
||||
throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location");
|
||||
}
|
||||
// fprintf(stderr, "Cutting back to last : for object key+value\n");
|
||||
str = str.substr(0, last_pos + 1) + (out.healing_marker.json_dump_marker = "\"" + magic_seed) + "\"" + closing;
|
||||
}
|
||||
} else {
|
||||
throw std::runtime_error("Cannot heal a truncated JSON object stopped in an unknown location");
|
||||
}
|
||||
// fprintf(stderr, "HEALED:\nSTRING <<<\n%s\n>>>\n\nmagic_cut: <<<\n%s\n>>>\n\n", str.c_str(), out.healing_marker.json_dump_marker.c_str());
|
||||
out.json = json::parse(str);
|
||||
it = temptative_end;
|
||||
return true;
|
||||
}
|
||||
// handle unclosed top-level primitive
|
||||
if (err_loc.position != 0 && !healing_marker.empty() && err_loc.stack.empty()) {
|
||||
std::string str(it, temptative_end);
|
||||
const auto & magic_seed = out.healing_marker.marker = healing_marker;
|
||||
if (can_parse(str + "\"")) {
|
||||
// Was inside an string
|
||||
str += (out.healing_marker.json_dump_marker = magic_seed) + "\"";
|
||||
} else if (str[str.length() - 1] == '\\' && can_parse(str + "\\\"")) {
|
||||
// Was inside an string after an escape
|
||||
str += (out.healing_marker.json_dump_marker = "\\" + magic_seed) + "\"";
|
||||
} else {
|
||||
// TODO: handle more unclosed top-level primitive if the stack was empty but we got an error (e.g. "tru", "\"", etc...)
|
||||
// fprintf(stderr, "Closing: TODO\n");
|
||||
return false;
|
||||
}
|
||||
out.json = json::parse(str);
|
||||
it = temptative_end;
|
||||
return true;
|
||||
}
|
||||
return false;
|
||||
}
|
||||
out.json = json::parse(it, end);
|
||||
it = end;
|
||||
return true;
|
||||
}
|
||||
38
common/json-partial.h
Normal file
38
common/json-partial.h
Normal file
@@ -0,0 +1,38 @@
|
||||
#pragma once
|
||||
|
||||
#include <nlohmann/json.hpp>
|
||||
|
||||
// Healing marker (empty if the JSON was fully parsed / wasn't healed).
|
||||
struct common_healing_marker {
|
||||
// Raw marker.
|
||||
std::string marker;
|
||||
|
||||
// Cutting the `common_json.json.dump()` string at the (only) occurrence of this marker should yield the original partial JSON string (modulo spaces / if it had the same dump format).
|
||||
std::string json_dump_marker;
|
||||
};
|
||||
|
||||
// Represents a parsed JSON object, with its optional healing marker (a JSON dump fragment that can be used to find the position of healing in the JSON dump string)
|
||||
struct common_json {
|
||||
nlohmann::ordered_json json;
|
||||
|
||||
common_healing_marker healing_marker;
|
||||
};
|
||||
|
||||
// Parse the JSON string, healing (closing) any partial JSON if `healing_marker` is not empty.
|
||||
//
|
||||
// Healing completes partial JSON strings by adding a (possibly modified) healing marker, then whatever is needed to close the JSON.
|
||||
// This allows to parse the resulting healed JSON string, yet be able to cut it again if needed at the healing marker.
|
||||
// (this is used when parsing JSON outputs from the models, then crafting partial JSONs for the partial tool calls in OAI format).
|
||||
//
|
||||
// For instance, parsing `{` with a healing marker `foo` will produce a healed JSON `{"foo":1}`, w/ json_dump_marker = `"foo"` (which can be used to break the JSON again).
|
||||
bool common_json_parse(
|
||||
const std::string & input,
|
||||
const std::string & healing_marker,
|
||||
common_json & out);
|
||||
|
||||
// Parse the JSON string (see overload above), but advancing an iterator to the end of the input when the (potentially partial) parsing succeeds.
|
||||
bool common_json_parse(
|
||||
std::string::const_iterator & it,
|
||||
const std::string::const_iterator & end,
|
||||
const std::string & healing_marker,
|
||||
common_json & out);
|
||||
1153
common/json-schema-to-grammar.cpp
Normal file
1153
common/json-schema-to-grammar.cpp
Normal file
File diff suppressed because it is too large
Load Diff
43
common/json-schema-to-grammar.h
Normal file
43
common/json-schema-to-grammar.h
Normal file
@@ -0,0 +1,43 @@
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#pragma once
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#include <nlohmann/json_fwd.hpp>
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#include <functional>
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#include <memory>
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#include <string>
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std::string json_schema_to_grammar(const nlohmann::ordered_json & schema,
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bool force_gbnf = false);
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class common_schema_converter;
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// Probes a JSON schema to extract information about its structure and type constraints.
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class common_schema_info {
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std::unique_ptr<common_schema_converter> impl_;
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public:
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common_schema_info();
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~common_schema_info();
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common_schema_info(const common_schema_info &) = delete;
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common_schema_info & operator=(const common_schema_info &) = delete;
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common_schema_info(common_schema_info &&) noexcept;
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common_schema_info & operator=(common_schema_info &&) noexcept;
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void resolve_refs(nlohmann::ordered_json & schema);
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bool resolves_to_string(const nlohmann::ordered_json & schema);
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};
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struct common_grammar_builder {
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std::function<std::string(const std::string &, const std::string &)> add_rule;
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std::function<std::string(const std::string &, const nlohmann::ordered_json &)> add_schema;
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std::function<void(nlohmann::ordered_json &)> resolve_refs;
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};
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struct common_grammar_options {
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bool dotall = false;
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};
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std::string gbnf_format_literal(const std::string & literal);
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std::string build_grammar(const std::function<void(const common_grammar_builder &)> & cb, const common_grammar_options & options = {});
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258
common/llguidance.cpp
Normal file
258
common/llguidance.cpp
Normal file
@@ -0,0 +1,258 @@
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#include "sampling.h"
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#include "log.h"
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#ifdef LLAMA_USE_LLGUIDANCE
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# include "llguidance.h"
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# include <cmath>
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struct llama_sampler_llg {
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const llama_vocab * vocab;
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std::string grammar_kind;
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std::string grammar_data;
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LlgTokenizer * tokenizer;
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LlgMatcher * grammar;
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};
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static LlgMatcher * llama_sampler_llg_new(LlgTokenizer * tokenizer, const char * grammar_kind,
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const char * grammar_data) {
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LlgConstraintInit cinit;
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llg_constraint_init_set_defaults(&cinit, tokenizer);
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const char * log_level = getenv("LLGUIDANCE_LOG_LEVEL");
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if (log_level && *log_level) {
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cinit.log_stderr_level = atoi(log_level);
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}
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auto c = llg_new_matcher(&cinit, grammar_kind, grammar_data);
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if (llg_matcher_get_error(c)) {
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||||
LOG_ERR("llg error: %s\n", llg_matcher_get_error(c));
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llg_free_matcher(c);
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||||
return nullptr;
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||||
}
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return c;
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}
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static const char * llama_sampler_llg_name(const llama_sampler * /*smpl*/) {
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return "llguidance";
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}
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||||
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static void llama_sampler_llg_accept_impl(llama_sampler * smpl, llama_token token) {
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auto * ctx = (llama_sampler_llg *) smpl->ctx;
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if (ctx->grammar) {
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llg_matcher_consume_token(ctx->grammar, token);
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}
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}
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static void llama_sampler_llg_apply(llama_sampler * smpl, llama_token_data_array * cur_p) {
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auto * ctx = (llama_sampler_llg *) smpl->ctx;
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if (ctx->grammar) {
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||||
const uint32_t * mask = llg_matcher_get_mask(ctx->grammar);
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||||
if (mask == nullptr) {
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if (llg_matcher_compute_mask(ctx->grammar) == 0) {
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mask = llg_matcher_get_mask(ctx->grammar);
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||||
} else {
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||||
LOG_ERR("llg error: %s\n", llg_matcher_get_error(ctx->grammar));
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llg_free_matcher(ctx->grammar);
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ctx->grammar = nullptr;
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return;
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}
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}
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for (size_t i = 0; i < cur_p->size; ++i) {
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auto token = cur_p->data[i].id;
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||||
if ((mask[token / 32] & (1 << (token % 32))) == 0) {
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cur_p->data[i].logit = -INFINITY;
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||||
}
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||||
}
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}
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}
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static void llama_sampler_llg_reset(llama_sampler * smpl) {
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auto * ctx = (llama_sampler_llg *) smpl->ctx;
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if (ctx->grammar) {
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llg_matcher_reset(ctx->grammar);
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||||
}
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}
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||||
static llama_sampler * llama_sampler_llg_clone(const llama_sampler * smpl) {
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const auto * ctx = (const llama_sampler_llg *) smpl->ctx;
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auto * result = llama_sampler_init_llg(ctx->vocab, nullptr, nullptr);
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// copy the state
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||||
{
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auto * result_ctx = (llama_sampler_llg *) result->ctx;
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if (ctx->grammar) {
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result_ctx->grammar_kind = ctx->grammar_kind;
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result_ctx->grammar_data = ctx->grammar_data;
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result_ctx->grammar = llg_clone_matcher(ctx->grammar);
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result_ctx->tokenizer = llg_clone_tokenizer(ctx->tokenizer);
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||||
}
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}
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return result;
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}
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||||
static void llama_sampler_llg_free(llama_sampler * smpl) {
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const auto * ctx = (llama_sampler_llg *) smpl->ctx;
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if (ctx->grammar) {
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llg_free_matcher(ctx->grammar);
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llg_free_tokenizer(ctx->tokenizer);
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}
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||||
delete ctx;
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||||
}
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||||
static llama_sampler_i llama_sampler_llg_i = {
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/* .name = */ llama_sampler_llg_name,
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/* .accept = */ llama_sampler_llg_accept_impl,
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||||
/* .apply = */ llama_sampler_llg_apply,
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||||
/* .reset = */ llama_sampler_llg_reset,
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||||
/* .clone = */ llama_sampler_llg_clone,
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/* .free = */ llama_sampler_llg_free,
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||||
/* .backend_init = */ NULL,
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||||
/* .backend_accept = */ NULL,
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/* .backend_apply = */ NULL,
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||||
/* .backend_set_input = */ NULL,
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||||
};
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||||
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||||
static size_t llama_sampler_llg_tokenize_fn(const void * user_data, const uint8_t * bytes, size_t bytes_len,
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uint32_t * output_tokens, size_t output_tokens_len) {
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const llama_vocab * vocab = (const llama_vocab *) user_data;
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||||
int r = 0;
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try {
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r = llama_tokenize(vocab, (const char *) bytes, bytes_len, (int32_t *) output_tokens, output_tokens_len, false,
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true);
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} catch (const std::exception & e) {
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||||
GGML_ABORT("llama_tokenize failed: %s\n", e.what());
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||||
}
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||||
if (r < 0) {
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return -r;
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||||
}
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return r;
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}
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static LlgTokenizer * llama_sampler_llg_new_tokenizer(const llama_vocab * vocab) {
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// TODO store the tokenizer in the vocab somehow
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static const llama_vocab * vocab_cache;
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static LlgTokenizer * tokenizer_cache;
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if (vocab_cache == vocab) {
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return llg_clone_tokenizer(tokenizer_cache);
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||||
}
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auto tok_eos = llama_vocab_eot(vocab);
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if (tok_eos == LLAMA_TOKEN_NULL) {
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tok_eos = llama_vocab_eos(vocab);
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}
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size_t vocab_size = llama_vocab_n_tokens(vocab);
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auto token_lens = new uint32_t[vocab_size];
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// we typically have ~7 bytes per token; let's go on the safe side here
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auto token_bytes_size = vocab_size * 16 + 1024 * 1024;
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auto token_bytes = new uint8_t[token_bytes_size];
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size_t offset = 0;
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for (size_t i = 0; i < vocab_size; i++) {
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size_t max_token = 1024;
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if (token_bytes_size - offset < max_token) {
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GGML_ABORT("token_bytes buffer too small\n");
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||||
}
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|
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llama_token token = i;
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auto dp = (char *) token_bytes + offset;
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auto size = llama_detokenize(vocab, &token, 1, dp, max_token, false, false);
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||||
if (size < 0) {
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GGML_ABORT("llama_detokenize failed\n");
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||||
}
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if (size == 0) {
|
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size = llama_detokenize(vocab, &token, 1, dp + 1, max_token - 1, false, true);
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if (size < 0) {
|
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GGML_ABORT("llama_detokenize failed\n");
|
||||
}
|
||||
if (size != 0) {
|
||||
*dp = '\xff'; // special token prefix marker
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size += 1;
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}
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}
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|
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token_lens[i] = size;
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||||
offset += size;
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||||
}
|
||||
|
||||
LlgTokenizerInit tinit = {
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||||
/* .vocab_size = */ (uint32_t) vocab_size,
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||||
/* .tok_eos = */ (uint32_t) tok_eos,
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/* .token_lens = */ token_lens,
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/* .token_bytes = */ token_bytes,
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||||
/* .tokenizer_json = */ nullptr,
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||||
/* .tokenize_assumes_string = */ true,
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||||
/* .tokenize_fn = */ llama_sampler_llg_tokenize_fn,
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/* .use_approximate_greedy_tokenize_fn = */ false,
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||||
/* .tokenize_user_data = */ vocab,
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||||
/* .slices = */ nullptr,
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||||
};
|
||||
|
||||
char error_buffer[1024];
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||||
LlgTokenizer * tokenizer = llg_new_tokenizer(&tinit, error_buffer, sizeof(error_buffer));
|
||||
|
||||
delete[] token_bytes;
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||||
delete[] token_lens;
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||||
|
||||
if (tokenizer == nullptr) {
|
||||
LOG_ERR("llg tokenizer error: %s\n", error_buffer);
|
||||
return tokenizer;
|
||||
}
|
||||
|
||||
if (tokenizer_cache) {
|
||||
llg_free_tokenizer(tokenizer_cache);
|
||||
}
|
||||
vocab_cache = vocab;
|
||||
tokenizer_cache = tokenizer;
|
||||
|
||||
return llg_clone_tokenizer(tokenizer_cache);
|
||||
}
|
||||
|
||||
llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind,
|
||||
const char * grammar_data) {
|
||||
auto * ctx = new llama_sampler_llg;
|
||||
|
||||
if (grammar_kind != nullptr && grammar_kind[0] != '\0') {
|
||||
auto tokenizer = llama_sampler_llg_new_tokenizer(vocab);
|
||||
*ctx = {
|
||||
/* .vocab = */ vocab,
|
||||
/* .grammar_kind = */ grammar_kind,
|
||||
/* .grammar_data = */ grammar_data,
|
||||
/* .tokenizer = */ tokenizer,
|
||||
/* .grammar = */ llama_sampler_llg_new(tokenizer, grammar_kind, grammar_data),
|
||||
};
|
||||
if (ctx->grammar) {
|
||||
GGML_ASSERT(((size_t) llama_vocab_n_tokens(vocab) + 31) / 32 * 4 ==
|
||||
llg_matcher_get_mask_byte_size(ctx->grammar));
|
||||
}
|
||||
} else {
|
||||
*ctx = {
|
||||
/* .vocab = */ vocab,
|
||||
/* .grammar_kind = */ {},
|
||||
/* .grammar_data = */ {},
|
||||
/* .tokenizer = */ nullptr,
|
||||
/* .grammar = */ nullptr,
|
||||
};
|
||||
}
|
||||
|
||||
return llama_sampler_init(
|
||||
/* .iface = */ &llama_sampler_llg_i,
|
||||
/* .ctx = */ ctx);
|
||||
}
|
||||
|
||||
#else
|
||||
|
||||
llama_sampler * llama_sampler_init_llg(const llama_vocab *, const char *, const char *) {
|
||||
LOG_WRN("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
#endif // LLAMA_USE_LLGUIDANCE
|
||||
446
common/log.cpp
Normal file
446
common/log.cpp
Normal file
@@ -0,0 +1,446 @@
|
||||
#include "common.h"
|
||||
#include "log.h"
|
||||
|
||||
#include <chrono>
|
||||
#include <condition_variable>
|
||||
#include <cstdarg>
|
||||
#include <cstdio>
|
||||
#include <cstdlib>
|
||||
#include <cstring>
|
||||
#include <mutex>
|
||||
#include <sstream>
|
||||
#include <thread>
|
||||
#include <vector>
|
||||
|
||||
#if defined(_WIN32)
|
||||
# include <io.h>
|
||||
# include <windows.h>
|
||||
# define isatty _isatty
|
||||
# define fileno _fileno
|
||||
#else
|
||||
# include <unistd.h>
|
||||
#endif // defined(_WIN32)
|
||||
|
||||
int common_log_verbosity_thold = LOG_DEFAULT_LLAMA;
|
||||
|
||||
void common_log_set_verbosity_thold(int verbosity) {
|
||||
common_log_verbosity_thold = verbosity;
|
||||
}
|
||||
|
||||
static int64_t t_us() {
|
||||
return std::chrono::duration_cast<std::chrono::microseconds>(std::chrono::system_clock::now().time_since_epoch()).count();
|
||||
}
|
||||
|
||||
// colors
|
||||
enum common_log_col : int {
|
||||
COMMON_LOG_COL_DEFAULT = 0,
|
||||
COMMON_LOG_COL_BOLD,
|
||||
COMMON_LOG_COL_RED,
|
||||
COMMON_LOG_COL_GREEN,
|
||||
COMMON_LOG_COL_YELLOW,
|
||||
COMMON_LOG_COL_BLUE,
|
||||
COMMON_LOG_COL_MAGENTA,
|
||||
COMMON_LOG_COL_CYAN,
|
||||
COMMON_LOG_COL_WHITE,
|
||||
};
|
||||
|
||||
// disable colors by default
|
||||
static std::vector<const char *> g_col = {
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
"",
|
||||
};
|
||||
|
||||
struct common_log_entry {
|
||||
enum ggml_log_level level;
|
||||
|
||||
bool prefix;
|
||||
|
||||
int64_t timestamp;
|
||||
|
||||
std::vector<char> msg;
|
||||
|
||||
// signals the worker thread to stop
|
||||
bool is_end;
|
||||
|
||||
void print(FILE * file = nullptr) const {
|
||||
FILE * fcur = file;
|
||||
if (!fcur) {
|
||||
// stderr displays DBG messages only when their verbosity level is not higher than the threshold
|
||||
// these messages will still be logged to a file
|
||||
if (level == GGML_LOG_LEVEL_DEBUG && common_log_verbosity_thold < LOG_DEFAULT_DEBUG) {
|
||||
return;
|
||||
}
|
||||
|
||||
fcur = stdout;
|
||||
|
||||
if (level != GGML_LOG_LEVEL_NONE) {
|
||||
fcur = stderr;
|
||||
}
|
||||
}
|
||||
|
||||
if (level != GGML_LOG_LEVEL_NONE && level != GGML_LOG_LEVEL_CONT && prefix) {
|
||||
if (timestamp) {
|
||||
// [M.s.ms.us]
|
||||
fprintf(fcur, "%s%d.%02d.%03d.%03d%s ",
|
||||
g_col[COMMON_LOG_COL_BLUE],
|
||||
(int) (timestamp / 1000000 / 60),
|
||||
(int) (timestamp / 1000000 % 60),
|
||||
(int) (timestamp / 1000 % 1000),
|
||||
(int) (timestamp % 1000),
|
||||
g_col[COMMON_LOG_COL_DEFAULT]);
|
||||
}
|
||||
|
||||
switch (level) {
|
||||
case GGML_LOG_LEVEL_INFO: fprintf(fcur, "%sI %s", g_col[COMMON_LOG_COL_GREEN], g_col[COMMON_LOG_COL_DEFAULT]); break;
|
||||
case GGML_LOG_LEVEL_WARN: fprintf(fcur, "%sW %s", g_col[COMMON_LOG_COL_MAGENTA], "" ); break;
|
||||
case GGML_LOG_LEVEL_ERROR: fprintf(fcur, "%sE %s", g_col[COMMON_LOG_COL_RED], "" ); break;
|
||||
case GGML_LOG_LEVEL_DEBUG: fprintf(fcur, "%sD %s", g_col[COMMON_LOG_COL_YELLOW], "" ); break;
|
||||
default:
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
fprintf(fcur, "%s", msg.data());
|
||||
|
||||
if (level == GGML_LOG_LEVEL_WARN || level == GGML_LOG_LEVEL_ERROR || level == GGML_LOG_LEVEL_DEBUG) {
|
||||
fprintf(fcur, "%s", g_col[COMMON_LOG_COL_DEFAULT]);
|
||||
}
|
||||
|
||||
fflush(fcur);
|
||||
}
|
||||
};
|
||||
|
||||
struct common_log {
|
||||
// default capacity - will be expanded if needed
|
||||
common_log() : common_log(256) {}
|
||||
|
||||
common_log(size_t capacity) {
|
||||
file = nullptr;
|
||||
prefix = false;
|
||||
timestamps = false;
|
||||
running = false;
|
||||
t_start = t_us();
|
||||
|
||||
// initial message size - will be expanded if longer messages arrive
|
||||
entries.resize(capacity);
|
||||
for (auto & entry : entries) {
|
||||
entry.msg.resize(256);
|
||||
}
|
||||
|
||||
head = 0;
|
||||
tail = 0;
|
||||
|
||||
resume();
|
||||
}
|
||||
|
||||
~common_log() {
|
||||
pause();
|
||||
if (file) {
|
||||
fclose(file);
|
||||
}
|
||||
}
|
||||
|
||||
private:
|
||||
std::mutex mtx;
|
||||
std::thread thrd;
|
||||
std::condition_variable cv;
|
||||
|
||||
FILE * file;
|
||||
|
||||
bool prefix;
|
||||
bool timestamps;
|
||||
bool running;
|
||||
|
||||
int64_t t_start;
|
||||
|
||||
// ring buffer of entries
|
||||
std::vector<common_log_entry> entries;
|
||||
size_t head;
|
||||
size_t tail;
|
||||
|
||||
// worker thread copies into this
|
||||
common_log_entry cur;
|
||||
|
||||
public:
|
||||
void add(enum ggml_log_level level, const char * fmt, va_list args) {
|
||||
std::lock_guard<std::mutex> lock(mtx);
|
||||
|
||||
if (!running) {
|
||||
// discard messages while the worker thread is paused
|
||||
return;
|
||||
}
|
||||
|
||||
auto & entry = entries[tail];
|
||||
|
||||
{
|
||||
// cannot use args twice, so make a copy in case we need to expand the buffer
|
||||
va_list args_copy;
|
||||
va_copy(args_copy, args);
|
||||
|
||||
#if 1
|
||||
const size_t n = vsnprintf(entry.msg.data(), entry.msg.size(), fmt, args);
|
||||
if (n >= entry.msg.size()) {
|
||||
entry.msg.resize(n + 1);
|
||||
vsnprintf(entry.msg.data(), entry.msg.size(), fmt, args_copy);
|
||||
}
|
||||
#else
|
||||
// hack for bolding arguments
|
||||
|
||||
std::stringstream ss;
|
||||
for (int i = 0; fmt[i] != 0; i++) {
|
||||
if (fmt[i] == '%') {
|
||||
ss << LOG_COL_BOLD;
|
||||
while (fmt[i] != ' ' && fmt[i] != ')' && fmt[i] != ']' && fmt[i] != 0) ss << fmt[i++];
|
||||
ss << LOG_COL_DEFAULT;
|
||||
if (fmt[i] == 0) break;
|
||||
}
|
||||
ss << fmt[i];
|
||||
}
|
||||
const size_t n = vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args);
|
||||
if (n >= entry.msg.size()) {
|
||||
entry.msg.resize(n + 1);
|
||||
vsnprintf(entry.msg.data(), entry.msg.size(), ss.str().c_str(), args_copy);
|
||||
}
|
||||
#endif
|
||||
va_end(args_copy);
|
||||
}
|
||||
|
||||
entry.level = level;
|
||||
entry.prefix = prefix;
|
||||
entry.timestamp = 0;
|
||||
if (timestamps) {
|
||||
entry.timestamp = t_us() - t_start;
|
||||
}
|
||||
entry.is_end = false;
|
||||
|
||||
tail = (tail + 1) % entries.size();
|
||||
if (tail == head) {
|
||||
// expand the buffer
|
||||
std::vector<common_log_entry> new_entries(2*entries.size());
|
||||
|
||||
size_t new_tail = 0;
|
||||
|
||||
do {
|
||||
new_entries[new_tail] = std::move(entries[head]);
|
||||
|
||||
head = (head + 1) % entries.size();
|
||||
new_tail = (new_tail + 1);
|
||||
} while (head != tail);
|
||||
|
||||
head = 0;
|
||||
tail = new_tail;
|
||||
|
||||
for (size_t i = tail; i < new_entries.size(); i++) {
|
||||
new_entries[i].msg.resize(256);
|
||||
}
|
||||
|
||||
entries = std::move(new_entries);
|
||||
}
|
||||
|
||||
cv.notify_one();
|
||||
}
|
||||
|
||||
void resume() {
|
||||
std::lock_guard<std::mutex> lock(mtx);
|
||||
|
||||
if (running) {
|
||||
return;
|
||||
}
|
||||
|
||||
running = true;
|
||||
|
||||
thrd = std::thread([this]() {
|
||||
while (true) {
|
||||
{
|
||||
std::unique_lock<std::mutex> lock(mtx);
|
||||
cv.wait(lock, [this]() { return head != tail; });
|
||||
|
||||
cur = entries[head];
|
||||
|
||||
head = (head + 1) % entries.size();
|
||||
}
|
||||
|
||||
if (cur.is_end) {
|
||||
break;
|
||||
}
|
||||
|
||||
cur.print(); // stdout and stderr
|
||||
|
||||
if (file) {
|
||||
cur.print(file);
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
void pause() {
|
||||
{
|
||||
std::lock_guard<std::mutex> lock(mtx);
|
||||
|
||||
if (!running) {
|
||||
return;
|
||||
}
|
||||
|
||||
running = false;
|
||||
|
||||
// push an entry to signal the worker thread to stop
|
||||
{
|
||||
auto & entry = entries[tail];
|
||||
entry.is_end = true;
|
||||
|
||||
tail = (tail + 1) % entries.size();
|
||||
}
|
||||
|
||||
cv.notify_one();
|
||||
}
|
||||
|
||||
thrd.join();
|
||||
}
|
||||
|
||||
void set_file(const char * path) {
|
||||
pause();
|
||||
|
||||
if (file) {
|
||||
fclose(file);
|
||||
}
|
||||
|
||||
if (path) {
|
||||
file = fopen(path, "w");
|
||||
} else {
|
||||
file = nullptr;
|
||||
}
|
||||
|
||||
resume();
|
||||
}
|
||||
|
||||
void set_colors(bool colors) {
|
||||
pause();
|
||||
|
||||
if (colors) {
|
||||
g_col[COMMON_LOG_COL_DEFAULT] = LOG_COL_DEFAULT;
|
||||
g_col[COMMON_LOG_COL_BOLD] = LOG_COL_BOLD;
|
||||
g_col[COMMON_LOG_COL_RED] = LOG_COL_RED;
|
||||
g_col[COMMON_LOG_COL_GREEN] = LOG_COL_GREEN;
|
||||
g_col[COMMON_LOG_COL_YELLOW] = LOG_COL_YELLOW;
|
||||
g_col[COMMON_LOG_COL_BLUE] = LOG_COL_BLUE;
|
||||
g_col[COMMON_LOG_COL_MAGENTA] = LOG_COL_MAGENTA;
|
||||
g_col[COMMON_LOG_COL_CYAN] = LOG_COL_CYAN;
|
||||
g_col[COMMON_LOG_COL_WHITE] = LOG_COL_WHITE;
|
||||
} else {
|
||||
for (size_t i = 0; i < g_col.size(); i++) {
|
||||
g_col[i] = "";
|
||||
}
|
||||
}
|
||||
|
||||
resume();
|
||||
}
|
||||
|
||||
void set_prefix(bool prefix) {
|
||||
std::lock_guard<std::mutex> lock(mtx);
|
||||
|
||||
this->prefix = prefix;
|
||||
}
|
||||
|
||||
void set_timestamps(bool timestamps) {
|
||||
std::lock_guard<std::mutex> lock(mtx);
|
||||
|
||||
this->timestamps = timestamps;
|
||||
}
|
||||
};
|
||||
|
||||
//
|
||||
// public API
|
||||
//
|
||||
|
||||
struct common_log * common_log_init() {
|
||||
return new common_log;
|
||||
}
|
||||
|
||||
struct common_log * common_log_main() {
|
||||
static struct common_log log;
|
||||
static std::once_flag init_flag;
|
||||
std::call_once(init_flag, [&]() {
|
||||
// Set default to auto-detect colors
|
||||
log.set_colors(tty_can_use_colors());
|
||||
});
|
||||
|
||||
return &log;
|
||||
}
|
||||
|
||||
void common_log_pause(struct common_log * log) {
|
||||
log->pause();
|
||||
}
|
||||
|
||||
void common_log_resume(struct common_log * log) {
|
||||
log->resume();
|
||||
}
|
||||
|
||||
void common_log_free(struct common_log * log) {
|
||||
delete log;
|
||||
}
|
||||
|
||||
void common_log_add(struct common_log * log, enum ggml_log_level level, const char * fmt, ...) {
|
||||
va_list args;
|
||||
va_start(args, fmt);
|
||||
log->add(level, fmt, args);
|
||||
va_end(args);
|
||||
}
|
||||
|
||||
void common_log_set_file(struct common_log * log, const char * file) {
|
||||
log->set_file(file);
|
||||
}
|
||||
|
||||
void common_log_set_colors(struct common_log * log, log_colors colors) {
|
||||
if (colors == LOG_COLORS_AUTO) {
|
||||
log->set_colors(tty_can_use_colors());
|
||||
return;
|
||||
}
|
||||
|
||||
if (colors == LOG_COLORS_DISABLED) {
|
||||
log->set_colors(false);
|
||||
return;
|
||||
}
|
||||
|
||||
GGML_ASSERT(colors == LOG_COLORS_ENABLED);
|
||||
log->set_colors(true);
|
||||
}
|
||||
|
||||
void common_log_set_prefix(struct common_log * log, bool prefix) {
|
||||
log->set_prefix(prefix);
|
||||
}
|
||||
|
||||
void common_log_set_timestamps(struct common_log * log, bool timestamps) {
|
||||
log->set_timestamps(timestamps);
|
||||
}
|
||||
|
||||
void common_log_flush(struct common_log * log) {
|
||||
log->pause();
|
||||
log->resume();
|
||||
}
|
||||
|
||||
static int common_get_verbosity(enum ggml_log_level level) {
|
||||
switch (level) {
|
||||
case GGML_LOG_LEVEL_DEBUG: return LOG_LEVEL_DEBUG;
|
||||
case GGML_LOG_LEVEL_INFO: return LOG_LEVEL_INFO;
|
||||
case GGML_LOG_LEVEL_WARN: return LOG_LEVEL_WARN;
|
||||
case GGML_LOG_LEVEL_ERROR: return LOG_LEVEL_ERROR;
|
||||
case GGML_LOG_LEVEL_CONT: return LOG_LEVEL_INFO; // same as INFO
|
||||
case GGML_LOG_LEVEL_NONE:
|
||||
default:
|
||||
return LOG_LEVEL_OUTPUT;
|
||||
}
|
||||
}
|
||||
|
||||
void common_log_default_callback(enum ggml_log_level level, const char * text, void * /*user_data*/) {
|
||||
auto verbosity = common_get_verbosity(level);
|
||||
if (verbosity <= common_log_verbosity_thold) {
|
||||
common_log_add(common_log_main(), level, "%s", text);
|
||||
}
|
||||
}
|
||||
119
common/log.h
Normal file
119
common/log.h
Normal file
@@ -0,0 +1,119 @@
|
||||
#pragma once
|
||||
|
||||
#include "ggml.h" // for ggml_log_level
|
||||
|
||||
#define LOG_CLR_TO_EOL "\033[K\r"
|
||||
#define LOG_COL_DEFAULT "\033[0m"
|
||||
#define LOG_COL_BOLD "\033[1m"
|
||||
#define LOG_COL_RED "\033[31m"
|
||||
#define LOG_COL_GREEN "\033[32m"
|
||||
#define LOG_COL_YELLOW "\033[33m"
|
||||
#define LOG_COL_BLUE "\033[34m"
|
||||
#define LOG_COL_MAGENTA "\033[35m"
|
||||
#define LOG_COL_CYAN "\033[36m"
|
||||
#define LOG_COL_WHITE "\033[37m"
|
||||
|
||||
#ifndef __GNUC__
|
||||
# define LOG_ATTRIBUTE_FORMAT(...)
|
||||
#elif defined(__MINGW32__) && !defined(__clang__)
|
||||
# define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__)))
|
||||
#else
|
||||
# define LOG_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__)))
|
||||
#endif
|
||||
|
||||
#define LOG_LEVEL_DEBUG 4
|
||||
#define LOG_LEVEL_INFO 3
|
||||
#define LOG_LEVEL_WARN 2
|
||||
#define LOG_LEVEL_ERROR 1
|
||||
#define LOG_LEVEL_OUTPUT 0 // output data from tools
|
||||
|
||||
#define LOG_DEFAULT_DEBUG LOG_LEVEL_DEBUG
|
||||
#define LOG_DEFAULT_LLAMA LOG_LEVEL_INFO
|
||||
|
||||
enum log_colors {
|
||||
LOG_COLORS_AUTO = -1,
|
||||
LOG_COLORS_DISABLED = 0,
|
||||
LOG_COLORS_ENABLED = 1,
|
||||
};
|
||||
|
||||
// needed by the LOG_TMPL macro to avoid computing log arguments if the verbosity lower
|
||||
// set via common_log_set_verbosity()
|
||||
extern int common_log_verbosity_thold;
|
||||
|
||||
void common_log_set_verbosity_thold(int verbosity); // not thread-safe
|
||||
|
||||
void common_log_default_callback(enum ggml_log_level level, const char * text, void * user_data);
|
||||
|
||||
// the common_log uses an internal worker thread to print/write log messages
|
||||
// when the worker thread is paused, incoming log messages are discarded
|
||||
struct common_log;
|
||||
|
||||
struct common_log * common_log_init();
|
||||
struct common_log * common_log_main(); // singleton, automatically destroys itself on exit
|
||||
void common_log_pause (struct common_log * log); // pause the worker thread, not thread-safe
|
||||
void common_log_resume(struct common_log * log); // resume the worker thread, not thread-safe
|
||||
void common_log_free (struct common_log * log);
|
||||
|
||||
LOG_ATTRIBUTE_FORMAT(3, 4)
|
||||
void common_log_add(struct common_log * log, enum ggml_log_level level, const char * fmt, ...);
|
||||
|
||||
// defaults: file = NULL, colors = false, prefix = false, timestamps = false
|
||||
//
|
||||
// regular log output:
|
||||
//
|
||||
// ggml_backend_metal_log_allocated_size: allocated buffer, size = 6695.84 MiB, ( 6695.91 / 21845.34)
|
||||
// llm_load_tensors: ggml ctx size = 0.27 MiB
|
||||
// llm_load_tensors: offloading 32 repeating layers to GPU
|
||||
// llm_load_tensors: offloading non-repeating layers to GPU
|
||||
//
|
||||
// with prefix = true, timestamps = true, the log output will look like this:
|
||||
//
|
||||
// 0.00.035.060 D ggml_backend_metal_log_allocated_size: allocated buffer, size = 6695.84 MiB, ( 6695.91 / 21845.34)
|
||||
// 0.00.035.064 I llm_load_tensors: ggml ctx size = 0.27 MiB
|
||||
// 0.00.090.578 I llm_load_tensors: offloading 32 repeating layers to GPU
|
||||
// 0.00.090.579 I llm_load_tensors: offloading non-repeating layers to GPU
|
||||
//
|
||||
// D - debug (stderr, V = LOG_DEFAULT_DEBUG)
|
||||
// I - info (stdout, V = LOG_DEFAULT_INFO)
|
||||
// W - warning (stderr, V = LOG_DEFAULT_WARN)
|
||||
// E - error (stderr, V = LOG_DEFAULT_ERROR)
|
||||
// O - output (stdout, V = LOG_DEFAULT_OUTPUT)
|
||||
//
|
||||
|
||||
void common_log_set_file (struct common_log * log, const char * file); // not thread-safe
|
||||
void common_log_set_colors (struct common_log * log, log_colors colors); // not thread-safe
|
||||
void common_log_set_prefix (struct common_log * log, bool prefix); // whether to output prefix to each log
|
||||
void common_log_set_timestamps(struct common_log * log, bool timestamps); // whether to output timestamps in the prefix
|
||||
void common_log_flush (struct common_log * log); // flush all pending log messages
|
||||
|
||||
// helper macros for logging
|
||||
// use these to avoid computing log arguments if the verbosity of the log is higher than the threshold
|
||||
//
|
||||
// for example:
|
||||
//
|
||||
// LOG_DBG("this is a debug message: %d\n", expensive_function());
|
||||
//
|
||||
// this will avoid calling expensive_function() if LOG_DEFAULT_DEBUG > common_log_verbosity_thold
|
||||
//
|
||||
|
||||
#define LOG_TMPL(level, verbosity, ...) \
|
||||
do { \
|
||||
if ((verbosity) <= common_log_verbosity_thold) { \
|
||||
common_log_add(common_log_main(), (level), __VA_ARGS__); \
|
||||
} \
|
||||
} while (0)
|
||||
|
||||
#define LOG(...) LOG_TMPL(GGML_LOG_LEVEL_NONE, LOG_LEVEL_OUTPUT, __VA_ARGS__)
|
||||
#define LOGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_NONE, verbosity, __VA_ARGS__)
|
||||
|
||||
#define LOG_DBG(...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, LOG_LEVEL_DEBUG, __VA_ARGS__)
|
||||
#define LOG_INF(...) LOG_TMPL(GGML_LOG_LEVEL_INFO, LOG_LEVEL_INFO, __VA_ARGS__)
|
||||
#define LOG_WRN(...) LOG_TMPL(GGML_LOG_LEVEL_WARN, LOG_LEVEL_WARN, __VA_ARGS__)
|
||||
#define LOG_ERR(...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, LOG_LEVEL_ERROR, __VA_ARGS__)
|
||||
#define LOG_CNT(...) LOG_TMPL(GGML_LOG_LEVEL_CONT, LOG_LEVEL_INFO, __VA_ARGS__) // same as INFO
|
||||
|
||||
#define LOG_INFV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_INFO, verbosity, __VA_ARGS__)
|
||||
#define LOG_WRNV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_WARN, verbosity, __VA_ARGS__)
|
||||
#define LOG_ERRV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_ERROR, verbosity, __VA_ARGS__)
|
||||
#define LOG_DBGV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_DEBUG, verbosity, __VA_ARGS__)
|
||||
#define LOG_CNTV(verbosity, ...) LOG_TMPL(GGML_LOG_LEVEL_CONT, verbosity, __VA_ARGS__)
|
||||
286
common/ngram-cache.cpp
Normal file
286
common/ngram-cache.cpp
Normal file
@@ -0,0 +1,286 @@
|
||||
#include "ngram-cache.h"
|
||||
#include "common.h"
|
||||
#include "log.h"
|
||||
|
||||
#include <cinttypes>
|
||||
#include <cstdint>
|
||||
#include <cstdio>
|
||||
#include <fstream>
|
||||
#include <thread>
|
||||
#include <algorithm>
|
||||
|
||||
void common_ngram_cache_update(common_ngram_cache & ngram_cache, int ngram_min, int ngram_max,
|
||||
std::vector<llama_token> & inp, int nnew, bool print_progress) {
|
||||
const int64_t t_start_ms = ggml_time_ms();
|
||||
const int64_t inp_size = inp.size();
|
||||
|
||||
const int64_t n_todo = inp_size * (ngram_max - ngram_min + 1);
|
||||
int64_t n_done = 0;
|
||||
|
||||
for (int64_t ngram_size = ngram_min; ngram_size <= ngram_max; ++ngram_size) {
|
||||
const int64_t i_start = std::max(inp_size - nnew, ngram_size);
|
||||
for (int64_t i = i_start; i < inp_size; ++i) {
|
||||
const int64_t ngram_start = i - ngram_size;
|
||||
common_ngram ngram(&inp[ngram_start], ngram_size);
|
||||
const llama_token token = inp[i];
|
||||
|
||||
common_ngram_cache::iterator part_it = ngram_cache.find(ngram);
|
||||
if (part_it == ngram_cache.end()) {
|
||||
common_ngram_cache_part part;
|
||||
part.emplace(token, 1);
|
||||
ngram_cache.emplace(ngram, part);
|
||||
} else {
|
||||
common_ngram_cache_part::iterator token_count_it = part_it->second.find(token);
|
||||
if (token_count_it == part_it->second.end()) {
|
||||
part_it->second.emplace(token, 1);
|
||||
} else {
|
||||
token_count_it->second++;
|
||||
}
|
||||
}
|
||||
++n_done;
|
||||
|
||||
if (print_progress && n_done % 10000000 == 0) {
|
||||
const int64_t t_now_ms = ggml_time_ms();
|
||||
const int64_t eta_ms = (inp_size*(ngram_max-ngram_min+1) - n_done) * (t_now_ms - t_start_ms) / n_done;
|
||||
const int64_t eta_min = eta_ms / (60*1000);
|
||||
const int64_t eta_s = (eta_ms - 60*1000*eta_min) / 1000;
|
||||
|
||||
fprintf(stderr, "%s: %" PRId64 "/%" PRId64 " done, ETA: %02" PRId64 ":%02" PRId64 "\n", __func__, n_done, n_todo, eta_min, eta_s);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// Helper function to get a token from the combined, speculative sequence of inp and draft.
|
||||
static llama_token get_token(const std::vector<llama_token> & inp, const std::vector<llama_token> & draft, const size_t i) {
|
||||
return i < inp.size() ? inp[i] : draft[1 + i - inp.size()];
|
||||
}
|
||||
|
||||
// If sample size or percentage are below these thresholds the draft is aborted early:
|
||||
constexpr int draft_min_sample_size_lax[LLAMA_NGRAM_MAX] = { 2, 2, 1, 1};
|
||||
constexpr int draft_min_percent_lax[LLAMA_NGRAM_MAX] = {66, 50, 50, 50};
|
||||
constexpr int draft_min_sample_size_strict[LLAMA_NGRAM_MAX] = { 4, 3, 2, 2};
|
||||
constexpr int draft_min_percent_strict[LLAMA_NGRAM_MAX] = {75, 66, 66, 66};
|
||||
|
||||
// Helper function that tries to draft a token from only the static ngram cache:
|
||||
static llama_token try_draft(common_ngram_cache & nc_static, const common_ngram ngram_static) {
|
||||
common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
|
||||
if (part_static_it == nc_static.end()) {
|
||||
return LLAMA_TOKEN_NULL;
|
||||
}
|
||||
const common_ngram_cache_part part_static = part_static_it->second;
|
||||
|
||||
int max_count_static = 0;
|
||||
int sum_count_static = 0;
|
||||
llama_token max_token = LLAMA_TOKEN_NULL;
|
||||
|
||||
for (std::pair<llama_token, int> token_count_static : part_static) {
|
||||
const llama_token token = token_count_static.first;
|
||||
const int32_t count_static = token_count_static.second;
|
||||
|
||||
if (count_static > max_count_static) {
|
||||
max_token = token;
|
||||
max_count_static = count_static;
|
||||
}
|
||||
sum_count_static += count_static;
|
||||
}
|
||||
|
||||
if (sum_count_static < draft_min_sample_size_lax[LLAMA_NGRAM_STATIC-1]) {
|
||||
return LLAMA_TOKEN_NULL;
|
||||
}
|
||||
if (100*max_count_static < draft_min_percent_lax[LLAMA_NGRAM_STATIC-1]*sum_count_static) {
|
||||
return LLAMA_TOKEN_NULL;
|
||||
}
|
||||
return max_token;
|
||||
}
|
||||
|
||||
// Try to draft a token from primary cache (context/dynamic), validate with static cache:
|
||||
static llama_token try_draft(
|
||||
common_ngram_cache & nc_primary, const std::vector<common_ngram> & ngrams_primary, common_ngram_cache_part & part_static,
|
||||
const int * min_sample_size, const int * min_percent) {
|
||||
|
||||
llama_token drafted_token = LLAMA_TOKEN_NULL;
|
||||
|
||||
for (int i = ngrams_primary.size()-1; i >= 0 && drafted_token == LLAMA_TOKEN_NULL; --i) {
|
||||
const common_ngram ngram_primary = ngrams_primary[i];
|
||||
|
||||
common_ngram_cache::iterator part_primary_it = nc_primary.find(ngram_primary);
|
||||
if (part_primary_it == nc_primary.end()) {
|
||||
continue;
|
||||
}
|
||||
const common_ngram_cache_part part_primary = part_primary_it->second;
|
||||
|
||||
int max_count_primary = 0;
|
||||
int max_count_static = 0;
|
||||
int sum_count_primary = 0;
|
||||
llama_token max_token = LLAMA_TOKEN_NULL;
|
||||
|
||||
for (std::pair<llama_token, int> token_count_primary : part_primary) {
|
||||
const llama_token token = token_count_primary.first;
|
||||
|
||||
common_ngram_cache_part::iterator token_count_static_it = part_static.find(token);
|
||||
|
||||
const int32_t count_primary = token_count_primary.second;
|
||||
const int32_t count_static = token_count_static_it != part_static.end() ? 100*token_count_static_it->second : 1;
|
||||
|
||||
if (count_primary*count_static > max_count_primary*max_count_static) {
|
||||
max_token = token;
|
||||
max_count_primary = count_primary;
|
||||
max_count_static = count_static;
|
||||
}
|
||||
sum_count_primary += count_primary;
|
||||
}
|
||||
|
||||
if (sum_count_primary < min_sample_size[i]) {
|
||||
continue;
|
||||
}
|
||||
if (100*max_count_primary < min_percent[i]*sum_count_primary) {
|
||||
continue;;
|
||||
}
|
||||
drafted_token = max_token;
|
||||
}
|
||||
|
||||
return drafted_token;
|
||||
}
|
||||
|
||||
void common_ngram_cache_draft(
|
||||
std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
|
||||
common_ngram_cache & nc_context, common_ngram_cache & nc_dynamic, common_ngram_cache & nc_static
|
||||
) {
|
||||
GGML_ASSERT(draft.size() == 1);
|
||||
const int inp_size = inp.size();
|
||||
|
||||
if (inp_size < LLAMA_NGRAM_STATIC) {
|
||||
return;
|
||||
}
|
||||
|
||||
while ((int) draft.size()-1 < n_draft) {
|
||||
llama_token drafted_token = LLAMA_TOKEN_NULL;
|
||||
|
||||
const int ngram_start_static = inp_size-LLAMA_NGRAM_STATIC + draft.size()-1;
|
||||
common_ngram ngram_static;
|
||||
for (int j = ngram_start_static; j < ngram_start_static + LLAMA_NGRAM_STATIC; ++j) {
|
||||
ngram_static.tokens[j-ngram_start_static] = get_token(inp, draft, j);
|
||||
}
|
||||
common_ngram_cache::iterator part_static_it = nc_static.find(ngram_static);
|
||||
common_ngram_cache_part part_static;
|
||||
if (part_static_it != nc_static.end()) {
|
||||
part_static = part_static_it->second;
|
||||
}
|
||||
|
||||
// cd = context + dynamic
|
||||
std::vector<common_ngram> ngrams_cd;
|
||||
for (int ngram_size_cd = ngram_min; ngram_size_cd <= ngram_max; ++ngram_size_cd) {
|
||||
const int ngram_start_cd = inp_size-ngram_size_cd + draft.size()-1;
|
||||
common_ngram ngram_cd;
|
||||
for (int j = ngram_start_cd; j < ngram_start_cd + ngram_size_cd; ++j) {
|
||||
ngram_cd.tokens[j-ngram_start_cd] = get_token(inp, draft, j);
|
||||
}
|
||||
ngrams_cd.push_back(ngram_cd);
|
||||
}
|
||||
if (drafted_token == LLAMA_TOKEN_NULL) {
|
||||
drafted_token = try_draft(nc_context, ngrams_cd, part_static, draft_min_sample_size_lax, draft_min_percent_lax);
|
||||
}
|
||||
if (drafted_token == LLAMA_TOKEN_NULL) {
|
||||
drafted_token = try_draft(nc_dynamic, ngrams_cd, part_static, draft_min_sample_size_strict, draft_min_percent_strict);
|
||||
}
|
||||
if (drafted_token == LLAMA_TOKEN_NULL) {
|
||||
drafted_token = try_draft(nc_static, ngram_static);
|
||||
}
|
||||
|
||||
if (drafted_token == LLAMA_TOKEN_NULL) {
|
||||
break;
|
||||
}
|
||||
|
||||
LOG(" - draft candidate: token=%d\n", drafted_token);
|
||||
draft.push_back(drafted_token);
|
||||
}
|
||||
}
|
||||
|
||||
void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename) {
|
||||
std::ofstream file_out(filename, std::ios::binary);
|
||||
for (std::pair<common_ngram, common_ngram_cache_part> item : ngram_cache) {
|
||||
const common_ngram ngram = item.first;
|
||||
common_ngram_cache_part token_counts = item.second;
|
||||
GGML_ASSERT(!token_counts.empty());
|
||||
const int32_t ntokens = token_counts.size();
|
||||
GGML_ASSERT(ntokens > 0);
|
||||
|
||||
file_out.write(reinterpret_cast<const char *>(&ngram), sizeof(common_ngram));
|
||||
file_out.write(reinterpret_cast<const char *>(&ntokens), sizeof(int32_t));
|
||||
for (std::pair<llama_token, int32_t> item2 : token_counts) {
|
||||
const llama_token token = item2.first;
|
||||
const int32_t count = item2.second;
|
||||
GGML_ASSERT(count > 0);
|
||||
|
||||
file_out.write(reinterpret_cast<const char *>(&token), sizeof(llama_token));
|
||||
file_out.write(reinterpret_cast<const char *>(&count), sizeof(int32_t));
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
common_ngram_cache common_ngram_cache_load(std::string & filename) {
|
||||
std::ifstream hashmap_file(filename, std::ios::binary);
|
||||
if (!hashmap_file) {
|
||||
throw std::ifstream::failure("Unable to open file " + filename);
|
||||
}
|
||||
common_ngram_cache ngram_cache;
|
||||
|
||||
common_ngram ngram;
|
||||
int32_t ntokens;
|
||||
llama_token token;
|
||||
int32_t count;
|
||||
|
||||
char * ngramc = reinterpret_cast<char*>(&ngram);
|
||||
char * ntokensc = reinterpret_cast<char*>(&ntokens);
|
||||
char * tokenc = reinterpret_cast<char*>(&token);
|
||||
char * countc = reinterpret_cast<char*>(&count);
|
||||
while(hashmap_file.read(ngramc, sizeof(common_ngram))) {
|
||||
GGML_ASSERT(!hashmap_file.eof());
|
||||
GGML_ASSERT(hashmap_file.read(ntokensc, sizeof(int32_t)));
|
||||
GGML_ASSERT(ntokens > 0);
|
||||
common_ngram_cache_part token_counts;
|
||||
|
||||
for (int i = 0; i < ntokens; ++i) {
|
||||
GGML_ASSERT(!hashmap_file.eof());
|
||||
GGML_ASSERT(hashmap_file.read(tokenc, sizeof(llama_token)));
|
||||
GGML_ASSERT(!hashmap_file.eof());
|
||||
GGML_ASSERT(hashmap_file.read(countc, sizeof(int32_t)));
|
||||
GGML_ASSERT(count > 0);
|
||||
token_counts.emplace(token, count);
|
||||
}
|
||||
|
||||
ngram_cache.emplace(ngram, token_counts);
|
||||
}
|
||||
GGML_ASSERT(hashmap_file.eof());
|
||||
|
||||
return ngram_cache;
|
||||
}
|
||||
|
||||
void common_ngram_cache_merge(common_ngram_cache & ngram_cache_target, common_ngram_cache & ngram_cache_add) {
|
||||
for (std::pair<common_ngram, common_ngram_cache_part> ngram_part : ngram_cache_add) {
|
||||
const common_ngram ngram = ngram_part.first;
|
||||
common_ngram_cache_part part = ngram_part.second;
|
||||
|
||||
common_ngram_cache::iterator part_merged_it = ngram_cache_target.find(ngram);
|
||||
if (part_merged_it == ngram_cache_target.end()) {
|
||||
ngram_cache_target.emplace(ngram, part);
|
||||
continue;
|
||||
}
|
||||
|
||||
for (std::pair<llama_token, int32_t> token_count : part) {
|
||||
const llama_token token = token_count.first;
|
||||
const int32_t count = token_count.second;
|
||||
GGML_ASSERT(count > 0);
|
||||
|
||||
common_ngram_cache_part::iterator token_count_merged_it = part_merged_it->second.find(token);
|
||||
if (token_count_merged_it == part_merged_it->second.end()) {
|
||||
part_merged_it->second.emplace(token, count);
|
||||
continue;
|
||||
}
|
||||
|
||||
token_count_merged_it->second += count;
|
||||
}
|
||||
}
|
||||
}
|
||||
101
common/ngram-cache.h
Normal file
101
common/ngram-cache.h
Normal file
@@ -0,0 +1,101 @@
|
||||
#pragma once
|
||||
|
||||
#include "llama.h"
|
||||
|
||||
#include <unordered_map>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
#define LLAMA_NGRAM_MIN 1
|
||||
#define LLAMA_NGRAM_MAX 4
|
||||
#define LLAMA_NGRAM_STATIC 2
|
||||
|
||||
// Data structures to map n-grams to empirical token probabilities:
|
||||
|
||||
struct common_ngram {
|
||||
llama_token tokens[LLAMA_NGRAM_MAX];
|
||||
|
||||
common_ngram() {
|
||||
for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
|
||||
tokens[i] = LLAMA_TOKEN_NULL;
|
||||
}
|
||||
}
|
||||
|
||||
common_ngram(const llama_token * input, const int ngram_size) {
|
||||
for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
|
||||
tokens[i] = i < ngram_size ? input[i] : LLAMA_TOKEN_NULL;
|
||||
}
|
||||
}
|
||||
|
||||
bool operator==(const common_ngram & other) const {
|
||||
for (int i = 0; i < LLAMA_NGRAM_MAX; ++i) {
|
||||
if (tokens[i] != other.tokens[i]) {
|
||||
return false;
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
};
|
||||
|
||||
struct common_token_hash_function {
|
||||
size_t operator()(const llama_token token) const {
|
||||
// see https://probablydance.com/2018/06/16/fibonacci-hashing-the-optimization-that-the-world-forgot-or-a-better-alternative-to-integer-modulo/
|
||||
return token * 11400714819323198485llu;
|
||||
}
|
||||
};
|
||||
|
||||
struct common_ngram_hash_function {
|
||||
size_t operator()(const common_ngram & ngram) const {
|
||||
size_t hash = common_token_hash_function{}(ngram.tokens[0]);
|
||||
for (int i = 1; i < LLAMA_NGRAM_MAX; ++i) {
|
||||
hash ^= common_token_hash_function{}(ngram.tokens[i]);
|
||||
}
|
||||
return hash;
|
||||
}
|
||||
};
|
||||
|
||||
// token -> number of times token has been seen
|
||||
typedef std::unordered_map<llama_token, int32_t> common_ngram_cache_part;
|
||||
|
||||
// n-gram -> empirical distribution of following tokens
|
||||
typedef std::unordered_map<common_ngram, common_ngram_cache_part, common_ngram_hash_function> common_ngram_cache;
|
||||
|
||||
|
||||
// Update an ngram cache with tokens.
|
||||
// ngram_cache: the cache to modify.
|
||||
// ngram_min/ngram_max: the min/max size of the ngrams to extract from inp_data.
|
||||
// inp_data: the token sequence with which to update ngram_cache.
|
||||
// nnew: how many new tokens have been appended to inp_data since the last call to this function.
|
||||
// print_progress: whether to print progress to stderr.
|
||||
//
|
||||
// In order to get correct results inp_data can ONLY BE APPENDED TO.
|
||||
// Changes in the middle need a complete rebuild.
|
||||
void common_ngram_cache_update(
|
||||
common_ngram_cache & ngram_cache, int ngram_min, int ngram_max, std::vector<llama_token> & inp_data, int nnew, bool print_progress);
|
||||
|
||||
// Try to draft tokens from ngram caches.
|
||||
// inp: the tokens generated so far.
|
||||
// draft: the token sequence to draft. Expected to initially contain the previously sampled token.
|
||||
// n_draft: maximum number of tokens to add to draft.
|
||||
// ngram_min/gram_max: the min/max size of the ngrams in nc_context and nc_dynamic.
|
||||
// nc_context: ngram cache based on current context.
|
||||
// nc_dynamic: ngram cache based on previous user generations.
|
||||
// nc_static: ngram cache generated from a large text corpus, used for validation.
|
||||
void common_ngram_cache_draft(
|
||||
std::vector<llama_token> & inp, std::vector<llama_token> & draft, int n_draft, int ngram_min, int ngram_max,
|
||||
common_ngram_cache & nc_context, common_ngram_cache & nc_dynamic, common_ngram_cache & nc_static);
|
||||
|
||||
// Save an ngram cache to a file.
|
||||
// ngram_cache: the ngram cache to save.
|
||||
// filename: the path under which to save the ngram cache.
|
||||
void common_ngram_cache_save(common_ngram_cache & ngram_cache, std::string & filename);
|
||||
|
||||
// Load an ngram cache saved with common_ngram_cache_save.
|
||||
// filename: the path from which to load the ngram cache.
|
||||
// returns: an ngram cache containing the information saved to filename.
|
||||
common_ngram_cache common_ngram_cache_load(std::string & filename);
|
||||
|
||||
// Merge two ngram caches.
|
||||
// ngram_cache_target: the ngram cache to which to add the information from ngram_cache_add.
|
||||
// ngram_cache_add: the ngram cache to add to ngram_cache_target.
|
||||
void common_ngram_cache_merge(common_ngram_cache & ngram_cache_target, common_ngram_cache & ngram_cache_add);
|
||||
1712
common/peg-parser.cpp
Normal file
1712
common/peg-parser.cpp
Normal file
File diff suppressed because it is too large
Load Diff
459
common/peg-parser.h
Normal file
459
common/peg-parser.h
Normal file
@@ -0,0 +1,459 @@
|
||||
#pragma once
|
||||
|
||||
#include <nlohmann/json_fwd.hpp>
|
||||
|
||||
#include <memory>
|
||||
#include <unordered_map>
|
||||
#include <string>
|
||||
#include <string_view>
|
||||
#include <functional>
|
||||
#include <vector>
|
||||
#include <variant>
|
||||
|
||||
struct common_grammar_builder;
|
||||
|
||||
class common_peg_parser_builder;
|
||||
|
||||
using common_peg_parser_id = size_t;
|
||||
constexpr common_peg_parser_id COMMON_PEG_INVALID_PARSER_ID = static_cast<common_peg_parser_id>(-1);
|
||||
|
||||
using common_peg_ast_id = size_t;
|
||||
constexpr common_peg_ast_id COMMON_PEG_INVALID_AST_ID = static_cast<common_peg_ast_id>(-1);
|
||||
|
||||
// Lightweight wrapper around common_peg_parser_id for convenience
|
||||
class common_peg_parser {
|
||||
common_peg_parser_id id_;
|
||||
common_peg_parser_builder & builder_;
|
||||
|
||||
public:
|
||||
common_peg_parser(const common_peg_parser & other) : id_(other.id_), builder_(other.builder_) {}
|
||||
common_peg_parser(common_peg_parser_id id, common_peg_parser_builder & builder) : id_(id), builder_(builder) {}
|
||||
|
||||
common_peg_parser & operator=(const common_peg_parser & other);
|
||||
common_peg_parser & operator+=(const common_peg_parser & other);
|
||||
common_peg_parser & operator|=(const common_peg_parser & other);
|
||||
|
||||
operator common_peg_parser_id() const { return id_; }
|
||||
common_peg_parser_id id() const { return id_; }
|
||||
|
||||
common_peg_parser_builder & builder() const { return builder_; }
|
||||
|
||||
// Creates a sequence
|
||||
common_peg_parser operator+(const common_peg_parser & other) const;
|
||||
|
||||
// Creates a sequence separated by spaces.
|
||||
common_peg_parser operator<<(const common_peg_parser & other) const;
|
||||
|
||||
// Creates a choice
|
||||
common_peg_parser operator|(const common_peg_parser & other) const;
|
||||
|
||||
common_peg_parser operator+(const char * str) const;
|
||||
common_peg_parser operator+(const std::string & str) const;
|
||||
common_peg_parser operator<<(const char * str) const;
|
||||
common_peg_parser operator<<(const std::string & str) const;
|
||||
common_peg_parser operator|(const char * str) const;
|
||||
common_peg_parser operator|(const std::string & str) const;
|
||||
};
|
||||
|
||||
common_peg_parser operator+(const char * str, const common_peg_parser & p);
|
||||
common_peg_parser operator+(const std::string & str, const common_peg_parser & p);
|
||||
common_peg_parser operator<<(const char * str, const common_peg_parser & p);
|
||||
common_peg_parser operator<<(const std::string & str, const common_peg_parser & p);
|
||||
common_peg_parser operator|(const char * str, const common_peg_parser & p);
|
||||
common_peg_parser operator|(const std::string & str, const common_peg_parser & p);
|
||||
|
||||
enum common_peg_parse_result_type {
|
||||
COMMON_PEG_PARSE_RESULT_FAIL = 0,
|
||||
COMMON_PEG_PARSE_RESULT_SUCCESS = 1,
|
||||
COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT = 2,
|
||||
};
|
||||
|
||||
const char * common_peg_parse_result_type_name(common_peg_parse_result_type type);
|
||||
|
||||
struct common_peg_ast_node {
|
||||
common_peg_ast_id id;
|
||||
std::string rule;
|
||||
std::string tag;
|
||||
size_t start;
|
||||
size_t end;
|
||||
std::string_view text;
|
||||
std::vector<common_peg_ast_id> children;
|
||||
|
||||
bool is_partial = false;
|
||||
};
|
||||
|
||||
struct common_peg_parse_result;
|
||||
|
||||
using common_peg_ast_visitor = std::function<void(const common_peg_ast_node & node)>;
|
||||
|
||||
class common_peg_ast_arena {
|
||||
std::vector<common_peg_ast_node> nodes_;
|
||||
public:
|
||||
common_peg_ast_id add_node(
|
||||
const std::string & rule,
|
||||
const std::string & tag,
|
||||
size_t start,
|
||||
size_t end,
|
||||
std::string_view text,
|
||||
std::vector<common_peg_ast_id> children,
|
||||
bool is_partial = false
|
||||
) {
|
||||
common_peg_ast_id id = nodes_.size();
|
||||
nodes_.push_back({id, rule, tag, start, end, text, std::move(children), is_partial});
|
||||
return id;
|
||||
}
|
||||
|
||||
const common_peg_ast_node & get(common_peg_ast_id id) const { return nodes_.at(id); }
|
||||
|
||||
size_t size() const { return nodes_.size(); }
|
||||
|
||||
void clear() { nodes_.clear(); }
|
||||
|
||||
void visit(common_peg_ast_id id, const common_peg_ast_visitor & visitor) const;
|
||||
void visit(const common_peg_parse_result & result, const common_peg_ast_visitor & visitor) const;
|
||||
};
|
||||
|
||||
struct common_peg_parse_result {
|
||||
common_peg_parse_result_type type = COMMON_PEG_PARSE_RESULT_FAIL;
|
||||
size_t start = 0;
|
||||
size_t end = 0;
|
||||
|
||||
std::vector<common_peg_ast_id> nodes;
|
||||
|
||||
common_peg_parse_result() = default;
|
||||
|
||||
common_peg_parse_result(common_peg_parse_result_type type, size_t start)
|
||||
: type(type), start(start), end(start) {}
|
||||
|
||||
common_peg_parse_result(common_peg_parse_result_type type, size_t start, size_t end)
|
||||
: type(type), start(start), end(end) {}
|
||||
|
||||
common_peg_parse_result(common_peg_parse_result_type type, size_t start, size_t end, std::vector<common_peg_ast_id> nodes)
|
||||
: type(type), start(start), end(end), nodes(std::move(nodes)) {}
|
||||
|
||||
bool fail() const { return type == COMMON_PEG_PARSE_RESULT_FAIL; }
|
||||
bool need_more_input() const { return type == COMMON_PEG_PARSE_RESULT_NEED_MORE_INPUT; }
|
||||
bool success() const { return type == COMMON_PEG_PARSE_RESULT_SUCCESS; }
|
||||
};
|
||||
|
||||
struct common_peg_parse_context {
|
||||
std::string input;
|
||||
bool is_partial;
|
||||
common_peg_ast_arena ast;
|
||||
|
||||
int parse_depth;
|
||||
|
||||
common_peg_parse_context()
|
||||
: is_partial(false), parse_depth(0) {}
|
||||
|
||||
common_peg_parse_context(const std::string & input)
|
||||
: input(input), is_partial(false), parse_depth(0) {}
|
||||
|
||||
common_peg_parse_context(const std::string & input, bool is_partial)
|
||||
: input(input), is_partial(is_partial), parse_depth(0) {}
|
||||
};
|
||||
|
||||
class common_peg_arena;
|
||||
|
||||
// Parser variants
|
||||
struct common_peg_epsilon_parser {};
|
||||
|
||||
struct common_peg_start_parser {};
|
||||
|
||||
struct common_peg_end_parser {};
|
||||
|
||||
struct common_peg_literal_parser {
|
||||
std::string literal;
|
||||
};
|
||||
|
||||
struct common_peg_sequence_parser {
|
||||
std::vector<common_peg_parser_id> children;
|
||||
};
|
||||
|
||||
struct common_peg_choice_parser {
|
||||
std::vector<common_peg_parser_id> children;
|
||||
};
|
||||
|
||||
struct common_peg_repetition_parser {
|
||||
common_peg_parser_id child;
|
||||
int min_count;
|
||||
int max_count; // -1 for unbounded
|
||||
};
|
||||
|
||||
struct common_peg_and_parser {
|
||||
common_peg_parser_id child;
|
||||
};
|
||||
|
||||
struct common_peg_not_parser {
|
||||
common_peg_parser_id child;
|
||||
};
|
||||
|
||||
struct common_peg_any_parser {};
|
||||
|
||||
struct common_peg_space_parser {};
|
||||
|
||||
struct common_peg_chars_parser {
|
||||
struct char_range {
|
||||
uint32_t start;
|
||||
uint32_t end;
|
||||
bool contains(uint32_t codepoint) const { return codepoint >= start && codepoint <= end; }
|
||||
};
|
||||
|
||||
std::string pattern;
|
||||
std::vector<char_range> ranges;
|
||||
bool negated;
|
||||
int min_count;
|
||||
int max_count; // -1 for unbounded
|
||||
};
|
||||
|
||||
struct common_peg_json_string_parser {};
|
||||
|
||||
struct common_peg_until_parser {
|
||||
std::vector<std::string> delimiters;
|
||||
};
|
||||
|
||||
struct common_peg_schema_parser {
|
||||
common_peg_parser_id child;
|
||||
std::string name;
|
||||
std::shared_ptr<nlohmann::ordered_json> schema;
|
||||
|
||||
// Indicates if the GBNF should accept a raw string that matches the schema.
|
||||
bool raw;
|
||||
};
|
||||
|
||||
struct common_peg_rule_parser {
|
||||
std::string name;
|
||||
common_peg_parser_id child;
|
||||
bool trigger;
|
||||
};
|
||||
|
||||
struct common_peg_ref_parser {
|
||||
std::string name;
|
||||
};
|
||||
|
||||
struct common_peg_atomic_parser {
|
||||
common_peg_parser_id child;
|
||||
};
|
||||
|
||||
struct common_peg_tag_parser {
|
||||
common_peg_parser_id child;
|
||||
std::string tag;
|
||||
};
|
||||
|
||||
// Variant holding all parser types
|
||||
using common_peg_parser_variant = std::variant<
|
||||
common_peg_epsilon_parser,
|
||||
common_peg_start_parser,
|
||||
common_peg_end_parser,
|
||||
common_peg_literal_parser,
|
||||
common_peg_sequence_parser,
|
||||
common_peg_choice_parser,
|
||||
common_peg_repetition_parser,
|
||||
common_peg_and_parser,
|
||||
common_peg_not_parser,
|
||||
common_peg_any_parser,
|
||||
common_peg_space_parser,
|
||||
common_peg_chars_parser,
|
||||
common_peg_json_string_parser,
|
||||
common_peg_until_parser,
|
||||
common_peg_schema_parser,
|
||||
common_peg_rule_parser,
|
||||
common_peg_ref_parser,
|
||||
common_peg_atomic_parser,
|
||||
common_peg_tag_parser
|
||||
>;
|
||||
|
||||
class common_peg_arena {
|
||||
std::vector<common_peg_parser_variant> parsers_;
|
||||
std::unordered_map<std::string, common_peg_parser_id> rules_;
|
||||
common_peg_parser_id root_ = COMMON_PEG_INVALID_PARSER_ID;
|
||||
|
||||
public:
|
||||
const common_peg_parser_variant & get(common_peg_parser_id id) const { return parsers_.at(id); }
|
||||
common_peg_parser_variant & get(common_peg_parser_id id) { return parsers_.at(id); }
|
||||
|
||||
size_t size() const { return parsers_.size(); }
|
||||
bool empty() const { return parsers_.empty(); }
|
||||
|
||||
common_peg_parser_id get_rule(const std::string & name) const;
|
||||
bool has_rule(const std::string & name) const { return rules_.find(name) != rules_.end(); }
|
||||
|
||||
common_peg_parser_id root() const { return root_; }
|
||||
void set_root(common_peg_parser_id id) { root_ = id; }
|
||||
|
||||
common_peg_parse_result parse(common_peg_parse_context & ctx, size_t start = 0) const;
|
||||
common_peg_parse_result parse(common_peg_parser_id id, common_peg_parse_context & ctx, size_t start) const;
|
||||
|
||||
void resolve_refs();
|
||||
|
||||
void build_grammar(const common_grammar_builder & builder, bool lazy = false) const;
|
||||
|
||||
std::string dump(common_peg_parser_id id) const;
|
||||
|
||||
nlohmann::json to_json() const;
|
||||
static common_peg_arena from_json(const nlohmann::json & j);
|
||||
|
||||
std::string save() const;
|
||||
void load(const std::string & data);
|
||||
|
||||
friend class common_peg_parser_builder;
|
||||
|
||||
private:
|
||||
common_peg_parser_id add_parser(common_peg_parser_variant parser);
|
||||
void add_rule(const std::string & name, common_peg_parser_id id);
|
||||
|
||||
common_peg_parser_id resolve_ref(common_peg_parser_id id);
|
||||
};
|
||||
|
||||
class common_peg_parser_builder {
|
||||
common_peg_arena arena_;
|
||||
|
||||
common_peg_parser wrap(common_peg_parser_id id) { return common_peg_parser(id, *this); }
|
||||
common_peg_parser add(const common_peg_parser_variant & p) { return wrap(arena_.add_parser(p)); }
|
||||
|
||||
public:
|
||||
common_peg_parser_builder();
|
||||
|
||||
// Match nothing, always succeed.
|
||||
// S -> ε
|
||||
common_peg_parser eps() { return add(common_peg_epsilon_parser{}); }
|
||||
|
||||
// Matches the start of the input.
|
||||
// S -> ^
|
||||
common_peg_parser start() { return add(common_peg_start_parser{}); }
|
||||
|
||||
// Matches the end of the input.
|
||||
// S -> $
|
||||
common_peg_parser end() { return add(common_peg_end_parser{}); }
|
||||
|
||||
// Matches an exact literal string.
|
||||
// S -> "hello"
|
||||
common_peg_parser literal(const std::string & literal) { return add(common_peg_literal_parser{literal}); }
|
||||
|
||||
// Matches a sequence of parsers in order, all must succeed.
|
||||
// S -> A B C
|
||||
common_peg_parser sequence() { return add(common_peg_sequence_parser{}); }
|
||||
common_peg_parser sequence(const std::vector<common_peg_parser_id> & parsers);
|
||||
common_peg_parser sequence(const std::vector<common_peg_parser> & parsers);
|
||||
common_peg_parser sequence(std::initializer_list<common_peg_parser> parsers);
|
||||
|
||||
// Matches the first parser that succeeds from a list of alternatives.
|
||||
// S -> A | B | C
|
||||
common_peg_parser choice() { return add(common_peg_choice_parser{}); }
|
||||
common_peg_parser choice(const std::vector<common_peg_parser_id> & parsers);
|
||||
common_peg_parser choice(const std::vector<common_peg_parser> & parsers);
|
||||
common_peg_parser choice(std::initializer_list<common_peg_parser> parsers);
|
||||
|
||||
// Matches one or more repetitions of a parser.
|
||||
// S -> A+
|
||||
common_peg_parser one_or_more(const common_peg_parser & p) { return repeat(p, 1, -1); }
|
||||
|
||||
// Matches zero or more repetitions of a parser, always succeeds.
|
||||
// S -> A*
|
||||
common_peg_parser zero_or_more(const common_peg_parser & p) { return repeat(p, 0, -1); }
|
||||
|
||||
// Matches zero or one occurrence of a parser, always succeeds.
|
||||
// S -> A?
|
||||
common_peg_parser optional(const common_peg_parser & p) { return repeat(p, 0, 1); }
|
||||
|
||||
// Positive lookahead: succeeds if child parser succeeds, consumes no input.
|
||||
// S -> &A
|
||||
common_peg_parser peek(const common_peg_parser & p) { return add(common_peg_and_parser{p}); }
|
||||
|
||||
// Negative lookahead: succeeds if child parser fails, consumes no input.
|
||||
// S -> !A
|
||||
common_peg_parser negate(const common_peg_parser & p) { return add(common_peg_not_parser{p}); }
|
||||
|
||||
// Matches any single character.
|
||||
// S -> .
|
||||
common_peg_parser any() { return add(common_peg_any_parser{}); }
|
||||
|
||||
// Matches between min and max repetitions of characters from a character class.
|
||||
// S -> [a-z]{m,n}
|
||||
//
|
||||
// Use -1 for max to represent unbounded repetition (equivalent to {m,})
|
||||
common_peg_parser chars(const std::string & classes, int min = 1, int max = -1);
|
||||
|
||||
// Creates a lightweight reference to a named rule (resolved during build()).
|
||||
// Use this for forward references in recursive grammars.
|
||||
// expr_ref -> expr
|
||||
common_peg_parser ref(const std::string & name) { return add(common_peg_ref_parser{name}); }
|
||||
|
||||
// Matches zero or more whitespace characters (space, tab, newline).
|
||||
// S -> [ \t\n]*
|
||||
common_peg_parser space() { return add(common_peg_space_parser{}); }
|
||||
|
||||
// Matches all characters until a delimiter is found (delimiter not consumed).
|
||||
// S -> (!delim .)*
|
||||
common_peg_parser until(const std::string & delimiter) { return add(common_peg_until_parser{{delimiter}}); }
|
||||
|
||||
// Matches all characters until one of the delimiters in the list is found (delimiter not consumed).
|
||||
// S -> (!delim .)*
|
||||
common_peg_parser until_one_of(const std::vector<std::string> & delimiters) { return add(common_peg_until_parser{delimiters}); }
|
||||
|
||||
// Matches everything
|
||||
// S -> .*
|
||||
common_peg_parser rest() { return until_one_of({}); }
|
||||
|
||||
// Matches between min and max repetitions of a parser (inclusive).
|
||||
// S -> A{m,n}
|
||||
// Use -1 for max to represent unbounded repetition (equivalent to {m,})
|
||||
common_peg_parser repeat(const common_peg_parser & p, int min, int max) { return add(common_peg_repetition_parser{p, min,max}); }
|
||||
|
||||
// Matches exactly n repetitions of a parser.
|
||||
// S -> A{n}
|
||||
common_peg_parser repeat(const common_peg_parser & p, int n) { return repeat(p, n, n); }
|
||||
|
||||
// Creates a complete JSON parser supporting objects, arrays, strings, numbers, booleans, and null.
|
||||
// value -> object | array | string | number | true | false | null
|
||||
common_peg_parser json();
|
||||
common_peg_parser json_object();
|
||||
common_peg_parser json_string();
|
||||
common_peg_parser json_array();
|
||||
common_peg_parser json_number();
|
||||
common_peg_parser json_bool();
|
||||
common_peg_parser json_null();
|
||||
|
||||
// Matches JSON string content without the surrounding quotes.
|
||||
// Useful for extracting content within a JSON string.
|
||||
common_peg_parser json_string_content();
|
||||
|
||||
// Matches a JSON object member with a key and associated parser as the
|
||||
// value.
|
||||
common_peg_parser json_member(const std::string & key, const common_peg_parser & p);
|
||||
|
||||
// Wraps a parser with JSON schema metadata for grammar generation.
|
||||
// Used internally to convert JSON schemas to GBNF grammar rules.
|
||||
common_peg_parser schema(const common_peg_parser & p, const std::string & name, const nlohmann::ordered_json & schema, bool raw = false);
|
||||
|
||||
// Creates a named rule, stores it in the grammar, and returns a ref.
|
||||
// If trigger=true, marks this rule as an entry point for lazy grammar generation.
|
||||
// auto json = p.rule("json", json_obj | json_arr | ...)
|
||||
common_peg_parser rule(const std::string & name, const common_peg_parser & p, bool trigger = false);
|
||||
|
||||
// Creates a named rule using a builder function, and returns a ref.
|
||||
// If trigger=true, marks this rule as an entry point for lazy grammar generation.
|
||||
// auto json = p.rule("json", [&]() { return json_object() | json_array() | ... })
|
||||
common_peg_parser rule(const std::string & name, const std::function<common_peg_parser()> & builder, bool trigger = false);
|
||||
|
||||
// Creates a trigger rule. When generating a lazy grammar from the parser,
|
||||
// only trigger rules and descendents are emitted.
|
||||
common_peg_parser trigger_rule(const std::string & name, const common_peg_parser & p) { return rule(name, p, true); }
|
||||
common_peg_parser trigger_rule(const std::string & name, const std::function<common_peg_parser()> & builder) { return rule(name, builder, true); }
|
||||
|
||||
// Creates an atomic parser. Atomic parsers do not create an AST node if
|
||||
// the child results in a partial parse, i.e. NEEDS_MORE_INPUT. This is
|
||||
// intended for situations where partial output is undesirable.
|
||||
common_peg_parser atomic(const common_peg_parser & p) { return add(common_peg_atomic_parser{p}); }
|
||||
|
||||
// Tags create nodes in the generated AST for semantic purposes.
|
||||
// Unlike rules, you can tag multiple nodes with the same tag.
|
||||
common_peg_parser tag(const std::string & tag, const common_peg_parser & p) { return add(common_peg_tag_parser{p.id(), tag}); }
|
||||
|
||||
void set_root(const common_peg_parser & p);
|
||||
|
||||
common_peg_arena build();
|
||||
};
|
||||
|
||||
// Helper function for building parsers
|
||||
common_peg_arena build_peg_parser(const std::function<common_peg_parser(common_peg_parser_builder & builder)> & fn);
|
||||
483
common/preset.cpp
Normal file
483
common/preset.cpp
Normal file
@@ -0,0 +1,483 @@
|
||||
#include "arg.h"
|
||||
#include "preset.h"
|
||||
#include "peg-parser.h"
|
||||
#include "log.h"
|
||||
#include "download.h"
|
||||
|
||||
#include <fstream>
|
||||
#include <sstream>
|
||||
#include <filesystem>
|
||||
|
||||
static std::string rm_leading_dashes(const std::string & str) {
|
||||
size_t pos = 0;
|
||||
while (pos < str.size() && str[pos] == '-') {
|
||||
++pos;
|
||||
}
|
||||
return str.substr(pos);
|
||||
}
|
||||
|
||||
// only allow a subset of args for remote presets for security reasons
|
||||
// do not add more args unless absolutely necessary
|
||||
// args that output to files are strictly prohibited
|
||||
static std::set<std::string> get_remote_preset_whitelist(const std::map<std::string, common_arg> & key_to_opt) {
|
||||
static const std::set<std::string> allowed_options = {
|
||||
"model-url",
|
||||
"hf-repo",
|
||||
"hf-repo-draft",
|
||||
"hf-repo-v", // vocoder
|
||||
"hf-file-v", // vocoder
|
||||
"mmproj-url",
|
||||
"pooling",
|
||||
"jinja",
|
||||
"batch-size",
|
||||
"ubatch-size",
|
||||
"cache-reuse",
|
||||
"chat-template-kwargs",
|
||||
"mmap",
|
||||
// note: sampling params are automatically allowed by default
|
||||
// negated args will be added automatically if the positive arg is specified above
|
||||
};
|
||||
|
||||
std::set<std::string> allowed_keys;
|
||||
|
||||
for (const auto & it : key_to_opt) {
|
||||
const std::string & key = it.first;
|
||||
const common_arg & opt = it.second;
|
||||
if (allowed_options.find(key) != allowed_options.end() || opt.is_sparam) {
|
||||
allowed_keys.insert(key);
|
||||
// also add variant keys (args without leading dashes and env vars)
|
||||
for (const auto & arg : opt.get_args()) {
|
||||
allowed_keys.insert(rm_leading_dashes(arg));
|
||||
}
|
||||
for (const auto & env : opt.get_env()) {
|
||||
allowed_keys.insert(env);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return allowed_keys;
|
||||
}
|
||||
|
||||
std::vector<std::string> common_preset::to_args(const std::string & bin_path) const {
|
||||
std::vector<std::string> args;
|
||||
|
||||
if (!bin_path.empty()) {
|
||||
args.push_back(bin_path);
|
||||
}
|
||||
|
||||
for (const auto & [opt, value] : options) {
|
||||
if (opt.is_preset_only) {
|
||||
continue; // skip preset-only options (they are not CLI args)
|
||||
}
|
||||
|
||||
// use the last arg as the main arg (i.e. --long-form)
|
||||
args.push_back(opt.args.back());
|
||||
|
||||
// handle value(s)
|
||||
if (opt.value_hint == nullptr && opt.value_hint_2 == nullptr) {
|
||||
// flag option, no value
|
||||
if (common_arg_utils::is_falsey(value)) {
|
||||
// use negative arg if available
|
||||
if (!opt.args_neg.empty()) {
|
||||
args.back() = opt.args_neg.back();
|
||||
} else {
|
||||
// otherwise, skip the flag
|
||||
// TODO: maybe throw an error instead?
|
||||
args.pop_back();
|
||||
}
|
||||
}
|
||||
}
|
||||
if (opt.value_hint != nullptr) {
|
||||
// single value
|
||||
args.push_back(value);
|
||||
}
|
||||
if (opt.value_hint != nullptr && opt.value_hint_2 != nullptr) {
|
||||
throw std::runtime_error(string_format(
|
||||
"common_preset::to_args(): option '%s' has two values, which is not supported yet",
|
||||
opt.args.back()
|
||||
));
|
||||
}
|
||||
}
|
||||
|
||||
return args;
|
||||
}
|
||||
|
||||
std::string common_preset::to_ini() const {
|
||||
std::ostringstream ss;
|
||||
|
||||
ss << "[" << name << "]\n";
|
||||
for (const auto & [opt, value] : options) {
|
||||
auto espaced_value = value;
|
||||
string_replace_all(espaced_value, "\n", "\\\n");
|
||||
ss << rm_leading_dashes(opt.args.back()) << " = ";
|
||||
ss << espaced_value << "\n";
|
||||
}
|
||||
ss << "\n";
|
||||
|
||||
return ss.str();
|
||||
}
|
||||
|
||||
void common_preset::set_option(const common_preset_context & ctx, const std::string & env, const std::string & value) {
|
||||
// try if option exists, update it
|
||||
for (auto & [opt, val] : options) {
|
||||
if (opt.env && env == opt.env) {
|
||||
val = value;
|
||||
return;
|
||||
}
|
||||
}
|
||||
// if option does not exist, we need to add it
|
||||
if (ctx.key_to_opt.find(env) == ctx.key_to_opt.end()) {
|
||||
throw std::runtime_error(string_format(
|
||||
"%s: option with env '%s' not found in ctx_params",
|
||||
__func__, env.c_str()
|
||||
));
|
||||
}
|
||||
options[ctx.key_to_opt.at(env)] = value;
|
||||
}
|
||||
|
||||
void common_preset::unset_option(const std::string & env) {
|
||||
for (auto it = options.begin(); it != options.end(); ) {
|
||||
const common_arg & opt = it->first;
|
||||
if (opt.env && env == opt.env) {
|
||||
it = options.erase(it);
|
||||
return;
|
||||
} else {
|
||||
++it;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
bool common_preset::get_option(const std::string & env, std::string & value) const {
|
||||
for (const auto & [opt, val] : options) {
|
||||
if (opt.env && env == opt.env) {
|
||||
value = val;
|
||||
return true;
|
||||
}
|
||||
}
|
||||
return false;
|
||||
}
|
||||
|
||||
void common_preset::merge(const common_preset & other) {
|
||||
for (const auto & [opt, val] : other.options) {
|
||||
options[opt] = val; // overwrite existing options
|
||||
}
|
||||
}
|
||||
|
||||
void common_preset::apply_to_params(common_params & params) const {
|
||||
for (const auto & [opt, val] : options) {
|
||||
// apply each option to params
|
||||
if (opt.handler_string) {
|
||||
opt.handler_string(params, val);
|
||||
} else if (opt.handler_int) {
|
||||
opt.handler_int(params, std::stoi(val));
|
||||
} else if (opt.handler_bool) {
|
||||
opt.handler_bool(params, common_arg_utils::is_truthy(val));
|
||||
} else if (opt.handler_str_str) {
|
||||
// not supported yet
|
||||
throw std::runtime_error(string_format(
|
||||
"%s: option with two values is not supported yet",
|
||||
__func__
|
||||
));
|
||||
} else if (opt.handler_void) {
|
||||
opt.handler_void(params);
|
||||
} else {
|
||||
GGML_ABORT("unknown handler type");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static std::map<std::string, std::map<std::string, std::string>> parse_ini_from_file(const std::string & path) {
|
||||
std::map<std::string, std::map<std::string, std::string>> parsed;
|
||||
|
||||
if (!std::filesystem::exists(path)) {
|
||||
throw std::runtime_error("preset file does not exist: " + path);
|
||||
}
|
||||
|
||||
std::ifstream file(path);
|
||||
if (!file.good()) {
|
||||
throw std::runtime_error("failed to open server preset file: " + path);
|
||||
}
|
||||
|
||||
std::string contents((std::istreambuf_iterator<char>(file)), std::istreambuf_iterator<char>());
|
||||
|
||||
static const auto parser = build_peg_parser([](auto & p) {
|
||||
// newline ::= "\r\n" / "\n" / "\r"
|
||||
auto newline = p.rule("newline", p.literal("\r\n") | p.literal("\n") | p.literal("\r"));
|
||||
|
||||
// ws ::= [ \t]*
|
||||
auto ws = p.rule("ws", p.chars("[ \t]", 0, -1));
|
||||
|
||||
// comment ::= [;#] (!newline .)*
|
||||
auto comment = p.rule("comment", p.chars("[;#]", 1, 1) + p.zero_or_more(p.negate(newline) + p.any()));
|
||||
|
||||
// eol ::= ws comment? (newline / EOF)
|
||||
auto eol = p.rule("eol", ws + p.optional(comment) + (newline | p.end()));
|
||||
|
||||
// ident ::= [a-zA-Z_] [a-zA-Z0-9_.-]*
|
||||
auto ident = p.rule("ident", p.chars("[a-zA-Z_]", 1, 1) + p.chars("[a-zA-Z0-9_.-]", 0, -1));
|
||||
|
||||
// value ::= (!eol-start .)*
|
||||
auto eol_start = p.rule("eol-start", ws + (p.chars("[;#]", 1, 1) | newline | p.end()));
|
||||
auto value = p.rule("value", p.zero_or_more(p.negate(eol_start) + p.any()));
|
||||
|
||||
// header-line ::= "[" ws ident ws "]" eol
|
||||
auto header_line = p.rule("header-line", "[" + ws + p.tag("section-name", p.chars("[^]]")) + ws + "]" + eol);
|
||||
|
||||
// kv-line ::= ident ws "=" ws value eol
|
||||
auto kv_line = p.rule("kv-line", p.tag("key", ident) + ws + "=" + ws + p.tag("value", value) + eol);
|
||||
|
||||
// comment-line ::= ws comment (newline / EOF)
|
||||
auto comment_line = p.rule("comment-line", ws + comment + (newline | p.end()));
|
||||
|
||||
// blank-line ::= ws (newline / EOF)
|
||||
auto blank_line = p.rule("blank-line", ws + (newline | p.end()));
|
||||
|
||||
// line ::= header-line / kv-line / comment-line / blank-line
|
||||
auto line = p.rule("line", header_line | kv_line | comment_line | blank_line);
|
||||
|
||||
// ini ::= line* EOF
|
||||
auto ini = p.rule("ini", p.zero_or_more(line) + p.end());
|
||||
|
||||
return ini;
|
||||
});
|
||||
|
||||
common_peg_parse_context ctx(contents);
|
||||
const auto result = parser.parse(ctx);
|
||||
if (!result.success()) {
|
||||
throw std::runtime_error("failed to parse server config file: " + path);
|
||||
}
|
||||
|
||||
std::string current_section = COMMON_PRESET_DEFAULT_NAME;
|
||||
std::string current_key;
|
||||
|
||||
ctx.ast.visit(result, [&](const auto & node) {
|
||||
if (node.tag == "section-name") {
|
||||
const std::string section = std::string(node.text);
|
||||
current_section = section;
|
||||
parsed[current_section] = {};
|
||||
} else if (node.tag == "key") {
|
||||
const std::string key = std::string(node.text);
|
||||
current_key = key;
|
||||
} else if (node.tag == "value" && !current_key.empty() && !current_section.empty()) {
|
||||
parsed[current_section][current_key] = std::string(node.text);
|
||||
current_key.clear();
|
||||
}
|
||||
});
|
||||
|
||||
return parsed;
|
||||
}
|
||||
|
||||
static std::map<std::string, common_arg> get_map_key_opt(common_params_context & ctx_params) {
|
||||
std::map<std::string, common_arg> mapping;
|
||||
for (const auto & opt : ctx_params.options) {
|
||||
for (const auto & env : opt.get_env()) {
|
||||
mapping[env] = opt;
|
||||
}
|
||||
for (const auto & arg : opt.get_args()) {
|
||||
mapping[rm_leading_dashes(arg)] = opt;
|
||||
}
|
||||
}
|
||||
return mapping;
|
||||
}
|
||||
|
||||
static bool is_bool_arg(const common_arg & arg) {
|
||||
return !arg.args_neg.empty();
|
||||
}
|
||||
|
||||
static std::string parse_bool_arg(const common_arg & arg, const std::string & key, const std::string & value) {
|
||||
// if this is a negated arg, we need to reverse the value
|
||||
for (const auto & neg_arg : arg.args_neg) {
|
||||
if (rm_leading_dashes(neg_arg) == key) {
|
||||
return common_arg_utils::is_truthy(value) ? "false" : "true";
|
||||
}
|
||||
}
|
||||
// otherwise, not negated
|
||||
return value;
|
||||
}
|
||||
|
||||
common_preset_context::common_preset_context(llama_example ex, bool only_remote_allowed)
|
||||
: ctx_params(common_params_parser_init(default_params, ex)) {
|
||||
common_params_add_preset_options(ctx_params.options);
|
||||
key_to_opt = get_map_key_opt(ctx_params);
|
||||
|
||||
// setup allowed keys if only_remote_allowed is true
|
||||
if (only_remote_allowed) {
|
||||
filter_allowed_keys = true;
|
||||
allowed_keys = get_remote_preset_whitelist(key_to_opt);
|
||||
}
|
||||
}
|
||||
|
||||
common_presets common_preset_context::load_from_ini(const std::string & path, common_preset & global) const {
|
||||
common_presets out;
|
||||
auto ini_data = parse_ini_from_file(path);
|
||||
|
||||
for (auto section : ini_data) {
|
||||
common_preset preset;
|
||||
if (section.first.empty()) {
|
||||
preset.name = COMMON_PRESET_DEFAULT_NAME;
|
||||
} else {
|
||||
preset.name = section.first;
|
||||
}
|
||||
LOG_DBG("loading preset: %s\n", preset.name.c_str());
|
||||
for (const auto & [key, value] : section.second) {
|
||||
if (key == "version") {
|
||||
// skip version key (reserved for future use)
|
||||
continue;
|
||||
}
|
||||
|
||||
LOG_DBG("option: %s = %s\n", key.c_str(), value.c_str());
|
||||
if (filter_allowed_keys && allowed_keys.find(key) == allowed_keys.end()) {
|
||||
throw std::runtime_error(string_format(
|
||||
"option '%s' is not allowed in remote presets",
|
||||
key.c_str()
|
||||
));
|
||||
}
|
||||
if (key_to_opt.find(key) != key_to_opt.end()) {
|
||||
const auto & opt = key_to_opt.at(key);
|
||||
if (is_bool_arg(opt)) {
|
||||
preset.options[opt] = parse_bool_arg(opt, key, value);
|
||||
} else {
|
||||
preset.options[opt] = value;
|
||||
}
|
||||
LOG_DBG("accepted option: %s = %s\n", key.c_str(), preset.options[opt].c_str());
|
||||
} else {
|
||||
throw std::runtime_error(string_format(
|
||||
"option '%s' not recognized in preset '%s'",
|
||||
key.c_str(), preset.name.c_str()
|
||||
));
|
||||
}
|
||||
}
|
||||
|
||||
if (preset.name == "*") {
|
||||
// handle global preset
|
||||
global = preset;
|
||||
} else {
|
||||
out[preset.name] = preset;
|
||||
}
|
||||
}
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
common_presets common_preset_context::load_from_cache() const {
|
||||
common_presets out;
|
||||
|
||||
auto cached_models = common_list_cached_models();
|
||||
for (const auto & model : cached_models) {
|
||||
common_preset preset;
|
||||
preset.name = model.to_string();
|
||||
preset.set_option(*this, "LLAMA_ARG_HF_REPO", model.to_string());
|
||||
out[preset.name] = preset;
|
||||
}
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
struct local_model {
|
||||
std::string name;
|
||||
std::string path;
|
||||
std::string path_mmproj;
|
||||
};
|
||||
|
||||
common_presets common_preset_context::load_from_models_dir(const std::string & models_dir) const {
|
||||
if (!std::filesystem::exists(models_dir) || !std::filesystem::is_directory(models_dir)) {
|
||||
throw std::runtime_error(string_format("error: '%s' does not exist or is not a directory\n", models_dir.c_str()));
|
||||
}
|
||||
|
||||
std::vector<local_model> models;
|
||||
auto scan_subdir = [&models](const std::string & subdir_path, const std::string & name) {
|
||||
auto files = fs_list(subdir_path, false);
|
||||
common_file_info model_file;
|
||||
common_file_info first_shard_file;
|
||||
common_file_info mmproj_file;
|
||||
for (const auto & file : files) {
|
||||
if (string_ends_with(file.name, ".gguf")) {
|
||||
if (file.name.find("mmproj") != std::string::npos) {
|
||||
mmproj_file = file;
|
||||
} else if (file.name.find("-00001-of-") != std::string::npos) {
|
||||
first_shard_file = file;
|
||||
} else {
|
||||
model_file = file;
|
||||
}
|
||||
}
|
||||
}
|
||||
// single file model
|
||||
local_model model{
|
||||
/* name */ name,
|
||||
/* path */ first_shard_file.path.empty() ? model_file.path : first_shard_file.path,
|
||||
/* path_mmproj */ mmproj_file.path // can be empty
|
||||
};
|
||||
if (!model.path.empty()) {
|
||||
models.push_back(model);
|
||||
}
|
||||
};
|
||||
|
||||
auto files = fs_list(models_dir, true);
|
||||
for (const auto & file : files) {
|
||||
if (file.is_dir) {
|
||||
scan_subdir(file.path, file.name);
|
||||
} else if (string_ends_with(file.name, ".gguf")) {
|
||||
// single file model
|
||||
std::string name = file.name;
|
||||
string_replace_all(name, ".gguf", "");
|
||||
local_model model{
|
||||
/* name */ name,
|
||||
/* path */ file.path,
|
||||
/* path_mmproj */ ""
|
||||
};
|
||||
models.push_back(model);
|
||||
}
|
||||
}
|
||||
|
||||
// convert local models to presets
|
||||
common_presets out;
|
||||
for (const auto & model : models) {
|
||||
common_preset preset;
|
||||
preset.name = model.name;
|
||||
preset.set_option(*this, "LLAMA_ARG_MODEL", model.path);
|
||||
if (!model.path_mmproj.empty()) {
|
||||
preset.set_option(*this, "LLAMA_ARG_MMPROJ", model.path_mmproj);
|
||||
}
|
||||
out[preset.name] = preset;
|
||||
}
|
||||
|
||||
return out;
|
||||
}
|
||||
|
||||
common_preset common_preset_context::load_from_args(int argc, char ** argv) const {
|
||||
common_preset preset;
|
||||
preset.name = COMMON_PRESET_DEFAULT_NAME;
|
||||
|
||||
bool ok = common_params_to_map(argc, argv, ctx_params.ex, preset.options);
|
||||
if (!ok) {
|
||||
throw std::runtime_error("failed to parse CLI arguments into preset");
|
||||
}
|
||||
|
||||
return preset;
|
||||
}
|
||||
|
||||
common_presets common_preset_context::cascade(const common_presets & base, const common_presets & added) const {
|
||||
common_presets out = base; // copy
|
||||
for (const auto & [name, preset_added] : added) {
|
||||
if (out.find(name) != out.end()) {
|
||||
// if exists, merge
|
||||
common_preset & target = out[name];
|
||||
target.merge(preset_added);
|
||||
} else {
|
||||
// otherwise, add directly
|
||||
out[name] = preset_added;
|
||||
}
|
||||
}
|
||||
return out;
|
||||
}
|
||||
|
||||
common_presets common_preset_context::cascade(const common_preset & base, const common_presets & presets) const {
|
||||
common_presets out;
|
||||
for (const auto & [name, preset] : presets) {
|
||||
common_preset tmp = base; // copy
|
||||
tmp.name = name;
|
||||
tmp.merge(preset);
|
||||
out[name] = std::move(tmp);
|
||||
}
|
||||
return out;
|
||||
}
|
||||
83
common/preset.h
Normal file
83
common/preset.h
Normal file
@@ -0,0 +1,83 @@
|
||||
#pragma once
|
||||
|
||||
#include "common.h"
|
||||
#include "arg.h"
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include <map>
|
||||
#include <set>
|
||||
|
||||
//
|
||||
// INI preset parser and writer
|
||||
//
|
||||
|
||||
constexpr const char * COMMON_PRESET_DEFAULT_NAME = "default";
|
||||
|
||||
struct common_preset_context;
|
||||
|
||||
struct common_preset {
|
||||
std::string name;
|
||||
|
||||
// options are stored as common_arg to string mapping, representing CLI arg and its value
|
||||
std::map<common_arg, std::string> options;
|
||||
|
||||
// convert preset to CLI argument list
|
||||
std::vector<std::string> to_args(const std::string & bin_path = "") const;
|
||||
|
||||
// convert preset to INI format string
|
||||
std::string to_ini() const;
|
||||
|
||||
// TODO: maybe implement to_env() if needed
|
||||
|
||||
// modify preset options where argument is identified by its env variable
|
||||
void set_option(const common_preset_context & ctx, const std::string & env, const std::string & value);
|
||||
|
||||
// unset option by its env variable
|
||||
void unset_option(const std::string & env);
|
||||
|
||||
// get option value by its env variable, return false if not found
|
||||
bool get_option(const std::string & env, std::string & value) const;
|
||||
|
||||
// merge another preset into this one, overwriting existing options
|
||||
void merge(const common_preset & other);
|
||||
|
||||
// apply preset options to common_params
|
||||
void apply_to_params(common_params & params) const;
|
||||
};
|
||||
|
||||
// interface for multiple presets in one file
|
||||
using common_presets = std::map<std::string, common_preset>;
|
||||
|
||||
// context for loading and editing presets
|
||||
struct common_preset_context {
|
||||
common_params default_params; // unused for now
|
||||
common_params_context ctx_params;
|
||||
std::map<std::string, common_arg> key_to_opt;
|
||||
|
||||
bool filter_allowed_keys = false;
|
||||
std::set<std::string> allowed_keys;
|
||||
|
||||
// if only_remote_allowed is true, only accept whitelisted keys
|
||||
common_preset_context(llama_example ex, bool only_remote_allowed = false);
|
||||
|
||||
// load presets from INI file
|
||||
common_presets load_from_ini(const std::string & path, common_preset & global) const;
|
||||
|
||||
// generate presets from cached models
|
||||
common_presets load_from_cache() const;
|
||||
|
||||
// generate presets from local models directory
|
||||
// for the directory structure, see "Using multiple models" in server/README.md
|
||||
common_presets load_from_models_dir(const std::string & models_dir) const;
|
||||
|
||||
// generate one preset from CLI arguments
|
||||
common_preset load_from_args(int argc, char ** argv) const;
|
||||
|
||||
// cascade multiple presets if exist on both: base < added
|
||||
// if preset does not exist in base, it will be added without modification
|
||||
common_presets cascade(const common_presets & base, const common_presets & added) const;
|
||||
|
||||
// apply presets over a base preset (same idea as CSS cascading)
|
||||
common_presets cascade(const common_preset & base, const common_presets & presets) const;
|
||||
};
|
||||
204
common/regex-partial.cpp
Normal file
204
common/regex-partial.cpp
Normal file
@@ -0,0 +1,204 @@
|
||||
#include "regex-partial.h"
|
||||
#include "common.h"
|
||||
#include <functional>
|
||||
#include <optional>
|
||||
|
||||
common_regex::common_regex(const std::string & pattern) :
|
||||
pattern(pattern),
|
||||
rx(pattern),
|
||||
rx_reversed_partial(regex_to_reversed_partial_regex(pattern)) {}
|
||||
|
||||
common_regex_match common_regex::search(const std::string & input, size_t pos, bool as_match) const {
|
||||
std::smatch match;
|
||||
if (pos > input.size()) {
|
||||
throw std::runtime_error("Position out of bounds");
|
||||
}
|
||||
auto start = input.begin() + pos;
|
||||
auto found = as_match
|
||||
? std::regex_match(start, input.end(), match, rx)
|
||||
: std::regex_search(start, input.end(), match, rx);
|
||||
if (found) {
|
||||
common_regex_match res;
|
||||
res.type = COMMON_REGEX_MATCH_TYPE_FULL;
|
||||
for (size_t i = 0; i < match.size(); ++i) {
|
||||
auto begin = pos + match.position(i);
|
||||
res.groups.emplace_back(begin, begin + match.length(i));
|
||||
}
|
||||
return res;
|
||||
}
|
||||
std::match_results<std::string::const_reverse_iterator> srmatch;
|
||||
if (std::regex_search(input.rbegin(), input.rend() - pos, srmatch, rx_reversed_partial, std::regex_constants::match_continuous)) {
|
||||
auto group = srmatch[1].str();
|
||||
if (group.length() != 0) {
|
||||
auto it = srmatch[1].second.base();
|
||||
// auto position = static_cast<size_t>(std::distance(input.begin(), it));
|
||||
if ((!as_match) || it == input.begin()) {
|
||||
common_regex_match res;
|
||||
res.type = COMMON_REGEX_MATCH_TYPE_PARTIAL;
|
||||
const size_t begin = std::distance(input.begin(), it);
|
||||
const size_t end = input.size();
|
||||
if (begin == std::string::npos || end == std::string::npos || begin > end) {
|
||||
throw std::runtime_error("Invalid range");
|
||||
}
|
||||
res.groups.push_back({begin, end});
|
||||
return res;
|
||||
}
|
||||
}
|
||||
}
|
||||
return {};
|
||||
}
|
||||
|
||||
/*
|
||||
Transforms a regex pattern to a partial match pattern that operates on a reversed input string to find partial final matches of the original pattern.
|
||||
|
||||
Ideally we'd like to use boost::match_partial (https://beta.boost.org/doc/libs/1_59_0/libs/regex/doc/html/boost_regex/partial_matches.html)
|
||||
to see if a string ends with a partial regex match, but but it's not in std::regex yet.
|
||||
Instead, we'll the regex into a partial match regex operating as a full match on the reverse iterators of the input.
|
||||
|
||||
- /abcd/ -> ^(dcba|cba|ba|a) -> ^((?:(?:(?:(?:d)?c)?b)?a)
|
||||
- /a|b/ -> ^(a|b)
|
||||
- /a*?/ -> error, could match ""
|
||||
- /a*b/ -> ^((?:b)?a*+) (final repetitions become eager)
|
||||
- /.*?ab/ -> ^((?:b)?a) (omit .*)
|
||||
- /a.*?b/ -> ^((?:b)?.*?a) (keep reluctant matches)
|
||||
- /a(bc)d/ -> ^((?:(?:d)?(?:(?:c)?b))?a)
|
||||
- /a(bc|de)/ -> ^((?:(?:(?:e)?d)?|(?:(?:c)?b)?)?a)
|
||||
- /ab{2,4}c/ -> ^cbbb?b?a -> ^((?:(?:(?:(?:(?:c)?b)?b)?b?)?b?)?a)
|
||||
|
||||
The regex will match a reversed string fully, and the end of the first (And only) capturing group will indicate the reversed start of the original partial pattern.
|
||||
All other groups are turned into non-capturing groups, and reluctant quantifiers are ignored.
|
||||
*/
|
||||
std::string regex_to_reversed_partial_regex(const std::string & pattern) {
|
||||
auto it = pattern.begin();
|
||||
const auto end = pattern.end();
|
||||
|
||||
std::function<std::string()> process = [&]() {
|
||||
std::vector<std::vector<std::string>> alternatives(1);
|
||||
std::vector<std::string> * sequence = &alternatives.back();
|
||||
|
||||
while (it != end) {
|
||||
if (*it == '[') {
|
||||
auto start = it;
|
||||
++it;
|
||||
while (it != end) {
|
||||
if ((*it == '\\') && (++it != end)) {
|
||||
++it;
|
||||
} else if ((it != end) && (*it == ']')) {
|
||||
break;
|
||||
} else {
|
||||
++it;
|
||||
}
|
||||
}
|
||||
if (it == end) {
|
||||
throw std::runtime_error("Unmatched '[' in pattern");
|
||||
}
|
||||
++it;
|
||||
sequence->push_back(std::string(start, it));
|
||||
} else if (*it == '*' || *it == '?' || *it == '+') {
|
||||
if (sequence->empty()) {
|
||||
throw std::runtime_error("Quantifier without preceding element");
|
||||
}
|
||||
sequence->back() += *it;
|
||||
auto is_star = *it == '*';
|
||||
++it;
|
||||
if (is_star) {
|
||||
if (*it == '?') {
|
||||
++it;
|
||||
}
|
||||
}
|
||||
} else if (*it == '{') {
|
||||
if (sequence->empty()) {
|
||||
throw std::runtime_error("Repetition without preceding element");
|
||||
}
|
||||
++it;
|
||||
auto start = it;
|
||||
while (it != end && *it != '}') {
|
||||
++it;
|
||||
}
|
||||
if (it == end) {
|
||||
throw std::runtime_error("Unmatched '{' in pattern");
|
||||
}
|
||||
auto parts = string_split(std::string(start, it), ",");
|
||||
++it;
|
||||
if (parts.size() > 2) {
|
||||
throw std::runtime_error("Invalid repetition range in pattern");
|
||||
}
|
||||
|
||||
auto parseOptInt = [&](const std::string & s, const std::optional<int> & def = std::nullopt) -> std::optional<int> {
|
||||
if (s.empty()) {
|
||||
return def;
|
||||
}
|
||||
return std::stoi(s);
|
||||
};
|
||||
auto min = parseOptInt(parts[0], 0);
|
||||
auto max = parts.size() == 1 ? min : parseOptInt(parts[1]);
|
||||
if (min && max && *max < *min) {
|
||||
throw std::runtime_error("Invalid repetition range in pattern");
|
||||
}
|
||||
// Brutal but... let's repeat at least min times, then ? for the delta between min & max (or * for unbounded)
|
||||
auto part = sequence->back();
|
||||
sequence->pop_back();
|
||||
for (int i = 0; i < *min; i++) {
|
||||
sequence->push_back(part);
|
||||
}
|
||||
if (max) {
|
||||
for (int i = *min; i < *max; i++) {
|
||||
sequence->push_back(part + "?");
|
||||
}
|
||||
} else {
|
||||
sequence->push_back(part + "*");
|
||||
}
|
||||
} else if (*it == '(') {
|
||||
++it;
|
||||
if (it != end && *it == '?' && (it + 1 != end) && *(it + 1) == ':') {
|
||||
it += 2;
|
||||
}
|
||||
auto sub = process();
|
||||
if (*it != ')') {
|
||||
throw std::runtime_error("Unmatched '(' in pattern");
|
||||
}
|
||||
++it;
|
||||
auto & part = sequence->emplace_back("(?:");
|
||||
part += sub;
|
||||
part += ")";
|
||||
} else if (*it == ')') {
|
||||
break;
|
||||
} else if (*it == '|') {
|
||||
++it;
|
||||
alternatives.emplace_back();
|
||||
sequence = &alternatives.back();
|
||||
} else if (*it == '\\' && (++it != end)) {
|
||||
auto str = std::string("\\") + *it;
|
||||
sequence->push_back(str);
|
||||
++it;
|
||||
} else if (it != end) {
|
||||
sequence->push_back(std::string(1, *it));
|
||||
++it;
|
||||
}
|
||||
}
|
||||
|
||||
// /abcd/ -> ^(dcba|cba|ba|a) -> ^((?:(?:(?:d)?c)?b)?a)
|
||||
// if n(=4) parts, opening n-1(=3) non-capturing groups after the 1 capturing group
|
||||
// We'll do the outermost capturing group and final .* in the enclosing function.
|
||||
std::vector<std::string> res_alts;
|
||||
for (const auto & parts : alternatives) {
|
||||
auto & res = res_alts.emplace_back();
|
||||
for (size_t i = 0; i < parts.size() - 1; i++) {
|
||||
res += "(?:";
|
||||
}
|
||||
for (auto it = parts.rbegin(); it != parts.rend(); ++it) {
|
||||
res += *it;
|
||||
if (it != parts.rend() - 1) {
|
||||
res += ")?";
|
||||
}
|
||||
}
|
||||
}
|
||||
return string_join(res_alts, "|");
|
||||
};
|
||||
auto res = process();
|
||||
if (it != end) {
|
||||
throw std::runtime_error("Unmatched '(' in pattern");
|
||||
}
|
||||
|
||||
return "^(" + res + ")";
|
||||
}
|
||||
56
common/regex-partial.h
Normal file
56
common/regex-partial.h
Normal file
@@ -0,0 +1,56 @@
|
||||
#pragma once
|
||||
|
||||
#include <regex>
|
||||
#include <string>
|
||||
|
||||
enum common_regex_match_type {
|
||||
COMMON_REGEX_MATCH_TYPE_NONE,
|
||||
COMMON_REGEX_MATCH_TYPE_PARTIAL,
|
||||
COMMON_REGEX_MATCH_TYPE_FULL,
|
||||
};
|
||||
|
||||
struct common_string_range {
|
||||
size_t begin;
|
||||
size_t end;
|
||||
common_string_range(size_t begin, size_t end) : begin(begin), end(end) {
|
||||
if (begin > end) {
|
||||
throw std::runtime_error("Invalid range");
|
||||
}
|
||||
}
|
||||
// prevent default ctor
|
||||
common_string_range() = delete;
|
||||
bool empty() const {
|
||||
return begin == end;
|
||||
}
|
||||
bool operator==(const common_string_range & other) const {
|
||||
return begin == other.begin && end == other.end;
|
||||
}
|
||||
};
|
||||
|
||||
struct common_regex_match {
|
||||
common_regex_match_type type = COMMON_REGEX_MATCH_TYPE_NONE;
|
||||
std::vector<common_string_range> groups;
|
||||
|
||||
bool operator==(const common_regex_match & other) const {
|
||||
return type == other.type && groups == other.groups;
|
||||
}
|
||||
bool operator!=(const common_regex_match & other) const {
|
||||
return !(*this == other);
|
||||
}
|
||||
};
|
||||
|
||||
class common_regex {
|
||||
std::string pattern;
|
||||
std::regex rx;
|
||||
std::regex rx_reversed_partial;
|
||||
|
||||
public:
|
||||
explicit common_regex(const std::string & pattern);
|
||||
|
||||
common_regex_match search(const std::string & input, size_t pos, bool as_match = false) const;
|
||||
|
||||
const std::string & str() const { return pattern; }
|
||||
};
|
||||
|
||||
// For testing only (pretty print of failures).
|
||||
std::string regex_to_reversed_partial_regex(const std::string & pattern);
|
||||
745
common/sampling.cpp
Normal file
745
common/sampling.cpp
Normal file
@@ -0,0 +1,745 @@
|
||||
#include "sampling.h"
|
||||
|
||||
#include "common.h"
|
||||
#include "log.h"
|
||||
|
||||
#include <algorithm>
|
||||
#include <cmath>
|
||||
#include <cstring>
|
||||
#include <unordered_map>
|
||||
|
||||
// the ring buffer works similarly to std::deque, but with a fixed capacity
|
||||
// TODO: deduplicate with llama-impl.h
|
||||
template<typename T>
|
||||
struct ring_buffer {
|
||||
ring_buffer(size_t cap) : capacity(cap), data(cap) {}
|
||||
|
||||
T & front() {
|
||||
if (sz == 0) {
|
||||
throw std::runtime_error("ring buffer is empty");
|
||||
}
|
||||
return data[first];
|
||||
}
|
||||
|
||||
const T & front() const {
|
||||
if (sz == 0) {
|
||||
throw std::runtime_error("ring buffer is empty");
|
||||
}
|
||||
return data[first];
|
||||
}
|
||||
|
||||
T & back() {
|
||||
if (sz == 0) {
|
||||
throw std::runtime_error("ring buffer is empty");
|
||||
}
|
||||
return data[pos];
|
||||
}
|
||||
|
||||
const T & back() const {
|
||||
if (sz == 0) {
|
||||
throw std::runtime_error("ring buffer is empty");
|
||||
}
|
||||
return data[pos];
|
||||
}
|
||||
|
||||
void push_back(const T & value) {
|
||||
if (sz == capacity) {
|
||||
// advance the start when buffer is full
|
||||
first = (first + 1) % capacity;
|
||||
} else {
|
||||
sz++;
|
||||
}
|
||||
data[pos] = value;
|
||||
pos = (pos + 1) % capacity;
|
||||
}
|
||||
|
||||
T pop_front() {
|
||||
if (sz == 0) {
|
||||
throw std::runtime_error("ring buffer is empty");
|
||||
}
|
||||
T value = data[first];
|
||||
first = (first + 1) % capacity;
|
||||
sz--;
|
||||
return value;
|
||||
}
|
||||
|
||||
const T & rat(size_t i) const {
|
||||
if (i >= sz) {
|
||||
throw std::runtime_error("ring buffer: index out of bounds");
|
||||
}
|
||||
return data[(first + sz - i - 1) % capacity];
|
||||
}
|
||||
|
||||
std::vector<T> to_vector() const {
|
||||
std::vector<T> result;
|
||||
result.reserve(sz);
|
||||
for (size_t i = 0; i < sz; i++) {
|
||||
result.push_back(data[(first + i) % capacity]);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
void clear() {
|
||||
// here only reset the status of the buffer
|
||||
sz = 0;
|
||||
first = 0;
|
||||
pos = 0;
|
||||
}
|
||||
|
||||
bool empty() const {
|
||||
return sz == 0;
|
||||
}
|
||||
|
||||
size_t size() const {
|
||||
return sz;
|
||||
}
|
||||
|
||||
size_t capacity = 0;
|
||||
size_t sz = 0;
|
||||
size_t first = 0;
|
||||
size_t pos = 0;
|
||||
std::vector<T> data;
|
||||
};
|
||||
|
||||
struct common_sampler {
|
||||
common_params_sampling params;
|
||||
|
||||
struct llama_sampler * grmr;
|
||||
struct llama_sampler * chain;
|
||||
|
||||
ring_buffer<llama_token> prev;
|
||||
|
||||
std::vector<llama_token_data> cur;
|
||||
|
||||
llama_token_data_array cur_p;
|
||||
|
||||
void reset() {
|
||||
prev.clear();
|
||||
|
||||
llama_sampler_reset(chain);
|
||||
}
|
||||
|
||||
void set_logits(struct llama_context * ctx, int idx) {
|
||||
const float * sampled_probs = llama_get_sampled_probs_ith (ctx, idx);
|
||||
const float * sampled_logits = llama_get_sampled_logits_ith (ctx, idx);
|
||||
const llama_token * sampled_ids = llama_get_sampled_candidates_ith(ctx, idx);
|
||||
|
||||
const llama_model * model = llama_get_model(ctx);
|
||||
const llama_vocab * vocab = llama_model_get_vocab(model);
|
||||
|
||||
const int n_vocab = llama_vocab_n_tokens(vocab);
|
||||
|
||||
if (sampled_probs) {
|
||||
const uint32_t sampled_probs_count = llama_get_sampled_probs_count_ith(ctx, idx);
|
||||
cur.resize(sampled_probs_count);
|
||||
for (uint32_t i = 0; i < sampled_probs_count; ++i) {
|
||||
cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], sampled_probs[i]};
|
||||
}
|
||||
} else if (sampled_logits) {
|
||||
const uint32_t sampled_logits_count = llama_get_sampled_logits_count_ith(ctx, idx);
|
||||
cur.resize(sampled_logits_count);
|
||||
for (uint32_t i = 0; i < sampled_logits_count; i++) {
|
||||
cur[i] = llama_token_data{sampled_ids[i], sampled_logits[i], 0.0f};
|
||||
}
|
||||
} else {
|
||||
const auto * logits = llama_get_logits_ith(ctx, idx);
|
||||
GGML_ASSERT(logits != nullptr);
|
||||
cur.resize(n_vocab);
|
||||
for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
|
||||
cur[token_id] = llama_token_data{token_id, logits[token_id], 0.0f};
|
||||
}
|
||||
}
|
||||
|
||||
cur_p = { cur.data(), cur.size(), -1, false };
|
||||
}
|
||||
|
||||
common_time_meas tm() {
|
||||
return common_time_meas(t_total_us, params.no_perf);
|
||||
}
|
||||
|
||||
mutable int64_t t_total_us = 0;
|
||||
};
|
||||
|
||||
std::string common_params_sampling::print() const {
|
||||
char result[1024];
|
||||
|
||||
snprintf(result, sizeof(result),
|
||||
"\trepeat_last_n = %d, repeat_penalty = %.3f, frequency_penalty = %.3f, presence_penalty = %.3f\n"
|
||||
"\tdry_multiplier = %.3f, dry_base = %.3f, dry_allowed_length = %d, dry_penalty_last_n = %d\n"
|
||||
"\ttop_k = %d, top_p = %.3f, min_p = %.3f, xtc_probability = %.3f, xtc_threshold = %.3f, typical_p = %.3f, top_n_sigma = %.3f, temp = %.3f\n"
|
||||
"\tmirostat = %d, mirostat_lr = %.3f, mirostat_ent = %.3f, adaptive_target = %.3f, adaptive_decay = %.3f",
|
||||
penalty_last_n, penalty_repeat, penalty_freq, penalty_present,
|
||||
dry_multiplier, dry_base, dry_allowed_length, dry_penalty_last_n,
|
||||
top_k, top_p, min_p, xtc_probability, xtc_threshold, typ_p, top_n_sigma, temp,
|
||||
mirostat, mirostat_eta, mirostat_tau, adaptive_target, adaptive_decay);
|
||||
|
||||
return std::string(result);
|
||||
}
|
||||
|
||||
struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params) {
|
||||
const llama_vocab * vocab = llama_model_get_vocab(model);
|
||||
|
||||
llama_sampler_chain_params lparams = llama_sampler_chain_default_params();
|
||||
|
||||
lparams.no_perf = params.no_perf;
|
||||
|
||||
llama_sampler * grmr = nullptr;
|
||||
llama_sampler * chain = llama_sampler_chain_init(lparams);
|
||||
|
||||
std::vector<llama_sampler *> samplers;
|
||||
|
||||
if (params.grammar.compare(0, 11, "%llguidance") == 0) {
|
||||
#ifdef LLAMA_USE_LLGUIDANCE
|
||||
grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str());
|
||||
#else
|
||||
GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled");
|
||||
#endif // LLAMA_USE_LLGUIDANCE
|
||||
} else {
|
||||
std::vector<std::string> trigger_patterns;
|
||||
std::vector<llama_token> trigger_tokens;
|
||||
for (const auto & trigger : params.grammar_triggers) {
|
||||
switch (trigger.type) {
|
||||
case COMMON_GRAMMAR_TRIGGER_TYPE_WORD:
|
||||
{
|
||||
const auto & word = trigger.value;
|
||||
trigger_patterns.push_back(regex_escape(word));
|
||||
break;
|
||||
}
|
||||
case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN:
|
||||
{
|
||||
trigger_patterns.push_back(trigger.value);
|
||||
break;
|
||||
}
|
||||
case COMMON_GRAMMAR_TRIGGER_TYPE_PATTERN_FULL:
|
||||
{
|
||||
const auto & pattern = trigger.value;
|
||||
std::string anchored = "^$";
|
||||
if (!pattern.empty()) {
|
||||
anchored = (pattern.front() != '^' ? "^" : "")
|
||||
+ pattern
|
||||
+ (pattern.back() != '$' ? "$" : "");
|
||||
}
|
||||
trigger_patterns.push_back(anchored);
|
||||
break;
|
||||
}
|
||||
case COMMON_GRAMMAR_TRIGGER_TYPE_TOKEN:
|
||||
{
|
||||
const auto token = trigger.token;
|
||||
trigger_tokens.push_back(token);
|
||||
break;
|
||||
}
|
||||
default:
|
||||
GGML_ASSERT(false && "unknown trigger type");
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<const char *> trigger_patterns_c;
|
||||
trigger_patterns_c.reserve(trigger_patterns.size());
|
||||
for (const auto & regex : trigger_patterns) {
|
||||
trigger_patterns_c.push_back(regex.c_str());
|
||||
}
|
||||
|
||||
if (!params.grammar.empty()) {
|
||||
if (params.grammar_lazy) {
|
||||
grmr = llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root",
|
||||
trigger_patterns_c.data(), trigger_patterns_c.size(),
|
||||
trigger_tokens.data(), trigger_tokens.size());
|
||||
} else {
|
||||
grmr = llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (params.has_logit_bias()) {
|
||||
samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data()));
|
||||
}
|
||||
|
||||
if (params.mirostat == 0) {
|
||||
|
||||
bool use_adaptive_p = false; // see below
|
||||
|
||||
for (const auto & cnstr : params.samplers) {
|
||||
switch (cnstr) {
|
||||
case COMMON_SAMPLER_TYPE_DRY:
|
||||
{
|
||||
std::vector<const char *> c_breakers;
|
||||
c_breakers.reserve(params.dry_sequence_breakers.size());
|
||||
for (const auto & str : params.dry_sequence_breakers) {
|
||||
c_breakers.push_back(str.c_str());
|
||||
}
|
||||
samplers.push_back(llama_sampler_init_dry(vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size()));
|
||||
}
|
||||
break;
|
||||
case COMMON_SAMPLER_TYPE_TOP_K:
|
||||
samplers.push_back(llama_sampler_init_top_k(params.top_k));
|
||||
break;
|
||||
case COMMON_SAMPLER_TYPE_TOP_P:
|
||||
samplers.push_back(llama_sampler_init_top_p(params.top_p, params.min_keep));
|
||||
break;
|
||||
case COMMON_SAMPLER_TYPE_TOP_N_SIGMA:
|
||||
samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma));
|
||||
break;
|
||||
case COMMON_SAMPLER_TYPE_MIN_P:
|
||||
samplers.push_back(llama_sampler_init_min_p(params.min_p, params.min_keep));
|
||||
break;
|
||||
case COMMON_SAMPLER_TYPE_XTC:
|
||||
samplers.push_back(llama_sampler_init_xtc(params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed));
|
||||
break;
|
||||
case COMMON_SAMPLER_TYPE_TYPICAL_P:
|
||||
samplers.push_back(llama_sampler_init_typical(params.typ_p, params.min_keep));
|
||||
break;
|
||||
case COMMON_SAMPLER_TYPE_TEMPERATURE:
|
||||
samplers.push_back(llama_sampler_init_temp_ext(params.temp, params.dynatemp_range, params.dynatemp_exponent));
|
||||
break;
|
||||
case COMMON_SAMPLER_TYPE_INFILL:
|
||||
samplers.push_back(llama_sampler_init_infill(vocab));
|
||||
break;
|
||||
case COMMON_SAMPLER_TYPE_PENALTIES:
|
||||
samplers.push_back(llama_sampler_init_penalties(params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present));
|
||||
break;
|
||||
case COMMON_SAMPLER_TYPE_ADAPTIVE_P:
|
||||
// the `adaptive-p` sampler is like `dist` and `mirostat` in that it selects
|
||||
// a single token, so we will add `dist` at the end of the chain by default,
|
||||
// unless the user specifically included `adaptive-p`. we set this flag here
|
||||
// so we know to add the sampler at the very end.
|
||||
use_adaptive_p = true;
|
||||
break;
|
||||
default:
|
||||
GGML_ASSERT(false && "unknown sampler type");
|
||||
}
|
||||
}
|
||||
if (use_adaptive_p) {
|
||||
// only if user explicitly included adaptive-p sampler
|
||||
samplers.push_back(llama_sampler_init_adaptive_p(params.adaptive_target, params.adaptive_decay, params.seed));
|
||||
} else {
|
||||
// default: sample from distribution
|
||||
samplers.push_back(llama_sampler_init_dist(params.seed));
|
||||
}
|
||||
} else if (params.mirostat == 1) {
|
||||
samplers.push_back(llama_sampler_init_temp(params.temp));
|
||||
samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100));
|
||||
} else if (params.mirostat == 2) {
|
||||
samplers.push_back(llama_sampler_init_temp(params.temp));
|
||||
samplers.push_back(llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta));
|
||||
} else {
|
||||
GGML_ASSERT(false && "unknown mirostat version");
|
||||
}
|
||||
|
||||
for (auto * smpl : samplers) {
|
||||
llama_sampler_chain_add(chain, smpl);
|
||||
}
|
||||
|
||||
if (grmr && params.backend_sampling) {
|
||||
LOG_WRN("%s: backend sampling is not compatible with grammar, disabling\n", __func__);
|
||||
|
||||
params.backend_sampling = false;
|
||||
}
|
||||
|
||||
auto * result = new common_sampler {
|
||||
/* .params = */ params,
|
||||
/* .grmr = */ grmr,
|
||||
/* .chain = */ chain,
|
||||
/* .prev = */ ring_buffer<llama_token>(std::max(32, params.n_prev)),
|
||||
/* .cur = */ {},
|
||||
/* .cur_p = */ {},
|
||||
};
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
void common_sampler_free(struct common_sampler * gsmpl) {
|
||||
if (!gsmpl) {
|
||||
return;
|
||||
}
|
||||
|
||||
llama_sampler_free(gsmpl->grmr);
|
||||
llama_sampler_free(gsmpl->chain);
|
||||
|
||||
delete gsmpl;
|
||||
}
|
||||
|
||||
void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) {
|
||||
if (!gsmpl) {
|
||||
return;
|
||||
}
|
||||
|
||||
const auto tm = gsmpl->tm();
|
||||
|
||||
if (gsmpl->grmr && accept_grammar) {
|
||||
llama_sampler_accept(gsmpl->grmr, token);
|
||||
}
|
||||
|
||||
llama_sampler_accept(gsmpl->chain, token);
|
||||
|
||||
gsmpl->prev.push_back(token);
|
||||
}
|
||||
|
||||
void common_sampler_reset(struct common_sampler * gsmpl) {
|
||||
if (!gsmpl) {
|
||||
return;
|
||||
}
|
||||
|
||||
gsmpl->reset();
|
||||
}
|
||||
|
||||
struct common_sampler * common_sampler_clone(common_sampler * gsmpl) {
|
||||
return new common_sampler {
|
||||
/* .params = */ gsmpl->params,
|
||||
/* .grmr = */ llama_sampler_clone(gsmpl->grmr),
|
||||
/* .chain = */ llama_sampler_clone(gsmpl->chain),
|
||||
/* .prev = */ gsmpl->prev,
|
||||
/* .cur = */ gsmpl->cur,
|
||||
/* .cur_p = */ gsmpl->cur_p,
|
||||
};
|
||||
}
|
||||
|
||||
void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl) {
|
||||
// TODO: measure grammar performance
|
||||
|
||||
const double t_sampling_ms = gsmpl ? 1e-3*gsmpl->t_total_us : 0;
|
||||
|
||||
llama_perf_sampler_data data_smpl;
|
||||
llama_perf_context_data data_ctx;
|
||||
|
||||
memset(&data_smpl, 0, sizeof(data_smpl));
|
||||
memset(&data_ctx, 0, sizeof(data_ctx));
|
||||
|
||||
if (gsmpl) {
|
||||
auto & data = data_smpl;
|
||||
|
||||
data = llama_perf_sampler(gsmpl->chain);
|
||||
|
||||
// note: the sampling time includes the samplers time + extra time spent in common/sampling
|
||||
LOG_INF("%s: sampling time = %10.2f ms\n", __func__, t_sampling_ms);
|
||||
LOG_INF("%s: samplers time = %10.2f ms / %5d tokens\n", __func__, data.t_sample_ms, data.n_sample);
|
||||
}
|
||||
|
||||
if (ctx) {
|
||||
auto & data = data_ctx;
|
||||
|
||||
data = llama_perf_context(ctx);
|
||||
|
||||
const double t_end_ms = 1e-3 * ggml_time_us();
|
||||
|
||||
const double t_total_ms = t_end_ms - data.t_start_ms;
|
||||
const double t_unacc_ms = t_total_ms - (t_sampling_ms + data.t_p_eval_ms + data.t_eval_ms);
|
||||
const double t_unacc_pc = 100.0 * t_unacc_ms / t_total_ms;
|
||||
|
||||
LOG_INF("%s: load time = %10.2f ms\n", __func__, data.t_load_ms);
|
||||
LOG_INF("%s: prompt eval time = %10.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n",
|
||||
__func__, data.t_p_eval_ms, data.n_p_eval, data.t_p_eval_ms / data.n_p_eval, 1e3 / data.t_p_eval_ms * data.n_p_eval);
|
||||
LOG_INF("%s: eval time = %10.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n",
|
||||
__func__, data.t_eval_ms, data.n_eval, data.t_eval_ms / data.n_eval, 1e3 / data.t_eval_ms * data.n_eval);
|
||||
LOG_INF("%s: total time = %10.2f ms / %5d tokens\n", __func__, (t_end_ms - data.t_start_ms), (data.n_p_eval + data.n_eval));
|
||||
LOG_INF("%s: unaccounted time = %10.2f ms / %5.1f %% (total - sampling - prompt eval - eval) / (total)\n", __func__, t_unacc_ms, t_unacc_pc);
|
||||
LOG_INF("%s: graphs reused = %10d\n", __func__, data.n_reused);
|
||||
|
||||
llama_memory_breakdown_print(ctx);
|
||||
}
|
||||
}
|
||||
|
||||
struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl) {
|
||||
if (!gsmpl) {
|
||||
return nullptr;
|
||||
}
|
||||
|
||||
return gsmpl->chain;
|
||||
}
|
||||
|
||||
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) {
|
||||
llama_synchronize(ctx);
|
||||
|
||||
// start measuring sampling time after the llama_context synchronization in order to not measure any ongoing async operations
|
||||
const auto tm = gsmpl->tm();
|
||||
|
||||
llama_token id = LLAMA_TOKEN_NULL;
|
||||
|
||||
auto & grmr = gsmpl->grmr;
|
||||
auto & chain = gsmpl->chain;
|
||||
auto & cur_p = gsmpl->cur_p; // initialized by set_logits
|
||||
|
||||
// Check if a backend sampler has already sampled a token in which case we
|
||||
// return that token id directly.
|
||||
{
|
||||
id = llama_get_sampled_token_ith(ctx, idx);
|
||||
|
||||
if (id != LLAMA_TOKEN_NULL) {
|
||||
LOG_DBG("%s: Backend sampler selected token: '%d'. Will not run any CPU samplers\n", __func__, id);
|
||||
|
||||
GGML_ASSERT(!gsmpl->grmr && "using grammar in combination with backend sampling is not supported");
|
||||
|
||||
// TODO: simplify
|
||||
gsmpl->cur.resize(1);
|
||||
gsmpl->cur[0] = { id, 0.0f, 1.0f };
|
||||
cur_p = { gsmpl->cur.data(), gsmpl->cur.size(), 0, true };
|
||||
|
||||
return id;
|
||||
}
|
||||
}
|
||||
|
||||
gsmpl->set_logits(ctx, idx);
|
||||
|
||||
if (grammar_first) {
|
||||
llama_sampler_apply(grmr, &cur_p);
|
||||
}
|
||||
|
||||
llama_sampler_apply(chain, &cur_p);
|
||||
|
||||
id = cur_p.data[cur_p.selected].id;
|
||||
|
||||
if (grammar_first) {
|
||||
return id;
|
||||
}
|
||||
|
||||
// check if it the sampled token fits the grammar (grammar-based rejection sampling)
|
||||
{
|
||||
llama_token_data single_token_data = { id, 1.0f, 0.0f };
|
||||
llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false };
|
||||
|
||||
llama_sampler_apply(grmr, &single_token_data_array);
|
||||
|
||||
const bool is_valid = single_token_data_array.data[0].logit != -INFINITY;
|
||||
if (is_valid) {
|
||||
return id;
|
||||
}
|
||||
}
|
||||
|
||||
// resampling:
|
||||
// if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain
|
||||
gsmpl->set_logits(ctx, idx);
|
||||
|
||||
llama_sampler_apply(grmr, &cur_p);
|
||||
llama_sampler_apply(chain, &cur_p);
|
||||
|
||||
GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration");
|
||||
|
||||
id = cur_p.data[cur_p.selected].id;
|
||||
|
||||
return id;
|
||||
}
|
||||
|
||||
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first) {
|
||||
GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1");
|
||||
|
||||
std::vector<llama_token> result;
|
||||
result.reserve(idxs.size());
|
||||
|
||||
size_t i = 0;
|
||||
for (; i < draft.size(); i++) {
|
||||
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
|
||||
|
||||
common_sampler_accept(gsmpl, id, true);
|
||||
|
||||
result.push_back(id);
|
||||
|
||||
if (draft[i] != id) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (i == draft.size()) {
|
||||
const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first);
|
||||
|
||||
common_sampler_accept(gsmpl, id, true);
|
||||
|
||||
result.push_back(id);
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) {
|
||||
std::vector<int> idxs(draft.size() + 1);
|
||||
for (size_t i = 0; i < idxs.size(); ++i) {
|
||||
idxs[i] = i;
|
||||
}
|
||||
|
||||
return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first);
|
||||
}
|
||||
|
||||
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) {
|
||||
return llama_sampler_get_seed(gsmpl->chain);
|
||||
}
|
||||
|
||||
// helpers
|
||||
|
||||
llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl, bool do_sort) {
|
||||
const auto tm = gsmpl->tm();
|
||||
|
||||
auto * res = &gsmpl->cur_p;
|
||||
|
||||
if (do_sort && !res->sorted) {
|
||||
// remember the selected token before sorting
|
||||
const llama_token id = res->data[res->selected].id;
|
||||
|
||||
std::sort(res->data, res->data + res->size, [](const llama_token_data & a, const llama_token_data & b) {
|
||||
return a.p > b.p;
|
||||
});
|
||||
|
||||
// restore the selected token after sorting
|
||||
for (size_t i = 0; i < res->size; ++i) {
|
||||
if (res->data[i].id == id) {
|
||||
res->selected = i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
res->sorted = true;
|
||||
}
|
||||
|
||||
return res;
|
||||
}
|
||||
|
||||
llama_token common_sampler_last(const struct common_sampler * gsmpl) {
|
||||
return gsmpl->prev.rat(0);
|
||||
}
|
||||
|
||||
std::string common_sampler_print(const struct common_sampler * gsmpl) {
|
||||
std::string result = "logits ";
|
||||
|
||||
for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) {
|
||||
const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i);
|
||||
result += std::string("-> ");
|
||||
result += std::string(llama_sampler_name(smpl)) + " ";
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx_main, int n) {
|
||||
n = std::min(n, (int) gsmpl->prev.size());
|
||||
|
||||
if (n <= 0) {
|
||||
return "";
|
||||
}
|
||||
|
||||
std::string result;
|
||||
result.reserve(8*n); // 8 is the average length of a token [citation needed], TODO: compute this from the vocab
|
||||
|
||||
for (int i = n - 1; i >= 0; i--) {
|
||||
const llama_token id = gsmpl->prev.rat(i);
|
||||
|
||||
GGML_ASSERT(id != LLAMA_TOKEN_NULL && "null token in the sampling history - should not happen");
|
||||
|
||||
result += common_token_to_piece(ctx_main, id);
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
char common_sampler_type_to_chr(enum common_sampler_type cnstr) {
|
||||
switch (cnstr) {
|
||||
case COMMON_SAMPLER_TYPE_DRY: return 'd';
|
||||
case COMMON_SAMPLER_TYPE_TOP_K: return 'k';
|
||||
case COMMON_SAMPLER_TYPE_TYPICAL_P: return 'y';
|
||||
case COMMON_SAMPLER_TYPE_TOP_P: return 'p';
|
||||
case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: return 's';
|
||||
case COMMON_SAMPLER_TYPE_MIN_P: return 'm';
|
||||
case COMMON_SAMPLER_TYPE_TEMPERATURE: return 't';
|
||||
case COMMON_SAMPLER_TYPE_XTC: return 'x';
|
||||
case COMMON_SAMPLER_TYPE_INFILL: return 'i';
|
||||
case COMMON_SAMPLER_TYPE_PENALTIES: return 'e';
|
||||
case COMMON_SAMPLER_TYPE_ADAPTIVE_P: return 'a';
|
||||
default : return '?';
|
||||
}
|
||||
}
|
||||
|
||||
std::string common_sampler_type_to_str(enum common_sampler_type cnstr) {
|
||||
switch (cnstr) {
|
||||
case COMMON_SAMPLER_TYPE_DRY: return "dry";
|
||||
case COMMON_SAMPLER_TYPE_TOP_K: return "top_k";
|
||||
case COMMON_SAMPLER_TYPE_TYPICAL_P: return "typ_p";
|
||||
case COMMON_SAMPLER_TYPE_TOP_P: return "top_p";
|
||||
case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: return "top_n_sigma";
|
||||
case COMMON_SAMPLER_TYPE_MIN_P: return "min_p";
|
||||
case COMMON_SAMPLER_TYPE_TEMPERATURE: return "temperature";
|
||||
case COMMON_SAMPLER_TYPE_XTC: return "xtc";
|
||||
case COMMON_SAMPLER_TYPE_INFILL: return "infill";
|
||||
case COMMON_SAMPLER_TYPE_PENALTIES: return "penalties";
|
||||
case COMMON_SAMPLER_TYPE_ADAPTIVE_P: return "adaptive_p";
|
||||
default : return "";
|
||||
}
|
||||
}
|
||||
|
||||
std::vector<common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names) {
|
||||
std::unordered_map<std::string, common_sampler_type> sampler_canonical_name_map {
|
||||
{ "dry", COMMON_SAMPLER_TYPE_DRY },
|
||||
{ "top_k", COMMON_SAMPLER_TYPE_TOP_K },
|
||||
{ "top_p", COMMON_SAMPLER_TYPE_TOP_P },
|
||||
{ "top_n_sigma", COMMON_SAMPLER_TYPE_TOP_N_SIGMA },
|
||||
{ "typ_p", COMMON_SAMPLER_TYPE_TYPICAL_P },
|
||||
{ "min_p", COMMON_SAMPLER_TYPE_MIN_P },
|
||||
{ "temperature", COMMON_SAMPLER_TYPE_TEMPERATURE },
|
||||
{ "xtc", COMMON_SAMPLER_TYPE_XTC },
|
||||
{ "infill", COMMON_SAMPLER_TYPE_INFILL },
|
||||
{ "penalties", COMMON_SAMPLER_TYPE_PENALTIES },
|
||||
{ "adaptive_p", COMMON_SAMPLER_TYPE_ADAPTIVE_P },
|
||||
};
|
||||
|
||||
// since samplers names are written multiple ways
|
||||
// make it ready for both system names and input names
|
||||
std::unordered_map<std::string, common_sampler_type> sampler_alt_name_map {
|
||||
{ "top-k", COMMON_SAMPLER_TYPE_TOP_K },
|
||||
{ "top-p", COMMON_SAMPLER_TYPE_TOP_P },
|
||||
{ "top-n-sigma", COMMON_SAMPLER_TYPE_TOP_N_SIGMA },
|
||||
{ "nucleus", COMMON_SAMPLER_TYPE_TOP_P },
|
||||
{ "typical-p", COMMON_SAMPLER_TYPE_TYPICAL_P },
|
||||
{ "typical", COMMON_SAMPLER_TYPE_TYPICAL_P },
|
||||
{ "typ-p", COMMON_SAMPLER_TYPE_TYPICAL_P },
|
||||
{ "typ", COMMON_SAMPLER_TYPE_TYPICAL_P },
|
||||
{ "min-p", COMMON_SAMPLER_TYPE_MIN_P },
|
||||
{ "temp", COMMON_SAMPLER_TYPE_TEMPERATURE },
|
||||
{ "adaptive-p", COMMON_SAMPLER_TYPE_ADAPTIVE_P },
|
||||
};
|
||||
|
||||
std::vector<common_sampler_type> samplers;
|
||||
samplers.reserve(names.size());
|
||||
|
||||
for (const auto & name : names) {
|
||||
auto sampler = sampler_canonical_name_map.find(name);
|
||||
if (sampler != sampler_canonical_name_map.end()) {
|
||||
samplers.push_back(sampler->second);
|
||||
continue;
|
||||
}
|
||||
if (allow_alt_names) {
|
||||
sampler = sampler_alt_name_map.find(name);
|
||||
if (sampler != sampler_alt_name_map.end()) {
|
||||
samplers.push_back(sampler->second);
|
||||
continue;
|
||||
}
|
||||
}
|
||||
LOG_WRN("%s: unable to match sampler by name '%s'\n", __func__, name.c_str());
|
||||
}
|
||||
|
||||
return samplers;
|
||||
}
|
||||
|
||||
std::vector<common_sampler_type> common_sampler_types_from_chars(const std::string & chars) {
|
||||
std::unordered_map<char, common_sampler_type> sampler_name_map = {
|
||||
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_DRY), COMMON_SAMPLER_TYPE_DRY },
|
||||
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_K), COMMON_SAMPLER_TYPE_TOP_K },
|
||||
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TYPICAL_P), COMMON_SAMPLER_TYPE_TYPICAL_P },
|
||||
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_P), COMMON_SAMPLER_TYPE_TOP_P },
|
||||
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TOP_N_SIGMA), COMMON_SAMPLER_TYPE_TOP_N_SIGMA },
|
||||
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_MIN_P), COMMON_SAMPLER_TYPE_MIN_P },
|
||||
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_TEMPERATURE), COMMON_SAMPLER_TYPE_TEMPERATURE },
|
||||
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_XTC), COMMON_SAMPLER_TYPE_XTC },
|
||||
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_INFILL), COMMON_SAMPLER_TYPE_INFILL },
|
||||
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_PENALTIES), COMMON_SAMPLER_TYPE_PENALTIES },
|
||||
{ common_sampler_type_to_chr(COMMON_SAMPLER_TYPE_ADAPTIVE_P), COMMON_SAMPLER_TYPE_ADAPTIVE_P },
|
||||
};
|
||||
|
||||
std::vector<common_sampler_type> samplers;
|
||||
samplers.reserve(chars.size());
|
||||
|
||||
for (const auto & c : chars) {
|
||||
const auto sampler = sampler_name_map.find(c);
|
||||
if (sampler != sampler_name_map.end()) {
|
||||
samplers.push_back(sampler->second);
|
||||
} else {
|
||||
LOG_WRN("%s: unable to match sampler by char '%c'\n", __func__, c);
|
||||
}
|
||||
}
|
||||
|
||||
return samplers;
|
||||
}
|
||||
119
common/sampling.h
Normal file
119
common/sampling.h
Normal file
@@ -0,0 +1,119 @@
|
||||
#pragma once
|
||||
|
||||
#include "llama.h"
|
||||
|
||||
#include "common.h"
|
||||
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
// common_sampler extends llama_sampler with additional functionality:
|
||||
//
|
||||
// - grammar support
|
||||
// - custom sampler logic based on the parameters
|
||||
// - history of the last accepted tokens
|
||||
// - performance metrics
|
||||
//
|
||||
// This goal is to have a common implementation of the sampling logic shared across the examples.
|
||||
// For example, depending on the temperature, the sampling chain can be very simple (greedy) or more
|
||||
// complex (top-k, top-p, etc).
|
||||
//
|
||||
// Another example is related to the grammar. In general, the grammar constraints applied on the full
|
||||
// vocabulary can be very taxing. To improve performance, the grammar can be applied only to the sampled
|
||||
// token in order to verify if it fits the grammar. And only if the token doesn't fit the grammar, the
|
||||
// grammar constraints are applied to the full vocabulary and the token is resampled.
|
||||
//
|
||||
// The common_sampler also maintains a container with the last accepted tokens. In the future, this can
|
||||
// be moved into the core llama library.
|
||||
//
|
||||
// For convenience, the common_sampler also maintains a container with the current candidate tokens.
|
||||
// This can be used to access the probabilities of the rest of the non-sampled tokens.
|
||||
//
|
||||
// TODO: measure grammar performance
|
||||
//
|
||||
|
||||
struct common_sampler;
|
||||
|
||||
// llama_sampler API overloads
|
||||
|
||||
// note: can mutate params in some cases
|
||||
struct common_sampler * common_sampler_init(const struct llama_model * model, struct common_params_sampling & params);
|
||||
|
||||
void common_sampler_free(struct common_sampler * gsmpl);
|
||||
|
||||
// if accept_grammar is true, the token is accepted both by the sampling chain and the grammar
|
||||
void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar);
|
||||
void common_sampler_reset (struct common_sampler * gsmpl);
|
||||
struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl);
|
||||
|
||||
// arguments can be nullptr to skip printing
|
||||
void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl);
|
||||
|
||||
// get the underlying llama_sampler_chain
|
||||
struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl);
|
||||
|
||||
// extended sampling implementation:
|
||||
//
|
||||
// - set logits
|
||||
// - apply the configured sampler chain
|
||||
// - check if the token fits the grammar (if any)
|
||||
// - if not: resample by first applying the grammar constraints and then sampling again (slower path)
|
||||
//
|
||||
// if grammar_first is true, the grammar is applied before the samplers (slower)
|
||||
// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar
|
||||
//
|
||||
llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false);
|
||||
|
||||
// generalized version of common_sampler_sample
|
||||
//
|
||||
// will cross-reference the sampled tokens with a batch of draft tokens and accept those that match
|
||||
// if the sampler disagrees at some point, we stop and return the accepted tokens up to now
|
||||
//
|
||||
// common_sampler_sample_n(gsmpl, ctx, { idx }, {});
|
||||
//
|
||||
// is equivalent to
|
||||
//
|
||||
// common_sampler_sample(gsmpl, ctx, idx);
|
||||
// common_sampler_accept(gsmpl, token, true);
|
||||
//
|
||||
// requires: idxs.size() == draft.size() + 1
|
||||
//
|
||||
// returns at least 1 token, up to idxs.size()
|
||||
//
|
||||
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector<int> & idxs, const llama_tokens & draft, bool grammar_first = false);
|
||||
|
||||
// assume idxs == [ 0, 1, 2, ..., draft.size() ]
|
||||
std::vector<llama_token> common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false);
|
||||
|
||||
uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl);
|
||||
|
||||
// helpers
|
||||
|
||||
// access the internal list of current candidate tokens
|
||||
// if do_sort == true, the candidates are guaranteed to be sorted afterwards (in descending order of probability)
|
||||
// the .sorted flag of the result indicates whether the returned candidates are sorted
|
||||
llama_token_data_array * common_sampler_get_candidates(struct common_sampler * gsmpl, bool do_sort);
|
||||
|
||||
// get the last accepted token
|
||||
llama_token common_sampler_last(const struct common_sampler * gsmpl);
|
||||
|
||||
// print the sampler chain into a string
|
||||
std::string common_sampler_print(const struct common_sampler * gsmpl);
|
||||
|
||||
// get a string representation of the last accepted tokens
|
||||
std::string common_sampler_prev_str(common_sampler * gsmpl, llama_context * ctx, int n);
|
||||
|
||||
char common_sampler_type_to_chr(enum common_sampler_type cnstr);
|
||||
std::string common_sampler_type_to_str(enum common_sampler_type cnstr);
|
||||
|
||||
std::vector<enum common_sampler_type> common_sampler_types_from_names(const std::vector<std::string> & names, bool allow_alt_names);
|
||||
std::vector<enum common_sampler_type> common_sampler_types_from_chars(const std::string & chars);
|
||||
|
||||
llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab,
|
||||
const char * grammar_kind, const char * grammar_data);
|
||||
|
||||
struct common_sampler_deleter {
|
||||
void operator()(common_sampler * s) { common_sampler_free(s); }
|
||||
};
|
||||
|
||||
typedef std::unique_ptr<common_sampler, common_sampler_deleter> common_sampler_ptr;
|
||||
361
common/speculative.cpp
Normal file
361
common/speculative.cpp
Normal file
@@ -0,0 +1,361 @@
|
||||
#include "speculative.h"
|
||||
|
||||
#include "ggml.h"
|
||||
#include "llama.h"
|
||||
#include "log.h"
|
||||
#include "common.h"
|
||||
#include "sampling.h"
|
||||
|
||||
#include <cstring>
|
||||
#include <algorithm>
|
||||
#include <map>
|
||||
|
||||
#define SPEC_VOCAB_MAX_SIZE_DIFFERENCE 128
|
||||
#define SPEC_VOCAB_CHECK_START_TOKEN_ID 5
|
||||
|
||||
struct common_speculative {
|
||||
struct llama_context * ctx_tgt; // only used for retokenizing from ctx_dft
|
||||
struct llama_context * ctx_dft;
|
||||
struct common_sampler * smpl;
|
||||
|
||||
llama_batch batch;
|
||||
llama_tokens prompt_dft;
|
||||
bool vocab_dft_compatible = true; // whether retokenization is needed
|
||||
std::map<std::string, std::string> tgt_dft_replacements = {};
|
||||
};
|
||||
|
||||
struct common_speculative * common_speculative_init(
|
||||
struct llama_context * ctx_tgt,
|
||||
struct llama_context * ctx_dft) {
|
||||
auto * result = new common_speculative {
|
||||
/* .ctx_tgt = */ ctx_tgt,
|
||||
/* .ctx_dft = */ ctx_dft,
|
||||
/* .smpl = */ nullptr,
|
||||
/* .batch = */ llama_batch_init(llama_n_batch(ctx_dft), 0, 1),
|
||||
/* .prompt_dft = */ {},
|
||||
/* .vocab_dft_compatible = */ false,
|
||||
};
|
||||
|
||||
// TODO: optimize or pass from outside?
|
||||
#if 0
|
||||
{
|
||||
common_params_sampling params;
|
||||
params.no_perf = false;
|
||||
|
||||
params.top_k = 40;
|
||||
params.top_p = 0.9;
|
||||
|
||||
params.samplers = {
|
||||
COMMON_SAMPLER_TYPE_TOP_K,
|
||||
COMMON_SAMPLER_TYPE_TOP_P,
|
||||
COMMON_SAMPLER_TYPE_INFILL,
|
||||
};
|
||||
|
||||
result->smpl = common_sampler_init(llama_get_model(ctx_dft), params);
|
||||
}
|
||||
#else
|
||||
{
|
||||
common_params_sampling params;
|
||||
params.no_perf = false;
|
||||
|
||||
params.top_k = 10;
|
||||
|
||||
params.samplers = {
|
||||
COMMON_SAMPLER_TYPE_TOP_K,
|
||||
};
|
||||
|
||||
result->smpl = common_sampler_init(llama_get_model(ctx_dft), params);
|
||||
}
|
||||
#endif
|
||||
|
||||
result->vocab_dft_compatible = common_speculative_are_compatible(ctx_tgt, ctx_dft);
|
||||
LOG_DBG("vocab_dft_compatible = %d\n", result->vocab_dft_compatible);
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
void common_speculative_free(struct common_speculative * spec) {
|
||||
if (spec == nullptr) {
|
||||
return;
|
||||
}
|
||||
|
||||
common_sampler_free(spec->smpl);
|
||||
|
||||
llama_batch_free(spec->batch);
|
||||
|
||||
delete spec;
|
||||
}
|
||||
|
||||
bool common_speculative_are_compatible(
|
||||
const struct llama_context * ctx_tgt,
|
||||
const struct llama_context * ctx_dft) {
|
||||
const struct llama_model * model_tgt = llama_get_model(ctx_tgt);
|
||||
const struct llama_model * model_dft = llama_get_model(ctx_dft);
|
||||
|
||||
const struct llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt);
|
||||
const struct llama_vocab * vocab_dft = llama_model_get_vocab(model_dft);
|
||||
|
||||
const bool vocab_type_tgt = llama_vocab_type(vocab_tgt);
|
||||
LOG_DBG("%s: vocab_type tgt: %d\n", __func__, vocab_type_tgt);
|
||||
|
||||
const bool vocab_type_dft = llama_vocab_type(vocab_dft);
|
||||
LOG_DBG("%s: vocab_type dft: %d\n", __func__, vocab_type_dft);
|
||||
|
||||
if (vocab_type_tgt != vocab_type_dft) {
|
||||
LOG_DBG("%s: draft model vocab type must match target model to use speculation but ", __func__);
|
||||
LOG_DBG("vocab_type_dft = %d while vocab_type_tgt = %d\n", vocab_type_dft, vocab_type_tgt);
|
||||
return false;
|
||||
}
|
||||
|
||||
if (
|
||||
llama_vocab_get_add_bos(vocab_tgt) != llama_vocab_get_add_bos(vocab_dft) ||
|
||||
llama_vocab_get_add_eos(vocab_tgt) != llama_vocab_get_add_eos(vocab_dft) ||
|
||||
llama_vocab_bos(vocab_tgt) != llama_vocab_bos(vocab_dft) ||
|
||||
llama_vocab_eos(vocab_tgt) != llama_vocab_eos(vocab_dft)
|
||||
) {
|
||||
LOG_DBG("%s: draft model special tokens must match target model to use speculation\n", __func__);
|
||||
return false;
|
||||
}
|
||||
|
||||
{
|
||||
const int n_vocab_tgt = llama_vocab_n_tokens(vocab_tgt);
|
||||
const int n_vocab_dft = llama_vocab_n_tokens(vocab_dft);
|
||||
const int vocab_diff = n_vocab_tgt > n_vocab_dft
|
||||
? n_vocab_tgt - n_vocab_dft
|
||||
: n_vocab_dft - n_vocab_tgt;
|
||||
|
||||
if (vocab_diff > SPEC_VOCAB_MAX_SIZE_DIFFERENCE) {
|
||||
LOG_DBG("%s: draft model vocab must closely match target model to use speculation but ", __func__);
|
||||
LOG_DBG("target vocab size %d does not match draft vocab size %d - difference %d, max allowed %d\n",
|
||||
n_vocab_tgt, llama_vocab_n_tokens(vocab_dft), vocab_diff, SPEC_VOCAB_MAX_SIZE_DIFFERENCE);
|
||||
return false;
|
||||
}
|
||||
|
||||
for (int i = SPEC_VOCAB_CHECK_START_TOKEN_ID; i < std::min(n_vocab_tgt, n_vocab_dft); ++i) {
|
||||
const char * token_text_tgt = llama_vocab_get_text(vocab_tgt, i);
|
||||
const char * token_text_dft = llama_vocab_get_text(vocab_dft, i);
|
||||
if (std::strcmp(token_text_tgt, token_text_dft) != 0) {
|
||||
LOG_DBG("%s: draft model vocab must match target model to use speculation but ", __func__);
|
||||
LOG_DBG("token %d content differs - target '%s', draft '%s'\n", i,
|
||||
common_token_to_piece(ctx_tgt, i).c_str(),
|
||||
common_token_to_piece(ctx_dft, i).c_str());
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return true;
|
||||
}
|
||||
|
||||
void common_speculative_add_replacement_tgt_dft(
|
||||
struct common_speculative * spec,
|
||||
const char *source, const char *dest) {
|
||||
spec->tgt_dft_replacements[source] = dest;
|
||||
}
|
||||
|
||||
static std::string replace_to_dft(
|
||||
struct common_speculative * spec,
|
||||
const std::string& input) {
|
||||
std::string result = input;
|
||||
for (const auto & pair : spec->tgt_dft_replacements) {
|
||||
size_t pos = result.find(pair.first);
|
||||
while (pos != std::string::npos) {
|
||||
result.replace(pos, pair.first.length(), pair.second);
|
||||
pos = result.find(pair.first, pos + pair.second.length());
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
static std::string replace_to_tgt(
|
||||
struct common_speculative * spec,
|
||||
const std::string& input) {
|
||||
std::string result = input;
|
||||
for (const auto& pair : spec->tgt_dft_replacements) {
|
||||
size_t pos = result.find(pair.second);
|
||||
while (pos != std::string::npos) {
|
||||
result.replace(pos, pair.second.length(), pair.first);
|
||||
pos = result.find(pair.second, pos + pair.first.length());
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
|
||||
llama_tokens common_speculative_gen_draft(
|
||||
struct common_speculative * spec,
|
||||
struct common_speculative_params params,
|
||||
const llama_tokens & prompt_tgt_main_model, // specified in target model vocab
|
||||
llama_token id_last) {
|
||||
auto & batch = spec->batch;
|
||||
auto & ctx_tgt = spec->ctx_tgt;
|
||||
auto & ctx_dft = spec->ctx_dft;
|
||||
auto & smpl = spec->smpl;
|
||||
auto & prompt_dft = spec->prompt_dft;
|
||||
|
||||
auto * mem_dft = llama_get_memory(ctx_dft);
|
||||
|
||||
int reuse_i = 0;
|
||||
int reuse_n = 0;
|
||||
|
||||
const int n_ctx = llama_n_ctx(ctx_dft) - params.n_draft;
|
||||
|
||||
llama_tokens prompt_tgt_draft_model;
|
||||
if (!spec->vocab_dft_compatible) {
|
||||
std::string text;
|
||||
text = common_detokenize(ctx_tgt, prompt_tgt_main_model, true);
|
||||
text = replace_to_dft(spec, text);
|
||||
LOG_DBG("%s: main->draft detokenized string: '%s'\n", __func__, text.c_str());
|
||||
prompt_tgt_draft_model = common_tokenize(ctx_dft, text, false, true);
|
||||
|
||||
// convert id_last to draft vocab. llama_detokenize is called directly to avoid an allocation
|
||||
const auto * model_tgt = llama_get_model(ctx_tgt);
|
||||
const auto * vocab_tgt = llama_model_get_vocab(model_tgt);
|
||||
|
||||
int32_t n_chars = llama_detokenize(vocab_tgt, &id_last, 1, nullptr, 0, false, false);
|
||||
GGML_ASSERT(n_chars < 0 && "failed to detokenize id_last");
|
||||
text.resize(-n_chars);
|
||||
llama_detokenize(vocab_tgt, &id_last, 1, text.data(), text.size(), false, false);
|
||||
text = replace_to_dft(spec, text);
|
||||
|
||||
LOG_DBG("main->draft detokenized id_last(%d): '%s'\n", id_last, text.c_str());
|
||||
id_last = common_tokenize(ctx_dft, text, false, true)[0];
|
||||
}
|
||||
// prompt_tgt's tokens will always be compatible with ctx_dft
|
||||
const llama_tokens &prompt_tgt =
|
||||
spec->vocab_dft_compatible ? prompt_tgt_main_model : prompt_tgt_draft_model;
|
||||
|
||||
const int i_start = std::max<int>(0, (int) prompt_tgt.size() - n_ctx);
|
||||
|
||||
// reuse as much as possible from the old draft context
|
||||
// ideally, the draft context should be as big as the target context and we will always reuse the entire prompt
|
||||
for (int i = 0; i < (int) prompt_dft.size(); ++i) {
|
||||
int cur = 0;
|
||||
while (i_start + cur < (int) prompt_tgt.size() &&
|
||||
i + cur < (int) prompt_dft.size() &&
|
||||
prompt_tgt[i_start + cur] == prompt_dft[i + cur]) {
|
||||
cur++;
|
||||
}
|
||||
|
||||
if ((cur >= params.n_reuse || n_ctx >= (int) prompt_tgt.size()) && cur > reuse_n) {
|
||||
reuse_i = i;
|
||||
reuse_n = cur;
|
||||
}
|
||||
}
|
||||
|
||||
LOG_DBG("%s: reuse_i = %d, reuse_n = %d, prompt = %d\n", __func__, reuse_i, reuse_n, (int) prompt_dft.size());
|
||||
|
||||
llama_tokens result;
|
||||
result.reserve(params.n_draft);
|
||||
|
||||
if (reuse_n == 0) {
|
||||
llama_memory_clear(mem_dft, false);
|
||||
prompt_dft.clear();
|
||||
} else {
|
||||
// this happens when a previous draft has been discarded (for example, due to being too small), but the
|
||||
// target model agreed with it. in this case, we simply pass back the previous results to save compute
|
||||
if (reuse_i + reuse_n < (int) prompt_dft.size() && prompt_dft[reuse_i + reuse_n] == id_last) {
|
||||
for (int i = reuse_i + reuse_n + 1; i < (int) prompt_dft.size(); ++i) {
|
||||
result.push_back(prompt_dft[i]);
|
||||
|
||||
if (params.n_draft <= (int) result.size()) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
if (reuse_i > 0) {
|
||||
llama_memory_seq_rm (mem_dft, 0, 0, reuse_i);
|
||||
llama_memory_seq_add(mem_dft, 0, reuse_i, -1, -reuse_i);
|
||||
|
||||
prompt_dft.erase(prompt_dft.begin(), prompt_dft.begin() + reuse_i);
|
||||
}
|
||||
|
||||
if (reuse_n < (int) prompt_dft.size()) {
|
||||
llama_memory_seq_rm (mem_dft, 0, reuse_n, -1);
|
||||
prompt_dft.erase(prompt_dft.begin() + reuse_n, prompt_dft.end());
|
||||
}
|
||||
}
|
||||
|
||||
// prepare a batch to evaluate any new tokens in the prompt
|
||||
common_batch_clear(batch);
|
||||
|
||||
for (size_t i = i_start + reuse_n; i < prompt_tgt.size(); ++i) {
|
||||
//LOG_DBG("i = %d, i_start = %d, reuse_n = %d, i - i_start = %d, id = %6d\n", i, i_start, reuse_n, i - i_start, prompt_tgt[i]);
|
||||
common_batch_add(batch, prompt_tgt[i], i - i_start, { 0 }, false);
|
||||
|
||||
prompt_dft.push_back(prompt_tgt[i]);
|
||||
}
|
||||
|
||||
// we should rarely end-up here during normal decoding
|
||||
if (batch.n_tokens > 0) {
|
||||
//LOG_DBG("%s: draft prompt batch: %s\n", __func__, string_from(ctx, batch).c_str());
|
||||
|
||||
llama_decode(ctx_dft, batch);
|
||||
}
|
||||
|
||||
const llama_pos n_past = prompt_dft.size();
|
||||
|
||||
LOG_DBG("%s: n_past = %d\n", __func__, n_past);
|
||||
|
||||
common_batch_clear(batch);
|
||||
common_batch_add (batch, id_last, n_past, { 0 }, true);
|
||||
|
||||
prompt_dft.push_back(id_last);
|
||||
|
||||
LOG_DBG("%s: draft prompt: %s\n", __func__, string_from(ctx_dft, prompt_dft).c_str());
|
||||
|
||||
llama_decode(ctx_dft, batch);
|
||||
|
||||
common_sampler_reset(smpl);
|
||||
|
||||
// sample n_draft tokens from the draft model
|
||||
for (int i = 0; i < params.n_draft; ++i) {
|
||||
common_batch_clear(batch);
|
||||
|
||||
common_sampler_sample(smpl, ctx_dft, 0, true);
|
||||
|
||||
const auto * cur_p = common_sampler_get_candidates(smpl, true);
|
||||
|
||||
for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
|
||||
LOG_DBG(" - draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
|
||||
k, i, cur_p->data[k].id, cur_p->data[k].p, common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
|
||||
}
|
||||
|
||||
// add drafted token for each sequence
|
||||
const llama_token id = cur_p->data[0].id;
|
||||
|
||||
common_sampler_accept(smpl, id, true);
|
||||
|
||||
result.push_back(id);
|
||||
|
||||
if (params.n_draft <= (int) result.size()) {
|
||||
break;
|
||||
}
|
||||
|
||||
// only collect very high-confidence draft tokens
|
||||
if (cur_p->data[0].p < params.p_min) {
|
||||
break;
|
||||
}
|
||||
|
||||
common_batch_add(batch, id, n_past + i + 1, { 0 }, true);
|
||||
|
||||
// evaluate the drafted tokens on the draft model
|
||||
llama_decode(ctx_dft, batch);
|
||||
|
||||
prompt_dft.push_back(id);
|
||||
}
|
||||
|
||||
if (!spec->vocab_dft_compatible) {
|
||||
std::string detokenized = common_detokenize(ctx_dft, result, true);
|
||||
detokenized = replace_to_tgt(spec, detokenized);
|
||||
LOG_DBG("draft->main detokenized string: '%s'\n", detokenized.c_str());
|
||||
result = common_tokenize(ctx_tgt, detokenized, false, true);
|
||||
if (result.size() > (size_t)params.n_draft) {
|
||||
result.resize(params.n_draft);
|
||||
}
|
||||
}
|
||||
return result;
|
||||
}
|
||||
35
common/speculative.h
Normal file
35
common/speculative.h
Normal file
@@ -0,0 +1,35 @@
|
||||
#pragma once
|
||||
|
||||
#include "llama.h"
|
||||
#include "common.h"
|
||||
|
||||
struct common_speculative;
|
||||
|
||||
struct common_speculative_params {
|
||||
int n_draft = 16; // max drafted tokens
|
||||
int n_reuse = 256;
|
||||
|
||||
float p_min = 0.75f; // min probability required to accept a token in the draft
|
||||
};
|
||||
|
||||
struct common_speculative * common_speculative_init(
|
||||
struct llama_context * ctx_tgt,
|
||||
struct llama_context * ctx_dft
|
||||
);
|
||||
|
||||
void common_speculative_free(struct common_speculative * spec);
|
||||
|
||||
bool common_speculative_are_compatible(
|
||||
const struct llama_context * ctx_tgt,
|
||||
const struct llama_context * ctx_dft);
|
||||
|
||||
void common_speculative_add_replacement_tgt_dft(
|
||||
struct common_speculative * spec,
|
||||
const char *source, const char *dest);
|
||||
|
||||
// sample up to n_draft tokens and add them to the batch using the draft model
|
||||
llama_tokens common_speculative_gen_draft(
|
||||
struct common_speculative * spec,
|
||||
struct common_speculative_params params,
|
||||
const llama_tokens & prompt,
|
||||
llama_token id_last);
|
||||
64
common/unicode.cpp
Normal file
64
common/unicode.cpp
Normal file
@@ -0,0 +1,64 @@
|
||||
#include "unicode.h"
|
||||
|
||||
// implementation adopted from src/unicode.cpp
|
||||
|
||||
size_t utf8_sequence_length(unsigned char first_byte) {
|
||||
const size_t lookup[] = { 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 3, 4 };
|
||||
uint8_t highbits = static_cast<uint8_t>(first_byte) >> 4;
|
||||
return lookup[highbits];
|
||||
}
|
||||
|
||||
utf8_parse_result parse_utf8_codepoint(std::string_view input, size_t offset) {
|
||||
if (offset >= input.size()) {
|
||||
return utf8_parse_result(utf8_parse_result::INCOMPLETE);
|
||||
}
|
||||
|
||||
// ASCII fast path
|
||||
if (!(input[offset] & 0x80)) {
|
||||
return utf8_parse_result(utf8_parse_result::SUCCESS, input[offset], 1);
|
||||
}
|
||||
|
||||
// Invalid: continuation byte as first byte
|
||||
if (!(input[offset] & 0x40)) {
|
||||
return utf8_parse_result(utf8_parse_result::INVALID);
|
||||
}
|
||||
|
||||
// 2-byte sequence
|
||||
if (!(input[offset] & 0x20)) {
|
||||
if (offset + 1 >= input.size()) {
|
||||
return utf8_parse_result(utf8_parse_result::INCOMPLETE);
|
||||
}
|
||||
if ((input[offset + 1] & 0xc0) != 0x80) {
|
||||
return utf8_parse_result(utf8_parse_result::INVALID);
|
||||
}
|
||||
auto result = ((input[offset] & 0x1f) << 6) | (input[offset + 1] & 0x3f);
|
||||
return utf8_parse_result(utf8_parse_result::SUCCESS, result, 2);
|
||||
}
|
||||
|
||||
// 3-byte sequence
|
||||
if (!(input[offset] & 0x10)) {
|
||||
if (offset + 2 >= input.size()) {
|
||||
return utf8_parse_result(utf8_parse_result::INCOMPLETE);
|
||||
}
|
||||
if ((input[offset + 1] & 0xc0) != 0x80 || (input[offset + 2] & 0xc0) != 0x80) {
|
||||
return utf8_parse_result(utf8_parse_result::INVALID);
|
||||
}
|
||||
auto result = ((input[offset] & 0x0f) << 12) | ((input[offset + 1] & 0x3f) << 6) | (input[offset + 2] & 0x3f);
|
||||
return utf8_parse_result(utf8_parse_result::SUCCESS, result, 3);
|
||||
}
|
||||
|
||||
// 4-byte sequence
|
||||
if (!(input[offset] & 0x08)) {
|
||||
if (offset + 3 >= input.size()) {
|
||||
return utf8_parse_result(utf8_parse_result::INCOMPLETE);
|
||||
}
|
||||
if ((input[offset + 1] & 0xc0) != 0x80 || (input[offset + 2] & 0xc0) != 0x80 || (input[offset + 3] & 0xc0) != 0x80) {
|
||||
return utf8_parse_result(utf8_parse_result::INVALID);
|
||||
}
|
||||
auto result = ((input[offset] & 0x07) << 18) | ((input[offset + 1] & 0x3f) << 12) | ((input[offset + 2] & 0x3f) << 6) | (input[offset + 3] & 0x3f);
|
||||
return utf8_parse_result(utf8_parse_result::SUCCESS, result, 4);
|
||||
}
|
||||
|
||||
// Invalid first byte
|
||||
return utf8_parse_result(utf8_parse_result::INVALID);
|
||||
}
|
||||
22
common/unicode.h
Normal file
22
common/unicode.h
Normal file
@@ -0,0 +1,22 @@
|
||||
#pragma once
|
||||
|
||||
#include <cstdint>
|
||||
#include <string_view>
|
||||
|
||||
// UTF-8 parsing utilities for streaming-aware unicode support
|
||||
|
||||
struct utf8_parse_result {
|
||||
uint32_t codepoint; // Decoded codepoint (only valid if status == SUCCESS)
|
||||
size_t bytes_consumed; // How many bytes this codepoint uses (1-4)
|
||||
enum status { SUCCESS, INCOMPLETE, INVALID } status;
|
||||
|
||||
utf8_parse_result(enum status s, uint32_t cp = 0, size_t bytes = 0)
|
||||
: codepoint(cp), bytes_consumed(bytes), status(s) {}
|
||||
};
|
||||
|
||||
// Determine the expected length of a UTF-8 sequence from its first byte
|
||||
// Returns 0 for invalid first bytes
|
||||
size_t utf8_sequence_length(unsigned char first_byte);
|
||||
|
||||
// Parse a single UTF-8 codepoint from input
|
||||
utf8_parse_result parse_utf8_codepoint(std::string_view input, size_t offset);
|
||||
11433
convert_hf_to_gguf.py
Executable file
11433
convert_hf_to_gguf.py
Executable file
File diff suppressed because it is too large
Load Diff
478
convert_hf_to_gguf_update.py
Executable file
478
convert_hf_to_gguf_update.py
Executable file
@@ -0,0 +1,478 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import logging
|
||||
import os
|
||||
import pathlib
|
||||
import re
|
||||
|
||||
import requests
|
||||
import json
|
||||
import shutil
|
||||
import argparse
|
||||
|
||||
from hashlib import sha256
|
||||
from enum import IntEnum, auto
|
||||
from transformers import AutoTokenizer
|
||||
|
||||
logging.basicConfig(level=logging.DEBUG)
|
||||
logger = logging.getLogger("convert_hf_to_gguf_update")
|
||||
sess = requests.Session()
|
||||
|
||||
convert_py_pth = pathlib.Path("convert_hf_to_gguf.py")
|
||||
convert_py = convert_py_pth.read_text(encoding="utf-8")
|
||||
hf_token_pth = pathlib.Path.home() / ".cache" / "huggingface" / "token"
|
||||
hf_token = hf_token_pth.read_text(encoding="utf-8").strip() if hf_token_pth.exists() else None
|
||||
|
||||
|
||||
class TOKENIZER_TYPE(IntEnum):
|
||||
SPM = auto()
|
||||
BPE = auto()
|
||||
WPM = auto()
|
||||
UGM = auto()
|
||||
|
||||
|
||||
DOC_STRING = """
|
||||
This script downloads the tokenizer models of the specified models from Huggingface and
|
||||
generates the get_vocab_base_pre() function for convert_hf_to_gguf.py
|
||||
|
||||
/!\\ It is intended to be used by contributors and is not meant to be run by end users
|
||||
|
||||
This is necessary in order to analyze the type of pre-tokenizer used by the model and
|
||||
provide the necessary information to llama.cpp via the GGUF header in order to implement
|
||||
the same pre-tokenizer.
|
||||
|
||||
ref: https://github.com/ggml-org/llama.cpp/pull/6920
|
||||
|
||||
Instructions:
|
||||
|
||||
- Add a new model to the "models" list
|
||||
- Run the script with your huggingface token
|
||||
By default, token will be read from ~/.cache/huggingface/token
|
||||
- The convert_hf_to_gguf.py script will have had its get_vocab_base_pre() function updated
|
||||
- Update llama.cpp with the new pre-tokenizer if necessary
|
||||
"""
|
||||
# TODO: generate tokenizer tests for llama.cpp
|
||||
|
||||
parser = argparse.ArgumentParser(description=DOC_STRING, formatter_class=argparse.RawTextHelpFormatter)
|
||||
parser.add_argument(
|
||||
"--full", action="store_true",
|
||||
help="download full list of models - make sure you have access to all of them",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--check-missing", action="store_true",
|
||||
help="only check for missing pre-tokenizer hashes",
|
||||
)
|
||||
parser.add_argument(
|
||||
"hf_token",
|
||||
help="optional HF token",
|
||||
nargs="?",
|
||||
)
|
||||
args = parser.parse_args()
|
||||
hf_token = args.hf_token if args.hf_token is not None else hf_token
|
||||
|
||||
if hf_token is None:
|
||||
logger.warning("HF token not found. You can provide it as an argument or set it in ~/.cache/huggingface/token")
|
||||
|
||||
if args.check_missing and args.full:
|
||||
logger.warning("Downloading full list of models requested, ignoring --check-missing!")
|
||||
args.check_missing = False
|
||||
|
||||
# TODO: this string has to exercise as much pre-tokenizer functionality as possible
|
||||
# will be updated with time - contributions welcome
|
||||
CHK_TXT = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL'
|
||||
|
||||
# TODO: add models here, base models preferred
|
||||
models = [
|
||||
{"name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", },
|
||||
{"name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", },
|
||||
{"name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", },
|
||||
{"name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", },
|
||||
{"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", },
|
||||
{"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", },
|
||||
{"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", },
|
||||
{"name": "falcon3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon3-7B-Base", },
|
||||
{"name": "bert-bge-large", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/BAAI/bge-large-zh-v1.5", },
|
||||
{"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", },
|
||||
{"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", },
|
||||
{"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", },
|
||||
{"name": "stablelm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", },
|
||||
{"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", },
|
||||
{"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", },
|
||||
{"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", },
|
||||
{"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", },
|
||||
{"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", },
|
||||
{"name": "jina-v1-en", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-reranker-v1-tiny-en", },
|
||||
{"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM!
|
||||
{"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", },
|
||||
{"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", },
|
||||
{"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", },
|
||||
{"name": "poro-chat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Poro-34B-chat", },
|
||||
{"name": "jina-v2-code", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-code", },
|
||||
{"name": "viking", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LumiOpen/Viking-7B", }, # Also used for Viking 13B and 33B
|
||||
{"name": "gemma", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2b", },
|
||||
{"name": "gemma-2", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/google/gemma-2-9b", },
|
||||
{"name": "jais", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/core42/jais-13b", },
|
||||
{"name": "t5", "tokt": TOKENIZER_TYPE.UGM, "repo": "https://huggingface.co/google-t5/t5-small", },
|
||||
{"name": "codeshell", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/WisdomShell/CodeShell-7B", },
|
||||
{"name": "tekken", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistralai/Mistral-Nemo-Base-2407", },
|
||||
{"name": "smollm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/HuggingFaceTB/SmolLM-135M", },
|
||||
{'name': "bloom", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigscience/bloom", },
|
||||
{'name': "gpt3-finnish", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/TurkuNLP/gpt3-finnish-small", },
|
||||
{"name": "exaone", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", },
|
||||
{"name": "phi-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/microsoft/phi-2", },
|
||||
{"name": "chameleon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/facebook/chameleon-7b", },
|
||||
{"name": "roberta-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sentence-transformers/stsb-roberta-base"},
|
||||
{"name": "gigachat", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct"},
|
||||
{"name": "megrez", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Infinigence/Megrez-3B-Instruct"},
|
||||
{"name": "deepseek-v3", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V3"},
|
||||
{"name": "deepseek-r1-qwen", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B"},
|
||||
{"name": "gpt-4o", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Xenova/gpt-4o", },
|
||||
{"name": "superbpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/UW/OLMo2-8B-SuperBPE-t180k", },
|
||||
{"name": "trillion", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/trillionlabs/Trillion-7B-preview", },
|
||||
{"name": "bailingmoe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-lite", },
|
||||
{"name": "llama4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", },
|
||||
{"name": "pixtral", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mistral-community/pixtral-12b", },
|
||||
{"name": "seed-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ByteDance-Seed/Seed-Coder-8B-Base", },
|
||||
{"name": "a.x-4.0", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/skt/A.X-4.0", },
|
||||
{"name": "midm-2.0", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/K-intelligence/Midm-2.0-Base-Instruct", },
|
||||
{"name": "lfm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LiquidAI/LFM2-Tokenizer"},
|
||||
{"name": "exaone4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/EXAONE-4.0-32B", },
|
||||
{"name": "mellum", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/JetBrains/Mellum-4b-base", },
|
||||
{"name": "modern-bert", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/answerdotai/ModernBERT-base", },
|
||||
{"name": "afmoe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/arcee-ai/Trinity-Tokenizer", },
|
||||
{"name": "bailingmoe2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/inclusionAI/Ling-mini-base-2.0", },
|
||||
{"name": "granite-docling", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/ibm-granite/granite-docling-258M", },
|
||||
{"name": "minimax-m2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2", },
|
||||
{"name": "kormo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/KORMo-Team/KORMo-tokenizer", },
|
||||
{"name": "youtu", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Youtu-LLM-2B", },
|
||||
{"name": "solar-open", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/upstage/Solar-Open-100B", },
|
||||
{"name": "exaone-moe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/LGAI-EXAONE/K-EXAONE-236B-A23B", },
|
||||
]
|
||||
|
||||
# some models are known to be broken upstream, so we will skip them as exceptions
|
||||
pre_computed_hashes = [
|
||||
# chatglm-bpe has 2 hashes, why?
|
||||
{"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "b6e8e1518dc4305be2fe39c313ed643381c4da5db34a98f6a04c093f8afbe99b"},
|
||||
{"name": "chatglm-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-chat", "chkhsh": "81d72c7348a9f0ebe86f23298d37debe0a5e71149e29bd283904c02262b27516"},
|
||||
{"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/THUDM/glm-4-9b-hf", "chkhsh": "a1336059768a55c99a734006ffb02203cd450fed003e9a71886c88acf24fdbc2"},
|
||||
{"name": "glm4", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/zai-org/GLM-4.5-Air", "chkhsh": "9ca2dd618e8afaf09731a7cf6e2105b373ba6a1821559f258b272fe83e6eb902"},
|
||||
{"name": "minerva-7b", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/sapienzanlp/Minerva-7B-base-v1.0", "chkhsh": "1431a23e583c97432bc230bff598d103ddb5a1f89960c8f1d1051aaa944d0b35"},
|
||||
{"name": "hunyuan", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-A13B-Instruct", "chkhsh": "7e57df22b1fe23a7b1e1c7f3dc4e3f96d43a4eb0836d0c6bdc3436d7b2f1c664"},
|
||||
{"name": "hunyuan-dense", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tencent/Hunyuan-4B-Instruct", "chkhsh": "bba3b3366b646dbdded5dbc42d59598b849371afc42f7beafa914afaa5b70aa6"},
|
||||
# falcon-h1 series uses 4 different tokenizers across model sizes (0.5b - 34b), hence we need to define 4 different hashes
|
||||
{"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-0.5B-Base", "chkhsh": "a6b57017d60e6edb4d88ecc2845188e0eb333a70357e45dcc9b53964a73bbae6"},
|
||||
{"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-1B-Base", "chkhsh": "60476e1243776c4fb1b993dbd7a5f15ac22f83c80afdf425fa5ae01c8d44ef86"},
|
||||
{"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-7B-Base", "chkhsh": "3eda48b4c4dc7de733d1a8b3e3b4a85243dbbf704da2ee9d42c6beced8897896"},
|
||||
{"name": "falcon-h1", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/Falcon-H1-34B-Base", "chkhsh": "48f8e02c0359c0bbdd82f26909171fac1c18a457bb47573ed1fe3bbb2c1cfd4b"},
|
||||
{"name": "kimi-k2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/moonshotai/Kimi-K2-Base", "chkhsh": "81212dc7cdb7e0c1074ca62c5aeab0d43c9f52b8a737be7b12a777c953027890"},
|
||||
{"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen3-Embedding-0.6B", "chkhsh": "d4540891389ea895b53b399da6ac824becc30f2fba0e9ddbb98f92e55ca0e97c"},
|
||||
{"name": "grok-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/alvarobartt/grok-2-tokenizer", "chkhsh": "66b8d4e19ab16c3bfd89bce5d785fb7e0155e8648708a1f42077cb9fe002c273"},
|
||||
# jina-v2-de variants
|
||||
{"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/aari1995/German_Semantic_V3", "chkhsh": "b3d1dd861f1d4c5c0d2569ce36baf3f90fe8a102db3de50dd71ff860d91be3df"},
|
||||
]
|
||||
|
||||
|
||||
def download_file_with_auth(url, token, save_path):
|
||||
headers = {"Authorization": f"Bearer {token}"} if token else None
|
||||
response = sess.get(url, headers=headers)
|
||||
response.raise_for_status()
|
||||
os.makedirs(os.path.dirname(save_path), exist_ok=True)
|
||||
with open(save_path, 'wb') as downloaded_file:
|
||||
downloaded_file.write(response.content)
|
||||
logger.info(f"File {save_path} downloaded successfully")
|
||||
|
||||
|
||||
def download_model(model):
|
||||
name = model["name"]
|
||||
repo = model["repo"]
|
||||
tokt = model["tokt"]
|
||||
|
||||
os.makedirs(f"models/tokenizers/{name}", exist_ok=True)
|
||||
|
||||
files = ["config.json", "tokenizer.json", "tokenizer_config.json"]
|
||||
|
||||
if name == "gpt-4o":
|
||||
# Xenova/gpt-4o is tokenizer-only, it does not contain config.json
|
||||
files = ["tokenizer.json", "tokenizer_config.json"]
|
||||
|
||||
if tokt == TOKENIZER_TYPE.SPM:
|
||||
files.append("tokenizer.model")
|
||||
|
||||
if tokt == TOKENIZER_TYPE.UGM:
|
||||
files.append("spiece.model")
|
||||
|
||||
if os.path.isdir(repo):
|
||||
# If repo is a path on the file system, copy the directory
|
||||
for file in files:
|
||||
src_path = os.path.join(repo, file)
|
||||
dst_path = f"models/tokenizers/{name}/{file}"
|
||||
if os.path.isfile(dst_path):
|
||||
logger.info(f"{name}: File {dst_path} already exists - skipping")
|
||||
continue
|
||||
if os.path.isfile(src_path):
|
||||
shutil.copy2(src_path, dst_path)
|
||||
logger.info(f"{name}: Copied {src_path} to {dst_path}")
|
||||
else:
|
||||
logger.warning(f"{name}: Source file {src_path} does not exist")
|
||||
else:
|
||||
# If repo is a URL, download the files
|
||||
for file in files:
|
||||
save_path = f"models/tokenizers/{name}/{file}"
|
||||
if os.path.isfile(save_path):
|
||||
logger.info(f"{name}: File {save_path} already exists - skipping")
|
||||
continue
|
||||
download_file_with_auth(f"{repo}/resolve/main/{file}", hf_token, save_path)
|
||||
|
||||
|
||||
# get list of existing models and chkhsh from the convert_hf_to_gguf.py file
|
||||
# returns mapping res --> chkhsh
|
||||
def get_existing_models(convert_py):
|
||||
pattern = r'if chkhsh == "([a-f0-9]{64})":\s*\n\s*.*\s*res = "([^"]+)"'
|
||||
matches = re.findall(pattern, convert_py)
|
||||
output = {}
|
||||
for chkhsh, res in matches:
|
||||
output[res] = chkhsh
|
||||
return output
|
||||
|
||||
|
||||
existing_models = {}
|
||||
all_models = models.copy()
|
||||
if not args.full:
|
||||
# Filter out models that already exist in convert_hf_to_gguf.py
|
||||
existing_models = get_existing_models(convert_py)
|
||||
all_models = models.copy()
|
||||
models = [model for model in all_models if model["name"] not in existing_models]
|
||||
|
||||
if not args.check_missing:
|
||||
logging.info(f"Downloading {len(models)} models...")
|
||||
for model in models:
|
||||
try:
|
||||
download_model(model)
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to download model {model['name']}. Error: {e}")
|
||||
|
||||
|
||||
# generate the source code for the convert_hf_to_gguf.py:get_vocab_base_pre() function:
|
||||
|
||||
src_ifs = ""
|
||||
for model in [*pre_computed_hashes, *all_models]:
|
||||
name = model["name"]
|
||||
tokt = model["tokt"]
|
||||
chkhsh = model.get("chkhsh")
|
||||
|
||||
if tokt == TOKENIZER_TYPE.SPM or tokt == TOKENIZER_TYPE.UGM:
|
||||
continue
|
||||
|
||||
# create the tokenizer
|
||||
if chkhsh is not None:
|
||||
# if the model has a pre-computed hash, use it
|
||||
logger.info(f"Using pre-computed hash for model {name}: {chkhsh}")
|
||||
elif name in existing_models:
|
||||
# if the model already exists in convert_hf_to_gguf.py, skip compute hash
|
||||
chkhsh = existing_models[name]
|
||||
else:
|
||||
# otherwise, compute the hash of the tokenizer
|
||||
|
||||
# Fail if the tokenizer folder with config does not exist or there are other download issues previously
|
||||
if not os.path.isfile(f"models/tokenizers/{name}/tokenizer_config.json"):
|
||||
raise OSError(f"Config for tokenizer {name} not found. The model may not exist or is not accessible with the provided token.")
|
||||
|
||||
try:
|
||||
logger.info(f"Loading tokenizer from {f'models/tokenizers/{name}'}...")
|
||||
if name == "t5":
|
||||
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
|
||||
else:
|
||||
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
|
||||
except Exception as e:
|
||||
raise OSError(f"Error loading tokenizer for model {name}.") from e
|
||||
|
||||
chktok = tokenizer.encode(CHK_TXT)
|
||||
chkhsh = sha256(str(chktok).encode()).hexdigest()
|
||||
|
||||
logger.info(f"model: {name}")
|
||||
logger.info(f"tokt: {tokt}")
|
||||
logger.info(f"repo: {model['repo']}")
|
||||
logger.info(f"chktok: {chktok}")
|
||||
logger.info(f"chkhsh: {chkhsh}")
|
||||
|
||||
# print the "pre_tokenizer" content from the tokenizer.json
|
||||
with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f:
|
||||
cfg = json.load(f)
|
||||
normalizer = cfg["normalizer"]
|
||||
logger.info("normalizer: " + json.dumps(normalizer, indent=4))
|
||||
pre_tokenizer = cfg["pre_tokenizer"]
|
||||
logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4))
|
||||
if "ignore_merges" in cfg["model"]:
|
||||
logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4))
|
||||
|
||||
logger.info("")
|
||||
|
||||
src_ifs += f" if chkhsh == \"{chkhsh}\":\n"
|
||||
src_ifs += f" # ref: {model['repo']}\n"
|
||||
src_ifs += f" res = \"{name}\"\n"
|
||||
|
||||
src_func = f"""
|
||||
def get_vocab_base_pre(self, tokenizer) -> str:
|
||||
# encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that
|
||||
# is specific for the BPE pre-tokenizer used by the model
|
||||
# we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can
|
||||
# use in llama.cpp to implement the same pre-tokenizer
|
||||
|
||||
chktxt = {repr(CHK_TXT)}
|
||||
|
||||
chktok = tokenizer.encode(chktxt)
|
||||
chkhsh = sha256(str(chktok).encode()).hexdigest()
|
||||
|
||||
logger.debug(f"chktok: {{chktok}}")
|
||||
logger.debug(f"chkhsh: {{chkhsh}}")
|
||||
|
||||
res = None
|
||||
|
||||
# NOTE: if you get an error here, you need to update the convert_hf_to_gguf_update.py script
|
||||
# or pull the latest version of the model from Huggingface
|
||||
# don't edit the hashes manually!
|
||||
{src_ifs}
|
||||
if res is None:
|
||||
logger.warning("\\n")
|
||||
logger.warning("**************************************************************************************")
|
||||
logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!")
|
||||
logger.warning("** There are 2 possible reasons for this:")
|
||||
logger.warning("** - the model has not been added to convert_hf_to_gguf_update.py yet")
|
||||
logger.warning("** - the pre-tokenization config has changed upstream")
|
||||
logger.warning("** Check your model files and convert_hf_to_gguf_update.py and update them accordingly.")
|
||||
logger.warning("** ref: https://github.com/ggml-org/llama.cpp/pull/6920")
|
||||
logger.warning("**")
|
||||
logger.warning(f"** chkhsh: {{chkhsh}}")
|
||||
logger.warning("**************************************************************************************")
|
||||
logger.warning("\\n")
|
||||
raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()")
|
||||
|
||||
logger.debug(f"tokenizer.ggml.pre: {{repr(res)}}")
|
||||
logger.debug(f"chkhsh: {{chkhsh}}")
|
||||
|
||||
return res
|
||||
"""
|
||||
|
||||
convert_py = re.sub(
|
||||
r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)",
|
||||
lambda m: m.group(1) + src_func + m.group(3),
|
||||
convert_py,
|
||||
flags=re.DOTALL | re.MULTILINE,
|
||||
)
|
||||
|
||||
convert_py_pth.write_text(convert_py, encoding="utf-8")
|
||||
|
||||
logger.info("+++ convert_hf_to_gguf.py was updated")
|
||||
|
||||
# generate tests for each tokenizer model
|
||||
|
||||
tests = [
|
||||
"ied 4 ½ months",
|
||||
"Äpfel",
|
||||
"",
|
||||
" ",
|
||||
" ",
|
||||
" ",
|
||||
"\t",
|
||||
"\n",
|
||||
"\n\n",
|
||||
"\n\n\n",
|
||||
"\t\n",
|
||||
"Hello world",
|
||||
" Hello world",
|
||||
"Hello World",
|
||||
" Hello World",
|
||||
" Hello World!",
|
||||
"Hello, world!",
|
||||
" Hello, world!",
|
||||
" this is 🦙.cpp",
|
||||
"w048 7tuijk dsdfhu",
|
||||
"нещо на Български",
|
||||
"កាន់តែពិសេសអាចខលចេញ",
|
||||
"🚀 (normal) 😶🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)",
|
||||
"Hello",
|
||||
" Hello",
|
||||
" Hello",
|
||||
" Hello",
|
||||
" Hello",
|
||||
" Hello\n Hello",
|
||||
" (",
|
||||
"\n =",
|
||||
"' era",
|
||||
"Hello, y'all! How are you 😁 ?我想在apple工作1314151天～",
|
||||
"!!!!!!",
|
||||
"3",
|
||||
"33",
|
||||
"333",
|
||||
"3333",
|
||||
"33333",
|
||||
"333333",
|
||||
"3333333",
|
||||
"33333333",
|
||||
"333333333",
|
||||
"Cửa Việt", # llama-bpe fails on this
|
||||
" discards",
|
||||
CHK_TXT,
|
||||
]
|
||||
|
||||
# write the tests to ./models/ggml-vocab-{name}.gguf.inp
|
||||
# the format is:
|
||||
#
|
||||
# test0
|
||||
# __ggml_vocab_test__
|
||||
# test1
|
||||
# __ggml_vocab_test__
|
||||
# ...
|
||||
#
|
||||
|
||||
# with each model, encode all tests and write the results in ./models/ggml-vocab-{name}.gguf.out
|
||||
# for each test, write the resulting tokens on a separate line
|
||||
|
||||
for model in models:
|
||||
name = model["name"]
|
||||
tokt = model["tokt"]
|
||||
|
||||
# Skip if the tokenizer folder does not exist or there are other download issues previously
|
||||
if not os.path.exists(f"models/tokenizers/{name}"):
|
||||
logger.warning(f"Directory for tokenizer {name} not found. Skipping...")
|
||||
continue
|
||||
|
||||
# create the tokenizer
|
||||
try:
|
||||
if name == "t5":
|
||||
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}", use_fast=False)
|
||||
else:
|
||||
tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}")
|
||||
except (OSError, TypeError) as e:
|
||||
logger.error(f"Failed to load tokenizer for model {name}. Error: {e}")
|
||||
continue # Skip this model and continue with the next one in the loop
|
||||
|
||||
if not os.path.exists(f"models/ggml-vocab-{name}.gguf"):
|
||||
logger.info(f"Skip vocab files for model {name}, no GGUF file found")
|
||||
continue
|
||||
|
||||
with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f:
|
||||
for text in tests:
|
||||
f.write(f"{text}")
|
||||
f.write("\n__ggml_vocab_test__\n")
|
||||
|
||||
with open(f"models/ggml-vocab-{name}.gguf.out", "w") as f:
|
||||
for text in tests:
|
||||
res = tokenizer.encode(text, add_special_tokens=False)
|
||||
for r in res:
|
||||
f.write(f" {r}")
|
||||
f.write("\n")
|
||||
|
||||
logger.info(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*")
|
||||
|
||||
# generate commands for creating vocab files
|
||||
|
||||
logger.info("\nRun the following commands to generate the vocab files for testing:\n")
|
||||
|
||||
for model in models:
|
||||
name = model["name"]
|
||||
|
||||
print(f"python3 convert_hf_to_gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100
|
||||
|
||||
logger.info("\n")
|
||||
450
convert_llama_ggml_to_gguf.py
Executable file
450
convert_llama_ggml_to_gguf.py
Executable file
@@ -0,0 +1,450 @@
|
||||
#!/usr/bin/env python3
|
||||
from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import argparse
|
||||
import os
|
||||
import struct
|
||||
import sys
|
||||
from enum import IntEnum
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
|
||||
if 'NO_LOCAL_GGUF' not in os.environ:
|
||||
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
|
||||
import gguf
|
||||
|
||||
logger = logging.getLogger("ggml-to-gguf")
|
||||
|
||||
|
||||
class GGMLFormat(IntEnum):
|
||||
GGML = 0
|
||||
GGMF = 1
|
||||
GGJT = 2
|
||||
|
||||
|
||||
class GGMLFType(IntEnum):
|
||||
ALL_F32 = 0
|
||||
MOSTLY_F16 = 1
|
||||
MOSTLY_Q4_0 = 2
|
||||
MOSTLY_Q4_1 = 3
|
||||
MOSTLY_Q4_1_SOME_F16 = 4
|
||||
MOSTLY_Q8_0 = 7
|
||||
MOSTLY_Q5_0 = 8
|
||||
MOSTLY_Q5_1 = 9
|
||||
MOSTLY_Q2_K = 10
|
||||
MOSTLY_Q3_K_S = 11
|
||||
MOSTLY_Q3_K_M = 12
|
||||
MOSTLY_Q3_K_L = 13
|
||||
MOSTLY_Q4_K_S = 14
|
||||
MOSTLY_Q4_K_M = 15
|
||||
MOSTLY_Q5_K_S = 16
|
||||
MOSTLY_Q5_K_M = 17
|
||||
MOSTLY_Q6_K = 18
|
||||
|
||||
|
||||
class Hyperparameters:
|
||||
def __init__(self):
|
||||
self.n_vocab = self.n_embd = self.n_mult = self.n_head = 0
|
||||
self.n_layer = self.n_rot = self.n_ff = 0
|
||||
self.ftype = GGMLFType.ALL_F32
|
||||
|
||||
def set_n_ff(self, model):
|
||||
ff_tensor_idx = model.tensor_map.get(b'layers.0.feed_forward.w1.weight')
|
||||
assert ff_tensor_idx is not None, 'Missing layer 0 FF tensor'
|
||||
ff_tensor = model.tensors[ff_tensor_idx]
|
||||
self.n_ff = ff_tensor.dims[1]
|
||||
|
||||
def load(self, data, offset):
|
||||
(
|
||||
self.n_vocab,
|
||||
self.n_embd,
|
||||
self.n_mult,
|
||||
self.n_head,
|
||||
self.n_layer,
|
||||
self.n_rot,
|
||||
ftype,
|
||||
) = struct.unpack('<7I', data[offset:offset + (4 * 7)])
|
||||
try:
|
||||
self.ftype = GGMLFType(ftype)
|
||||
except ValueError:
|
||||
raise ValueError(f'Invalid ftype {ftype}')
|
||||
return 4 * 7
|
||||
|
||||
def __str__(self):
|
||||
return f'<Hyperparameters: n_vocab={self.n_vocab}, n_embd={self.n_embd}, n_mult={self.n_mult}, n_head={self.n_head}, n_layer={self.n_layer}, n_rot={self.n_rot}, n_ff={self.n_ff}, ftype={self.ftype.name}>'
|
||||
|
||||
|
||||
class Vocab:
|
||||
def __init__(self, load_scores = True):
|
||||
self.items = []
|
||||
self.load_scores = load_scores
|
||||
|
||||
def load(self, data, offset, n_vocab):
|
||||
orig_offset = offset
|
||||
for _ in range(n_vocab):
|
||||
itemlen = struct.unpack('<I', data[offset:offset + 4])[0]
|
||||
assert itemlen < 4096, 'Absurd vocab item length'
|
||||
offset += 4
|
||||
item_text = bytes(data[offset:offset + itemlen])
|
||||
offset += itemlen
|
||||
if self.load_scores:
|
||||
item_score = struct.unpack('<f', data[offset:offset + 4])[0]
|
||||
offset += 4
|
||||
else:
|
||||
item_score = 0.0
|
||||
self.items.append((item_text, item_score))
|
||||
return offset - orig_offset
|
||||
|
||||
|
||||
class Tensor:
|
||||
def __init__(self, use_padding = True):
|
||||
self.name = None
|
||||
self.dims: tuple[int, ...] = ()
|
||||
self.dtype = None
|
||||
self.start_offset = 0
|
||||
self.len_bytes = np.int64(0)
|
||||
self.use_padding = use_padding
|
||||
|
||||
def load(self, data, offset):
|
||||
orig_offset = offset
|
||||
(n_dims, name_len, dtype) = struct.unpack('<3I', data[offset:offset + 12])
|
||||
assert n_dims >= 0 and n_dims <= 4, f'Invalid tensor dimensions {n_dims}'
|
||||
assert name_len < 4096, 'Absurd tensor name length'
|
||||
quant = gguf.GGML_QUANT_SIZES.get(dtype)
|
||||
assert quant is not None, 'Unknown tensor type'
|
||||
(blksize, tysize) = quant
|
||||
offset += 12
|
||||
self.dtype= gguf.GGMLQuantizationType(dtype)
|
||||
self.dims = struct.unpack(f'<{n_dims}I', data[offset:offset + (4 * n_dims)])
|
||||
offset += 4 * n_dims
|
||||
self.name = bytes(data[offset:offset + name_len])
|
||||
offset += name_len
|
||||
pad = ((offset + 31) & ~31) - offset if self.use_padding else 0
|
||||
offset += pad
|
||||
n_elems = np.prod(self.dims)
|
||||
n_bytes = np.int64(np.int64(n_elems) * np.int64(tysize)) // np.int64(blksize)
|
||||
self.start_offset = offset
|
||||
self.len_bytes = n_bytes
|
||||
offset += n_bytes
|
||||
return offset - orig_offset
|
||||
|
||||
|
||||
class GGMLModel:
|
||||
|
||||
file_format: GGMLFormat
|
||||
format_version: int
|
||||
|
||||
def __init__(self):
|
||||
self.hyperparameters = None
|
||||
self.vocab = None
|
||||
self.tensor_map = {}
|
||||
self.tensors = []
|
||||
|
||||
def validate_header(self, data, offset):
|
||||
magic = bytes(data[offset:offset + 4])
|
||||
if magic == b'GGUF':
|
||||
raise ValueError('File is already in GGUF format.')
|
||||
if magic == b'lmgg':
|
||||
self.file_format = GGMLFormat.GGML
|
||||
self.format_version = 1
|
||||
return 4
|
||||
version = struct.unpack('<I', data[offset + 4:offset + 8])[0]
|
||||
if magic == b'fmgg':
|
||||
if version != 1:
|
||||
raise ValueError(f'Cannot handle unexpected GGMF file version {version}')
|
||||
self.file_format = GGMLFormat.GGMF
|
||||
self.format_version = version
|
||||
return 8
|
||||
if magic == b'tjgg':
|
||||
if version < 1 or version > 3:
|
||||
raise ValueError(f'Cannot handle unexpected GGJT file version {version}')
|
||||
self.file_format = GGMLFormat.GGJT
|
||||
self.format_version = version
|
||||
return 8
|
||||
raise ValueError(f"Unexpected file magic {magic!r}! This doesn't look like a GGML format file.")
|
||||
|
||||
def validate_conversion(self, ftype):
|
||||
err = ''
|
||||
if (self.file_format < GGMLFormat.GGJT or self.format_version < 2):
|
||||
if ftype not in (GGMLFType.ALL_F32, GGMLFType.MOSTLY_F16):
|
||||
err = 'Quantizations changed in GGJTv2. Can only convert unquantized GGML files older than GGJTv2.'
|
||||
elif (self.file_format == GGMLFormat.GGJT and self.format_version == 2):
|
||||
if ftype in (GGMLFType.MOSTLY_Q4_0, GGMLFType.MOSTLY_Q4_1,
|
||||
GGMLFType.MOSTLY_Q4_1_SOME_F16, GGMLFType.MOSTLY_Q8_0):
|
||||
err = 'Q4 and Q8 quantizations changed in GGJTv3.'
|
||||
if len(err) > 0:
|
||||
raise ValueError(f'{err} Sorry, your {self.file_format.name}v{self.format_version} file of type {ftype.name} is not eligible for conversion.')
|
||||
|
||||
def load(self, data, offset):
|
||||
offset += self.validate_header(data, offset)
|
||||
hp = Hyperparameters()
|
||||
offset += hp.load(data, offset)
|
||||
logger.info(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}')
|
||||
self.validate_conversion(hp.ftype)
|
||||
vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML)
|
||||
offset += vocab.load(data, offset, hp.n_vocab)
|
||||
tensors: list[Tensor] = []
|
||||
tensor_map = {}
|
||||
while offset < len(data):
|
||||
tensor = Tensor(use_padding = self.file_format > GGMLFormat.GGMF)
|
||||
offset += tensor.load(data, offset)
|
||||
tensor_map[tensor.name] = len(tensors)
|
||||
tensors.append(tensor)
|
||||
self.hyperparameters = hp
|
||||
self.vocab = vocab
|
||||
self.tensors = tensors
|
||||
self.tensor_map = tensor_map
|
||||
hp.set_n_ff(self)
|
||||
return offset
|
||||
|
||||
|
||||
class GGMLToGGUF:
|
||||
def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None, special_vocab = None):
|
||||
hp = ggml_model.hyperparameters
|
||||
self.model = ggml_model
|
||||
self.data = data
|
||||
self.cfg = cfg
|
||||
self.params_override = params_override
|
||||
self.vocab_override = vocab_override
|
||||
self.special_vocab = special_vocab
|
||||
if params_override is not None:
|
||||
n_kv_head = params_override.n_head_kv
|
||||
else:
|
||||
if cfg.gqa == 1:
|
||||
n_kv_head = hp.n_head
|
||||
else:
|
||||
gqa = float(cfg.gqa)
|
||||
n_kv_head = None
|
||||
for x in range(1, 256):
|
||||
if float(hp.n_head) / float(x) == gqa:
|
||||
n_kv_head = x
|
||||
assert n_kv_head is not None, "Couldn't determine n_kv_head from GQA param"
|
||||
logger.info(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}')
|
||||
self.n_kv_head = n_kv_head
|
||||
self.name_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAMA, ggml_model.hyperparameters.n_layer)
|
||||
|
||||
def save(self):
|
||||
logger.info('* Preparing to save GGUF file')
|
||||
gguf_writer = gguf.GGUFWriter(
|
||||
self.cfg.output,
|
||||
gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA],
|
||||
use_temp_file = False)
|
||||
self.add_params(gguf_writer)
|
||||
self.add_vocab(gguf_writer)
|
||||
if self.special_vocab is not None:
|
||||
self.special_vocab.add_to_gguf(gguf_writer)
|
||||
self.add_tensors(gguf_writer)
|
||||
logger.info(" gguf: write header")
|
||||
gguf_writer.write_header_to_file()
|
||||
logger.info(" gguf: write metadata")
|
||||
gguf_writer.write_kv_data_to_file()
|
||||
logger.info(" gguf: write tensors")
|
||||
gguf_writer.write_tensors_to_file()
|
||||
gguf_writer.close()
|
||||
|
||||
def add_params(self, gguf_writer):
|
||||
hp = self.model.hyperparameters
|
||||
cfg = self.cfg
|
||||
if cfg.desc is not None:
|
||||
desc = cfg.desc
|
||||
else:
|
||||
desc = f'converted from legacy {self.model.file_format.name}v{self.model.format_version} {hp.ftype.name} format'
|
||||
try:
|
||||
# Filenames aren't necessarily valid UTF8.
|
||||
name = cfg.name if cfg.name is not None else cfg.input.name
|
||||
except UnicodeDecodeError:
|
||||
name = None
|
||||
logger.info('* Adding model parameters and KV items')
|
||||
if name is not None:
|
||||
gguf_writer.add_name(name)
|
||||
gguf_writer.add_description(desc)
|
||||
gguf_writer.add_file_type(int(hp.ftype))
|
||||
if self.params_override is not None:
|
||||
po = self.params_override
|
||||
assert po.n_embd == hp.n_embd, 'Model hyperparams mismatch'
|
||||
assert po.n_layer == hp.n_layer, 'Model hyperparams mismatch'
|
||||
assert po.n_head == hp.n_head, 'Model hyperparams mismatch'
|
||||
gguf_writer.add_context_length (po.n_ctx)
|
||||
gguf_writer.add_embedding_length (po.n_embd)
|
||||
gguf_writer.add_block_count (po.n_layer)
|
||||
gguf_writer.add_feed_forward_length (po.n_ff)
|
||||
gguf_writer.add_rope_dimension_count(po.n_embd // po.n_head)
|
||||
gguf_writer.add_head_count (po.n_head)
|
||||
gguf_writer.add_head_count_kv (po.n_head_kv)
|
||||
gguf_writer.add_layer_norm_rms_eps (po.f_norm_eps)
|
||||
return
|
||||
gguf_writer.add_context_length(cfg.context_length)
|
||||
gguf_writer.add_embedding_length(hp.n_embd)
|
||||
gguf_writer.add_block_count(hp.n_layer)
|
||||
gguf_writer.add_feed_forward_length(hp.n_ff)
|
||||
gguf_writer.add_rope_dimension_count(hp.n_embd // hp.n_head)
|
||||
gguf_writer.add_head_count(hp.n_head)
|
||||
gguf_writer.add_head_count_kv(self.n_kv_head)
|
||||
gguf_writer.add_layer_norm_rms_eps(float(cfg.eps))
|
||||
|
||||
def add_vocab(self, gguf_writer):
|
||||
hp = self.model.hyperparameters
|
||||
gguf_writer.add_tokenizer_model('llama')
|
||||
gguf_writer.add_tokenizer_pre('default')
|
||||
tokens = []
|
||||
scores = []
|
||||
toktypes = []
|
||||
if self.vocab_override is not None:
|
||||
vo = self.vocab_override
|
||||
logger.info('* Adding vocab item(s)')
|
||||
for (_, (vbytes, score, ttype)) in enumerate(vo.all_tokens()):
|
||||
tokens.append(vbytes)
|
||||
scores.append(score)
|
||||
toktypes.append(ttype)
|
||||
assert len(tokens) == hp.n_vocab, \
|
||||
f'Override vocab has a different number of items than hyperparameters - override = {len(tokens)} but n_vocab={hp.n_vocab}'
|
||||
gguf_writer.add_token_list(tokens)
|
||||
gguf_writer.add_token_scores(scores)
|
||||
if len(toktypes) > 0:
|
||||
gguf_writer.add_token_types(toktypes)
|
||||
return
|
||||
logger.info(f'* Adding {hp.n_vocab} vocab item(s)')
|
||||
assert len(self.model.vocab.items) >= 3, 'Cannot handle unexpectedly short model vocab'
|
||||
for (tokid, (vbytes, vscore)) in enumerate(self.model.vocab.items):
|
||||
tt = 1 # Normal
|
||||
# Special handling for UNK, BOS, EOS tokens.
|
||||
if tokid <= 2:
|
||||
if tokid == 0:
|
||||
vbytes = b'<unk>'
|
||||
tt = 2
|
||||
elif tokid == 1:
|
||||
vbytes = b'<s>'
|
||||
tt = 3
|
||||
else:
|
||||
vbytes = b'</s>'
|
||||
tt = 3
|
||||
elif len(vbytes) == 0:
|
||||
tt = 3 # Control
|
||||
elif tokid >= 3 and tokid <= 258 and len(vbytes) == 1:
|
||||
vbytes = bytes(f'<0x{vbytes[0]:02X}>', encoding = 'UTF-8')
|
||||
tt = 6 # Byte
|
||||
else:
|
||||
vbytes = vbytes.replace(b' ', b'\xe2\x96\x81')
|
||||
toktypes.append(tt)
|
||||
tokens.append(vbytes)
|
||||
scores.append(vscore)
|
||||
gguf_writer.add_token_list(tokens)
|
||||
gguf_writer.add_token_scores(scores)
|
||||
gguf_writer.add_token_types(toktypes)
|
||||
gguf_writer.add_unk_token_id(0)
|
||||
gguf_writer.add_bos_token_id(1)
|
||||
gguf_writer.add_eos_token_id(2)
|
||||
|
||||
def add_tensors(self, gguf_writer):
|
||||
tensor_map = self.name_map
|
||||
data = self.data
|
||||
logger.info(f'* Adding {len(self.model.tensors)} tensor(s)')
|
||||
for tensor in self.model.tensors:
|
||||
name = str(tensor.name, 'UTF-8')
|
||||
mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias"))
|
||||
assert mapped_name is not None, f'Bad name {name}'
|
||||
tempdims = list(tensor.dims[:])
|
||||
if len(tempdims) > 1:
|
||||
temp = tempdims[1]
|
||||
tempdims[1] = tempdims[0]
|
||||
tempdims[0] = temp
|
||||
gguf_writer.add_tensor(
|
||||
mapped_name,
|
||||
data[tensor.start_offset:tensor.start_offset + tensor.len_bytes],
|
||||
raw_shape = tempdims,
|
||||
raw_dtype = tensor.dtype)
|
||||
|
||||
|
||||
def handle_metadata(cfg, hp):
|
||||
import examples.convert_legacy_llama as convert
|
||||
|
||||
assert cfg.model_metadata_dir.is_dir(), 'Metadata dir is not a directory'
|
||||
hf_config_path = cfg.model_metadata_dir / "config.json"
|
||||
orig_config_path = cfg.model_metadata_dir / "params.json"
|
||||
# We pass a fake model here. "original" mode will check the shapes of some
|
||||
# tensors if information is missing in the .json file: other than that, the
|
||||
# model data isn't used so this should be safe (at least for now).
|
||||
fakemodel = {
|
||||
'tok_embeddings.weight': convert.LazyTensor.__new__(convert.LazyTensor),
|
||||
'layers.0.feed_forward.w1.weight': convert.LazyTensor.__new__(convert.LazyTensor),
|
||||
}
|
||||
fakemodel['tok_embeddings.weight'].shape = [hp.n_vocab]
|
||||
fakemodel['layers.0.feed_forward.w1.weight'].shape = [hp.n_ff]
|
||||
if hf_config_path.exists():
|
||||
params = convert.Params.loadHFTransformerJson(fakemodel, hf_config_path)
|
||||
elif orig_config_path.exists():
|
||||
params = convert.Params.loadOriginalParamsJson(fakemodel, orig_config_path)
|
||||
else:
|
||||
raise ValueError('Unable to load metadata')
|
||||
vocab_path = Path(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir)
|
||||
vocab_factory = convert.VocabFactory(vocab_path)
|
||||
vocab, special_vocab = vocab_factory.load_vocab(cfg.vocabtype.split(","), cfg.model_metadata_dir)
|
||||
convert.check_vocab_size(params, vocab)
|
||||
return params, vocab, special_vocab
|
||||
|
||||
|
||||
def handle_args():
|
||||
parser = argparse.ArgumentParser(description = 'Convert GGML models to GGUF')
|
||||
parser.add_argument('--input', '-i', type = Path, required = True,
|
||||
help = 'Input GGMLv3 filename')
|
||||
parser.add_argument('--output', '-o', type = Path, required = True,
|
||||
help ='Output GGUF filename')
|
||||
parser.add_argument('--name',
|
||||
help = 'Set model name')
|
||||
parser.add_argument('--desc',
|
||||
help = 'Set model description')
|
||||
parser.add_argument('--gqa', type = int, default = 1,
|
||||
help = 'grouped-query attention factor (use 8 for LLaMA2 70B)')
|
||||
parser.add_argument('--eps', default = '5.0e-06',
|
||||
help = 'RMS norm eps: Use 1e-6 for LLaMA1 and OpenLLaMA, use 1e-5 for LLaMA2')
|
||||
parser.add_argument('--context-length', '-c', type=int, default = 2048,
|
||||
help = 'Default max context length: LLaMA1 is typically 2048, LLaMA2 is typically 4096')
|
||||
parser.add_argument('--model-metadata-dir', '-m', type = Path,
|
||||
help ='Load HuggingFace/.pth vocab and metadata from the specified directory')
|
||||
parser.add_argument("--vocab-dir", type=Path,
|
||||
help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir")
|
||||
parser.add_argument("--vocabtype", default="spm,hfft",
|
||||
help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)")
|
||||
parser.add_argument("--verbose", action="store_true", help="increase output verbosity")
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main():
|
||||
cfg = handle_args()
|
||||
logging.basicConfig(level=logging.DEBUG if cfg.verbose else logging.INFO)
|
||||
logger.info(f'* Using config: {cfg}')
|
||||
logger.warning('=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===')
|
||||
if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'):
|
||||
logger.info('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".')
|
||||
data = np.memmap(cfg.input, mode = 'r')
|
||||
model = GGMLModel()
|
||||
logger.info('* Scanning GGML input file')
|
||||
offset = model.load(data, 0) # noqa
|
||||
logger.info(f'* GGML model hyperparameters: {model.hyperparameters}')
|
||||
vocab_override = None
|
||||
params_override = None
|
||||
special_vocab = None
|
||||
if cfg.model_metadata_dir is not None:
|
||||
(params_override, vocab_override, special_vocab) = handle_metadata(cfg, model.hyperparameters)
|
||||
logger.info('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.')
|
||||
logger.info(f'* Overriding params: {params_override}')
|
||||
logger.info(f'* Overriding vocab: {vocab_override}')
|
||||
logger.info(f'* Special vocab: {special_vocab}')
|
||||
else:
|
||||
logger.warning('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n')
|
||||
if model.file_format == GGMLFormat.GGML:
|
||||
logger.info('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!')
|
||||
converter = GGMLToGGUF(
|
||||
model, data, cfg,
|
||||
params_override = params_override,
|
||||
vocab_override = vocab_override,
|
||||
special_vocab = special_vocab
|
||||
)
|
||||
converter.save()
|
||||
logger.info(f'* Successful completion. Output saved to: {cfg.output}')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
493
convert_lora_to_gguf.py
Executable file
493
convert_lora_to_gguf.py
Executable file
@@ -0,0 +1,493 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from dataclasses import dataclass
|
||||
import logging
|
||||
import argparse
|
||||
import os
|
||||
import sys
|
||||
import json
|
||||
from math import prod
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any, Callable, Iterable, Iterator, Sequence, SupportsIndex, cast
|
||||
from transformers import AutoConfig, AutoTokenizer
|
||||
|
||||
import torch
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from torch import Tensor
|
||||
|
||||
if 'NO_LOCAL_GGUF' not in os.environ:
|
||||
sys.path.insert(1, str(Path(__file__).parent / 'gguf-py'))
|
||||
import gguf
|
||||
|
||||
# reuse model definitions from convert_hf_to_gguf.py
|
||||
from convert_hf_to_gguf import LazyTorchTensor, ModelBase
|
||||
|
||||
from gguf.constants import GGUFValueType
|
||||
|
||||
logger = logging.getLogger("lora-to-gguf")
|
||||
|
||||
|
||||
@dataclass
|
||||
class PartialLoraTensor:
|
||||
A: Tensor | None = None
|
||||
B: Tensor | None = None
|
||||
|
||||
|
||||
# magic to support tensor shape modifications and splitting
|
||||
class LoraTorchTensor:
|
||||
_lora_A: Tensor # (n_rank, row_size)
|
||||
_lora_B: Tensor # (col_size, n_rank)
|
||||
_rank: int
|
||||
|
||||
def __init__(self, A: Tensor, B: Tensor):
|
||||
assert len(A.shape) == len(B.shape)
|
||||
assert A.shape[-2] == B.shape[-1]
|
||||
if A.dtype != B.dtype:
|
||||
A = A.to(torch.float32)
|
||||
B = B.to(torch.float32)
|
||||
self._lora_A = A
|
||||
self._lora_B = B
|
||||
self._rank = B.shape[-1]
|
||||
|
||||
def get_lora_A_B(self) -> tuple[Tensor, Tensor]:
|
||||
return (self._lora_A, self._lora_B)
|
||||
|
||||
def __getitem__(
|
||||
self,
|
||||
indices: (
|
||||
SupportsIndex
|
||||
| slice
|
||||
| tuple[SupportsIndex | slice | Tensor, ...] # TODO: add ellipsis in the type signature
|
||||
),
|
||||
) -> LoraTorchTensor:
|
||||
shape = self.shape
|
||||
if isinstance(indices, SupportsIndex):
|
||||
if len(shape) > 2:
|
||||
return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices])
|
||||
else:
|
||||
raise NotImplementedError # can't return a vector
|
||||
elif isinstance(indices, slice):
|
||||
if len(shape) > 2:
|
||||
return LoraTorchTensor(self._lora_A[indices], self._lora_B[indices])
|
||||
else:
|
||||
return LoraTorchTensor(self._lora_A, self._lora_B[indices])
|
||||
elif isinstance(indices, tuple):
|
||||
assert len(indices) > 0
|
||||
if indices[-1] is Ellipsis:
|
||||
return self[indices[:-1]]
|
||||
# expand ellipsis
|
||||
indices = tuple(
|
||||
u
|
||||
for v in (
|
||||
(
|
||||
(slice(None, None) for _ in range(len(indices) - 1))
|
||||
if i is Ellipsis
|
||||
else (i,)
|
||||
)
|
||||
for i in indices
|
||||
)
|
||||
for u in v
|
||||
)
|
||||
|
||||
if len(indices) < len(shape):
|
||||
indices = (*indices, *(slice(None, None) for _ in range(len(indices), len(shape))))
|
||||
|
||||
# TODO: make sure this is correct
|
||||
indices_A = (
|
||||
*(
|
||||
(
|
||||
j.__index__() % self._lora_A.shape[i]
|
||||
if isinstance(j, SupportsIndex)
|
||||
else slice(None, None)
|
||||
)
|
||||
for i, j in enumerate(indices[:-2])
|
||||
),
|
||||
slice(None, None),
|
||||
indices[-1],
|
||||
)
|
||||
indices_B = indices[:-1]
|
||||
return LoraTorchTensor(self._lora_A[indices_A], self._lora_B[indices_B])
|
||||
else:
|
||||
raise NotImplementedError # unknown indice type
|
||||
|
||||
@property
|
||||
def dtype(self) -> torch.dtype:
|
||||
assert self._lora_A.dtype == self._lora_B.dtype
|
||||
return self._lora_A.dtype
|
||||
|
||||
@property
|
||||
def shape(self) -> tuple[int, ...]:
|
||||
assert len(self._lora_A.shape) == len(self._lora_B.shape)
|
||||
return (*self._lora_B.shape[:-1], self._lora_A.shape[-1])
|
||||
|
||||
def size(self, dim=None):
|
||||
assert dim is None
|
||||
return self.shape
|
||||
|
||||
def reshape(self, *shape: int | tuple[int, ...]) -> LoraTorchTensor:
|
||||
if isinstance(shape[0], tuple):
|
||||
new_shape: tuple[int, ...] = shape[0]
|
||||
else:
|
||||
new_shape = cast(tuple[int, ...], shape)
|
||||
orig_shape = self.shape
|
||||
if len(new_shape) < 2:
|
||||
raise NotImplementedError # can't become a vector
|
||||
|
||||
# expand -1 in the shape
|
||||
if any(dim == -1 for dim in new_shape):
|
||||
n_elems = prod(orig_shape)
|
||||
n_new_elems = prod(dim if dim != -1 else 1 for dim in new_shape)
|
||||
assert n_elems % n_new_elems == 0
|
||||
new_shape = (*(dim if dim != -1 else n_elems // n_new_elems for dim in new_shape),)
|
||||
|
||||
if new_shape[-1] != orig_shape[-1]:
|
||||
raise NotImplementedError # can't reshape the row size trivially
|
||||
|
||||
shape_A = (*(1 for _ in new_shape[:-2]), self._rank, orig_shape[-1])
|
||||
shape_B = (*new_shape[:-1], self._rank)
|
||||
return LoraTorchTensor(
|
||||
self._lora_A.reshape(shape_A),
|
||||
self._lora_B.reshape(shape_B),
|
||||
)
|
||||
|
||||
def reshape_as(self, other: Tensor) -> LoraTorchTensor:
|
||||
return self.reshape(*other.shape)
|
||||
|
||||
def view(self, *size: int) -> LoraTorchTensor:
|
||||
return self.reshape(*size)
|
||||
|
||||
def permute(self, *dims: int) -> LoraTorchTensor:
|
||||
shape = self.shape
|
||||
dims = tuple(dim - len(shape) if dim >= 0 else dim for dim in dims)
|
||||
if dims[-1] == -1:
|
||||
# TODO: support higher dimensional A shapes bigger than 1
|
||||
assert all(dim == 1 for dim in self._lora_A.shape[:-2])
|
||||
return LoraTorchTensor(self._lora_A, self._lora_B.permute(*dims))
|
||||
if len(shape) == 2 and dims[-1] == -2 and dims[-2] == -1:
|
||||
return LoraTorchTensor(self._lora_B.permute(*dims), self._lora_A.permute(*dims))
|
||||
else:
|
||||
# TODO: compose the above two
|
||||
raise NotImplementedError
|
||||
|
||||
def transpose(self, dim0: int, dim1: int) -> LoraTorchTensor:
|
||||
shape = self.shape
|
||||
dims = [i for i in range(len(shape))]
|
||||
dims[dim0], dims[dim1] = dims[dim1], dims[dim0]
|
||||
return self.permute(*dims)
|
||||
|
||||
def swapaxes(self, axis0: int, axis1: int) -> LoraTorchTensor:
|
||||
return self.transpose(axis0, axis1)
|
||||
|
||||
def to(self, *args, **kwargs):
|
||||
return LoraTorchTensor(self._lora_A.to(*args, **kwargs), self._lora_B.to(*args, **kwargs))
|
||||
|
||||
@classmethod
|
||||
def __torch_function__(cls, func: Callable, types, args=(), kwargs=None):
|
||||
del types # unused
|
||||
|
||||
if kwargs is None:
|
||||
kwargs = {}
|
||||
|
||||
if func is torch.permute:
|
||||
return type(args[0]).permute(*args, **kwargs)
|
||||
elif func is torch.reshape:
|
||||
return type(args[0]).reshape(*args, **kwargs)
|
||||
elif func is torch.stack:
|
||||
assert isinstance(args[0], Sequence)
|
||||
dim = kwargs.get("dim", 0)
|
||||
assert dim == 0
|
||||
return LoraTorchTensor(
|
||||
torch.stack([a._lora_A for a in args[0]], dim),
|
||||
torch.stack([b._lora_B for b in args[0]], dim),
|
||||
)
|
||||
elif func is torch.cat:
|
||||
assert isinstance(args[0], Sequence)
|
||||
dim = kwargs.get("dim", 0)
|
||||
assert dim == 0
|
||||
if len(args[0][0].shape) > 2:
|
||||
return LoraTorchTensor(
|
||||
torch.cat([a._lora_A for a in args[0]], dim),
|
||||
torch.cat([b._lora_B for b in args[0]], dim),
|
||||
)
|
||||
elif all(torch.equal(args[0][0]._lora_A, t._lora_A) for t in args[0][1:]):
|
||||
return LoraTorchTensor(
|
||||
args[0][0]._lora_A,
|
||||
torch.cat([b._lora_B for b in args[0]], dim),
|
||||
)
|
||||
else:
|
||||
raise NotImplementedError
|
||||
else:
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
def get_base_tensor_name(lora_tensor_name: str) -> str:
|
||||
base_name = lora_tensor_name.replace("base_model.model.", "")
|
||||
base_name = base_name.replace(".lora_A.weight", ".weight")
|
||||
base_name = base_name.replace(".lora_B.weight", ".weight")
|
||||
# models produced by mergekit-extract-lora have token embeddings in the adapter
|
||||
base_name = base_name.replace(".lora_embedding_A", ".weight")
|
||||
base_name = base_name.replace(".lora_embedding_B", ".weight")
|
||||
return base_name
|
||||
|
||||
|
||||
def parse_args() -> argparse.Namespace:
|
||||
parser = argparse.ArgumentParser(
|
||||
description="Convert a Hugging Face PEFT LoRA adapter to a GGUF file")
|
||||
parser.add_argument(
|
||||
"--outfile", type=Path,
|
||||
help="path to write to; default: based on input. {ftype} will be replaced by the outtype.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--outtype", type=str, choices=["f32", "f16", "bf16", "q8_0", "auto"], default="f32",
|
||||
help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, q8_0 for Q8_0, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--bigendian", action="store_true",
|
||||
help="model is executed on big endian machine",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--no-lazy", action="store_true",
|
||||
help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--verbose", action="store_true",
|
||||
help="increase output verbosity",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--dry-run", action="store_true",
|
||||
help="only print out what will be done, without writing any new files",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--base", type=Path,
|
||||
help="directory containing Hugging Face model config files (config.json, tokenizer.json) for the base model that the adapter is based on - only config is needed, actual model weights are not required. If base model is unspecified, it will be loaded from Hugging Face hub based on the adapter config",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--base-model-id", type=str,
|
||||
help="the model ID of the base model, if it is not available locally or in the adapter config. If specified, it will ignore --base and load the base model config from the Hugging Face hub (Example: 'meta-llama/Llama-3.2-1B-Instruct')",
|
||||
)
|
||||
parser.add_argument(
|
||||
"lora_path", type=Path,
|
||||
help="directory containing Hugging Face PEFT LoRA config (adapter_model.json) and weights (adapter_model.safetensors or adapter_model.bin)",
|
||||
)
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def load_hparams_from_hf(hf_model_id: str) -> tuple[dict[str, Any], Path | None]:
|
||||
from huggingface_hub import try_to_load_from_cache
|
||||
|
||||
# normally, adapter does not come with base model config, we need to load it from AutoConfig
|
||||
config = AutoConfig.from_pretrained(hf_model_id)
|
||||
cache_dir = try_to_load_from_cache(hf_model_id, "config.json")
|
||||
cache_dir = Path(cache_dir).parent if isinstance(cache_dir, str) else None
|
||||
|
||||
return config.to_dict(), cache_dir
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = parse_args()
|
||||
logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO)
|
||||
|
||||
ftype_map: dict[str, gguf.LlamaFileType] = {
|
||||
"f32": gguf.LlamaFileType.ALL_F32,
|
||||
"f16": gguf.LlamaFileType.MOSTLY_F16,
|
||||
"bf16": gguf.LlamaFileType.MOSTLY_BF16,
|
||||
"q8_0": gguf.LlamaFileType.MOSTLY_Q8_0,
|
||||
"auto": gguf.LlamaFileType.GUESSED,
|
||||
}
|
||||
|
||||
ftype = ftype_map[args.outtype]
|
||||
|
||||
dir_base_model: Path | None = args.base
|
||||
dir_lora: Path = args.lora_path
|
||||
base_model_id: str | None = args.base_model_id
|
||||
lora_config = dir_lora / "adapter_config.json"
|
||||
input_model = dir_lora / "adapter_model.safetensors"
|
||||
|
||||
if args.outfile is not None:
|
||||
fname_out = args.outfile
|
||||
else:
|
||||
# output in the same directory as the model by default
|
||||
fname_out = dir_lora
|
||||
|
||||
if os.path.exists(input_model):
|
||||
# lazy import load_file only if lora is in safetensors format.
|
||||
from safetensors.torch import load_file
|
||||
|
||||
lora_model = load_file(input_model, device="cpu")
|
||||
else:
|
||||
input_model = os.path.join(dir_lora, "adapter_model.bin")
|
||||
lora_model = torch.load(input_model, map_location="cpu", weights_only=True)
|
||||
|
||||
# load LoRA config
|
||||
with open(lora_config, "r") as f:
|
||||
lparams: dict[str, Any] = json.load(f)
|
||||
|
||||
# load base model
|
||||
if base_model_id is not None:
|
||||
logger.info(f"Loading base model from Hugging Face: {base_model_id}")
|
||||
hparams, dir_base_model = load_hparams_from_hf(base_model_id)
|
||||
elif dir_base_model is None:
|
||||
if "base_model_name_or_path" in lparams:
|
||||
model_id = lparams["base_model_name_or_path"]
|
||||
logger.info(f"Loading base model from Hugging Face: {model_id}")
|
||||
try:
|
||||
hparams, dir_base_model = load_hparams_from_hf(model_id)
|
||||
except OSError as e:
|
||||
logger.error(f"Failed to load base model config: {e}")
|
||||
logger.error("Please try downloading the base model and add its path to --base")
|
||||
sys.exit(1)
|
||||
else:
|
||||
logger.error("'base_model_name_or_path' is not found in adapter_config.json")
|
||||
logger.error("Base model config is required. Please download the base model and add its path to --base")
|
||||
sys.exit(1)
|
||||
else:
|
||||
logger.info(f"Loading base model: {dir_base_model.name}")
|
||||
hparams = ModelBase.load_hparams(dir_base_model, False)
|
||||
|
||||
with torch.inference_mode():
|
||||
try:
|
||||
model_class = ModelBase.from_model_architecture(hparams["architectures"][0])
|
||||
except NotImplementedError:
|
||||
logger.error(f"Model {hparams['architectures'][0]} is not supported")
|
||||
sys.exit(1)
|
||||
|
||||
class LoraModel(model_class):
|
||||
model_arch = model_class.model_arch
|
||||
|
||||
lora_alpha: float
|
||||
|
||||
def __init__(self, *args, dir_lora_model: Path, lora_alpha: float, **kwargs):
|
||||
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
self.dir_model_card = dir_lora_model
|
||||
self.lora_alpha = float(lora_alpha)
|
||||
|
||||
def set_vocab(self):
|
||||
pass
|
||||
|
||||
def set_type(self):
|
||||
self.gguf_writer.add_type(gguf.GGUFType.ADAPTER)
|
||||
self.gguf_writer.add_string(gguf.Keys.Adapter.TYPE, "lora")
|
||||
|
||||
def set_gguf_parameters(self):
|
||||
logger.debug("GGUF KV: %s = %d", gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
|
||||
self.gguf_writer.add_float32(gguf.Keys.Adapter.LORA_ALPHA, self.lora_alpha)
|
||||
alora_invocation_tokens = lparams.get("alora_invocation_tokens")
|
||||
invocation_string = lparams.get("invocation_string")
|
||||
if invocation_string and not alora_invocation_tokens:
|
||||
logger.debug("Tokenizing invocation_string -> alora_invocation_tokens")
|
||||
base_model_path_or_id = hparams.get("_name_or_path")
|
||||
try:
|
||||
tokenizer = AutoTokenizer.from_pretrained(base_model_path_or_id)
|
||||
except ValueError:
|
||||
logger.error("Unable to load tokenizer from %s", base_model_path_or_id)
|
||||
raise
|
||||
# NOTE: There's an off-by-one with the older aLoRAs where
|
||||
# the invocation string includes the "<|start_of_turn|>"
|
||||
# token, but the adapters themselves were trained to
|
||||
# activate _after_ that first token, so we drop it here.
|
||||
alora_invocation_tokens = tokenizer(invocation_string)["input_ids"][1:]
|
||||
if alora_invocation_tokens:
|
||||
logger.debug("GGUF KV: %s = %s", gguf.Keys.Adapter.ALORA_INVOCATION_TOKENS, alora_invocation_tokens)
|
||||
self.gguf_writer.add_key_value(
|
||||
gguf.Keys.Adapter.ALORA_INVOCATION_TOKENS,
|
||||
alora_invocation_tokens,
|
||||
GGUFValueType.ARRAY,
|
||||
GGUFValueType.UINT32,
|
||||
)
|
||||
|
||||
def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]:
|
||||
# Never add extra tensors (e.g. rope_freqs) for LoRA adapters
|
||||
return ()
|
||||
|
||||
def get_tensors(self) -> Iterator[tuple[str, Tensor]]:
|
||||
tensor_map: dict[str, PartialLoraTensor] = {}
|
||||
|
||||
for name, tensor in lora_model.items():
|
||||
if self.lazy:
|
||||
tensor = LazyTorchTensor.from_eager(tensor)
|
||||
base_name = get_base_tensor_name(name)
|
||||
# note: mergekit-extract-lora also adds token embeddings to the adapter
|
||||
is_lora_a = ".lora_A.weight" in name or ".lora_embedding_A" in name
|
||||
is_lora_b = ".lora_B.weight" in name or ".lora_embedding_B" in name
|
||||
if not is_lora_a and not is_lora_b:
|
||||
if ".base_layer.weight" in name:
|
||||
continue
|
||||
# mergekit-extract-lora add these layernorm to the adapter, we need to keep them
|
||||
if "_layernorm" in name or ".norm" in name:
|
||||
yield (base_name, tensor)
|
||||
continue
|
||||
logger.error(f"Unexpected name '{name}': Not a lora_A or lora_B tensor")
|
||||
if ".embed_tokens.weight" in name or ".lm_head.weight" in name:
|
||||
logger.error("Embeddings is present in the adapter. This can be due to new tokens added during fine tuning")
|
||||
logger.error("Please refer to https://github.com/ggml-org/llama.cpp/pull/9948")
|
||||
sys.exit(1)
|
||||
|
||||
if base_name in tensor_map:
|
||||
if is_lora_a:
|
||||
tensor_map[base_name].A = tensor
|
||||
else:
|
||||
tensor_map[base_name].B = tensor
|
||||
else:
|
||||
if is_lora_a:
|
||||
tensor_map[base_name] = PartialLoraTensor(A=tensor)
|
||||
else:
|
||||
tensor_map[base_name] = PartialLoraTensor(B=tensor)
|
||||
|
||||
for name, tensor in tensor_map.items():
|
||||
assert tensor.A is not None
|
||||
assert tensor.B is not None
|
||||
yield (name, cast(torch.Tensor, LoraTorchTensor(tensor.A, tensor.B)))
|
||||
|
||||
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
|
||||
dest = list(super().modify_tensors(data_torch, name, bid))
|
||||
# some archs may have the same tensor for lm_head and output (tie word embeddings)
|
||||
# in this case, adapters targeting lm_head will fail when using llama-export-lora
|
||||
# therefore, we ignore them for now
|
||||
# see: https://github.com/ggml-org/llama.cpp/issues/9065
|
||||
if name == "lm_head.weight" and len(dest) == 0:
|
||||
raise ValueError("lm_head is present in adapter, but is ignored in base model")
|
||||
for dest_name, dest_data in dest:
|
||||
# mergekit-extract-lora add these layernorm to the adapter
|
||||
if "_norm" in dest_name:
|
||||
assert dest_data.dim() == 1
|
||||
yield (dest_name, dest_data)
|
||||
continue
|
||||
|
||||
# otherwise, we must get the lora_A and lora_B tensors
|
||||
assert isinstance(dest_data, LoraTorchTensor)
|
||||
lora_a, lora_b = dest_data.get_lora_A_B()
|
||||
|
||||
# note: mergekit-extract-lora flip and transpose A and B
|
||||
# here we only need to transpose token_embd.lora_a, see llm_build_inp_embd()
|
||||
if "token_embd.weight" in dest_name:
|
||||
lora_a = lora_a.T
|
||||
|
||||
yield (dest_name + ".lora_a", lora_a)
|
||||
yield (dest_name + ".lora_b", lora_b)
|
||||
|
||||
alpha: float = lparams["lora_alpha"]
|
||||
|
||||
model_instance = LoraModel(
|
||||
dir_base_model,
|
||||
ftype,
|
||||
fname_out,
|
||||
is_big_endian=args.bigendian,
|
||||
use_temp_file=False,
|
||||
eager=args.no_lazy,
|
||||
dry_run=args.dry_run,
|
||||
dir_lora_model=dir_lora,
|
||||
lora_alpha=alpha,
|
||||
hparams=hparams,
|
||||
remote_hf_model_id=base_model_id,
|
||||
)
|
||||
|
||||
logger.info("Exporting model...")
|
||||
model_instance.write()
|
||||
logger.info(f"Model successfully exported to {model_instance.fname_out}")
|
||||
103
docs/android.md
Normal file
103
docs/android.md
Normal file
@@ -0,0 +1,103 @@
|
||||
|
||||
# Android
|
||||
|
||||
## Build GUI binding using Android Studio
|
||||
|
||||
Import the `examples/llama.android` directory into Android Studio, then perform a Gradle sync and build the project.
|
||||
|
||||
|
||||
This Android binding supports hardware acceleration up to `SME2` for **Arm** and `AMX` for **x86-64** CPUs on Android and ChromeOS devices.
|
||||
It automatically detects the host's hardware to load compatible kernels. As a result, it runs seamlessly on both the latest premium devices and older devices that may lack modern CPU features or have limited RAM, without requiring any manual configuration.
|
||||
|
||||
A minimal Android app frontend is included to showcase the binding’s core functionalities:
|
||||
1. **Parse GGUF metadata** via `GgufMetadataReader` from either a `ContentResolver` provided `Uri` from shared storage, or a local `File` from your app's private storage.
|
||||
2. **Obtain a `InferenceEngine`** instance through the `AiChat` facade and load your selected model via its app-private file path.
|
||||
3. **Send a raw user prompt** for automatic template formatting, prefill, and batch decoding. Then collect the generated tokens in a Kotlin `Flow`.
|
||||
|
||||
For a production-ready experience that leverages advanced features such as system prompts and benchmarks, plus friendly UI features such as model management and Arm feature visualizer, check out [Arm AI Chat](https://play.google.com/store/apps/details?id=com.arm.aichat) on Google Play.
|
||||
This project is made possible through a collaborative effort by Arm's **CT-ML**, **CE-ML** and **STE** groups:
|
||||
|
||||
|  |  |  |
|
||||
|:------------------------------------------------------:|:----------------------------------------------------:|:--------------------------------------------------------:|
|
||||
| Home screen | System prompt | "Haiku" |
|
||||
|
||||
## Build CLI on Android using Termux
|
||||
|
||||
[Termux](https://termux.dev/en/) is an Android terminal emulator and Linux environment app (no root required). As of writing, Termux is available experimentally in the Google Play Store; otherwise, it may be obtained directly from the project repo or on F-Droid.
|
||||
|
||||
With Termux, you can install and run `llama.cpp` as if the environment were Linux. Once in the Termux shell:
|
||||
|
||||
```
|
||||
$ apt update && apt upgrade -y
|
||||
$ apt install git cmake
|
||||
```
|
||||
|
||||
Then, follow the [build instructions](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md), specifically for CMake.
|
||||
|
||||
Once the binaries are built, download your model of choice (e.g., from Hugging Face). It's recommended to place it in the `~/` directory for best performance:
|
||||
|
||||
```
|
||||
$ curl -L {model-url} -o ~/{model}.gguf
|
||||
```
|
||||
|
||||
Then, if you are not already in the repo directory, `cd` into `llama.cpp` and:
|
||||
|
||||
```
|
||||
$ ./build/bin/llama-cli -m ~/{model}.gguf -c {context-size} -p "{your-prompt}"
|
||||
```
|
||||
|
||||
Here, we show `llama-cli`, but any of the executables under `examples` should work, in theory. Be sure to set `context-size` to a reasonable number (say, 4096) to start with; otherwise, memory could spike and kill your terminal.
|
||||
|
||||
To see what it might look like visually, here's an old demo of an interactive session running on a Pixel 5 phone:
|
||||
|
||||
https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4
|
||||
|
||||
## Cross-compile CLI using Android NDK
|
||||
It's possible to build `llama.cpp` for Android on your host system via CMake and the Android NDK. If you are interested in this path, ensure you already have an environment prepared to cross-compile programs for Android (i.e., install the Android SDK). Note that, unlike desktop environments, the Android environment ships with a limited set of native libraries, and so only those libraries are available to CMake when building with the Android NDK (see: https://developer.android.com/ndk/guides/stable_apis.)
|
||||
|
||||
Once you're ready and have cloned `llama.cpp`, invoke the following in the project directory:
|
||||
|
||||
```
|
||||
$ cmake \
|
||||
-DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
|
||||
-DANDROID_ABI=arm64-v8a \
|
||||
-DANDROID_PLATFORM=android-28 \
|
||||
-DCMAKE_C_FLAGS="-march=armv8.7a" \
|
||||
-DCMAKE_CXX_FLAGS="-march=armv8.7a" \
|
||||
-DGGML_OPENMP=OFF \
|
||||
-DGGML_LLAMAFILE=OFF \
|
||||
-B build-android
|
||||
```
|
||||
|
||||
Notes:
|
||||
- While later versions of Android NDK ship with OpenMP, it must still be installed by CMake as a dependency, which is not supported at this time
|
||||
- `llamafile` does not appear to support Android devices (see: https://github.com/Mozilla-Ocho/llamafile/issues/325)
|
||||
|
||||
The above command should configure `llama.cpp` with the most performant options for modern devices. Even if your device is not running `armv8.7a`, `llama.cpp` includes runtime checks for available CPU features it can use.
|
||||
|
||||
Feel free to adjust the Android ABI for your target. Once the project is configured:
|
||||
|
||||
```
|
||||
$ cmake --build build-android --config Release -j{n}
|
||||
$ cmake --install build-android --prefix {install-dir} --config Release
|
||||
```
|
||||
|
||||
After installing, go ahead and download the model of your choice to your host system. Then:
|
||||
|
||||
```
|
||||
$ adb shell "mkdir /data/local/tmp/llama.cpp"
|
||||
$ adb push {install-dir} /data/local/tmp/llama.cpp/
|
||||
$ adb push {model}.gguf /data/local/tmp/llama.cpp/
|
||||
$ adb shell
|
||||
```
|
||||
|
||||
In the `adb shell`:
|
||||
|
||||
```
|
||||
$ cd /data/local/tmp/llama.cpp
|
||||
$ LD_LIBRARY_PATH=lib ./bin/llama-simple -m {model}.gguf -c {context-size} -p "{your-prompt}"
|
||||
```
|
||||
|
||||
That's it!
|
||||
|
||||
Be aware that Android will not find the library path `lib` on its own, so we must specify `LD_LIBRARY_PATH` in order to run the installed executables. Android does support `RPATH` in later API levels, so this could change in the future. Refer to the previous section for information about `context-size` (very important!) and running other `examples`.
|
||||
BIN
docs/android/imported-into-android-studio.jpg
Normal file
BIN
docs/android/imported-into-android-studio.jpg
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 479 KiB |
60
docs/backend/BLIS.md
Normal file
60
docs/backend/BLIS.md
Normal file
@@ -0,0 +1,60 @@
|
||||
BLIS Installation Manual
|
||||
------------------------
|
||||
|
||||
BLIS is a portable software framework for high-performance BLAS-like dense linear algebra libraries. It has received awards and recognition, including the 2023 James H. Wilkinson Prize for Numerical Software and the 2020 SIAM Activity Group on Supercomputing Best Paper Prize. BLIS provides a new BLAS-like API and a compatibility layer for traditional BLAS routine calls. It offers features such as object-based API, typed API, BLAS and CBLAS compatibility layers.
|
||||
|
||||
Project URL: https://github.com/flame/blis
|
||||
|
||||
### Prepare:
|
||||
|
||||
Compile BLIS:
|
||||
|
||||
```bash
|
||||
git clone https://github.com/flame/blis
|
||||
cd blis
|
||||
./configure --enable-cblas -t openmp,pthreads auto
|
||||
# will install to /usr/local/ by default.
|
||||
make -j
|
||||
```
|
||||
|
||||
Install BLIS:
|
||||
|
||||
```bash
|
||||
sudo make install
|
||||
```
|
||||
|
||||
We recommend using openmp since it's easier to modify the cores being used.
|
||||
|
||||
### llama.cpp compilation
|
||||
|
||||
CMake:
|
||||
|
||||
```bash
|
||||
mkdir build
|
||||
cd build
|
||||
cmake -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=FLAME ..
|
||||
make -j
|
||||
```
|
||||
|
||||
### llama.cpp execution
|
||||
|
||||
According to the BLIS documentation, we could set the following
|
||||
environment variables to modify the behavior of openmp:
|
||||
|
||||
```bash
|
||||
export GOMP_CPU_AFFINITY="0-19"
|
||||
export BLIS_NUM_THREADS=14
|
||||
```
|
||||
|
||||
And then run the binaries as normal.
|
||||
|
||||
|
||||
### Intel specific issue
|
||||
|
||||
Some might get the error message saying that `libimf.so` cannot be found.
|
||||
Please follow this [stackoverflow page](https://stackoverflow.com/questions/70687930/intel-oneapi-2022-libimf-so-no-such-file-or-directory-during-openmpi-compila).
|
||||
|
||||
### Reference:
|
||||
|
||||
1. https://github.com/flame/blis#getting-started
|
||||
2. https://github.com/flame/blis/blob/master/docs/Multithreading.md
|
||||
333
docs/backend/CANN.md
Executable file
333
docs/backend/CANN.md
Executable file
@@ -0,0 +1,333 @@
|
||||
# llama.cpp for CANN
|
||||
|
||||
- [Background](#background)
|
||||
- [News](#news)
|
||||
- [OS](#os)
|
||||
- [Hardware](#hardware)
|
||||
- [Model Supports](#model-supports)
|
||||
- [DataType Supports](#datatype-supports)
|
||||
- [Docker](#docker)
|
||||
- [Linux](#linux)
|
||||
- [Environment variable setup](#environment-variable-setup)
|
||||
- [TODO](#todo)
|
||||
|
||||
|
||||
## Background
|
||||
|
||||
**Ascend NPU** is a range of AI processors using Neural Processing Unit. It will efficiently handle matrix-matrix multiplication, dot-product and scalars.
|
||||
|
||||
**CANN** (Compute Architecture for Neural Networks) is a heterogeneous computing architecture for AI scenarios, providing support for multiple AI frameworks on the top and serving AI processors and programming at the bottom. It plays a crucial role in bridging the gap between upper and lower layers, and is a key platform for improving the computing efficiency of Ascend AI processors. Meanwhile, it offers a highly efficient and easy-to-use programming interface for diverse application scenarios, allowing users to rapidly build AI applications and services based on the Ascend platform.
|
||||
|
||||
**Llama.cpp + CANN**
|
||||
|
||||
The llama.cpp CANN backend is designed to support Ascend NPU. It utilize the ability of AscendC and ACLNN which are intergrated to CANN Toolkit and kernels to using Ascend NPU directly.
|
||||
|
||||
## News
|
||||
|
||||
- 2024.11
|
||||
- Support F16 and F32 data type model for Ascend 310P NPU.
|
||||
- 2024.8
|
||||
- Support `Q4_0` and `Q8_0` data type for Ascend NPU.
|
||||
- 2024.7
|
||||
- Create CANN backend for Ascend NPU.
|
||||
|
||||
## OS
|
||||
|
||||
| OS | Status | Verified |
|
||||
|:-------:|:-------:|:----------------------------------------------:|
|
||||
| Linux | Support | Ubuntu 22.04, OpenEuler22.03 |
|
||||
|
||||
|
||||
## Hardware
|
||||
|
||||
### Ascend NPU
|
||||
|
||||
**Verified devices**
|
||||
|
||||
| Ascend NPU | Status |
|
||||
|:-----------------------------:|:-------:|
|
||||
| Atlas 300T A2 | Support |
|
||||
| Atlas 300I Duo | Support |
|
||||
|
||||
*Notes:*
|
||||
|
||||
- If you have trouble with Ascend NPU device, please create a issue with **[CANN]** prefix/tag.
|
||||
- If you run successfully with your Ascend NPU device, please help update the upper table.
|
||||
|
||||
|
||||
## Model Supports
|
||||
|
||||
| Model Name | FP16 | Q4_0 | Q8_0 |
|
||||
|:----------------------------|:-----:|:----:|:----:|
|
||||
| Llama-2 | √ | √ | √ |
|
||||
| Llama-3 | √ | √ | √ |
|
||||
| Mistral-7B | √ | √ | √ |
|
||||
| Mistral MOE | √ | √ | √ |
|
||||
| DBRX | - | - | - |
|
||||
| Falcon | √ | √ | √ |
|
||||
| Chinese LLaMA/Alpaca | √ | √ | √ |
|
||||
| Vigogne(French) | √ | √ | √ |
|
||||
| BERT | x | x | x |
|
||||
| Koala | √ | √ | √ |
|
||||
| Baichuan | √ | √ | √ |
|
||||
| Aquila 1 & 2 | √ | √ | √ |
|
||||
| Starcoder models | √ | √ | √ |
|
||||
| Refact | √ | √ | √ |
|
||||
| MPT | √ | √ | √ |
|
||||
| Bloom | √ | √ | √ |
|
||||
| Yi models | √ | √ | √ |
|
||||
| stablelm models | √ | √ | √ |
|
||||
| DeepSeek models | x | x | x |
|
||||
| Qwen models | √ | √ | √ |
|
||||
| PLaMo-13B | √ | √ | √ |
|
||||
| Phi models | √ | √ | √ |
|
||||
| PhiMoE | √ | √ | √ |
|
||||
| GPT-2 | √ | √ | √ |
|
||||
| Orion | √ | √ | √ |
|
||||
| InternlLM2 | √ | √ | √ |
|
||||
| CodeShell | √ | √ | √ |
|
||||
| Gemma | √ | √ | √ |
|
||||
| Mamba | √ | √ | √ |
|
||||
| Xverse | √ | √ | √ |
|
||||
| command-r models | √ | √ | √ |
|
||||
| Grok-1 | - | - | - |
|
||||
| SEA-LION | √ | √ | √ |
|
||||
| GritLM-7B | √ | √ | √ |
|
||||
| OLMo | √ | √ | √ |
|
||||
| OLMo 2 | √ | √ | √ |
|
||||
| OLMoE | √ | √ | √ |
|
||||
| Granite models | √ | √ | √ |
|
||||
| GPT-NeoX | √ | √ | √ |
|
||||
| Pythia | √ | √ | √ |
|
||||
| Snowflake-Arctic MoE | - | - | - |
|
||||
| Smaug | √ | √ | √ |
|
||||
| Poro 34B | √ | √ | √ |
|
||||
| Bitnet b1.58 models | √ | x | x |
|
||||
| Flan-T5 | √ | √ | √ |
|
||||
| Open Elm models | x | √ | √ |
|
||||
| chatGLM3-6B + ChatGLM4-9b + GLMEdge-1.5b + GLMEdge-4b | √ | √ | √ |
|
||||
| GLM-4-0414 | √ | √ | √ |
|
||||
| SmolLM | √ | √ | √ |
|
||||
| EXAONE-3.0-7.8B-Instruct | √ | √ | √ |
|
||||
| FalconMamba Models | √ | √ | √ |
|
||||
| Jais Models | - | x | x |
|
||||
| Bielik-11B-v2.3 | √ | √ | √ |
|
||||
| RWKV-6 | - | √ | √ |
|
||||
| QRWKV-6 | √ | √ | √ |
|
||||
| GigaChat-20B-A3B | x | x | x |
|
||||
| Trillion-7B-preview | √ | √ | √ |
|
||||
| Ling models | √ | √ | √ |
|
||||
|
||||
|
||||
**Multimodal**
|
||||
| Model Name | FP16 | Q4_0 | Q8_0 |
|
||||
|:----------------------------|:-----:|:----:|:----:|
|
||||
| LLaVA 1.5 models, LLaVA 1.6 models | x | x | x |
|
||||
| BakLLaVA | √ | √ | √ |
|
||||
| Obsidian | √ | - | - |
|
||||
| ShareGPT4V | x | - | - |
|
||||
| MobileVLM 1.7B/3B models | - | - | - |
|
||||
| Yi-VL | - | - | - |
|
||||
| Mini CPM | √ | √ | √ |
|
||||
| Moondream | √ | √ | √ |
|
||||
| Bunny | √ | - | - |
|
||||
| GLM-EDGE | √ | √ | √ |
|
||||
| Qwen2-VL | √ | √ | √ |
|
||||
|
||||
|
||||
|
||||
## DataType Supports
|
||||
|
||||
| DataType | Status |
|
||||
|:----------------------:|:-------:|
|
||||
| FP16 | Support |
|
||||
| Q8_0 | Support |
|
||||
| Q4_0 | Support |
|
||||
|
||||
## Docker
|
||||
|
||||
### Build Images
|
||||
You can get a image with llama.cpp in one command.
|
||||
```sh
|
||||
docker build -t llama-cpp-cann -f .devops/llama-cli-cann.Dockerfile .
|
||||
```
|
||||
|
||||
### Run container
|
||||
|
||||
```sh
|
||||
# Find all cards.
|
||||
npu-smi info
|
||||
|
||||
# Select the cards that you want to use, make sure these cards are not used by someone.
|
||||
# Following using cards of device0.
|
||||
docker run --name llamacpp --device /dev/davinci0 --device /dev/davinci_manager --device /dev/devmm_svm --device /dev/hisi_hdc -v /usr/local/dcmi:/usr/local/dcmi -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi -v /usr/local/Ascend/driver/lib64/:/usr/local/Ascend/driver/lib64/ -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info -v /PATH_TO_YOUR_MODELS/:/app/models -it llama-cpp-cann -m /app/models/MODEL_PATH -ngl 32 -p "Building a website can be done in 10 simple steps:"
|
||||
```
|
||||
|
||||
*Notes:*
|
||||
|
||||
- You may need to install Ascend Driver and firmware on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*.
|
||||
|
||||
## Linux
|
||||
|
||||
### I. Setup Environment
|
||||
|
||||
1. **Install Ascend Driver and firmware**
|
||||
|
||||
```sh
|
||||
# create driver running user.
|
||||
sudo groupadd -g HwHiAiUser
|
||||
sudo useradd -g HwHiAiUser -d /home/HwHiAiUser -m HwHiAiUser -s /bin/bash
|
||||
sudo usermod -aG HwHiAiUser $USER
|
||||
|
||||
# download driver from https://www.hiascend.com/hardware/firmware-drivers/community according to your system
|
||||
# and install driver.
|
||||
sudo sh Ascend-hdk-910b-npu-driver_x.x.x_linux-{arch}.run --full --install-for-all
|
||||
```
|
||||
|
||||
Once installed, run `npu-smi info` to check whether driver is installed successfully.
|
||||
```sh
|
||||
+-------------------------------------------------------------------------------------------+
|
||||
| npu-smi 24.1.rc2 Version: 24.1.rc2 |
|
||||
+----------------------+---------------+----------------------------------------------------+
|
||||
| NPU Name | Health | Power(W) Temp(C) Hugepages-Usage(page)|
|
||||
| Chip | Bus-Id | AICore(%) Memory-Usage(MB) HBM-Usage(MB) |
|
||||
+======================+===============+====================================================+
|
||||
| 2 xxx | OK | 64.4 51 15 / 15 |
|
||||
| 0 | 0000:01:00.0 | 0 1873 / 15077 0 / 32768 |
|
||||
+======================+===============+====================================================+
|
||||
| 5 xxx | OK | 64.0 52 15 / 15 |
|
||||
| 0 | 0000:81:00.0 | 0 1874 / 15077 0 / 32768 |
|
||||
+======================+===============+====================================================+
|
||||
| No running processes found in NPU 2 |
|
||||
+======================+===============+====================================================+
|
||||
| No running processes found in NPU 5 |
|
||||
+======================+===============+====================================================+
|
||||
```
|
||||
|
||||
2. **Install Ascend Firmware**
|
||||
```sh
|
||||
# download driver from https://www.hiascend.com/hardware/firmware-drivers/community according to your system
|
||||
# and install driver.
|
||||
sudo sh Ascend-hdk-910b-npu-firmware_x.x.x.x.X.run --full
|
||||
```
|
||||
If the following messaage appers, firmware is installed successfully.
|
||||
```sh
|
||||
Firmware package installed successfully!
|
||||
```
|
||||
|
||||
|
||||
3. **Install CANN toolkit and kernels**
|
||||
|
||||
CANN toolkit and kernels can be obtained from the official [CANN Toolkit](https://www.hiascend.com/zh/developer/download/community/result?module=cann) page.
|
||||
|
||||
Please download the corresponding version that satified your system. The minimum version required is 8.0.RC2.alpha002 and here is the install command.
|
||||
```sh
|
||||
pip3 install attrs numpy decorator sympy cffi pyyaml pathlib2 psutil protobuf scipy requests absl-py wheel typing_extensions
|
||||
sh Ascend-cann-toolkit_8.0.RC2.alpha002_linux-aarch64.run --install
|
||||
sh Ascend-cann-kernels-910b_8.0.RC2.alpha002_linux.run --install
|
||||
```
|
||||
|
||||
Set Ascend Variables:
|
||||
```sh
|
||||
echo "source ~/Ascend/ascend-toolkit/set_env.sh" >> ~/.bashrc
|
||||
source ~/.bashrc
|
||||
```
|
||||
|
||||
Upon a successful installation, CANN is enabled for the available ascend devices.
|
||||
|
||||
### II. Build llama.cpp
|
||||
|
||||
```sh
|
||||
cmake -B build -DGGML_CANN=on -DCMAKE_BUILD_TYPE=release
|
||||
cmake --build build --config release
|
||||
```
|
||||
|
||||
### III. Run the inference
|
||||
|
||||
1. **Retrieve and prepare model**
|
||||
|
||||
You can refer to the general [*Prepare and Quantize*](../../README.md#prepare-and-quantize) guide for model prepration.
|
||||
|
||||
**Notes**:
|
||||
|
||||
- CANN backend only supports FP16/Q4_0/Q8_0 models currently.
|
||||
|
||||
2. **Launch inference**
|
||||
|
||||
There are two device selection modes:
|
||||
|
||||
- Single device: Use one device target specified by the user.
|
||||
- Multiple devices: Automatically choose the devices with the same backend.
|
||||
|
||||
| Device selection | Parameter |
|
||||
|:----------------:|:--------------------------------------:|
|
||||
| Single device | --split-mode none --main-gpu DEVICE_ID |
|
||||
| Multiple devices | --split-mode layer (default) |
|
||||
|
||||
Examples:
|
||||
|
||||
- Use device 0:
|
||||
|
||||
```sh
|
||||
./build/bin/llama-cli -m path_to_model -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm none -mg 0
|
||||
```
|
||||
|
||||
- Use multiple devices:
|
||||
|
||||
```sh
|
||||
./build/bin/llama-cli -m path_to_model -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -sm layer
|
||||
```
|
||||
|
||||
### **GitHub contribution**:
|
||||
Please add the **[CANN]** prefix/tag in issues/PRs titles to help the CANN-team check/address them without delay.
|
||||
|
||||
## Updates
|
||||
### Basic Flash Attention Support
|
||||
The basic FA kernel with aclnnops has been added in aclnn_ops.cpp.
|
||||
Currently, the FA only supports the cases with FP16 KV tensors and NO logit softcap.
|
||||
Since the aclnn interface for flash attention cannot support the logit softcap, we will only update the quantized version in the future.
|
||||
|
||||
Authors from Peking University: Bizhao Shi (bshi@pku.edu.cn), Yuxin Yang (yxyang@pku.edu.cn), Ruiyang Ma (ruiyang@stu.pku.edu.cn), and Guojie Luo (gluo@pku.edu.cn).
|
||||
|
||||
We would like to thank Tuo Dai, Shanni Li, and all of the project maintainers from Huawei Technologies Co., Ltd for their help during the code development and pull request.
|
||||
|
||||
## Environment variable setup
|
||||
|
||||
### GGML_CANN_MEM_POOL
|
||||
|
||||
Specifies the memory pool management strategy, Default is vmm.
|
||||
|
||||
- vmm: Utilizes a virtual memory manager pool. If hardware support for VMM is unavailable, falls back to the legacy (leg) memory pool.
|
||||
|
||||
- prio: Employs a priority queue-based memory pool management.
|
||||
|
||||
- leg: Uses a fixed-size buffer pool.
|
||||
|
||||
### GGML_CANN_DISABLE_BUF_POOL_CLEAN
|
||||
|
||||
Controls automatic cleanup of the memory pool. This option is only effective when using the prio or leg memory pool strategies.
|
||||
|
||||
### GGML_CANN_WEIGHT_NZ
|
||||
|
||||
Converting the matmul weight format from ND to NZ to improve performance. Enabled by default.
|
||||
|
||||
### GGML_CANN_ACL_GRAPH
|
||||
|
||||
Operators are executed using ACL graph execution, rather than in op-by-op (eager) mode. Enabled by default. This option is only effective if `USE_ACL_GRAPH` was enabled at compilation time. To enable it, recompile using:
|
||||
|
||||
```sh
|
||||
cmake -B build -DGGML_CANN=on -DCMAKE_BUILD_TYPE=release -DUSE_ACL_GRAPH=ON
|
||||
cmake --build build --config release
|
||||
```
|
||||
|
||||
### GGML_CANN_GRAPH_CACHE_CAPACITY
|
||||
|
||||
Maximum number of compiled CANN graphs kept in the LRU cache, default is 12. When the number of cached graphs exceeds this capacity, the least recently used graph will be evicted.
|
||||
|
||||
### GGML_CANN_PREFILL_USE_GRAPH
|
||||
|
||||
Enable ACL graph execution during the prefill stage, default is false. This option is only effective when FA is enabled.
|
||||
|
||||
### GGML_CANN_OPERATOR_FUSION
|
||||
|
||||
Enable operator fusion during computation, default is false. This option fuses compatible operators (e.g., ADD + RMS_NORM) to reduce overhead and improve performance.
|
||||
283
docs/backend/CUDA-FEDORA.md
Normal file
283
docs/backend/CUDA-FEDORA.md
Normal file
@@ -0,0 +1,283 @@
|
||||
# Setting Up CUDA on Fedora
|
||||
|
||||
In this guide we setup [Nvidia CUDA](https://docs.nvidia.com/cuda/) in a toolbox container. This guide is applicable for:
|
||||
|
||||
- [Fedora Workstation](https://fedoraproject.org/workstation/)
|
||||
- [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/)
|
||||
- [Fedora Spins](https://fedoraproject.org/spins)
|
||||
- [Other Distributions](https://containertoolbx.org/distros/), including `Red Hat Enterprise Linux >= 8.5`, `Arch Linux`, and `Ubuntu`.
|
||||
|
||||
## Table of Contents
|
||||
|
||||
- [Prerequisites](#prerequisites)
|
||||
- [Using the Fedora 41 CUDA Repository](#using-the-fedora-41-cuda-repository)
|
||||
- [Creating a Fedora Toolbox Environment](#creating-a-fedora-toolbox-environment)
|
||||
- [Installing Essential Development Tools](#installing-essential-development-tools)
|
||||
- [Adding the CUDA Repository](#adding-the-cuda-repository)
|
||||
- [Installing Nvidia Driver Libraries](#installing-nvidia-driver-libraries)
|
||||
- [Installing the CUDA Meta-Package](#installing-the-cuda-meta-package)
|
||||
- [Configuring the Environment](#configuring-the-environment)
|
||||
- [Verifying the Installation](#verifying-the-installation)
|
||||
- [Conclusion](#conclusion)
|
||||
- [Troubleshooting](#troubleshooting)
|
||||
- [Additional Notes](#additional-notes)
|
||||
- [References](#references)
|
||||
|
||||
## Prerequisites
|
||||
|
||||
- **Toolbox Installed on the Host System** `Fedora Silverblue` and `Fedora Workstation` both have toolbox by default, other distributions may need to install the [toolbox package](https://containertoolbx.org/install/).
|
||||
- **NVIDIA Drivers and Graphics Card installed on Host System (recommended)** To run CUDA program, such as `llama.cpp`, the host should be setup to access your NVIDIA hardware. Fedora Hosts can use the [RPM Fusion Repository](https://rpmfusion.org/Howto/NVIDIA).
|
||||
- **Internet connectivity** to download packages.
|
||||
|
||||
### Using the Fedora 41 CUDA Repository
|
||||
|
||||
The latest release is 41.
|
||||
|
||||
- [Fedora 41 CUDA Repository](https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/)
|
||||
|
||||
**Note:** We recommend using a toolbox environment to prevent system conflicts.
|
||||
|
||||
## Creating a Fedora Toolbox Environment
|
||||
|
||||
This guide focuses on Fedora hosts, but with small adjustments, it can work for other hosts. Using the Fedora Toolbox allows us to install the necessary packages without affecting the host system.
|
||||
|
||||
**Note:** Toolbox is available for other systems, and even without Toolbox, it is possible to use Podman or Docker.
|
||||
|
||||
1. **Create a Fedora 41 Toolbox:**
|
||||
|
||||
```bash
|
||||
toolbox create --image registry.fedoraproject.org/fedora-toolbox:41 --container fedora-toolbox-41-cuda
|
||||
```
|
||||
|
||||
2. **Enter the Toolbox:**
|
||||
|
||||
```bash
|
||||
toolbox enter --container fedora-toolbox-41-cuda
|
||||
```
|
||||
|
||||
Inside the toolbox, you have root privileges and can install packages without affecting the host system.
|
||||
|
||||
## Installing Essential Development Tools
|
||||
|
||||
1. **Synchronize the DNF Package Manager:**
|
||||
|
||||
```bash
|
||||
sudo dnf distro-sync
|
||||
```
|
||||
|
||||
2. **Install **Vim** the default text editor (Optional):**
|
||||
|
||||
```bash
|
||||
sudo dnf install vim-default-editor --allowerasing
|
||||
```
|
||||
|
||||
The `--allowerasing` flag will allow the removal of the conflicting `nano-default-editor` package.
|
||||
|
||||
3. **Install Development Tools and Libraries:**
|
||||
|
||||
```bash
|
||||
sudo dnf install @c-development @development-tools cmake
|
||||
```
|
||||
|
||||
This installs essential packages for compiling software, including `gcc`, `make`, and other development headers.
|
||||
|
||||
## Adding the CUDA Repository
|
||||
|
||||
Add the NVIDIA CUDA repository to your DNF configuration:
|
||||
|
||||
```bash
|
||||
sudo dnf config-manager addrepo --from-repofile=https://developer.download.nvidia.com/compute/cuda/repos/fedora41/x86_64/cuda-fedora41.repo
|
||||
```
|
||||
|
||||
After adding the repository, synchronize the package manager again:
|
||||
|
||||
```bash
|
||||
sudo dnf distro-sync
|
||||
```
|
||||
|
||||
## Installing Nvidia Driver Libraries
|
||||
|
||||
First, we need to detect if the host is supplying the [NVIDIA driver libraries into the toolbox](https://github.com/containers/toolbox/blob/main/src/pkg/nvidia/nvidia.go):
|
||||
|
||||
```bash
|
||||
ls -la /usr/lib64/libcuda.so.1
|
||||
```
|
||||
|
||||
### If *`libcuda.so.1`* is missing:
|
||||
|
||||
```
|
||||
ls: cannot access '/usr/lib64/libcuda.so.1': No such file or directory
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
The host dose not supply the CUDA drivers, **install them now:**
|
||||
|
||||
#### Install the Nvidia Driver Libraries on Guest:
|
||||
|
||||
```bash
|
||||
sudo dnf install nvidia-driver-cuda nvidia-driver-libs nvidia-driver-cuda-libs nvidia-persistenced
|
||||
```
|
||||
|
||||
### If *`libcuda.so.1`* exists:
|
||||
```
|
||||
lrwxrwxrwx. 1 root root 21 Mar 24 11:26 /usr/lib64/libcuda.so.1 -> libcuda.so.570.133.07
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
The host is supply the CUDA drivers, **we need to update the guest RPM Database accordingly:**
|
||||
|
||||
#### Update the Toolbox RPM Database to include the Host-Supplied Libraries:
|
||||
|
||||
Note: we do not actually install the libraries, we just update the DB so that the guest system knows they are supplied by the host.
|
||||
|
||||
##### 1. Download `nvidia-` parts that are supplied by the host RPM's (with dependencies)
|
||||
|
||||
```bash
|
||||
sudo dnf download --destdir=/tmp/nvidia-driver-libs --resolve --arch x86_64 nvidia-driver-cuda nvidia-driver-libs nvidia-driver-cuda-libs nvidia-persistenced
|
||||
```
|
||||
|
||||
##### 2. Update the RPM database to assume the installation of these packages.
|
||||
|
||||
```bash
|
||||
sudo rpm --install --verbose --hash --justdb /tmp/nvidia-driver-libs/*
|
||||
```
|
||||
|
||||
**Note:**
|
||||
|
||||
- The `--justdb` option only updates the RPM database, without touching the filesystem elsewhere.
|
||||
|
||||
##### Check that the RPM Database has been correctly updated:
|
||||
|
||||
**Note:** This is the same command as in the *"Install the Nvidia Driver Libraries on Guest"* for if *`libcuda.so.1`* was missing.
|
||||
|
||||
|
||||
```bash
|
||||
sudo dnf install nvidia-driver-cuda nvidia-driver-libs nvidia-driver-cuda-libs nvidia-persistenced
|
||||
```
|
||||
|
||||
*(this time it will not install anything, as the database things that these packages are already installed)*
|
||||
|
||||
```
|
||||
Updating and loading repositories:
|
||||
Repositories loaded.
|
||||
Package "nvidia-driver-cuda-3:570.124.06-1.fc41.x86_64" is already installed.
|
||||
Package "nvidia-driver-libs-3:570.124.06-1.fc41.x86_64" is already installed.
|
||||
Package "nvidia-driver-cuda-libs-3:570.124.06-1.fc41.x86_64" is already installed.
|
||||
Package "nvidia-persistenced-3:570.124.06-1.fc41.x86_64" is already installed.
|
||||
|
||||
Nothing to do.
|
||||
```
|
||||
|
||||
## Installing the CUDA Meta-Package
|
||||
|
||||
Now that the driver libraries are installed, proceed to install CUDA:
|
||||
|
||||
```bash
|
||||
sudo dnf install cuda
|
||||
```
|
||||
|
||||
This installs the CUDA toolkit and associated packages.
|
||||
|
||||
## Configuring the Environment
|
||||
|
||||
To use CUDA, add its binary directory to your system's `PATH`.
|
||||
|
||||
1. **Create a Profile Script:**
|
||||
|
||||
```bash
|
||||
sudo sh -c 'echo "export PATH=\$PATH:/usr/local/cuda/bin" >> /etc/profile.d/cuda.sh'
|
||||
```
|
||||
|
||||
**Explanation:**
|
||||
|
||||
- We add to `/etc/profile.d/` as the `/etc/` folder is unique to this particular container, and is not shared with other containers or the host system.
|
||||
- The backslash `\` before `$PATH` ensures the variable is correctly written into the script.
|
||||
|
||||
2. **Make the Script Executable:**
|
||||
|
||||
```bash
|
||||
sudo chmod +x /etc/profile.d/cuda.sh
|
||||
```
|
||||
|
||||
3. **Source the Script to Update Your Environment:**
|
||||
|
||||
```bash
|
||||
source /etc/profile.d/cuda.sh
|
||||
```
|
||||
|
||||
**Note:** This command updates your current shell session with the new `PATH`. The `/etc/profile.d/cuda.sh` script ensures that the CUDA binaries are available in your `PATH` for all future sessions.
|
||||
|
||||
## Verifying the Installation
|
||||
|
||||
To confirm that CUDA is correctly installed and configured, check the version of the NVIDIA CUDA Compiler (`nvcc`):
|
||||
|
||||
```bash
|
||||
nvcc --version
|
||||
```
|
||||
|
||||
You should see output similar to:
|
||||
|
||||
```
|
||||
nvcc: NVIDIA (R) Cuda compiler driver
|
||||
Copyright (c) 2005-2025 NVIDIA Corporation
|
||||
Built on Fri_Feb_21_20:23:50_PST_2025
|
||||
Cuda compilation tools, release 12.8, V12.8.93
|
||||
Build cuda_12.8.r12.8/compiler.35583870_0
|
||||
```
|
||||
|
||||
This output confirms that the CUDA compiler is accessible and indicates the installed version.
|
||||
|
||||
## Conclusion
|
||||
|
||||
You have successfully set up CUDA on Fedora within a toolbox environment using the Fedora 41 CUDA repository. By manually updating the RPM db and configuring the environment, you can develop CUDA applications without affecting your host system.
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
- **Installation Failures:**
|
||||
|
||||
- If you encounter errors during installation, carefully read the error messages. They often indicate conflicting files or missing dependencies.
|
||||
- You may use the `--excludepath` option with `rpm` to exclude conflicting files during manual RPM installations.
|
||||
|
||||
- **Rebooting the Container:**
|
||||
|
||||
- Sometimes there may be a bug in the NVIDIA driver host passthrough (such as missing a shared library). Rebooting the container may solve this issue:
|
||||
|
||||
```bash
|
||||
# on the host system
|
||||
podman container restart --all
|
||||
```
|
||||
|
||||
- **Environment Variables Not Set:**
|
||||
- If `nvcc` is not found after installation, ensure that `/usr/local/cuda/bin` is in your `PATH`.
|
||||
- Run `echo $PATH` to check if the path is included.
|
||||
- Re-source the profile script or open a new terminal session.
|
||||
|
||||
## Additional Notes
|
||||
|
||||
- **Updating CUDA in the Future:**
|
||||
|
||||
- Keep an eye on the official NVIDIA repositories for updates to your Fedora version.
|
||||
- When an updated repository becomes available, adjust your `dnf` configuration accordingly.
|
||||
|
||||
- **Building `llama.cpp`:**
|
||||
|
||||
- With CUDA installed, you can follow these [build instructions for `llama.cpp`](https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md) to compile it with CUDA support.
|
||||
- Ensure that any CUDA-specific build flags or paths are correctly set in your build configuration.
|
||||
|
||||
- **Using the Toolbox Environment:**
|
||||
- The toolbox environment is isolated from your host system, which helps prevent conflicts.
|
||||
- Remember that system files and configurations inside the toolbox are separate from the host. By default the home directory of the user is shared between the host and the toolbox.
|
||||
|
||||
---
|
||||
|
||||
**Disclaimer:** Manually installing and modifying system packages can lead to instability of the container. The above steps are provided as a guideline and may need adjustments based on your specific system configuration. Always back up important data before making significant system changes, especially as your home folder is writable and shared with he toolbox.
|
||||
|
||||
**Acknowledgments:** Special thanks to the Fedora community and NVIDIA documentation for providing resources that assisted in creating this guide.
|
||||
|
||||
## References
|
||||
|
||||
- [Fedora Toolbox Documentation](https://docs.fedoraproject.org/en-US/fedora-silverblue/toolbox/)
|
||||
- [NVIDIA CUDA Installation Guide](https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html)
|
||||
- [Podman Documentation](https://podman.io/get-started)
|
||||
|
||||
---
|
||||
281
docs/backend/OPENCL.md
Normal file
281
docs/backend/OPENCL.md
Normal file
@@ -0,0 +1,281 @@
|
||||
# llama.cpp for OpenCL
|
||||
|
||||
- [Background](#background)
|
||||
- [OS](#os)
|
||||
- [Hardware](#hardware)
|
||||
- [DataType Supports](#datatype-supports)
|
||||
- [Model Preparation](#model-preparation)
|
||||
- [CMake Options](#cmake-options)
|
||||
- [Android](#android)
|
||||
- [Windows 11 Arm64](#windows-11-arm64)
|
||||
- [Known Issue](#known-issues)
|
||||
- [TODO](#todo)
|
||||
|
||||
## Background
|
||||
|
||||
OpenCL (Open Computing Language) is an open, royalty-free standard for cross-platform, parallel programming of diverse accelerators found in supercomputers, cloud servers, personal computers, mobile devices and embedded platforms. OpenCL specifies a programming language (based on C99) for programming these devices and application programming interfaces (APIs) to control the platform and execute programs on the compute devices. Similar to CUDA, OpenCL has been widely used to program GPUs and is supported by most GPU vendors.
|
||||
|
||||
### Llama.cpp + OpenCL
|
||||
|
||||
The llama.cpp OpenCL backend is designed to enable llama.cpp on **Qualcomm Adreno GPU** firstly via OpenCL. Thanks to the portabilty of OpenCL, the OpenCL backend can also run on certain Intel GPUs such as those that do not have [SYCL](/docs/backend/SYCL.md) support although the performance is not optimal.
|
||||
|
||||
## OS
|
||||
|
||||
| OS | Status | Verified |
|
||||
|---------|---------|------------------------------------------------|
|
||||
| Android | Support | Snapdragon 8 Gen 3, Snapdragon 8 Elite |
|
||||
| Windows | Support | Windows 11 Arm64 with Snapdragon X Elite |
|
||||
| Linux | Support | Ubuntu 22.04 WSL2 with Intel 12700H |
|
||||
|
||||
## Hardware
|
||||
|
||||
### Adreno GPU
|
||||
|
||||
**Verified devices**
|
||||
|
||||
| Adreno GPU | Status |
|
||||
|:------------------------------------:|:-------:|
|
||||
| Adreno 750 (Snapdragon 8 Gen 3) | Support |
|
||||
| Adreno 830 (Snapdragon 8 Elite) | Support |
|
||||
| Adreno X85 (Snapdragon X Elite) | Support |
|
||||
|
||||
> A6x GPUs with a recent driver and compiler are supported; they are usually found in IoT platforms.
|
||||
However, A6x GPUs in phones are likely not supported due to the outdated driver and compiler.
|
||||
|
||||
## DataType Supports
|
||||
|
||||
| DataType | Status |
|
||||
|:----------------------:|:--------------------------:|
|
||||
| Q4_0 | Support |
|
||||
| Q6_K | Support, but not optimized |
|
||||
| Q8_0 | Support |
|
||||
| MXFP4 | Support |
|
||||
|
||||
## Model Preparation
|
||||
|
||||
You can refer to the general [llama-quantize tool](/tools/quantize/README.md) for steps to convert a model in Hugging Face safetensor format to GGUF with quantization.
|
||||
|
||||
Currently we support `Q4_0` quantization and have optimized for it. To achieve best performance on Adreno GPU, add `--pure` to `llama-quantize` (i.e., make all weights in `Q4_0`). For example,
|
||||
|
||||
```sh
|
||||
./llama-quantize --pure ggml-model-qwen2.5-3b-f16.gguf ggml-model-qwen-3b-Q4_0.gguf Q4_0
|
||||
```
|
||||
|
||||
Since `Q6_K` is also supported, `Q4_0` quantization without `--pure` will also work. However, the performance will be worse compared to pure `Q4_0` quantization.
|
||||
|
||||
### `MXFP4` MoE Models
|
||||
|
||||
OpenAI gpt-oss models are MoE models in `MXFP4`. The quantized model will be in `MXFP4_MOE`, a mixture of `MXFP4` and `Q8_0`.
|
||||
For this quantization, there is no need to specify `--pure`.
|
||||
For gpt-oss-20b model, you can directly [download](https://huggingface.co/ggml-org/gpt-oss-20b-GGUF) the quantized GGUF file in `MXFP4_MOE` from Hugging Face.
|
||||
|
||||
Although it is possible to quantize gpt-oss-20b model in pure `Q4_0` (all weights in `Q4_0`), it is not recommended since `MXFP4` has been optimized for MoE while `Q4_0` is not. In addition, accuracy should degrade with such pure `Q4_0` quantization.
|
||||
Hence, using the default `MXFP4_MOE` quantization (see the link above) is recommended for this model.
|
||||
|
||||
> Note that the `Q4_0` model found [here](https://huggingface.co/unsloth/gpt-oss-20b-GGUF/blob/main/gpt-oss-20b-Q4_0.gguf) is a mixture of `Q4_0`, `Q8_0` and `MXFP4` and gives better performance than `MXFP4_MOE` quantization.
|
||||
|
||||
## CMake Options
|
||||
|
||||
The OpenCL backend has the following CMake options that control the behavior of the backend.
|
||||
|
||||
| CMake options | Default value | Description |
|
||||
|:---------------------------------:|:--------------:|:------------------------------------------|
|
||||
| `GGML_OPENCL_EMBED_KERNELS` | `ON` | Embed OpenCL kernels into the executable. |
|
||||
| `GGML_OPENCL_USE_ADRENO_KERNELS` | `ON` | Use kernels optimized for Adreno. |
|
||||
|
||||
## Android
|
||||
|
||||
Ubuntu 22.04 is used for targeting Android. Make sure the following tools are accessible from command line,
|
||||
|
||||
* Git
|
||||
* CMake 3.29
|
||||
* Ninja
|
||||
* Python3
|
||||
|
||||
### I. Setup Environment
|
||||
|
||||
1. **Install NDK**
|
||||
|
||||
```sh
|
||||
cd ~
|
||||
wget https://dl.google.com/android/repository/commandlinetools-linux-8512546_latest.zip && \
|
||||
unzip commandlinetools-linux-8512546_latest.zip && \
|
||||
mkdir -p ~/android-sdk/cmdline-tools && \
|
||||
mv cmdline-tools latest && \
|
||||
mv latest ~/android-sdk/cmdline-tools/ && \
|
||||
rm -rf commandlinetools-linux-8512546_latest.zip
|
||||
|
||||
yes | ~/android-sdk/cmdline-tools/latest/bin/sdkmanager "ndk;26.3.11579264"
|
||||
```
|
||||
|
||||
2. **Install OpenCL Headers and Library**
|
||||
|
||||
```sh
|
||||
mkdir -p ~/dev/llm
|
||||
cd ~/dev/llm
|
||||
|
||||
git clone https://github.com/KhronosGroup/OpenCL-Headers && \
|
||||
cd OpenCL-Headers && \
|
||||
cp -r CL ~/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
|
||||
|
||||
cd ~/dev/llm
|
||||
|
||||
git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && \
|
||||
cd OpenCL-ICD-Loader && \
|
||||
mkdir build_ndk26 && cd build_ndk26 && \
|
||||
cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \
|
||||
-DCMAKE_TOOLCHAIN_FILE=$HOME/android-sdk/ndk/26.3.11579264/build/cmake/android.toolchain.cmake \
|
||||
-DOPENCL_ICD_LOADER_HEADERS_DIR=$HOME/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \
|
||||
-DANDROID_ABI=arm64-v8a \
|
||||
-DANDROID_PLATFORM=24 \
|
||||
-DANDROID_STL=c++_shared && \
|
||||
ninja && \
|
||||
cp libOpenCL.so ~/android-sdk/ndk/26.3.11579264/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
|
||||
```
|
||||
|
||||
### II. Build llama.cpp
|
||||
|
||||
```sh
|
||||
cd ~/dev/llm
|
||||
|
||||
git clone https://github.com/ggml-org/llama.cpp && \
|
||||
cd llama.cpp && \
|
||||
mkdir build-android && cd build-android
|
||||
|
||||
cmake .. -G Ninja \
|
||||
-DCMAKE_TOOLCHAIN_FILE=$HOME/android-sdk/ndk/26.3.11579264/build/cmake/android.toolchain.cmake \
|
||||
-DANDROID_ABI=arm64-v8a \
|
||||
-DANDROID_PLATFORM=android-28 \
|
||||
-DBUILD_SHARED_LIBS=OFF \
|
||||
-DGGML_OPENCL=ON
|
||||
|
||||
ninja
|
||||
```
|
||||
|
||||
## Windows 11 Arm64
|
||||
|
||||
A Snapdragon X Elite device with Windows 11 Arm64 is used. Make sure the following tools are accessible from command line,
|
||||
|
||||
* Git
|
||||
* CMake 3.29
|
||||
* Clang 19
|
||||
* Ninja
|
||||
* Visual Studio 2022
|
||||
* Powershell 7
|
||||
* Python
|
||||
|
||||
Visual Studio provides necessary headers and libraries although it is not directly used for building.
|
||||
Alternatively, Visual Studio Build Tools can be installed instead of the full Visual Studio.
|
||||
|
||||
> Note that building using Visual Studio's cl compiler is not supported. Clang must be used. Clang depends on libraries provided by Visual Studio to work. Therefore, Visual Studio must be installed. Alternatively, Visual Studio Build Tools can be installed instead of the full Visual Studio.
|
||||
|
||||
Powershell 7 is used for the following commands.
|
||||
If an older version of Powershell is used, these commands may not work as they are.
|
||||
|
||||
### I. Setup Environment
|
||||
|
||||
1. **Install OpenCL Headers and Library**
|
||||
|
||||
```powershell
|
||||
mkdir -p ~/dev/llm
|
||||
|
||||
cd ~/dev/llm
|
||||
git clone https://github.com/KhronosGroup/OpenCL-Headers && cd OpenCL-Headers
|
||||
mkdir build && cd build
|
||||
cmake .. -G Ninja `
|
||||
-DBUILD_TESTING=OFF `
|
||||
-DOPENCL_HEADERS_BUILD_TESTING=OFF `
|
||||
-DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
|
||||
-DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl"
|
||||
cmake --build . --target install
|
||||
|
||||
cd ~/dev/llm
|
||||
git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && cd OpenCL-ICD-Loader
|
||||
mkdir build && cd build
|
||||
cmake .. -G Ninja `
|
||||
-DCMAKE_BUILD_TYPE=Release `
|
||||
-DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" `
|
||||
-DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl"
|
||||
cmake --build . --target install
|
||||
```
|
||||
|
||||
### II. Build llama.cpp
|
||||
|
||||
```powershell
|
||||
|
||||
mkdir -p ~/dev/llm
|
||||
cd ~/dev/llm
|
||||
|
||||
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
|
||||
mkdir build && cd build
|
||||
|
||||
cmake .. -G Ninja `
|
||||
-DCMAKE_TOOLCHAIN_FILE="$HOME/dev/llm/llama.cpp/cmake/arm64-windows-llvm.cmake" `
|
||||
-DCMAKE_BUILD_TYPE=Release `
|
||||
-DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" `
|
||||
-DBUILD_SHARED_LIBS=OFF `
|
||||
-DGGML_OPENCL=ON
|
||||
ninja
|
||||
```
|
||||
|
||||
## Linux
|
||||
|
||||
The two steps just above also apply to Linux. When building for linux, the commands are mostly the same as those for PowerShell on Windows, but in the second step they do not have the `-DCMAKE_TOOLCHAIN_FILE` parameter, and then in both steps the backticks are replaced with back slashes.
|
||||
|
||||
If not installed already, install Git, CMake, Clang, Ninja and Python, then run in the terminal the following:
|
||||
|
||||
### I. Setup Environment
|
||||
|
||||
1. **Install OpenCL Headers and Library**
|
||||
|
||||
```bash
|
||||
mkdir -p ~/dev/llm
|
||||
|
||||
cd ~/dev/llm
|
||||
git clone https://github.com/KhronosGroup/OpenCL-Headers && cd OpenCL-Headers
|
||||
mkdir build && cd build
|
||||
cmake .. -G Ninja \
|
||||
-DBUILD_TESTING=OFF \
|
||||
-DOPENCL_HEADERS_BUILD_TESTING=OFF \
|
||||
-DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF \
|
||||
-DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl"
|
||||
cmake --build . --target install
|
||||
|
||||
cd ~/dev/llm
|
||||
git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && cd OpenCL-ICD-Loader
|
||||
mkdir build && cd build
|
||||
cmake .. -G Ninja \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" \
|
||||
-DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl"
|
||||
cmake --build . --target install
|
||||
```
|
||||
|
||||
### II. Build llama.cpp
|
||||
|
||||
```bash
|
||||
mkdir -p ~/dev/llm
|
||||
cd ~/dev/llm
|
||||
|
||||
git clone https://github.com/ggml-org/llama.cpp && cd llama.cpp
|
||||
mkdir build && cd build
|
||||
|
||||
cmake .. -G Ninja \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" \
|
||||
-DBUILD_SHARED_LIBS=OFF \
|
||||
-DGGML_OPENCL=ON
|
||||
ninja
|
||||
```
|
||||
|
||||
## Known Issues
|
||||
|
||||
- Flash attention does not always improve performance.
|
||||
- Currently OpenCL backend works on A6xx GPUs with recent drivers and compilers (usually found in IoT platforms).
|
||||
However, it does not work on A6xx GPUs found in phones with old drivers and compilers.
|
||||
|
||||
## TODO
|
||||
|
||||
- Optimization for Q6_K
|
||||
- Support and optimization for Q4_K
|
||||
- Improve flash attention
|
||||
858
docs/backend/SYCL.md
Normal file
858
docs/backend/SYCL.md
Normal file
@@ -0,0 +1,858 @@
|
||||
# llama.cpp for SYCL
|
||||
|
||||
- [Background](#background)
|
||||
- [Recommended Release](#recommended-release)
|
||||
- [News](#news)
|
||||
- [OS](#os)
|
||||
- [Hardware](#hardware)
|
||||
- [Docker](#docker)
|
||||
- [Linux](#linux)
|
||||
- [Windows](#windows)
|
||||
- [Environment Variable](#environment-variable)
|
||||
- [Known Issue](#known-issues)
|
||||
- [Q&A](#qa)
|
||||
- [TODO](#todo)
|
||||
|
||||
## Background
|
||||
|
||||
**SYCL** is a high-level parallel programming model designed to improve developers productivity writing code across various hardware accelerators such as CPUs, GPUs, and FPGAs. It is a single-source language designed for heterogeneous computing and based on standard C++17.
|
||||
|
||||
**oneAPI** is an open ecosystem and a standard-based specification, supporting multiple architectures including but not limited to Intel CPUs, GPUs and FPGAs. The key components of the oneAPI ecosystem include:
|
||||
|
||||
- **DPCPP** *(Data Parallel C++)*: The primary oneAPI SYCL implementation, which includes the icpx/icx Compilers.
|
||||
- **oneAPI Libraries**: A set of highly optimized libraries targeting multiple domains *(e.g. Intel oneMKL, oneMath and oneDNN)*.
|
||||
- **oneAPI LevelZero**: A high performance low level interface for fine-grained control over Intel iGPUs and dGPUs.
|
||||
- **Nvidia & AMD Plugins**: These are plugins extending oneAPI's DPCPP support to SYCL on Nvidia and AMD GPU targets.
|
||||
|
||||
### Llama.cpp + SYCL
|
||||
|
||||
The llama.cpp SYCL backend is primarily designed for **Intel GPUs**.
|
||||
SYCL cross-platform capabilities enable support for Nvidia GPUs as well, with limited support for AMD.
|
||||
|
||||
## Recommended Release
|
||||
|
||||
The following releases are verified and recommended:
|
||||
|
||||
|Commit ID|Tag|Release|Verified Platform| Update date|
|
||||
|-|-|-|-|-|
|
||||
|24e86cae7219b0f3ede1d5abdf5bf3ad515cccb8|b5377 |[llama-b5377-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b5377/llama-b5377-bin-win-sycl-x64.zip) |ArcB580/Linux/oneAPI 2025.1<br>LNL Arc GPU/Windows 11/oneAPI 2025.1.1|2025-05-15|
|
||||
|3bcd40b3c593d14261fb2abfabad3c0fb5b9e318|b4040 |[llama-b4040-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b4040/llama-b4040-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1| 2024-11-19|
|
||||
|fb76ec31a9914b7761c1727303ab30380fd4f05c|b3038 |[llama-b3038-bin-win-sycl-x64.zip](https://github.com/ggml-org/llama.cpp/releases/download/b3038/llama-b3038-bin-win-sycl-x64.zip) |Arc770/Linux/oneAPI 2024.1<br>MTL Arc GPU/Windows 11/oneAPI 2024.1||
|
||||
|
||||
|
||||
## News
|
||||
|
||||
- 2025.11
|
||||
- Support malloc memory on device more than 4GB.
|
||||
|
||||
- 2025.2
|
||||
- Optimize MUL_MAT Q4_0 on Intel GPU for all dGPUs and built-in GPUs since MTL. Increase the performance of LLM (llama-2-7b.Q4_0.gguf) 21%-87% on Intel GPUs (MTL, ARL-H, Arc, Flex, PVC).
|
||||
|GPU|Base tokens/s|Increased tokens/s|Percent|
|
||||
|-|-|-|-|
|
||||
|PVC 1550|39|73|+87%|
|
||||
|Flex 170|39|50|+28%|
|
||||
|Arc770|42|55|+30%|
|
||||
|MTL|13|16|+23%|
|
||||
|ARL-H|14|17|+21%|
|
||||
|
||||
- 2024.11
|
||||
- Use syclcompat to improve the performance on some platforms. This requires to use oneAPI 2025.0 or newer.
|
||||
|
||||
- 2024.8
|
||||
- Use oneDNN as the default GEMM library, improve the compatibility for new Intel GPUs.
|
||||
|
||||
- 2024.5
|
||||
- Performance is increased: 34 -> 37 tokens/s of llama-2-7b.Q4_0 on Arc770.
|
||||
- Arch Linux is verified successfully.
|
||||
|
||||
- 2024.4
|
||||
- Support data types: GGML_TYPE_IQ4_NL, GGML_TYPE_IQ4_XS, GGML_TYPE_IQ3_XXS, GGML_TYPE_IQ3_S, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, GGML_TYPE_IQ2_S, GGML_TYPE_IQ1_S, GGML_TYPE_IQ1_M.
|
||||
|
||||
- 2024.3
|
||||
- Release binary files of Windows.
|
||||
- A blog is published: **Run LLM on all Intel GPUs Using llama.cpp**: [intel.com](https://www.intel.com/content/www/us/en/developer/articles/technical/run-llm-on-all-gpus-using-llama-cpp-artical.html) or [medium.com](https://medium.com/@jianyu_neo/run-llm-on-all-intel-gpus-using-llama-cpp-fd2e2dcbd9bd).
|
||||
- New base line is ready: [tag b2437](https://github.com/ggml-org/llama.cpp/tree/b2437).
|
||||
- Support multiple cards: **--split-mode**: [none|layer]; not support [row], it's on developing.
|
||||
- Support to assign main GPU by **--main-gpu**, replace $GGML_SYCL_DEVICE.
|
||||
- Support detecting all GPUs with level-zero and same top **Max compute units**.
|
||||
- Support OPs
|
||||
- hardsigmoid
|
||||
- hardswish
|
||||
- pool2d
|
||||
|
||||
- 2024.1
|
||||
- Create SYCL backend for Intel GPU.
|
||||
- Support Windows build
|
||||
|
||||
## OS
|
||||
|
||||
| OS | Status | Verified |
|
||||
|---------|---------|------------------------------------------------|
|
||||
| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39, Arch Linux |
|
||||
| Windows | Support | Windows 11 |
|
||||
|
||||
|
||||
## Hardware
|
||||
|
||||
### Intel GPU
|
||||
|
||||
SYCL backend supports Intel GPU Family:
|
||||
|
||||
- Intel Data Center Max Series
|
||||
- Intel Flex Series, Arc Series
|
||||
- Intel Built-in Arc GPU
|
||||
- Intel iGPU in Core CPU (11th Generation Core CPU and newer, refer to [oneAPI supported GPU](https://www.intel.com/content/www/us/en/developer/articles/system-requirements/intel-oneapi-base-toolkit-system-requirements.html#inpage-nav-1-1)).
|
||||
|
||||
On older Intel GPUs, you may try [OpenCL](/docs/backend/OPENCL.md) although the performance is not optimal, and some GPUs may not support OpenCL nor have any GPGPU capabilities.
|
||||
|
||||
#### Verified devices
|
||||
|
||||
| Intel GPU | Status | Verified Model |
|
||||
|-------------------------------|---------|---------------------------------------|
|
||||
| Intel Data Center Max Series | Support | Max 1550, 1100 |
|
||||
| Intel Data Center Flex Series | Support | Flex 170 |
|
||||
| Intel Arc Series | Support | Arc 770, 730M, Arc A750, B580 |
|
||||
| Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake, Arrow Lake, Lunar Lake |
|
||||
| Intel iGPU | Support | iGPU in 13700k, 13400, i5-1250P, i7-1260P, i7-1165G7 |
|
||||
|
||||
*Notes:*
|
||||
|
||||
- **Memory**
|
||||
- The device memory is a limitation when running a large model. The loaded model size, *`llm_load_tensors: buffer_size`*, is displayed in the log when running `./bin/llama-cli`.
|
||||
- Please make sure the GPU shared memory from the host is large enough to account for the model's size. For e.g. the *llama-2-7b.Q4_0* requires at least 8.0GB for integrated GPU and 4.0GB for discrete GPU.
|
||||
|
||||
- **Execution Unit (EU)**
|
||||
- If the iGPU has less than 80 EUs, the inference speed will likely be too slow for practical use.
|
||||
|
||||
### Other Vendor GPU
|
||||
|
||||
**Verified devices**
|
||||
|
||||
| Nvidia GPU | Status | Verified Model |
|
||||
|--------------------------|-----------|----------------|
|
||||
| Ampere Series | Supported | A100, A4000 |
|
||||
| Ampere Series *(Mobile)* | Supported | RTX 40 Series |
|
||||
|
||||
| AMD GPU | Status | Verified Model |
|
||||
|--------------------------|--------------|----------------|
|
||||
| Radeon Pro | Experimental | W6800 |
|
||||
| Radeon RX | Experimental | 6700 XT |
|
||||
|
||||
Note: AMD GPU support is highly experimental and is incompatible with F16.
|
||||
Additionally, it only supports GPUs with a sub_group_size (warp size) of 32.
|
||||
|
||||
## Docker
|
||||
|
||||
The docker build option is currently limited to *Intel GPU* targets.
|
||||
|
||||
### Build image
|
||||
|
||||
```sh
|
||||
# Using FP16
|
||||
docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=ON" --target light -f .devops/intel.Dockerfile .
|
||||
|
||||
# Using FP32
|
||||
docker build -t llama-cpp-sycl --build-arg="GGML_SYCL_F16=OFF" --target light -f .devops/intel.Dockerfile .
|
||||
```
|
||||
|
||||
*Notes*:
|
||||
|
||||
You can also use the `.devops/llama-server-intel.Dockerfile`, which builds the *"server"* alternative.
|
||||
Check the [documentation for Docker](../docker.md) to see the available images.
|
||||
|
||||
### Run container
|
||||
|
||||
```sh
|
||||
# First, find all the DRI cards
|
||||
ls -la /dev/dri
|
||||
# Then, pick the card that you want to use (here for e.g. /dev/dri/card1).
|
||||
docker run -it --rm -v "/path/to/models:/models" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card0:/dev/dri/card0 llama-cpp-sycl -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 -c 4096 -s 0
|
||||
```
|
||||
|
||||
*Notes:*
|
||||
- Docker has been tested successfully on native Linux. WSL support has not been verified yet.
|
||||
- You may need to install Intel GPU driver on the **host** machine *(Please refer to the [Linux configuration](#linux) for details)*.
|
||||
|
||||
## Linux
|
||||
|
||||
### I. Setup Environment
|
||||
|
||||
1. **Install GPU drivers**
|
||||
|
||||
- **Intel GPU**
|
||||
|
||||
Intel data center GPUs drivers installation guide and download page can be found here: [Get intel dGPU Drivers](https://dgpu-docs.intel.com/driver/installation.html#ubuntu-install-steps).
|
||||
|
||||
*Note*: for client GPUs *(iGPU & Arc A-Series)*, please refer to the [client iGPU driver installation](https://dgpu-docs.intel.com/driver/client/overview.html).
|
||||
|
||||
Once installed, add the user(s) to the `video` and `render` groups.
|
||||
|
||||
```sh
|
||||
sudo usermod -aG render $USER
|
||||
sudo usermod -aG video $USER
|
||||
```
|
||||
|
||||
*Note*: logout/re-login for the changes to take effect.
|
||||
|
||||
Verify installation through `clinfo`:
|
||||
|
||||
```sh
|
||||
sudo apt install clinfo
|
||||
sudo clinfo -l
|
||||
```
|
||||
|
||||
Sample output:
|
||||
|
||||
```sh
|
||||
Platform #0: Intel(R) OpenCL Graphics
|
||||
`-- Device #0: Intel(R) Arc(TM) A770 Graphics
|
||||
|
||||
Platform #0: Intel(R) OpenCL HD Graphics
|
||||
`-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49]
|
||||
```
|
||||
|
||||
- **Nvidia GPU**
|
||||
|
||||
In order to target Nvidia GPUs through SYCL, please make sure the CUDA/CUBLAS native requirements *-found [here](README.md#cuda)-* are installed.
|
||||
|
||||
- **AMD GPU**
|
||||
|
||||
To target AMD GPUs with SYCL, the ROCm stack must be installed first.
|
||||
|
||||
2. **Install Intel® oneAPI Base toolkit**
|
||||
|
||||
SYCL backend depends on:
|
||||
- Intel® oneAPI DPC++/C++ compiler/running-time.
|
||||
- Intel® oneAPI DPC++/C++ library (oneDPL).
|
||||
- Intel® oneAPI Deep Neural Network Library (oneDNN).
|
||||
- Intel® oneAPI Math Kernel Library (oneMKL).
|
||||
|
||||
- **For Intel GPU**
|
||||
|
||||
All above are included in both **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** packages.
|
||||
|
||||
It's recommended to install **Intel® Deep Learning Essentials** which only provides the necessary libraries with less size.
|
||||
|
||||
The **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
|
||||
|
||||
Please follow the instructions for downloading and installing the Toolkit for Linux, and preferably keep the default installation values unchanged, notably the installation path *(`/opt/intel/oneapi` by default)*.
|
||||
|
||||
Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
|
||||
|
||||
Upon a successful installation, SYCL is enabled for the available intel devices, along with relevant libraries such as oneAPI oneDNN for Intel GPUs.
|
||||
|
||||
|Verified release|
|
||||
|-|
|
||||
|2025.2.1|
|
||||
|2025.1|
|
||||
|2024.1|
|
||||
|
||||
- **Adding support to Nvidia GPUs**
|
||||
|
||||
**oneAPI Plugin**: In order to enable SYCL support on Nvidia GPUs, please install the [Codeplay oneAPI Plugin for Nvidia GPUs](https://developer.codeplay.com/products/oneapi/nvidia/download). User should also make sure the plugin version matches the installed base toolkit one *(previous step)* for a seamless "oneAPI on Nvidia GPU" setup.
|
||||
|
||||
**oneDNN**: The current oneDNN releases *(shipped with the oneAPI base-toolkit)* do not include the NVIDIA backend. Therefore, oneDNN must be compiled from source to enable the NVIDIA target:
|
||||
|
||||
```sh
|
||||
git clone https://github.com/oneapi-src/oneDNN.git
|
||||
cd oneDNN
|
||||
cmake -GNinja -Bbuild-nvidia -DDNNL_CPU_RUNTIME=DPCPP -DDNNL_GPU_RUNTIME=DPCPP -DDNNL_GPU_VENDOR=NVIDIA -DONEDNN_BUILD_GRAPH=OFF -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
|
||||
cmake --build build-nvidia --config Release
|
||||
```
|
||||
|
||||
- **Adding support to AMD GPUs**
|
||||
|
||||
**oneAPI Plugin**: In order to enable SYCL support on AMD GPUs, please install the [Codeplay oneAPI Plugin for AMD GPUs](https://developer.codeplay.com/products/oneapi/amd/download). As with Nvidia GPUs, the user should also make sure the plugin version matches the installed base toolkit.
|
||||
|
||||
3. **Verify installation and environment**
|
||||
|
||||
In order to check the available SYCL devices on the machine, please use the `sycl-ls` command.
|
||||
```sh
|
||||
source /opt/intel/oneapi/setvars.sh
|
||||
sycl-ls
|
||||
```
|
||||
|
||||
- **Intel GPU**
|
||||
|
||||
When targeting an intel GPU, the user should expect one or more devices among the available SYCL devices. Please make sure that at least one GPU is present via `sycl-ls`, for instance `[level_zero:gpu]` in the sample output below:
|
||||
|
||||
```
|
||||
[level_zero:gpu][level_zero:0] Intel(R) oneAPI Unified Runtime over Level-Zero, Intel(R) Arc(TM) A770 Graphics 12.55.8 [1.3.29735+27]
|
||||
[level_zero:gpu][level_zero:1] Intel(R) oneAPI Unified Runtime over Level-Zero, Intel(R) UHD Graphics 730 12.2.0 [1.3.29735+27]
|
||||
[opencl:cpu][opencl:0] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i5-13400 OpenCL 3.0 (Build 0) [2025.20.8.0.06_160000]
|
||||
[opencl:gpu][opencl:1] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [24.39.31294]
|
||||
[opencl:gpu][opencl:2] Intel(R) OpenCL Graphics, Intel(R) UHD Graphics 730 OpenCL 3.0 NEO [24.39.31294]
|
||||
```
|
||||
|
||||
- **Nvidia GPU**
|
||||
|
||||
Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA device [`cuda:gpu`] as below:
|
||||
|
||||
```
|
||||
[opencl:acc][opencl:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.12.0.12_195853.xmain-hotfix]
|
||||
[opencl:cpu][opencl:1] Intel(R) OpenCL, Intel(R) Xeon(R) Gold 6326 CPU @ 2.90GHz OpenCL 3.0 (Build 0) [2023.16.12.0.12_195853.xmain-hotfix]
|
||||
[cuda:gpu][cuda:0] NVIDIA CUDA BACKEND, NVIDIA A100-PCIE-40GB 8.0 [CUDA 12.5]
|
||||
```
|
||||
|
||||
- **AMD GPU**
|
||||
|
||||
For AMD GPUs we should expect at least one SYCL-HIP device [`hip:gpu`]:
|
||||
|
||||
```
|
||||
[opencl:cpu][opencl:0] Intel(R) OpenCL, 12th Gen Intel(R) Core(TM) i9-12900K OpenCL 3.0 (Build 0) [2024.18.6.0.02_160000]
|
||||
[hip:gpu][hip:0] AMD HIP BACKEND, AMD Radeon PRO W6800 gfx1030 [HIP 60140.9]
|
||||
```
|
||||
|
||||
### II. Build llama.cpp
|
||||
|
||||
#### Intel GPU
|
||||
|
||||
```sh
|
||||
./examples/sycl/build.sh
|
||||
```
|
||||
|
||||
or
|
||||
|
||||
```sh
|
||||
# Export relevant ENV variables
|
||||
source /opt/intel/oneapi/setvars.sh
|
||||
|
||||
# Option 1: Use FP32 (recommended for better performance in most cases)
|
||||
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
|
||||
|
||||
# Option 2: Use FP16
|
||||
cmake -B build -DGGML_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON
|
||||
|
||||
# build all binary
|
||||
cmake --build build --config Release -j -v
|
||||
```
|
||||
|
||||
It is possible to come across some precision issues when running tests that stem from using faster
|
||||
instructions, which can be circumvented by setting the environment variable `SYCL_PROGRAM_COMPILE_OPTIONS`
|
||||
as `-cl-fp32-correctly-rounded-divide-sqrt`
|
||||
|
||||
#### Nvidia GPU
|
||||
|
||||
The SYCL backend depends on [oneMath](https://github.com/uxlfoundation/oneMath) for Nvidia and AMD devices.
|
||||
By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`.
|
||||
|
||||
```sh
|
||||
# Build LLAMA with Nvidia BLAS acceleration through SYCL
|
||||
# Setting GGML_SYCL_DEVICE_ARCH is optional but can improve performance
|
||||
GGML_SYCL_DEVICE_ARCH=sm_80 # Example architecture
|
||||
|
||||
# Option 1: Use FP32 (recommended for better performance in most cases)
|
||||
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl
|
||||
|
||||
# Option 2: Use FP16
|
||||
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=NVIDIA -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_SYCL_F16=ON -DDNNL_DIR=/path/to/oneDNN/build-nvidia/install/lib/cmake/dnnl
|
||||
|
||||
# build all binary
|
||||
cmake --build build --config Release -j -v
|
||||
```
|
||||
|
||||
It is possible to come across some precision issues when running tests that stem from using faster
|
||||
instructions, which can be circumvented by passing the `-fno-fast-math` flag to the compiler.
|
||||
|
||||
#### AMD GPU
|
||||
|
||||
The SYCL backend depends on [oneMath](https://github.com/uxlfoundation/oneMath) for Nvidia and AMD devices.
|
||||
By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`.
|
||||
|
||||
```sh
|
||||
# Build LLAMA with rocBLAS acceleration through SYCL
|
||||
|
||||
## AMD
|
||||
# Use FP32, FP16 is not supported
|
||||
# Find your GGML_SYCL_DEVICE_ARCH with rocminfo, under the key 'Name:'
|
||||
GGML_SYCL_DEVICE_ARCH=gfx90a # Example architecture
|
||||
cmake -B build -DGGML_SYCL=ON -DGGML_SYCL_TARGET=AMD -DGGML_SYCL_DEVICE_ARCH=${GGML_SYCL_DEVICE_ARCH} -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx
|
||||
|
||||
# build all binary
|
||||
cmake --build build --config Release -j -v
|
||||
```
|
||||
|
||||
### III. Run the inference
|
||||
|
||||
#### Retrieve and prepare model
|
||||
|
||||
You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/resolve/main/llama-2-7b.Q4_0.gguf?download=true) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf).
|
||||
|
||||
##### Check device
|
||||
|
||||
1. Enable oneAPI running environment
|
||||
|
||||
```sh
|
||||
source /opt/intel/oneapi/setvars.sh
|
||||
```
|
||||
|
||||
2. List devices information
|
||||
|
||||
Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
|
||||
|
||||
```sh
|
||||
./build/bin/llama-ls-sycl-device
|
||||
```
|
||||
|
||||
This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *intel GPU* it would look like the following:
|
||||
```
|
||||
found 2 SYCL devices:
|
||||
|
||||
| | | |Compute |Max compute|Max work|Max sub| |
|
||||
|ID| Device Type| Name|capability|units |group |group |Global mem size|
|
||||
|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
|
||||
| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136|
|
||||
| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216|
|
||||
```
|
||||
|
||||
#### Choose level-zero devices
|
||||
|
||||
|Chosen Device ID|Setting|
|
||||
|-|-|
|
||||
|0|`export ONEAPI_DEVICE_SELECTOR="level_zero:0"` or no action|
|
||||
|1|`export ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
|
||||
|0 & 1|`export ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"`|
|
||||
|
||||
#### Execute
|
||||
|
||||
Choose one of following methods to run.
|
||||
|
||||
1. Script
|
||||
|
||||
- Use device 0:
|
||||
|
||||
```sh
|
||||
./examples/sycl/run-llama2.sh 0
|
||||
# OR
|
||||
./examples/sycl/run-llama3.sh 0
|
||||
```
|
||||
- Use multiple devices:
|
||||
|
||||
```sh
|
||||
./examples/sycl/run-llama2.sh
|
||||
# OR
|
||||
./examples/sycl/run-llama3.sh
|
||||
```
|
||||
|
||||
2. Command line
|
||||
Launch inference
|
||||
|
||||
There are two device selection modes:
|
||||
|
||||
- Single device: Use one device assigned by user. Default device id is 0.
|
||||
- Multiple devices: Automatically choose the devices with the same backend.
|
||||
|
||||
In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR.
|
||||
|
||||
| Device selection | Parameter |
|
||||
|------------------|----------------------------------------|
|
||||
| Single device | --split-mode none --main-gpu DEVICE_ID |
|
||||
| Multiple devices | --split-mode layer (default) |
|
||||
|
||||
Examples:
|
||||
|
||||
- Use device 0:
|
||||
|
||||
```sh
|
||||
ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm none -mg 0
|
||||
```
|
||||
|
||||
- Use multiple devices:
|
||||
|
||||
```sh
|
||||
ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -no-cnv -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 99 -sm layer
|
||||
```
|
||||
|
||||
*Notes:*
|
||||
|
||||
- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
|
||||
|
||||
```sh
|
||||
detect 1 SYCL GPUs: [0] with top Max compute units:512
|
||||
```
|
||||
Or
|
||||
```sh
|
||||
use 1 SYCL GPUs: [0] with Max compute units:512
|
||||
```
|
||||
|
||||
## Windows
|
||||
|
||||
### I. Setup Environment
|
||||
|
||||
1. Install GPU driver
|
||||
|
||||
Intel GPU drivers instructions guide and download page can be found here: [Get Intel GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html).
|
||||
|
||||
2. Install Visual Studio
|
||||
|
||||
If you already have a recent version of Microsoft Visual Studio, you can skip this step. Otherwise, please refer to the official download page for [Microsoft Visual Studio](https://visualstudio.microsoft.com/).
|
||||
|
||||
3. Install Intel® oneAPI Base toolkit
|
||||
|
||||
SYCL backend depends on:
|
||||
- Intel® oneAPI DPC++/C++ compiler/running-time.
|
||||
- Intel® oneAPI DPC++/C++ library (oneDPL).
|
||||
- Intel® oneAPI Deep Neural Network Library (oneDNN).
|
||||
- Intel® oneAPI Math Kernel Library (oneMKL).
|
||||
|
||||
All above are included in both **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** packages.
|
||||
|
||||
It's recommended to install **Intel® Deep Learning Essentials** which only provides the necessary libraries with less size.
|
||||
|
||||
The **Intel® oneAPI Base toolkit** and **Intel® Deep Learning Essentials** can be obtained from the official [Intel® oneAPI Base Toolkit](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html) page.
|
||||
|
||||
Please follow the instructions for downloading and installing the Toolkit for Windows, and preferably keep the default installation values unchanged, notably the installation path *(`C:\Program Files (x86)\Intel\oneAPI` by default)*.
|
||||
|
||||
Following guidelines/code snippets assume the default installation values. Otherwise, please make sure the necessary changes are reflected where applicable.
|
||||
|
||||
b. Enable oneAPI running environment:
|
||||
|
||||
- Type "oneAPI" in the search bar, then open the `Intel oneAPI command prompt for Intel 64 for Visual Studio 2022` App.
|
||||
|
||||
- On the command prompt, enable the runtime environment with the following:
|
||||
```
|
||||
"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
|
||||
```
|
||||
|
||||
- if you are using Powershell, enable the runtime environment with the following:
|
||||
|
||||
```
|
||||
cmd.exe "/K" '"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" && powershell'
|
||||
```
|
||||
|
||||
c. Verify installation
|
||||
|
||||
In the oneAPI command line, run the following to print the available SYCL devices:
|
||||
|
||||
```
|
||||
sycl-ls.exe
|
||||
```
|
||||
|
||||
There should be one or more *level-zero* GPU devices displayed as **[ext_oneapi_level_zero:gpu]**. Below is example of such output detecting an *intel Iris Xe* GPU as a Level-zero SYCL device:
|
||||
|
||||
Output (example):
|
||||
```
|
||||
[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000]
|
||||
[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000]
|
||||
[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186]
|
||||
[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044]
|
||||
```
|
||||
|
||||
4. Install build tools
|
||||
|
||||
a. Download & install cmake for Windows: https://cmake.org/download/ (CMake can also be installed from Visual Studio Installer)
|
||||
b. The new Visual Studio will install Ninja as default. (If not, please install it manually: https://ninja-build.org/)
|
||||
|
||||
|
||||
### II. Build llama.cpp
|
||||
|
||||
You could download the release package for Windows directly, which including binary files and depended oneAPI dll files.
|
||||
|
||||
Choose one of following methods to build from source code.
|
||||
|
||||
#### 1. Script
|
||||
|
||||
```sh
|
||||
.\examples\sycl\win-build-sycl.bat
|
||||
```
|
||||
|
||||
#### 2. CMake
|
||||
|
||||
On the oneAPI command line window, step into the llama.cpp main directory and run the following:
|
||||
|
||||
```
|
||||
@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
|
||||
|
||||
# Option 1: Use FP32 (recommended for better performance in most cases)
|
||||
cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release
|
||||
|
||||
# Option 2: Or FP16
|
||||
cmake -B build -G "Ninja" -DGGML_SYCL=ON -DCMAKE_C_COMPILER=cl -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DGGML_SYCL_F16=ON
|
||||
|
||||
cmake --build build --config Release -j
|
||||
```
|
||||
|
||||
Or, use CMake presets to build:
|
||||
|
||||
```sh
|
||||
cmake --preset x64-windows-sycl-release
|
||||
cmake --build build-x64-windows-sycl-release -j --target llama-cli
|
||||
|
||||
cmake -DGGML_SYCL_F16=ON --preset x64-windows-sycl-release
|
||||
cmake --build build-x64-windows-sycl-release -j --target llama-cli
|
||||
|
||||
cmake --preset x64-windows-sycl-debug
|
||||
cmake --build build-x64-windows-sycl-debug -j --target llama-cli
|
||||
```
|
||||
|
||||
#### 3. Visual Studio
|
||||
|
||||
You have two options to use Visual Studio to build llama.cpp:
|
||||
- As CMake Project using CMake presets.
|
||||
- Creating a Visual Studio solution to handle the project.
|
||||
|
||||
**Note**:
|
||||
|
||||
All following commands are executed in PowerShell.
|
||||
|
||||
##### - Open as a CMake Project
|
||||
|
||||
You can use Visual Studio to open the `llama.cpp` folder directly as a CMake project. Before compiling, select one of the SYCL CMake presets:
|
||||
|
||||
- `x64-windows-sycl-release`
|
||||
|
||||
- `x64-windows-sycl-debug`
|
||||
|
||||
*Notes:*
|
||||
- For a minimal experimental setup, you can build only the inference executable using:
|
||||
|
||||
```Powershell
|
||||
cmake --build build --config Release -j --target llama-cli
|
||||
```
|
||||
|
||||
##### - Generating a Visual Studio Solution
|
||||
|
||||
You can use Visual Studio solution to build and work on llama.cpp on Windows. You need to convert the CMake Project into a `.sln` file.
|
||||
|
||||
If you want to use the Intel C++ Compiler for the entire `llama.cpp` project, run the following command:
|
||||
|
||||
```Powershell
|
||||
cmake -B build -G "Visual Studio 17 2022" -T "Intel C++ Compiler 2025" -A x64 -DGGML_SYCL=ON -DCMAKE_BUILD_TYPE=Release
|
||||
```
|
||||
|
||||
If you prefer to use the Intel C++ Compiler only for `ggml-sycl`, ensure that `ggml` and its backend libraries are built as shared libraries ( i.e. `-DBUILD_SHARED_LIBRARIES=ON`, this is default behaviour):
|
||||
|
||||
```Powershell
|
||||
cmake -B build -G "Visual Studio 17 2022" -A x64 -DGGML_SYCL=ON -DCMAKE_BUILD_TYPE=Release \
|
||||
-DSYCL_INCLUDE_DIR="C:\Program Files (x86)\Intel\oneAPI\compiler\latest\include" \
|
||||
-DSYCL_LIBRARY_DIR="C:\Program Files (x86)\Intel\oneAPI\compiler\latest\lib"
|
||||
```
|
||||
|
||||
If successful the build files have been written to: *path/to/llama.cpp/build*
|
||||
Open the project file **build/llama.cpp.sln** with Visual Studio.
|
||||
|
||||
Once the Visual Studio solution is created, follow these steps:
|
||||
|
||||
1. Open the solution in Visual Studio.
|
||||
|
||||
2. Right-click on `ggml-sycl` and select **Properties**.
|
||||
|
||||
3. In the left column, expand **C/C++** and select **DPC++**.
|
||||
|
||||
4. In the right panel, find **Enable SYCL Offload** and set it to `Yes`.
|
||||
|
||||
5. Apply the changes and save.
|
||||
|
||||
|
||||
*Navigation Path:*
|
||||
|
||||
```
|
||||
Properties -> C/C++ -> DPC++ -> Enable SYCL Offload (Yes)
|
||||
```
|
||||
|
||||
Now, you can build `llama.cpp` with the SYCL backend as a Visual Studio project.
|
||||
To do it from menu: `Build -> Build Solution`.
|
||||
Once it is completed, final results will be in **build/Release/bin**
|
||||
|
||||
*Additional Note*
|
||||
|
||||
- You can avoid specifying `SYCL_INCLUDE_DIR` and `SYCL_LIBRARY_DIR` in the CMake command by setting the environment variables:
|
||||
|
||||
- `SYCL_INCLUDE_DIR_HINT`
|
||||
|
||||
- `SYCL_LIBRARY_DIR_HINT`
|
||||
|
||||
- Above instruction has been tested with Visual Studio 17 Community edition and oneAPI 2025.0. We expect them to work also with future version if the instructions are adapted accordingly.
|
||||
|
||||
### III. Run the inference
|
||||
|
||||
#### Retrieve and prepare model
|
||||
|
||||
You can refer to the general [*Prepare and Quantize*](README.md#prepare-and-quantize) guide for model preparation, or download an already quantized model like [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) or [Meta-Llama-3-8B-Instruct-Q4_0.gguf](https://huggingface.co/aptha/Meta-Llama-3-8B-Instruct-Q4_0-GGUF/resolve/main/Meta-Llama-3-8B-Instruct-Q4_0.gguf).
|
||||
|
||||
##### Check device
|
||||
|
||||
1. Enable oneAPI running environment
|
||||
|
||||
On the oneAPI command line window, run the following and step into the llama.cpp directory:
|
||||
```
|
||||
"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64
|
||||
```
|
||||
|
||||
2. List devices information
|
||||
|
||||
Similar to the native `sycl-ls`, available SYCL devices can be queried as follow:
|
||||
|
||||
```
|
||||
build\bin\llama-ls-sycl-device.exe
|
||||
```
|
||||
|
||||
This command will only display the selected backend that is supported by SYCL. The default backend is level_zero. For example, in a system with 2 *Intel GPU* it would look like the following:
|
||||
```
|
||||
found 2 SYCL devices:
|
||||
| | | |Compute |Max compute|Max work|Max sub| |
|
||||
|ID| Device Type| Name|capability|units |group |group |Global mem size|
|
||||
|--|------------------|---------------------------------------------|----------|-----------|--------|-------|---------------|
|
||||
| 0|[level_zero:gpu:0]| Intel(R) Arc(TM) A770 Graphics| 1.3| 512| 1024| 32| 16225243136|
|
||||
| 1|[level_zero:gpu:1]| Intel(R) UHD Graphics 770| 1.3| 32| 512| 32| 53651849216|
|
||||
|
||||
```
|
||||
|
||||
#### Choose level-zero devices
|
||||
|
||||
|Chosen Device ID|Setting|
|
||||
|-|-|
|
||||
|0|Default option. You may also want to `set ONEAPI_DEVICE_SELECTOR="level_zero:0"`|
|
||||
|1|`set ONEAPI_DEVICE_SELECTOR="level_zero:1"`|
|
||||
|0 & 1|`set ONEAPI_DEVICE_SELECTOR="level_zero:0;level_zero:1"` or `set ONEAPI_DEVICE_SELECTOR="level_zero:*"`|
|
||||
|
||||
#### Execute
|
||||
|
||||
Choose one of following methods to run.
|
||||
|
||||
1. Script
|
||||
|
||||
```
|
||||
examples\sycl\win-run-llama-2.bat
|
||||
```
|
||||
|
||||
or
|
||||
|
||||
```
|
||||
examples\sycl\win-run-llama-3.bat
|
||||
```
|
||||
|
||||
2. Command line
|
||||
|
||||
Launch inference
|
||||
|
||||
There are two device selection modes:
|
||||
|
||||
- Single device: Use one device assigned by user. Default device id is 0.
|
||||
- Multiple devices: Automatically choose the devices with the same backend.
|
||||
|
||||
In two device selection modes, the default SYCL backend is level_zero, you can choose other backend supported by SYCL by setting environment variable ONEAPI_DEVICE_SELECTOR.
|
||||
|
||||
| Device selection | Parameter |
|
||||
|------------------|----------------------------------------|
|
||||
| Single device | --split-mode none --main-gpu DEVICE_ID |
|
||||
| Multiple devices | --split-mode layer (default) |
|
||||
|
||||
Examples:
|
||||
|
||||
- Use device 0:
|
||||
|
||||
```
|
||||
build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm none -mg 0
|
||||
```
|
||||
|
||||
- Use multiple devices:
|
||||
|
||||
```
|
||||
build\bin\llama-cli.exe -no-cnv -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 99 -sm layer
|
||||
```
|
||||
|
||||
|
||||
Note:
|
||||
|
||||
- Upon execution, verify the selected device(s) ID(s) in the output log, which can for instance be displayed as follow:
|
||||
|
||||
```sh
|
||||
detect 1 SYCL GPUs: [0] with top Max compute units:512
|
||||
```
|
||||
|
||||
Or
|
||||
|
||||
```sh
|
||||
use 1 SYCL GPUs: [0] with Max compute units:512
|
||||
```
|
||||
|
||||
|
||||
## Environment Variable
|
||||
|
||||
#### Build
|
||||
|
||||
| Name | Value | Function |
|
||||
|--------------------|---------------------------------------|---------------------------------------------|
|
||||
| GGML_SYCL | ON (mandatory) | Enable build with SYCL code path. |
|
||||
| GGML_SYCL_TARGET | INTEL *(default)* \| NVIDIA \| AMD | Set the SYCL target device type. |
|
||||
| GGML_SYCL_DEVICE_ARCH | Optional (except for AMD) | Set the SYCL device architecture, optional except for AMD. Setting the device architecture can improve the performance. See the table [--offload-arch](https://github.com/intel/llvm/blob/sycl/sycl/doc/design/OffloadDesign.md#--offload-arch) for a list of valid architectures. |
|
||||
| GGML_SYCL_F16 | OFF *(default)* \|ON *(optional)* | Enable FP16 build with SYCL code path. (1.) |
|
||||
| GGML_SYCL_GRAPH | ON *(default)* \|OFF *(Optional)* | Enable build with [SYCL Graph extension](https://github.com/intel/llvm/blob/sycl/sycl/doc/extensions/experimental/sycl_ext_oneapi_graph.asciidoc). |
|
||||
| GGML_SYCL_DNN | ON *(default)* \|OFF *(Optional)* | Enable build with oneDNN. |
|
||||
| CMAKE_C_COMPILER | `icx` *(Linux)*, `icx/cl` *(Windows)* | Set `icx` compiler for SYCL code path. |
|
||||
| CMAKE_CXX_COMPILER | `icpx` *(Linux)*, `icx` *(Windows)* | Set `icpx/icx` compiler for SYCL code path. |
|
||||
|
||||
1. FP16 is recommended for better prompt processing performance on quantized models. Performance is equivalent in text generation but set `GGML_SYCL_F16=OFF` if you are experiencing issues with FP16 builds.
|
||||
|
||||
#### Runtime
|
||||
|
||||
| Name | Value | Function |
|
||||
|-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
|
||||
| GGML_SYCL_DEBUG | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG |
|
||||
| GGML_SYCL_DISABLE_OPT | 0 (default) or 1 | Disable optimize features for Intel GPUs. (Recommended to 1 for intel devices older than Gen 10) |
|
||||
| GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because graph performance isn't yet better than non-graph performance. |
|
||||
| GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
|
||||
| ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
|
||||
| UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Support malloc device memory more than 4GB.|
|
||||
|
||||
|
||||
|
||||
## Known Issues
|
||||
|
||||
- `Split-mode:[row]` is not supported.
|
||||
|
||||
## Q&A
|
||||
|
||||
- Error: `error while loading shared libraries: libsycl.so: cannot open shared object file: No such file or directory`.
|
||||
|
||||
- Potential cause: Unavailable oneAPI installation or not set ENV variables.
|
||||
- Solution: Install *oneAPI base toolkit* and enable its ENV through: `source /opt/intel/oneapi/setvars.sh`.
|
||||
|
||||
- General compiler error:
|
||||
|
||||
- Remove **build** folder or try a clean-build.
|
||||
|
||||
- I can **not** see `[ext_oneapi_level_zero:gpu]` afer installing the GPU driver on Linux.
|
||||
|
||||
Please double-check with `sudo sycl-ls`.
|
||||
|
||||
If it's present in the list, please add video/render group to your user then **logout/login** or restart your system:
|
||||
|
||||
```
|
||||
sudo usermod -aG render $USER
|
||||
sudo usermod -aG video $USER
|
||||
```
|
||||
Otherwise, please double-check the GPU driver installation steps.
|
||||
|
||||
- Can I report Ollama issue on Intel GPU to llama.cpp SYCL backend?
|
||||
|
||||
No. We can't support Ollama issue directly, because we aren't familiar with Ollama.
|
||||
|
||||
Suggest reproducing on llama.cpp and report similar issue to llama.cpp. We will support it.
|
||||
|
||||
It's same for other projects including llama.cpp SYCL backend.
|
||||
|
||||
- `Native API failed. Native API returns: 39 (UR_RESULT_ERROR_OUT_OF_DEVICE_MEMORY)`, `ggml_backend_sycl_buffer_type_alloc_buffer: can't allocate 3503030272 Bytes of memory on device`, or `failed to allocate SYCL0 buffer`
|
||||
|
||||
You are running out of Device Memory.
|
||||
|
||||
|Reason|Solution|
|
||||
|-|-|
|
||||
| The default context is too big. It leads to excessive memory usage.|Set `-c 8192` or a smaller value.|
|
||||
| The model is too big and requires more memory than what is available.|Choose a smaller model or change to a smaller quantization, like Q5 -> Q4;<br>Alternatively, use more than one device to load model.|
|
||||
|
||||
- `ggml_backend_sycl_buffer_type_alloc_buffer: can't allocate 5000000000 Bytes of memory on device`
|
||||
|
||||
You need to enable to support 4GB memory malloc by:
|
||||
```
|
||||
export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
|
||||
set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
|
||||
```
|
||||
|
||||
### **GitHub contribution**:
|
||||
Please add the `SYCL :` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay.
|
||||
|
||||
## TODO
|
||||
|
||||
- Review ZES_ENABLE_SYSMAN: https://github.com/intel/compute-runtime/blob/master/programmers-guide/SYSMAN.md#support-and-limitations
|
||||
258
docs/backend/ZenDNN.md
Normal file
258
docs/backend/ZenDNN.md
Normal file
@@ -0,0 +1,258 @@
|
||||
# llama.cpp for AMD ZenDNN
|
||||
|
||||
> [!WARNING]
|
||||
> **Note:** ZenDNN is **not** the same as zDNN.
|
||||
> - **ZenDNN** (this page): AMD's deep learning library for AMD EPYC CPUs
|
||||
> - **zDNN**: IBM's Deep Neural Network acceleration library for IBM Z & LinuxONE Mainframes ([see zDNN documentation](zDNN.md))
|
||||
|
||||
- [Background](#background)
|
||||
- [OS](#os)
|
||||
- [Hardware](#hardware)
|
||||
- [Supported Operations](#supported-operations)
|
||||
- [DataType Supports](#datatype-supports)
|
||||
- [Linux](#linux)
|
||||
- [Environment Variable](#environment-variable)
|
||||
- [Performance Optimization](#performance-optimization)
|
||||
- [Known Issues](#known-issues)
|
||||
- [TODO](#todo)
|
||||
|
||||
## Background
|
||||
|
||||
**ZenDNN** (Zen Deep Neural Network Library) is AMD's high-performance deep learning inference library optimized for AMD EPYC™ CPUs. It provides optimized implementations of key deep learning primitives and operations, delivering significant performance improvements for neural network workloads on AMD Zen-based processor architectures.
|
||||
|
||||
**Llama.cpp + ZenDNN**
|
||||
|
||||
The llama.cpp ZenDNN backend leverages AMD's optimized matrix multiplication primitives to accelerate inference on AMD CPUs. It utilizes ZenDNN's **LowOHA (Low Overhead Hardware Accelerated)** MatMul operator for efficient GEMM operations with minimal execution overhead, built-in weight caching, and direct access to backend libraries (AOCL BLIS, LibXSMM, OneDNN).
|
||||
|
||||
For more information about ZenDNN, visit: https://www.amd.com/en/developer/zendnn.html
|
||||
|
||||
## OS
|
||||
|
||||
| OS | Status | Verified |
|
||||
|:-------:|:-------:|:----------------------------------------------:|
|
||||
| Linux | Support | Ubuntu 20.04, 22.04, 24.04 |
|
||||
|
||||
For the latest list of supported operating systems, see the [ZenDNN Supported OS](https://github.com/amd/ZenDNN/blob/zendnnl/README.md#15-supported-os).
|
||||
|
||||
## Hardware
|
||||
|
||||
### AMD CPUs
|
||||
|
||||
**Recommended Processors**
|
||||
|
||||
ZenDNN is optimized for AMD EPYC™ processors and AMD Ryzen™ processors based on "Zen" microarchitecture and newer.
|
||||
|
||||
| CPU Family | Status | Notes |
|
||||
|:-----------------------------:|:-------:|:----------------------------------:|
|
||||
| AMD EPYC™ 9005 Series (Turin)| Support | 5th Gen - Zen 5 architecture |
|
||||
| AMD EPYC™ 9004 Series (Genoa)| Support | 4th Gen - Zen 4 architecture |
|
||||
| AMD EPYC™ 7003 Series (Milan)| Support | 3rd Gen - Zen 3 architecture |
|
||||
| AMD Ryzen™ AI MAX (Strix Halo)| Support | High-performance mobile processors |
|
||||
|
||||
*Notes:*
|
||||
|
||||
- Best performance is achieved on AMD EPYC™ processors with high core counts (e.g., EPYC 9005 series).
|
||||
- ZenDNN leverages AMD's advanced CPU features including AVX2 and AVX-512 instruction sets.
|
||||
- For optimal performance, ensure your system has sufficient memory bandwidth.
|
||||
|
||||
## Supported Operations
|
||||
|
||||
The ZenDNN backend currently accelerates **matrix multiplication (MUL_MAT)** operations only. Other operations are handled by the standard CPU backend.
|
||||
|
||||
| Operation | Status | Notes |
|
||||
|:-------------|:-------:|:----------------------------------------------:|
|
||||
| MUL_MAT | ✓ | Accelerated via ZenDNN LowOHA MatMul |
|
||||
|
||||
*Note:* Since only MUL_MAT is accelerated, models will benefit most from ZenDNN when matrix multiplications dominate the computational workload (which is typical for transformer-based LLMs).
|
||||
|
||||
## DataType Supports
|
||||
|
||||
| DataType | Status | Notes |
|
||||
|:----------------------:|:-------:|:---------------------------------------------:|
|
||||
| FP32 | Support | Full precision floating point |
|
||||
| BF16 | Support | BFloat16 (best performance on Zen 4/Zen 5) |
|
||||
|
||||
*Notes:*
|
||||
|
||||
- **BF16** provides best performance on Zen 4 and Zen 5 EPYC™ processors (Genoa, Turin).
|
||||
|
||||
## Linux
|
||||
|
||||
### I. Setup Environment
|
||||
|
||||
You have two options to set up ZenDNN:
|
||||
|
||||
#### Option 1: Automatic Download and Build (Recommended)
|
||||
|
||||
CMake will automatically download and build ZenDNN for you:
|
||||
|
||||
```sh
|
||||
# Build llama.cpp - ZenDNN will be automatically downloaded and built
|
||||
cmake -B build -DGGML_ZENDNN=ON -DCMAKE_BUILD_TYPE=Release
|
||||
cmake --build build --config Release -j $(nproc)
|
||||
```
|
||||
|
||||
No manual ZenDNN installation required. CMake will handle everything automatically.
|
||||
|
||||
#### Option 2: Use Custom ZenDNN Installation
|
||||
|
||||
If you want to build ZenDNN yourself or use a specific version:
|
||||
|
||||
**Step 1: Build ZenDNN from source**
|
||||
|
||||
```sh
|
||||
# Clone ZenDNN repository
|
||||
git clone https://github.com/amd/ZenDNN.git
|
||||
cd ZenDNN
|
||||
git checkout zendnnl
|
||||
|
||||
# Build and install (requires CMake >= 3.25)
|
||||
mkdir build && cd build
|
||||
cmake ..
|
||||
cmake --build . --target all
|
||||
```
|
||||
|
||||
Default installation path: `ZenDNN/build/install`
|
||||
|
||||
**For detailed build instructions**, refer to the [ZenDNN README](https://github.com/amd/ZenDNN/blob/zendnnl/README.md).
|
||||
|
||||
**Step 2: Build llama.cpp with custom ZenDNN path**
|
||||
|
||||
```sh
|
||||
# Using environment variable
|
||||
export ZENDNN_ROOT=/path/to/ZenDNN/build/install
|
||||
cmake -B build -DGGML_ZENDNN=ON -DCMAKE_BUILD_TYPE=Release
|
||||
cmake --build build --config Release -j $(nproc)
|
||||
|
||||
# OR specify path directly in CMake
|
||||
cmake -B build -DGGML_ZENDNN=ON -DZENDNN_ROOT=/path/to/ZenDNN/build/install -DCMAKE_BUILD_TYPE=Release
|
||||
cmake --build build --config Release -j $(nproc)
|
||||
```
|
||||
|
||||
### II. Run the Server
|
||||
|
||||
#### 1. Download Model
|
||||
|
||||
Download LLaMA 3.1 8B Instruct BF16 model:
|
||||
|
||||
```sh
|
||||
# Download from Hugging Face
|
||||
huggingface-cli download meta-llama/Llama-3.1-8B-Instruct-GGUF --local-dir models/
|
||||
```
|
||||
|
||||
#### 2. Start Server
|
||||
|
||||
Run llama.cpp server with ZenDNN acceleration:
|
||||
|
||||
```sh
|
||||
# Set optimal configuration
|
||||
export OMP_NUM_THREADS=64 # Adjust to your CPU core count
|
||||
export ZENDNNL_MATMUL_ALGO=2 # Blocked AOCL BLIS for best performance
|
||||
|
||||
# Start server
|
||||
./build/bin/llama-server \
|
||||
-m models/Llama-3.1-8B-Instruct.BF16.gguf \
|
||||
--host 0.0.0.0 \
|
||||
--port 8080 \
|
||||
-t 64
|
||||
```
|
||||
|
||||
Access the server at `http://localhost:8080`.
|
||||
|
||||
**Performance tips**:
|
||||
- Set `OMP_NUM_THREADS` to match your physical core count
|
||||
- Use `ZENDNNL_MATMUL_ALGO=2` for optimal performance
|
||||
- For NUMA systems: `numactl --cpunodebind=0 --membind=0 ./build/bin/llama-server ...`
|
||||
|
||||
## Environment Variable
|
||||
|
||||
### Build Time
|
||||
|
||||
| Name | Value | Function |
|
||||
|--------------------|---------------------------------------|---------------------------------------------|
|
||||
| GGML_ZENDNN | ON/OFF | Enable ZenDNN backend support |
|
||||
| ZENDNN_ROOT | Path to ZenDNN installation | Set ZenDNN installation directory |
|
||||
| GGML_OPENMP | ON/OFF (recommended: ON) | Enable OpenMP for multi-threading |
|
||||
|
||||
### Runtime
|
||||
|
||||
| Name | Value | Function |
|
||||
|-------------------------|--------------------------|-------------------------------------------------------------------|
|
||||
| OMP_NUM_THREADS | Number (e.g., 64) | Set number of OpenMP threads (recommended: physical core count) |
|
||||
| ZENDNNL_MATMUL_ALGO | 0-5 | Select MatMul backend algorithm (see Performance Optimization) |
|
||||
| ZENDNNL_PROFILE_LOG_LEVEL | 0-4 | Profiling log level (0=disabled, 4=verbose) |
|
||||
| ZENDNNL_ENABLE_PROFILER | 0 or 1 | Enable detailed profiling (1=enabled) |
|
||||
| ZENDNNL_API_LOG_LEVEL | 0-4 | API log level (0=disabled, 4=verbose) |
|
||||
|
||||
**Example**:
|
||||
|
||||
```sh
|
||||
export OMP_NUM_THREADS=64
|
||||
export ZENDNNL_MATMUL_ALGO=2 # Use Blocked AOCL BLIS for best performance
|
||||
./build/bin/llama-cli -m models/llama-2-7b.Q4_0.gguf -p "Test" -n 100
|
||||
```
|
||||
|
||||
## Performance Optimization
|
||||
|
||||
### MatMul Algorithm Selection
|
||||
|
||||
ZenDNN's LowOHA MatMul supports multiple backend algorithms. For **best performance**, use the **Blocked AOCL BLIS** algorithm:
|
||||
|
||||
```sh
|
||||
export ZENDNNL_MATMUL_ALGO=2 # Blocked AOCL BLIS (recommended)
|
||||
```
|
||||
|
||||
**Available algorithms**:
|
||||
|
||||
| Value | Algorithm | Description |
|
||||
|:-----:|:-----------------------|:----------------------------------------------|
|
||||
| 0 | Dynamic Dispatch | Automatic backend selection (default) |
|
||||
| 1 | AOCL BLIS | AOCL BLIS backend |
|
||||
| 2 | AOCL BLIS Blocked | **Blocked AOCL BLIS (recommended)** |
|
||||
| 3 | OneDNN | OneDNN backend |
|
||||
| 4 | OneDNN Blocked | Blocked OneDNN |
|
||||
| 5 | LibXSMM | LibXSMM backend |
|
||||
|
||||
### Profiling and Debugging
|
||||
|
||||
For detailed profiling and logging options, refer to the [ZenDNN Logging Documentation](https://github.com/amd/ZenDNN/blob/zendnnl/docs/logging.md).
|
||||
|
||||
## Known Issues
|
||||
|
||||
- **Limited operation support**: Currently only matrix multiplication (MUL_MAT) is accelerated via ZenDNN. Other operations fall back to the standard CPU backend.
|
||||
- **BF16 support**: BF16 operations require AMD Zen 4 or Zen 5 architecture (EPYC 9004/9005 series). On older CPUs, operations will use FP32.
|
||||
- **NUMA awareness**: For multi-socket systems, manual NUMA binding may be required for optimal performance.
|
||||
|
||||
## Q&A
|
||||
|
||||
**Q: How do I verify that ZenDNN backend is being used?**
|
||||
|
||||
A: Check the log output when running llama.cpp. You should see messages indicating the ZenDNN backend is initialized. You can also check the backend name in the output.
|
||||
|
||||
**Q: What performance improvement can I expect?**
|
||||
|
||||
A: Performance gains vary depending on the model size, batch size, and CPU architecture. On AMD EPYC processors, you can typically expect 1.1x-2x speedup compared to standard CPU inference for matrix multiplication operations.
|
||||
|
||||
**Q: Can I use ZenDNN on non-AMD processors?**
|
||||
|
||||
A: ZenDNN is optimized specifically for AMD processors. While it may work on other x86-64 CPUs, performance benefits are only guaranteed on AMD Zen-based architectures.
|
||||
|
||||
**Q: Does ZenDNN support quantized models?**
|
||||
|
||||
A: Currently, ZenDNN primarily supports FP32 and BF16 data types. Quantized model support is not available at this time.
|
||||
|
||||
**Q: Why is my inference not faster with ZenDNN?**
|
||||
|
||||
A: Ensure:
|
||||
1. You're using an AMD EPYC or Ryzen processor (Zen 2 or newer)
|
||||
2. `OMP_NUM_THREADS` is set appropriately (physical core count)
|
||||
3. `ZENDNNL_MATMUL_ALGO=2` is set for best performance (Blocked AOCL BLIS)
|
||||
4. You're using a sufficiently large model (small models may not benefit as much)
|
||||
5. Enable profiling to verify ZenDNN MatMul is being called
|
||||
|
||||
### **GitHub Contribution**:
|
||||
Please add the **[ZenDNN]** prefix/tag in issues/PRs titles to help the ZenDNN-team check/address them without delay.
|
||||
|
||||
## TODO
|
||||
|
||||
- Expand operation support beyond MUL_MAT (attention operations, activations, etc.)
|
||||
51
docs/backend/hexagon/CMakeUserPresets.json
Normal file
51
docs/backend/hexagon/CMakeUserPresets.json
Normal file
@@ -0,0 +1,51 @@
|
||||
{
|
||||
"version": 4,
|
||||
"configurePresets": [
|
||||
{
|
||||
"name": "arm64-android-snapdragon",
|
||||
"hidden": true,
|
||||
"architecture": { "value": "arm64", "strategy": "external" },
|
||||
"toolset": { "value": "host=x86_64", "strategy": "external" },
|
||||
"cacheVariables": {
|
||||
"ANDROID_ABI": "arm64-v8a",
|
||||
"ANDROID_PLATFORM": "android-31",
|
||||
"CMAKE_TOOLCHAIN_FILE": "$env{ANDROID_NDK_ROOT}/build/cmake/android.toolchain.cmake",
|
||||
"CMAKE_C_FLAGS": "-march=armv8.7a+fp16 -fvectorize -ffp-model=fast -fno-finite-math-only -flto -D_GNU_SOURCE",
|
||||
"CMAKE_CXX_FLAGS": "-march=armv8.7a+fp16 -fvectorize -ffp-model=fast -fno-finite-math-only -flto -D_GNU_SOURCE",
|
||||
"CMAKE_C_FLAGS_RELEASE": "-O3 -DNDEBUG",
|
||||
"CMAKE_CXX_FLAGS_RELEASE": "-O3 -DNDEBUG",
|
||||
"CMAKE_C_FLAGS_RELWITHDEBINFO": "-O3 -DNDEBUG -g",
|
||||
"CMAKE_CXX_FLAGS_RELWITHDEBINFO": "-O3 -DNDEBUG -g",
|
||||
"HEXAGON_SDK_ROOT": "$env{HEXAGON_SDK_ROOT}",
|
||||
"PREBUILT_LIB_DIR": "android_aarch64",
|
||||
"GGML_OPENMP": "OFF",
|
||||
"GGML_LLAMAFILE": "OFF",
|
||||
"GGML_OPENCL": "ON",
|
||||
"GGML_HEXAGON": "ON",
|
||||
"GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
|
||||
"LLAMA_OPENSSL": "OFF"
|
||||
}
|
||||
},
|
||||
|
||||
{
|
||||
"name": "arm64-windows-snapdragon",
|
||||
"inherits": [ "base", "arm64-windows-llvm" ],
|
||||
"cacheVariables": {
|
||||
"HEXAGON_SDK_ROOT": "$env{HEXAGON_SDK_ROOT}",
|
||||
"PREBUILT_LIB_DIR": "windows_aarch64",
|
||||
"GGML_OPENMP": "OFF",
|
||||
"GGML_LLAMAFILE": "OFF",
|
||||
"GGML_OPENCL": "ON",
|
||||
"GGML_HEXAGON": "ON",
|
||||
"GGML_HEXAGON_FP32_QUANTIZE_GROUP_SIZE": "128",
|
||||
"LLAMA_OPENSSL": "OFF"
|
||||
}
|
||||
},
|
||||
|
||||
{ "name": "arm64-android-snapdragon-debug" , "inherits": [ "base", "arm64-android-snapdragon", "debug" ] },
|
||||
{ "name": "arm64-android-snapdragon-release", "inherits": [ "base", "arm64-android-snapdragon", "release" ] },
|
||||
|
||||
{ "name": "arm64-windows-snapdragon-debug" , "inherits": [ "base", "arm64-windows-snapdragon", "debug" ] },
|
||||
{ "name": "arm64-windows-snapdragon-release", "inherits": [ "base", "arm64-windows-snapdragon", "release" ] }
|
||||
]
|
||||
}
|
||||
243
docs/backend/hexagon/README.md
Normal file
243
docs/backend/hexagon/README.md
Normal file
@@ -0,0 +1,243 @@
|
||||
# Snapdragon-based Android devices
|
||||
|
||||
## How to Build
|
||||
|
||||
The easiest way to build llama.cpp for a Snapdragon-based Android device is using the toolchain Docker image (see github.com/snapdragon-toolchain).
|
||||
This image includes Android NDK, OpenCL SDK, Hexagon SDK, CMake, etc.
|
||||
|
||||
This method works on Linux, macOS, and Windows. macOS and Windows users should install Docker Desktop.
|
||||
|
||||
```
|
||||
~/src/llama.cpp$ docker run -it -u $(id -u):$(id -g) --volume $(pwd):/workspace --platform linux/amd64 ghcr.io/snapdragon-toolchain/arm64-android:v0.3
|
||||
[d]/> cd /workspace
|
||||
```
|
||||
|
||||
The rest of the Android build process assumes that you're running inside the toolchain container.
|
||||
Let's build llama.cpp with CPU, OpenCL, and Hexagon backends via CMake presets:
|
||||
|
||||
```
|
||||
[d]/workspace> cp docs/backend/hexagon/CMakeUserPresets.json .
|
||||
|
||||
[d]/workspace> cmake --preset arm64-android-snapdragon-release -B build-snapdragon
|
||||
Preset CMake variables:
|
||||
ANDROID_ABI="arm64-v8a"
|
||||
...
|
||||
CMAKE_TOOLCHAIN_FILE="/opt/android-ndk-r28b/build/cmake/android.toolchain.cmake"
|
||||
GGML_HEXAGON="ON"
|
||||
GGML_OPENCL="ON"
|
||||
GGML_OPENMP="OFF"
|
||||
HEXAGON_SDK_ROOT="/opt/hexagon/6.4.0.2"
|
||||
...
|
||||
-- Including OpenCL backend
|
||||
-- Including Hexagon backend
|
||||
...
|
||||
-- Build files have been written to: /workspace/build-snapdragon
|
||||
|
||||
[d]/workspace> cmake --build build-snapdragon
|
||||
...
|
||||
[144/356] Performing build step for 'htp-v73'
|
||||
[1/16] Generating htp_iface_skel.c, htp_iface_stub.c, htp_iface.h
|
||||
[2/16] Building C object CMakeFiles/ggml-htp-v73.dir/hvx-sigmoid.c.obj
|
||||
[3/16] Building C object CMakeFiles/ggml-htp-v73.dir/htp-dma.c.obj
|
||||
[4/16] Building C object CMakeFiles/ggml-htp-v73.dir/worker-pool.c.obj
|
||||
...
|
||||
-- Installing: /workspace/build-snapdragon/ggml/src/ggml-hexagon/libggml-htp-v73.so
|
||||
-- Installing: /workspace/build-snapdragon/ggml/src/ggml-hexagon/libggml-htp-v75.so
|
||||
...
|
||||
```
|
||||
|
||||
To generate an installable "package" simply use cmake --install:
|
||||
|
||||
```
|
||||
[d]/workspace> cmake --install build-snapdragon --prefix pkg-adb/llama.cpp
|
||||
-- Install configuration: "Release"
|
||||
-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-cpu.so
|
||||
-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-opencl.so
|
||||
-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-hexagon.so
|
||||
-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v73.so
|
||||
-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v75.so
|
||||
-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v79.so
|
||||
-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml-htp-v81.so
|
||||
-- Installing: /workspace/pkg-adb/llama.cpp/lib/libggml.so
|
||||
...
|
||||
-- Installing: /workspace/pkg-adb/llama.cpp/bin/llama-bench
|
||||
-- Installing: /workspace/pkg-adb/llama.cpp/bin/llama-cli
|
||||
...
|
||||
```
|
||||
|
||||
## How to Install
|
||||
|
||||
For this step, your device needs to be configured for on-device development.
|
||||
Please see https://developer.android.com/studio/debug/dev-options for details.
|
||||
|
||||
Once ADB is enabled, use `adb push` to install `pkg-snapdragon` on the device.
|
||||
**Note that the toolchain Docker image doesn't have ADB and doesn't set up the ADB bridge. Please use native ADB on the host.**
|
||||
|
||||
```
|
||||
~/src/llama.cpp$ adb push pkg-adb/llama.cpp /data/local/tmp/
|
||||
pkg-adb/llama.cpp/bin/: 67 files pushed, 0 skipped. 190.2 MB/s (919095042 bytes in 4.607s)
|
||||
pkg-adb/llama.cpp/include/: 19 files pushed, 0 skipped. 20.5 MB/s (255173 bytes in 0.012s)
|
||||
pkg-adb/llama.cpp/lib/: 16 files pushed, 0 skipped. 144.4 MB/s (43801382 bytes in 0.289s)
|
||||
102 files pushed, 0 skipped. 186.9 MB/s (963151597 bytes in 4.914s)
|
||||
```
|
||||
|
||||
At this point, you should also install some models:
|
||||
|
||||
```
|
||||
~/src/llama.cpp$ wget https://huggingface.co/bartowski/Llama-3.2-1B-Instruct-GGUF/resolve/main/Llama-3.2-1B-Instruct-Q4_0.gguf
|
||||
...
|
||||
2025-10-11 12:04:52 (10.7 MB/s) - ‘Llama-3.2-1B-Instruct-Q4_0.gguf’ saved [773025920/773025920]
|
||||
|
||||
~/src/llama.cpp$ adb push Llama-3.2-1B-Instruct-Q4_0.gguf /data/local/tmp/gguf
|
||||
Llama-3.2-1B-Instruct-Q4_0.gguf: 1 file pushed, 0 skipped. 38.3 MB/s (773025920 bytes in 19.250s)
|
||||
```
|
||||
|
||||
## How to Run
|
||||
|
||||
The easiest way to run llama.cpp cli tools is using provided wrapper scripts that properly set up all required environment variables.
|
||||
|
||||
llama.cpp supports three backends on Snapdragon-based devices: CPU, Adreno GPU (GPUOpenCL), and Hexagon NPU (HTP0-4).
|
||||
You can select which backend to run the model on using the `D=` variable, which maps to the `--device` option.
|
||||
|
||||
Hexagon NPU behaves as a "GPU" device when it comes to `-ngl` and other offload-related options.
|
||||
|
||||
Here are some examples of running various llama.cpp tools via ADB.
|
||||
|
||||
Simple question for Llama-3.2-1B
|
||||
|
||||
```
|
||||
~/src/llama.cpp$ M=Llama-3.2-1B-Instruct-Q4_0.gguf D=HTP0 ./scripts/snapdragon/adb/run-completion.sh -p "what is the most popular cookie in the world?"
|
||||
...
|
||||
ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
|
||||
ggml-hex: Hexagon Arch version v79
|
||||
ggml-hex: allocating new session: HTP0
|
||||
ggml-hex: new session: HTP0 : session-id 0 domain-id 3 uri file:///libggml-htp-v79.so?htp_iface_skel_handle_invoke&_modver=1.0&_dom=cdsp&_session=0 handle 0xb4000072c7955e50
|
||||
...
|
||||
load_tensors: offloading output layer to GPU
|
||||
load_tensors: offloaded 17/17 layers to GPU
|
||||
load_tensors: CPU model buffer size = 225.49 MiB
|
||||
load_tensors: HTP0 model buffer size = 0.26 MiB
|
||||
load_tensors: HTP0-REPACK model buffer size = 504.00 MiB
|
||||
...
|
||||
I hope this helps you understand the world's most popular cookies! [end of text]
|
||||
...
|
||||
llama_perf_sampler_print: sampling time = 30.08 ms / 487 runs ( 0.06 ms per token, 16191.77 tokens per second)
|
||||
llama_perf_context_print: load time = 617.94 ms
|
||||
llama_perf_context_print: prompt eval time = 80.76 ms / 11 tokens ( 7.34 ms per token, 136.21 tokens per second)
|
||||
llama_perf_context_print: eval time = 9210.59 ms / 475 runs ( 19.39 ms per token, 51.57 tokens per second)
|
||||
llama_perf_context_print: total time = 9454.92 ms / 486 tokens
|
||||
llama_perf_context_print: graphs reused = 473
|
||||
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
|
||||
llama_memory_breakdown_print: | - HTP0 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 |
|
||||
llama_memory_breakdown_print: | - Host | 439 = 225 + 136 + 77 |
|
||||
llama_memory_breakdown_print: | - HTP0-REPACK | 504 = 504 + 0 + 0 |
|
||||
```
|
||||
|
||||
Summary request for OLMoE-1B-7B. This is a large model that requires two HTP sessions/devices
|
||||
|
||||
```
|
||||
~/src/llama.cpp$ M=OLMoE-1B-7B-0125-Instruct-Q4_0.gguf NDEV=2 D=HTP0,HTP1 ./scripts/snapdragon/adb/run-completion.sh -f surfing.txt
|
||||
...
|
||||
ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
|
||||
ggml-hex: Hexagon Arch version v81
|
||||
ggml-hex: allocating new session: HTP0
|
||||
ggml-hex: allocating new session: HTP1
|
||||
...
|
||||
load_tensors: offloading output layer to GPU
|
||||
load_tensors: offloaded 17/17 layers to GPU
|
||||
load_tensors: CPU model buffer size = 143.86 MiB
|
||||
load_tensors: HTP1 model buffer size = 0.23 MiB
|
||||
load_tensors: HTP1-REPACK model buffer size = 1575.00 MiB
|
||||
load_tensors: HTP0 model buffer size = 0.28 MiB
|
||||
load_tensors: HTP0-REPACK model buffer size = 2025.00 MiB
|
||||
...
|
||||
llama_context: CPU output buffer size = 0.19 MiB
|
||||
llama_kv_cache: HTP1 KV buffer size = 238.00 MiB
|
||||
llama_kv_cache: HTP0 KV buffer size = 306.00 MiB
|
||||
llama_kv_cache: size = 544.00 MiB ( 8192 cells, 16 layers, 1/1 seqs), K (q8_0): 272.00 MiB, V (q8_0): 272.00 MiB
|
||||
llama_context: HTP0 compute buffer size = 15.00 MiB
|
||||
llama_context: HTP1 compute buffer size = 15.00 MiB
|
||||
llama_context: CPU compute buffer size = 24.56 MiB
|
||||
...
|
||||
llama_perf_context_print: prompt eval time = 1730.57 ms / 212 tokens ( 8.16 ms per token, 122.50 tokens per second)
|
||||
llama_perf_context_print: eval time = 5624.75 ms / 257 runs ( 21.89 ms per token, 45.69 tokens per second)
|
||||
llama_perf_context_print: total time = 7377.33 ms / 469 tokens
|
||||
llama_perf_context_print: graphs reused = 255
|
||||
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
|
||||
llama_memory_breakdown_print: | - HTP0 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 |
|
||||
llama_memory_breakdown_print: | - HTP1 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 |
|
||||
llama_memory_breakdown_print: | - Host | 742 = 144 + 544 + 54 |
|
||||
llama_memory_breakdown_print: | - HTP1-REPACK | 1575 = 1575 + 0 + 0 |
|
||||
llama_memory_breakdown_print: | - HTP0-REPACK | 2025 = 2025 + 0 + 0 |
|
||||
```
|
||||
|
||||
Op test for MUL_MAT
|
||||
|
||||
```
|
||||
~/src/llama.cpp$ HB=0 ./scripts/snapdragon/adb/run-tool.sh test-backend-ops -b HTP0 -o MUL_MAT
|
||||
...
|
||||
Backend 2/3: HTP0
|
||||
Device description: Hexagon
|
||||
Device memory: 2048 MB (2048 MB free)
|
||||
MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
|
||||
MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
|
||||
MUL_MAT(type_a=q4_0,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],v=0,o=1): OK
|
||||
|
||||
~/src/llama.cpp-hexagon$ M=Llama-3.2-1B-Instruct-Q4_0.gguf ./scripts/snapdragon/adb/run-bench.sh -p 128 -n 64
|
||||
...
|
||||
ggml-hex: Hexagon backend (experimental) : allocating new registry : ndev 1
|
||||
ggml-hex: Hexagon Arch version v79
|
||||
ggml-hex: allocating new session: HTP0
|
||||
ggml-hex: new session: HTP0 : session-id 0 domain-id 3 uri file:///libggml-htp-v79.so?htp_iface_skel_handle_invoke&_modver=1.0&_dom=cdsp&_session=0 handle 0xb400007d4b231090
|
||||
| model | size | params | backend | ngl | threads | n_batch | mmap | test | t/s |
|
||||
| ---------------| ---------: | -----: | ---------- | --: | ------: | ------: | ---: | ----: | ------------: |
|
||||
| llama 1B Q4_0 | 729.75 MiB | 1.24 B | HTP | 99 | 4 | 128 | 0 | pp128 | 169.42 ± 1.75 |
|
||||
| llama 1B Q4_0 | 729.75 MiB | 1.24 B | HTP | 99 | 4 | 128 | 0 | tg64 | 51.54 ± 1.13 |
|
||||
|
||||
build: 6a8cf8914 (6733)
|
||||
```
|
||||
|
||||
## Environment variables
|
||||
|
||||
- `GGML_HEXAGON_NDEV=1`
|
||||
Controls the number of devices/sessions to allocate. The default is 1.
|
||||
Most quantized models under 4B fit into a single session; an 8B model needs two, and a 20B model needs four.
|
||||
|
||||
- `GGML_HEXAGON_NHVX=0`
|
||||
Controls the number of HVX hardware threads to use. The default is all (actual number varies depending on the hardware version).
|
||||
|
||||
- `GGML_HEXAGON_HOSTBUF=1`
|
||||
Controls whether the Hexagon backend allocates host buffers. By default, all buffers except for REPACK are host buffers.
|
||||
This option is required for testing Ops that require REPACK buffers (MUL_MAT and MUL_MAT_ID).
|
||||
|
||||
- `GGML_HEXAGON_EXPERIMENTAL=1`
|
||||
Controls whether the Hexagon backend enables experimental features.
|
||||
This option is required for enabling/testing experimental Ops (FLASH_ATTN_EXT).
|
||||
|
||||
- `GGML_HEXAGON_VERBOSE=1`
|
||||
Enables verbose logging of Ops from the backend. Example output:
|
||||
|
||||
```
|
||||
ggml-hex: HTP0 graph-compute n_nodes 2
|
||||
ggml-hex: HTP0 matmul : blk.27.ffn_up.weight x ffn_norm-27 -> ffn_up-27 : 3072:8192 x 3072:1 -> 8192:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x1
|
||||
ggml-hex: HTP0 matmul : blk.27.ffn_gate.weight x ffn_norm-27 -> ffn_gate-27 : 3072:8192 x 3072:1 -> 8192:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x3
|
||||
ggml-hex: HTP0 graph-compute n_nodes 1
|
||||
ggml-hex: HTP0 matmul : blk.27.ffn_down.weight x ffn_gate_par-27 -> ffn_out-27 : 8192:3072 x 8192:1 -> 3072:1 : q4_0 x f32 -> f32 : HTP0 x HTP0 -> HTP0 : flags 0x0
|
||||
ggml-hex: HTP0 get-tensor result_output : data 0x7592487000 offset 0 size 513024
|
||||
```
|
||||
|
||||
- `GGML_HEXAGON_PROFILE=1`
|
||||
Generates a host-side profile for the ggml-hexagon Ops.
|
||||
|
||||
- `GGML_HEXAGON_OPMASK=0x0`
|
||||
Allows enabling specific stages of the processing pipeline:
|
||||
|
||||
- `0x1` Enable Op Queue (i.e., queuing Ops into NPU)
|
||||
- `0x2` Enable Dynamic Quantizer (if needed for the Op)
|
||||
- `0x4` Enable Op Compute (MUL_MAT, etc.)
|
||||
|
||||
Examples:
|
||||
|
||||
`GGML_HEXAGON_OPMASK=0x1 llama-completion ...` - Ops are enqueued but NPU-side processing is stubbed out
|
||||
`GGML_HEXAGON_OPMASK=0x3 llama-completion ...` - NPU performs dynamic quantization and skips the rest
|
||||
`GGML_HEXAGON_OPMASK=0x7 llama-completion ...` - Full queuing and processing of Ops (default)
|
||||
109
docs/backend/hexagon/developer.md
Normal file
109
docs/backend/hexagon/developer.md
Normal file
@@ -0,0 +1,109 @@
|
||||
# Hexagon backend developer details
|
||||
|
||||
## Backend libraries
|
||||
|
||||
The Hexagon backend consist of two parts:
|
||||
|
||||
- `libggml-hexagon`
|
||||
This is the regular CPU-side GGML backend library, either shared or statically linked
|
||||
|
||||
- `libggml-htp-vNN`
|
||||
This is the NPU-side (HTP stands for Hexagon Tensor Processor) shared library that contains the Op dispatcher and kernels.
|
||||
The correct library is selected automatically at runtime based on the HW version.
|
||||
|
||||
Here is an example of the build artifacts
|
||||
|
||||
```
|
||||
~/src/llama.cpp$ ls -l pkg-adb/llama.cpp/lib/libggml*
|
||||
pkg-adb/llama.cpp/lib/libggml-base.so
|
||||
pkg-adb/llama.cpp/lib/libggml-cpu.so
|
||||
pkg-adb/llama.cpp/lib/libggml-hexagon.so <<< CPU library
|
||||
pkg-adb/llama.cpp/lib/libggml-htp-v73.so <<< HTP op/kernels for Hexagon v73
|
||||
pkg-adb/llama.cpp/lib/libggml-htp-v75.so
|
||||
pkg-adb/llama.cpp/lib/libggml-htp-v79.so
|
||||
pkg-adb/llama.cpp/lib/libggml-htp-v81.so
|
||||
```
|
||||
|
||||
## Memory buffers
|
||||
|
||||
Hexagon NPU backend takes advantage of the Snapdragon's unified memory model where all buffers are fully accessible by the CPU and GPU.
|
||||
The NPU does have a dedicated tightly-coupled memory called VTCM but that memory is used only for intermediate data (e.g. dynamically
|
||||
quantized tensors) or temporary data (chunks of the weight tensors fetched via DMA).
|
||||
|
||||
Please note that currently the Hexagon backend does not implement SET/GET_ROWS Ops because there is no advantage in offloading those
|
||||
to the NPU at this point.
|
||||
|
||||
The backend does allocates non-host buffers for the tensors with datatypes that require repacking: Q4_0, Q8_0, MXFP4.
|
||||
From the MMU perspective these buffers are still regular buffers (normal access by the CPU) they are marked as non-host simply to force
|
||||
the repacking.
|
||||
|
||||
## Large model handling
|
||||
|
||||
Hexagon NPU session (aka Process Domain (PD) in the Hexagon docs) is limited to a memory mapping of around 3.5GB.
|
||||
In llama.cpp/GGML the Hexagon session is mapped to a single GGML backend device (HTP0, HTP1, etc).
|
||||
|
||||
In order to map models larger than 3.5GB we need to allocate multiple devices and split the model.
|
||||
For this we're taking advantage of the llama.cpp/GGML multi-GPU layer-splitting support.
|
||||
Each Hexagon device behaves like a GPU from the offload and model splitting perspective.
|
||||
|
||||
Here is an example of running GPT-OSS-20B model on a newer Snapdragon device with 16GB of DDR.
|
||||
|
||||
```
|
||||
M=gpt-oss-20b-Q4_0.gguf NDEV=4 D=HTP0,HTP1,HTP2,HTP3 P=surfing.txt scripts/snapdragon/adb/run-completion.sh -f surfing.txt -n 32
|
||||
...
|
||||
LD_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib
|
||||
ADSP_LIBRARY_PATH=/data/local/tmp/llama.cpp/lib
|
||||
GGML_HEXAGON_NDEV=4 ./bin/llama-cli --no-mmap -m /data/local/tmp/llama.cpp/../gguf/gpt-oss-20b-Q4_0.gguf
|
||||
-t 4 --ctx-size 8192 --batch-size 128 -ctk q8_0 -ctv q8_0 -fa on -ngl 99 --device HTP0,HTP1,HTP2,HTP3 -no-cnv -f surfing.txt
|
||||
...
|
||||
llama_model_loader: - type f32: 289 tensors
|
||||
llama_model_loader: - type q4_0: 96 tensors
|
||||
llama_model_loader: - type q8_0: 2 tensors
|
||||
llama_model_loader: - type mxfp4: 72 tensors
|
||||
...
|
||||
load_tensors: offloaded 25/25 layers to GPU
|
||||
load_tensors: CPU model buffer size = 1182.09 MiB
|
||||
load_tensors: HTP1 model buffer size = 6.64 MiB
|
||||
load_tensors: HTP1-REPACK model buffer size = 2505.94 MiB
|
||||
load_tensors: HTP3 model buffer size = 5.55 MiB
|
||||
load_tensors: HTP3-REPACK model buffer size = 2088.28 MiB
|
||||
load_tensors: HTP0 model buffer size = 7.75 MiB
|
||||
load_tensors: HTP0-REPACK model buffer size = 2923.59 MiB
|
||||
load_tensors: HTP2 model buffer size = 6.64 MiB
|
||||
load_tensors: HTP2-REPACK model buffer size = 2505.94 MiB
|
||||
...
|
||||
llama_context: n_ctx_per_seq (8192) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
|
||||
llama_context: CPU output buffer size = 0.77 MiB
|
||||
llama_kv_cache_iswa: creating non-SWA KV cache, size = 8192 cells
|
||||
llama_kv_cache: HTP1 KV buffer size = 25.50 MiB
|
||||
llama_kv_cache: HTP3 KV buffer size = 25.50 MiB
|
||||
llama_kv_cache: HTP0 KV buffer size = 25.50 MiB
|
||||
llama_kv_cache: HTP2 KV buffer size = 25.50 MiB
|
||||
llama_kv_cache: size = 102.00 MiB ( 8192 cells, 12 layers, 1/1 seqs), K (q8_0): 51.00 MiB, V (q8_0): 51.00 MiB
|
||||
llama_kv_cache_iswa: creating SWA KV cache, size = 256 cells
|
||||
llama_kv_cache: HTP1 KV buffer size = 0.80 MiB
|
||||
llama_kv_cache: HTP3 KV buffer size = 0.53 MiB
|
||||
llama_kv_cache: HTP0 KV buffer size = 1.06 MiB
|
||||
llama_kv_cache: HTP2 KV buffer size = 0.80 MiB
|
||||
llama_kv_cache: size = 3.19 MiB ( 256 cells, 12 layers, 1/1 seqs), K (q8_0): 1.59 MiB, V (q8_0): 1.59 MiB
|
||||
llama_context: HTP0 compute buffer size = 16.06 MiB
|
||||
llama_context: HTP1 compute buffer size = 16.06 MiB
|
||||
llama_context: HTP2 compute buffer size = 16.06 MiB
|
||||
llama_context: HTP3 compute buffer size = 16.06 MiB
|
||||
llama_context: CPU compute buffer size = 98.19 MiB
|
||||
...
|
||||
llama_perf_context_print: prompt eval time = 3843.67 ms / 197 tokens ( 19.51 ms per token, 51.25 tokens per second)
|
||||
llama_perf_context_print: eval time = 1686.13 ms / 31 runs ( 54.39 ms per token, 18.39 tokens per second)
|
||||
llama_perf_context_print: total time = 6266.30 ms / 228 tokens
|
||||
llama_perf_context_print: graphs reused = 30
|
||||
llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted |
|
||||
llama_memory_breakdown_print: | - HTP0 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 |
|
||||
llama_memory_breakdown_print: | - HTP1 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 |
|
||||
llama_memory_breakdown_print: | - HTP2 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 |
|
||||
llama_memory_breakdown_print: | - HTP3 (Hexagon) | 2048 = 2048 + ( 0 = 0 + 0 + 0) + 0 |
|
||||
llama_memory_breakdown_print: | - Host | 1476 = 1208 + 105 + 162 |
|
||||
llama_memory_breakdown_print: | - HTP1-REPACK | 2505 = 2505 + 0 + 0 |
|
||||
llama_memory_breakdown_print: | - HTP3-REPACK | 2088 = 2088 + 0 + 0 |
|
||||
llama_memory_breakdown_print: | - HTP0-REPACK | 2923 = 2923 + 0 + 0 |
|
||||
llama_memory_breakdown_print: | - HTP2-REPACK | 2505 = 2505 + 0 + 0 |
|
||||
```
|
||||
66
docs/backend/zDNN.md
Normal file
66
docs/backend/zDNN.md
Normal file
@@ -0,0 +1,66 @@
|
||||
# llama.cpp for IBM zDNN Accelerator
|
||||
|
||||
> [!WARNING]
|
||||
> **Note:** zDNN is **not** the same as ZenDNN.
|
||||
> - **zDNN** (this page): IBM's Deep Neural Network acceleration library for IBM Z & LinuxONE Mainframes
|
||||
> - **ZenDNN**: AMD's deep learning library for AMD EPYC CPUs ([see ZenDNN documentation](ZenDNN.md))
|
||||
|
||||
## Background
|
||||
|
||||
IBM zDNN (Z Deep Neural Network) is a hardware acceleration library designed specifically to leverage the IBM NNPA (Neural Network Processor Assist) accelerator located within IBM Telum I and II processors. It provides significant performance improvements for neural network inference operations.
|
||||
|
||||
### Llama.cpp + IBM zDNN
|
||||
|
||||
The llama.cpp zDNN backend is designed to enable llama.cpp on IBM z17 and later systems via the IBM zDNN hardware acceleration library.
|
||||
|
||||
## Software & Hardware Support
|
||||
|
||||
| Hardware Level | Status | Verified |
|
||||
| -------------------- | ------------- | -------------------------- |
|
||||
| IBM z17 / LinuxONE 5 | Supported | RHEL 9.6, IBM z17, 40 IFLs |
|
||||
| IBM z16 / LinuxONE 4 | Not Supported | |
|
||||
|
||||
## Data Types Supported
|
||||
|
||||
| Data Type | Status |
|
||||
| --------- | --------- |
|
||||
| F32 | Supported |
|
||||
| F16 | Supported |
|
||||
| BF16 | Supported |
|
||||
|
||||
## CMake Options
|
||||
|
||||
The IBM zDNN backend has the following CMake options that control the behaviour of the backend.
|
||||
|
||||
| CMake Option | Default Value | Description |
|
||||
| ------------ | ------------- | ----------------------------------- |
|
||||
| `GGML_ZDNN` | `OFF` | Compile llama.cpp with zDNN support |
|
||||
| `ZDNN_ROOT` | `""` | Override zDNN library lookup |
|
||||
|
||||
## 1. Install zDNN Library
|
||||
|
||||
Note: Using the zDNN library provided via `apt` or `yum` may not work correctly as reported in [#15772](https://github.com/ggml-org/llama.cpp/issues/15772). It is preferred that you compile from source.
|
||||
|
||||
```sh
|
||||
git clone --recurse-submodules https://github.com/IBM/zDNN
|
||||
cd zDNN
|
||||
|
||||
autoreconf .
|
||||
./configure --prefix=/opt/zdnn-libs
|
||||
|
||||
make build
|
||||
sudo make install
|
||||
```
|
||||
|
||||
## 2. Build llama.cpp
|
||||
|
||||
```sh
|
||||
git clone https://github.com/ggml-org/llama.cpp
|
||||
cd llama.cpp
|
||||
|
||||
cmake -S . -G Ninja -B build \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DGGML_ZDNN=ON \
|
||||
-DZDNN_ROOT=/opt/zdnn-libs
|
||||
cmake --build build --config Release -j$(nproc)
|
||||
```
|
||||
90
docs/build-riscv64-spacemit.md
Normal file
90
docs/build-riscv64-spacemit.md
Normal file
@@ -0,0 +1,90 @@
|
||||
> [!IMPORTANT]
|
||||
> This build documentation is specific only to RISC-V SpacemiT SOCs.
|
||||
|
||||
## Build llama.cpp locally (for riscv64)
|
||||
|
||||
1. Prepare Toolchain For RISCV
|
||||
~~~
|
||||
wget https://archive.spacemit.com/toolchain/spacemit-toolchain-linux-glibc-x86_64-v1.1.2.tar.xz
|
||||
~~~
|
||||
|
||||
2. Build
|
||||
Below is the build script: it requires utilizing RISC-V vector instructions for acceleration. Ensure the `GGML_CPU_RISCV64_SPACEMIT` compilation option is enabled. The currently supported optimization version is `RISCV64_SPACEMIT_IME1`, corresponding to the `RISCV64_SPACEMIT_IME_SPEC` compilation option. Compiler configurations are defined in the `riscv64-spacemit-linux-gnu-gcc.cmake` file. Please ensure you have installed the RISC-V compiler and set the environment variable via `export RISCV_ROOT_PATH={your_compiler_path}`.
|
||||
```bash
|
||||
|
||||
cmake -B build \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DGGML_CPU_RISCV64_SPACEMIT=ON \
|
||||
-DLLAMA_OPENSSL=OFF \
|
||||
-DGGML_RVV=ON \
|
||||
-DGGML_RV_ZFH=ON \
|
||||
-DGGML_RV_ZICBOP=ON \
|
||||
-DGGML_RV_ZIHINTPAUSE=ON \
|
||||
-DRISCV64_SPACEMIT_IME_SPEC=RISCV64_SPACEMIT_IME1 \
|
||||
-DCMAKE_TOOLCHAIN_FILE=${PWD}/cmake/riscv64-spacemit-linux-gnu-gcc.cmake \
|
||||
-DCMAKE_INSTALL_PREFIX=build/installed
|
||||
|
||||
cmake --build build --parallel $(nproc) --config Release
|
||||
|
||||
pushd build
|
||||
make install
|
||||
popd
|
||||
```
|
||||
|
||||
## Simulation
|
||||
You can use QEMU to perform emulation on non-RISC-V architectures.
|
||||
|
||||
1. Download QEMU
|
||||
~~~
|
||||
wget https://archive.spacemit.com/spacemit-ai/qemu/jdsk-qemu-v0.0.14.tar.gz
|
||||
~~~
|
||||
|
||||
2. Run Simulation
|
||||
After build your llama.cpp, you can run the executable file via QEMU for simulation, for example:
|
||||
~~~
|
||||
export QEMU_ROOT_PATH={your QEMU file path}
|
||||
export RISCV_ROOT_PATH_IME1={your RISC-V compiler path}
|
||||
|
||||
${QEMU_ROOT_PATH}/bin/qemu-riscv64 -L ${RISCV_ROOT_PATH_IME1}/sysroot -cpu max,vlen=256,elen=64,vext_spec=v1.0 ${PWD}/build/bin/llama-cli -m ${PWD}/models/Qwen2.5-0.5B-Instruct-Q4_0.gguf -t 1
|
||||
~~~
|
||||
## Performance
|
||||
#### Quantization Support For Matrix
|
||||
~~~
|
||||
model name : Spacemit(R) X60
|
||||
isa : rv64imafdcv_zicbom_zicboz_zicntr_zicond_zicsr_zifencei_zihintpause_zihpm_zfh_zfhmin_zca_zcd_zba_zbb_zbc_zbs_zkt_zve32f_zve32x_zve64d_zve64f_zve64x_zvfh_zvfhmin_zvkt_sscofpmf_sstc_svinval_svnapot_svpbmt
|
||||
mmu : sv39
|
||||
uarch : spacemit,x60
|
||||
mvendorid : 0x710
|
||||
marchid : 0x8000000058000001
|
||||
~~~
|
||||
|
||||
Q4_0
|
||||
| Model | Size | Params | backend | threads | test | t/s |
|
||||
| -----------| -------- | ------ | ------- | ------- | ---- |------|
|
||||
Qwen2.5 0.5B |403.20 MiB|630.17 M| cpu | 4 | pp512|64.12 ± 0.26|
|
||||
Qwen2.5 0.5B |403.20 MiB|630.17 M| cpu | 4 | tg128|10.03 ± 0.01|
|
||||
Qwen2.5 1.5B |1011.16 MiB| 1.78 B | cpu | 4 | pp512|24.16 ± 0.02|
|
||||
Qwen2.5 1.5B |1011.16 MiB| 1.78 B | cpu | 4 | tg128|3.83 ± 0.06|
|
||||
Qwen2.5 3B | 1.86 GiB | 3.40 B | cpu | 4 | pp512|12.08 ± 0.02|
|
||||
Qwen2.5 3B | 1.86 GiB | 3.40 B | cpu | 4 | tg128|2.23 ± 0.02|
|
||||
|
||||
Q4_1
|
||||
| Model | Size | Params | backend | threads | test | t/s |
|
||||
| -----------| -------- | ------ | ------- | ------- | ---- |------|
|
||||
Qwen2.5 0.5B |351.50 MiB|494.03 M| cpu | 4 | pp512|62.07 ± 0.12|
|
||||
Qwen2.5 0.5B |351.50 MiB|494.03 M| cpu | 4 | tg128|9.91 ± 0.01|
|
||||
Qwen2.5 1.5B |964.06 MiB| 1.54 B | cpu | 4 | pp512|22.95 ± 0.25|
|
||||
Qwen2.5 1.5B |964.06 MiB| 1.54 B | cpu | 4 | tg128|4.01 ± 0.15|
|
||||
Qwen2.5 3B | 1.85 GiB | 3.09 B | cpu | 4 | pp512|11.55 ± 0.16|
|
||||
Qwen2.5 3B | 1.85 GiB | 3.09 B | cpu | 4 | tg128|2.25 ± 0.04|
|
||||
|
||||
|
||||
Q4_K
|
||||
| Model | Size | Params | backend | threads | test | t/s |
|
||||
| -----------| -------- | ------ | ------- | ------- | ---- |------|
|
||||
Qwen2.5 0.5B |462.96 MiB|630.17 M| cpu | 4 | pp512|9.29 ± 0.05|
|
||||
Qwen2.5 0.5B |462.96 MiB|630.17 M| cpu | 4 | tg128|5.67 ± 0.04|
|
||||
Qwen2.5 1.5B | 1.04 GiB | 1.78 B | cpu | 4 | pp512|10.38 ± 0.10|
|
||||
Qwen2.5 1.5B | 1.04 GiB | 1.78 B | cpu | 4 | tg128|3.17 ± 0.08|
|
||||
Qwen2.5 3B | 1.95 GiB | 3.40 B | cpu | 4 | pp512|4.23 ± 0.04|
|
||||
Qwen2.5 3B | 1.95 GiB | 3.40 B | cpu | 4 | tg128|1.73 ± 0.00|
|
||||
275
docs/build-s390x.md
Normal file
275
docs/build-s390x.md
Normal file
@@ -0,0 +1,275 @@
|
||||
> [!IMPORTANT]
|
||||
> This build documentation is specific only to IBM Z & LinuxONE mainframes (s390x). You can find the build documentation for other architectures: [build.md](build.md).
|
||||
|
||||
# Build llama.cpp locally (for s390x)
|
||||
|
||||
The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](../include/llama.h).
|
||||
|
||||
The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server.
|
||||
|
||||
**To get the code:**
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ggml-org/llama.cpp
|
||||
cd llama.cpp
|
||||
```
|
||||
|
||||
## CPU Build with BLAS
|
||||
|
||||
Building llama.cpp with BLAS support is highly recommended as it has shown to provide performance improvements. Make sure to have OpenBLAS installed in your environment.
|
||||
|
||||
```bash
|
||||
cmake -S . -B build \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DGGML_BLAS=ON \
|
||||
-DGGML_BLAS_VENDOR=OpenBLAS
|
||||
|
||||
cmake --build build --config Release -j $(nproc)
|
||||
```
|
||||
|
||||
**Notes**:
|
||||
|
||||
- For faster repeated compilation, install [ccache](https://ccache.dev/)
|
||||
- By default, VXE/VXE2 is enabled. To disable it (not recommended):
|
||||
|
||||
```bash
|
||||
cmake -S . -B build \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DGGML_BLAS=ON \
|
||||
-DGGML_BLAS_VENDOR=OpenBLAS \
|
||||
-DGGML_VXE=OFF
|
||||
|
||||
cmake --build build --config Release -j $(nproc)
|
||||
```
|
||||
|
||||
- For debug builds:
|
||||
|
||||
```bash
|
||||
cmake -S . -B build \
|
||||
-DCMAKE_BUILD_TYPE=Debug \
|
||||
-DGGML_BLAS=ON \
|
||||
-DGGML_BLAS_VENDOR=OpenBLAS
|
||||
cmake --build build --config Debug -j $(nproc)
|
||||
```
|
||||
|
||||
- For static builds, add `-DBUILD_SHARED_LIBS=OFF`:
|
||||
|
||||
```bash
|
||||
cmake -S . -B build \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DGGML_BLAS=ON \
|
||||
-DGGML_BLAS_VENDOR=OpenBLAS \
|
||||
-DBUILD_SHARED_LIBS=OFF
|
||||
|
||||
cmake --build build --config Release -j $(nproc)
|
||||
```
|
||||
|
||||
## IBM zDNN Accelerator
|
||||
|
||||
This provides acceleration using the IBM zAIU co-processor located in the Telum I and Telum II processors. Make sure to have the [IBM zDNN library](https://github.com/IBM/zDNN) installed.
|
||||
|
||||
#### Compile from source from IBM
|
||||
|
||||
You may find the official build instructions here: [Building and Installing zDNN](https://github.com/IBM/zDNN?tab=readme-ov-file#building-and-installing-zdnn)
|
||||
|
||||
### Compilation
|
||||
|
||||
```bash
|
||||
cmake -S . -B build \
|
||||
-DCMAKE_BUILD_TYPE=Release \
|
||||
-DGGML_ZDNN=ON
|
||||
cmake --build build --config Release -j$(nproc)
|
||||
```
|
||||
|
||||
## Getting GGUF Models
|
||||
|
||||
All models need to be converted to Big-Endian. You can achieve this in three cases:
|
||||
|
||||
1. **Use pre-converted models verified for use on IBM Z & LinuxONE (easiest)**
|
||||
|
||||
|
||||
|
||||
You can find popular models pre-converted and verified at [s390x Verified Models](https://huggingface.co/collections/taronaeo/s390x-verified-models-672765393af438d0ccb72a08) or [s390x Runnable Models](https://huggingface.co/collections/taronaeo/s390x-runnable-models-686e951824198df12416017e).
|
||||
|
||||
These models have already been converted from `safetensors` to `GGUF` Big-Endian and their respective tokenizers verified to run correctly on IBM z15 and later system.
|
||||
|
||||
2. **Convert safetensors model to GGUF Big-Endian directly (recommended)**
|
||||
|
||||
|
||||
|
||||
The model you are trying to convert must be in `safetensors` file format (for example [IBM Granite 3.3 2B](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct)). Make sure you have downloaded the model repository for this case.
|
||||
|
||||
Ensure that you have installed the required packages in advance
|
||||
|
||||
```bash
|
||||
pip3 install -r requirements.txt
|
||||
```
|
||||
|
||||
Convert the `safetensors` model to `GGUF`
|
||||
|
||||
```bash
|
||||
python3 convert_hf_to_gguf.py \
|
||||
--outfile model-name-be.f16.gguf \
|
||||
--outtype f16 \
|
||||
--bigendian \
|
||||
model-directory/
|
||||
```
|
||||
|
||||
For example,
|
||||
|
||||
```bash
|
||||
python3 convert_hf_to_gguf.py \
|
||||
--outfile granite-3.3-2b-instruct-be.f16.gguf \
|
||||
--outtype f16 \
|
||||
--bigendian \
|
||||
granite-3.3-2b-instruct/
|
||||
```
|
||||
|
||||
3. **Convert existing GGUF Little-Endian model to Big-Endian**
|
||||
|
||||
|
||||
|
||||
The model you are trying to convert must be in `gguf` file format (for example [IBM Granite 3.3 2B GGUF](https://huggingface.co/ibm-granite/granite-3.3-2b-instruct-GGUF)). Make sure you have downloaded the model file for this case.
|
||||
|
||||
```bash
|
||||
python3 gguf-py/gguf/scripts/gguf_convert_endian.py model-name.f16.gguf BIG
|
||||
```
|
||||
|
||||
For example,
|
||||
|
||||
```bash
|
||||
python3 gguf-py/gguf/scripts/gguf_convert_endian.py granite-3.3-2b-instruct-le.f16.gguf BIG
|
||||
mv granite-3.3-2b-instruct-le.f16.gguf granite-3.3-2b-instruct-be.f16.gguf
|
||||
```
|
||||
|
||||
**Notes:**
|
||||
|
||||
- The GGUF endian conversion script may not support all data types at the moment and may fail for some models/quantizations. When that happens, please try manually converting the safetensors model to GGUF Big-Endian via Step 2.
|
||||
|
||||
## IBM Accelerators
|
||||
|
||||
### 1. SIMD Acceleration
|
||||
|
||||
Only available in IBM z15/LinuxONE 3 or later system with the `-DGGML_VXE=ON` (turned on by default) compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z14/arch12. In such systems, the APIs can still run but will use a scalar implementation.
|
||||
|
||||
### 2. zDNN Accelerator (WIP)
|
||||
|
||||
Only available in IBM z17/LinuxONE 5 or later system with the `-DGGML_ZDNN=ON` compile flag. No hardware acceleration is possible with llama.cpp with older systems, such as IBM z15/arch13. In such systems, the APIs will default back to CPU routines.
|
||||
|
||||
### 3. Spyre Accelerator
|
||||
|
||||
_Only available with IBM z17 / LinuxONE 5 or later system. No support currently available._
|
||||
|
||||
## Performance Tuning
|
||||
|
||||
### 1. Virtualization Setup
|
||||
|
||||
It is strongly recommended to use only LPAR (Type-1) virtualization to get the most performance.
|
||||
|
||||
Note: Type-2 virtualization is not supported at the moment, while you can get it running, the performance will not be the best.
|
||||
|
||||
### 2. IFL (Core) Count
|
||||
|
||||
It is recommended to allocate a minimum of 8 shared IFLs assigned to the LPAR. Increasing the IFL count past 8 shared IFLs will only improve Prompt Processing performance but not Token Generation.
|
||||
|
||||
Note: IFL count does not equate to vCPU count.
|
||||
|
||||
### 3. SMT vs NOSMT (Simultaneous Multithreading)
|
||||
|
||||
It is strongly recommended to disable SMT via the kernel boot parameters as it negatively affects performance. Please refer to your Linux distribution's guide on disabling SMT via kernel boot parameters.
|
||||
|
||||
### 4. BLAS vs NOBLAS
|
||||
|
||||
IBM VXE/VXE2 SIMD acceleration depends on the BLAS implementation. It is strongly recommended to use BLAS.
|
||||
|
||||
## Frequently Asked Questions (FAQ)
|
||||
|
||||
1. I'm getting the following error message while trying to load a model: `gguf_init_from_file_impl: failed to load model: this GGUF file version 50331648 is extremely large, is there a mismatch between the host and model endianness?`
|
||||
|
||||
Answer: Please ensure that the model you have downloaded/converted is GGUFv3 Big-Endian. These models are usually denoted with the `-be` suffix, i.e., `granite-3.3-2b-instruct-be.F16.gguf`.
|
||||
|
||||
You may refer to the [Getting GGUF Models](#getting-gguf-models) section to manually convert a `safetensors` model to `GGUF` Big Endian.
|
||||
|
||||
2. I'm getting extremely poor performance when running inference on a model
|
||||
|
||||
Answer: Please refer to the [Appendix B: SIMD Support Matrix](#appendix-b-simd-support-matrix) to check if your model quantization is supported by SIMD acceleration.
|
||||
|
||||
3. I'm building on IBM z17 and getting the following error messages: `invalid switch -march=z17`
|
||||
|
||||
Answer: Please ensure that your GCC compiler is of minimum GCC 15.1.0 version, and have `binutils` updated to the latest version. If this does not fix the problem, kindly open an issue.
|
||||
|
||||
4. Failing to install the `sentencepiece` package using GCC 15+
|
||||
|
||||
Answer: The `sentencepiece` team are aware of this as seen in [this issue](https://github.com/google/sentencepiece/issues/1108).
|
||||
|
||||
As a temporary workaround, please run the installation command with the following environment variables.
|
||||
|
||||
```bash
|
||||
export CXXFLAGS="-include cstdint"
|
||||
```
|
||||
|
||||
For example,
|
||||
|
||||
```bash
|
||||
CXXFLAGS="-include cstdint" pip3 install -r requirements.txt
|
||||
```
|
||||
|
||||
## Getting Help on IBM Z & LinuxONE
|
||||
|
||||
1. **Bugs, Feature Requests**
|
||||
|
||||
Please file an issue in llama.cpp and ensure that the title contains "s390x".
|
||||
|
||||
2. **Other Questions**
|
||||
|
||||
Please reach out directly to [aionz@us.ibm.com](mailto:aionz@us.ibm.com).
|
||||
|
||||
## Appendix A: Hardware Support Matrix
|
||||
|
||||
| | Support | Minimum Compiler Version |
|
||||
| -------- | ------- | ------------------------ |
|
||||
| IBM z15 | ✅ | |
|
||||
| IBM z16 | ✅ | |
|
||||
| IBM z17 | ✅ | GCC 15.1.0 |
|
||||
| IBM zDNN | ✅ | |
|
||||
|
||||
- ✅ - supported and verified to run as intended
|
||||
- 🚫 - unsupported, we are unlikely able to provide support
|
||||
|
||||
## Appendix B: SIMD Support Matrix
|
||||
|
||||
| | VX/VXE/VXE2 | zDNN | Spyre |
|
||||
|------------|-------------|------|-------|
|
||||
| FP32 | ✅ | ✅ | ❓ |
|
||||
| FP16 | ✅ | ✅ | ❓ |
|
||||
| BF16 | 🚫 | ✅ | ❓ |
|
||||
| Q4_0 | ✅ | ❓ | ❓ |
|
||||
| Q4_1 | ✅ | ❓ | ❓ |
|
||||
| MXFP4 | 🚫 | ❓ | ❓ |
|
||||
| Q5_0 | ✅ | ❓ | ❓ |
|
||||
| Q5_1 | ✅ | ❓ | ❓ |
|
||||
| Q8_0 | ✅ | ❓ | ❓ |
|
||||
| Q2_K | 🚫 | ❓ | ❓ |
|
||||
| Q3_K | ✅ | ❓ | ❓ |
|
||||
| Q4_K | ✅ | ❓ | ❓ |
|
||||
| Q5_K | ✅ | ❓ | ❓ |
|
||||
| Q6_K | ✅ | ❓ | ❓ |
|
||||
| TQ1_0 | 🚫 | ❓ | ❓ |
|
||||
| TQ2_0 | 🚫 | ❓ | ❓ |
|
||||
| IQ2_XXS | 🚫 | ❓ | ❓ |
|
||||
| IQ2_XS | 🚫 | ❓ | ❓ |
|
||||
| IQ2_S | 🚫 | ❓ | ❓ |
|
||||
| IQ3_XXS | 🚫 | ❓ | ❓ |
|
||||
| IQ3_S | 🚫 | ❓ | ❓ |
|
||||
| IQ1_S | 🚫 | ❓ | ❓ |
|
||||
| IQ1_M | 🚫 | ❓ | ❓ |
|
||||
| IQ4_NL | ✅ | ❓ | ❓ |
|
||||
| IQ4_XS | ✅ | ❓ | ❓ |
|
||||
| FP32->FP16 | 🚫 | ❓ | ❓ |
|
||||
| FP16->FP32 | 🚫 | ❓ | ❓ |
|
||||
|
||||
- ✅ - acceleration available
|
||||
- 🚫 - acceleration unavailable, will still run using scalar implementation
|
||||
- ❓ - acceleration unknown, please contribute if you can test it yourself
|
||||
|
||||
Last Updated by **Aaron Teo (aaron.teo1@ibm.com)** on Sep 7, 2025.
|
||||
690
docs/build.md
Normal file
690
docs/build.md
Normal file
@@ -0,0 +1,690 @@
|
||||
# Build llama.cpp locally
|
||||
|
||||
The main product of this project is the `llama` library. Its C-style interface can be found in [include/llama.h](../include/llama.h).
|
||||
|
||||
The project also includes many example programs and tools using the `llama` library. The examples range from simple, minimal code snippets to sophisticated sub-projects such as an OpenAI-compatible HTTP server.
|
||||
|
||||
**To get the Code:**
|
||||
|
||||
```bash
|
||||
git clone https://github.com/ggml-org/llama.cpp
|
||||
cd llama.cpp
|
||||
```
|
||||
|
||||
The following sections describe how to build with different backends and options.
|
||||
|
||||
## CPU Build
|
||||
|
||||
Build llama.cpp using `CMake`:
|
||||
|
||||
```bash
|
||||
cmake -B build
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
**Notes**:
|
||||
|
||||
- For faster compilation, add the `-j` argument to run multiple jobs in parallel, or use a generator that does this automatically such as Ninja. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel.
|
||||
- For faster repeated compilation, install [ccache](https://ccache.dev/)
|
||||
- For debug builds, there are two cases:
|
||||
|
||||
1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag):
|
||||
|
||||
```bash
|
||||
cmake -B build -DCMAKE_BUILD_TYPE=Debug
|
||||
cmake --build build
|
||||
```
|
||||
|
||||
2. Multi-config generators (`-G` param set to Visual Studio, XCode...):
|
||||
|
||||
```bash
|
||||
cmake -B build -G "Xcode"
|
||||
cmake --build build --config Debug
|
||||
```
|
||||
|
||||
For more details and a list of supported generators, see the [CMake documentation](https://cmake.org/cmake/help/latest/manual/cmake-generators.7.html).
|
||||
- For static builds, add `-DBUILD_SHARED_LIBS=OFF`:
|
||||
```
|
||||
cmake -B build -DBUILD_SHARED_LIBS=OFF
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
- Building for Windows (x86, x64 and arm64) with MSVC or clang as compilers:
|
||||
- Install Visual Studio 2022, e.g. via the [Community Edition](https://visualstudio.microsoft.com/vs/community/). In the installer, select at least the following options (this also automatically installs the required additional tools like CMake,...):
|
||||
- Tab Workload: Desktop-development with C++
|
||||
- Tab Components (select quickly via search): C++-_CMake_ Tools for Windows, _Git_ for Windows, C++-_Clang_ Compiler for Windows, MS-Build Support for LLVM-Toolset (clang)
|
||||
- Please remember to always use a Developer Command Prompt / PowerShell for VS2022 for git, build, test
|
||||
- For Windows on ARM (arm64, WoA) build with:
|
||||
```bash
|
||||
cmake --preset arm64-windows-llvm-release -D GGML_OPENMP=OFF
|
||||
cmake --build build-arm64-windows-llvm-release
|
||||
```
|
||||
For building with ninja generator and clang compiler as default:
|
||||
-set path:set LIB=C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\x64;C:\Program Files\Microsoft Visual Studio\2022\Community\VC\Tools\MSVC\14.41.34120\lib\x64\uwp;C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\x64
|
||||
```bash
|
||||
cmake --preset x64-windows-llvm-release
|
||||
cmake --build build-x64-windows-llvm-release
|
||||
```
|
||||
- If you want HTTPS/TLS features, you may install OpenSSL development libraries. If not installed, the project will build and run without SSL support.
|
||||
- **Debian / Ubuntu:** `sudo apt-get install libssl-dev`
|
||||
- **Fedora / RHEL / Rocky / Alma:** `sudo dnf install openssl-devel`
|
||||
- **Arch / Manjaro:** `sudo pacman -S openssl`
|
||||
|
||||
## BLAS Build
|
||||
|
||||
Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Using BLAS doesn't affect the generation performance. There are currently several different BLAS implementations available for build and use:
|
||||
|
||||
### Accelerate Framework
|
||||
|
||||
This is only available on Mac PCs and it's enabled by default. You can just build using the normal instructions.
|
||||
|
||||
### OpenBLAS
|
||||
|
||||
This provides BLAS acceleration using only the CPU. Make sure to have OpenBLAS installed on your machine.
|
||||
|
||||
- Using `CMake` on Linux:
|
||||
|
||||
```bash
|
||||
cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=OpenBLAS
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
### BLIS
|
||||
|
||||
Check [BLIS.md](./backend/BLIS.md) for more information.
|
||||
|
||||
### Intel oneMKL
|
||||
|
||||
Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
|
||||
|
||||
- Using manual oneAPI installation:
|
||||
By default, `GGML_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DGGML_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps:
|
||||
```bash
|
||||
source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation
|
||||
cmake -B build -DGGML_BLAS=ON -DGGML_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DGGML_NATIVE=ON
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
- Using oneAPI docker image:
|
||||
If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above.
|
||||
|
||||
Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information.
|
||||
|
||||
### Other BLAS libraries
|
||||
|
||||
Any other BLAS library can be used by setting the `GGML_BLAS_VENDOR` option. See the [CMake documentation](https://cmake.org/cmake/help/latest/module/FindBLAS.html#blas-lapack-vendors) for a list of supported vendors.
|
||||
|
||||
## Metal Build
|
||||
|
||||
On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
|
||||
To disable the Metal build at compile time use the `-DGGML_METAL=OFF` cmake option.
|
||||
|
||||
When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers 0` command-line argument.
|
||||
|
||||
## SYCL
|
||||
|
||||
SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators.
|
||||
|
||||
llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU).
|
||||
|
||||
For detailed info, please refer to [llama.cpp for SYCL](./backend/SYCL.md).
|
||||
|
||||
## CUDA
|
||||
|
||||
This provides GPU acceleration using an NVIDIA GPU. Make sure to have the [CUDA toolkit](https://developer.nvidia.com/cuda-toolkit) installed.
|
||||
|
||||
#### Download directly from NVIDIA
|
||||
You may find the official downloads here: [NVIDIA developer site](https://developer.nvidia.com/cuda-downloads).
|
||||
|
||||
|
||||
#### Compile and run inside a Fedora Toolbox Container
|
||||
We also have a [guide](./backend/CUDA-FEDORA.md) for setting up CUDA toolkit in a Fedora [toolbox container](https://containertoolbx.org/).
|
||||
|
||||
**Recommended for:**
|
||||
- ***Necessary*** for users of [Atomic Desktops for Fedora](https://fedoraproject.org/atomic-desktops/); such as: [Silverblue](https://fedoraproject.org/atomic-desktops/silverblue/) and [Kinoite](https://fedoraproject.org/atomic-desktops/kinoite/).
|
||||
- (there are no supported CUDA packages for these systems)
|
||||
- ***Necessary*** for users that have a host that is not a: [Supported Nvidia CUDA Release Platform](https://developer.nvidia.com/cuda-downloads).
|
||||
- (for example, you may have [Fedora 42 Beta](https://fedoramagazine.org/announcing-fedora-linux-42-beta/) as your your host operating system)
|
||||
- ***Convenient*** For those running [Fedora Workstation](https://fedoraproject.org/workstation/) or [Fedora KDE Plasma Desktop](https://fedoraproject.org/spins/kde), and want to keep their host system clean.
|
||||
- *Optionally* toolbox packages are available: [Arch Linux](https://archlinux.org/), [Red Hat Enterprise Linux >= 8.5](https://www.redhat.com/en/technologies/linux-platforms/enterprise-linux), or [Ubuntu](https://ubuntu.com/download)
|
||||
|
||||
|
||||
### Compilation
|
||||
|
||||
Make sure to read the notes about the CPU build for general instructions for e.g. speeding up the compilation.
|
||||
|
||||
```bash
|
||||
cmake -B build -DGGML_CUDA=ON
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
### Non-Native Builds
|
||||
|
||||
By default llama.cpp will be built for the hardware that is connected to the system at that time.
|
||||
For a build covering all CUDA GPUs, disable `GGML_NATIVE`:
|
||||
|
||||
```bash
|
||||
cmake -B build -DGGML_CUDA=ON -DGGML_NATIVE=OFF
|
||||
```
|
||||
|
||||
The resulting binary should run on all CUDA GPUs with optimal performance, though some just-in-time compilation may be required.
|
||||
|
||||
### Override Compute Capability Specifications
|
||||
|
||||
If `nvcc` cannot detect your gpu, you may get compile warnings such as:
|
||||
```text
|
||||
nvcc warning : Cannot find valid GPU for '-arch=native', default arch is used
|
||||
```
|
||||
|
||||
One option is to do a non-native build as described above.
|
||||
However, this will result in a large binary that takes a long time to compile.
|
||||
Alternatively it is also possible to explicitly specify CUDA architectures.
|
||||
This may also make sense for a non-native build, for that one should look at the logic in `ggml/src/ggml-cuda/CMakeLists.txt` as a starting point.
|
||||
|
||||
To override the default CUDA architectures:
|
||||
|
||||
#### 1. Take note of the `Compute Capability` of your NVIDIA devices: ["CUDA: Your GPU Compute > Capability"](https://developer.nvidia.com/cuda-gpus).
|
||||
|
||||
```text
|
||||
GeForce RTX 4090 8.9
|
||||
GeForce RTX 3080 Ti 8.6
|
||||
GeForce RTX 3070 8.6
|
||||
```
|
||||
|
||||
#### 2. Manually list each varying `Compute Capability` in the `CMAKE_CUDA_ARCHITECTURES` list.
|
||||
|
||||
```bash
|
||||
cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="86;89"
|
||||
```
|
||||
|
||||
### Overriding the CUDA Version
|
||||
|
||||
If you have multiple CUDA installations on your system and want to compile llama.cpp for a specific one, e.g. for CUDA 11.7 installed under `/opt/cuda-11.7`:
|
||||
|
||||
```bash
|
||||
cmake -B build -DGGML_CUDA=ON -DCMAKE_CUDA_COMPILER=/opt/cuda-11.7/bin/nvcc -DCMAKE_INSTALL_RPATH="/opt/cuda-11.7/lib64;\$ORIGIN" -DCMAKE_BUILD_WITH_INSTALL_RPATH=ON
|
||||
```
|
||||
|
||||
#### Fixing Compatibility Issues with Old CUDA and New glibc
|
||||
|
||||
If you try to use an old CUDA version (e.g. v11.7) with a new glibc version you can get errors like this:
|
||||
|
||||
```
|
||||
/usr/include/bits/mathcalls.h(83): error: exception specification is
|
||||
incompatible with that of previous function "cospi"
|
||||
|
||||
|
||||
/opt/cuda-11.7/bin/../targets/x86_64-linux/include/crt/math_functions.h(5545):
|
||||
here
|
||||
```
|
||||
|
||||
It seems the least bad solution is to patch the CUDA installation to declare the correct signatures.
|
||||
Replace the following lines in `/path/to/your/cuda/installation/targets/x86_64-linux/include/crt/math_functions.h`:
|
||||
|
||||
```C++
|
||||
// original lines
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double cospi(double x);
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float cospif(float x);
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double sinpi(double x);
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float sinpif(float x);
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double rsqrt(double x);
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float rsqrtf(float x);
|
||||
|
||||
// edited lines
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double cospi(double x) noexcept (true);
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float cospif(float x) noexcept (true);
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double sinpi(double x) noexcept (true);
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float sinpif(float x) noexcept (true);
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ double rsqrt(double x) noexcept (true);
|
||||
extern __DEVICE_FUNCTIONS_DECL__ __device_builtin__ float rsqrtf(float x) noexcept (true);
|
||||
```
|
||||
|
||||
### Runtime CUDA environmental variables
|
||||
|
||||
You may set the [cuda environmental variables](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) at runtime.
|
||||
|
||||
```bash
|
||||
# Use `CUDA_VISIBLE_DEVICES` to hide the first compute device.
|
||||
CUDA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf
|
||||
```
|
||||
|
||||
### Unified Memory
|
||||
|
||||
The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted. In Windows this setting is available in the NVIDIA control panel as `System Memory Fallback`.
|
||||
|
||||
### Performance Tuning
|
||||
|
||||
The following compilation options are also available to tweak performance:
|
||||
|
||||
| Option | Legal values | Default | Description |
|
||||
|-------------------------------|------------------------|---------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|
||||
| GGML_CUDA_FORCE_MMQ | Boolean | false | Force the use of custom matrix multiplication kernels for quantized models instead of FP16 cuBLAS even if there is no int8 tensor core implementation available (affects V100, CDNA and RDNA3+). MMQ kernels are enabled by default on GPUs with int8 tensor core support. With MMQ force enabled, speed for large batch sizes will be worse but VRAM consumption will be lower. |
|
||||
| GGML_CUDA_FORCE_CUBLAS | Boolean | false | Force the use of FP16 cuBLAS instead of custom matrix multiplication kernels for quantized models. There may be issues with numerical overflows (except for CDNA and RDNA4) and memory use will be higher. Prompt processing may become faster on recent datacenter GPUs (the custom kernels were tuned primarily for RTX 3000/4000). |
|
||||
| GGML_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. |
|
||||
| GGML_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. |
|
||||
|
||||
## MUSA
|
||||
|
||||
This provides GPU acceleration using a Moore Threads GPU. Make sure to have the [MUSA SDK](https://developer.mthreads.com/musa/musa-sdk) installed.
|
||||
|
||||
#### Download directly from Moore Threads
|
||||
|
||||
You may find the official downloads here: [Moore Threads developer site](https://developer.mthreads.com/sdk/download/musa).
|
||||
|
||||
### Compilation
|
||||
|
||||
```bash
|
||||
cmake -B build -DGGML_MUSA=ON
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
#### Override Compute Capability Specifications
|
||||
|
||||
By default, all supported compute capabilities are enabled. To customize this behavior, you can specify the `MUSA_ARCHITECTURES` option in the CMake command:
|
||||
|
||||
```bash
|
||||
cmake -B build -DGGML_MUSA=ON -DMUSA_ARCHITECTURES="21"
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
This configuration enables only compute capability `2.1` (MTT S80) during compilation, which can help reduce compilation time.
|
||||
|
||||
#### Compilation options
|
||||
|
||||
Most of the compilation options available for CUDA should also be available for MUSA, though they haven't been thoroughly tested yet.
|
||||
|
||||
- For static builds, add `-DBUILD_SHARED_LIBS=OFF` and `-DCMAKE_POSITION_INDEPENDENT_CODE=ON`:
|
||||
```
|
||||
cmake -B build -DGGML_MUSA=ON \
|
||||
-DBUILD_SHARED_LIBS=OFF -DCMAKE_POSITION_INDEPENDENT_CODE=ON
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
### Runtime MUSA environmental variables
|
||||
|
||||
You may set the [musa environmental variables](https://docs.mthreads.com/musa-sdk/musa-sdk-doc-online/programming_guide/Z%E9%99%84%E5%BD%95/) at runtime.
|
||||
|
||||
```bash
|
||||
# Use `MUSA_VISIBLE_DEVICES` to hide the first compute device.
|
||||
MUSA_VISIBLE_DEVICES="-0" ./build/bin/llama-server --model /srv/models/llama.gguf
|
||||
```
|
||||
|
||||
### Unified Memory
|
||||
|
||||
The environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1` can be used to enable unified memory in Linux. This allows swapping to system RAM instead of crashing when the GPU VRAM is exhausted.
|
||||
|
||||
## HIP
|
||||
|
||||
This provides GPU acceleration on HIP-supported AMD GPUs.
|
||||
Make sure to have ROCm installed.
|
||||
You can download it from your Linux distro's package manager or from here: [ROCm Quick Start (Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html#rocm-install-quick).
|
||||
|
||||
- Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU):
|
||||
```bash
|
||||
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \
|
||||
cmake -S . -B build -DGGML_HIP=ON -DGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
|
||||
&& cmake --build build --config Release -- -j 16
|
||||
```
|
||||
|
||||
Note: `GPU_TARGETS` is optional, omitting it will build the code for all GPUs in the current system.
|
||||
|
||||
To enhance flash attention performance on RDNA3+ or CDNA architectures, you can utilize the rocWMMA library by enabling the `-DGGML_HIP_ROCWMMA_FATTN=ON` option. This requires rocWMMA headers to be installed on the build system.
|
||||
|
||||
The rocWMMA library is included by default when installing the ROCm SDK using the `rocm` meta package provided by AMD. Alternatively, if you are not using the meta package, you can install the library using the `rocwmma-dev` or `rocwmma-devel` package, depending on your system's package manager.
|
||||
|
||||
As an alternative, you can manually install the library by cloning it from the official [GitHub repository](https://github.com/ROCm/rocWMMA), checkout the corresponding version tag (e.g. `rocm-6.2.4`) and set `-DCMAKE_CXX_FLAGS="-I<path/to/rocwmma>/library/include/"` in CMake. This also works under Windows despite not officially supported by AMD.
|
||||
|
||||
Note that if you get the following error:
|
||||
```
|
||||
clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library
|
||||
```
|
||||
Try searching for a directory under `HIP_PATH` that contains the file
|
||||
`oclc_abi_version_400.bc`. Then, add the following to the start of the
|
||||
command: `HIP_DEVICE_LIB_PATH=<directory-you-just-found>`, so something
|
||||
like:
|
||||
```bash
|
||||
HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \
|
||||
HIP_DEVICE_LIB_PATH=<directory-you-just-found> \
|
||||
cmake -S . -B build -DGGML_HIP=ON -DGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \
|
||||
&& cmake --build build -- -j 16
|
||||
```
|
||||
|
||||
- Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU):
|
||||
```bash
|
||||
set PATH=%HIP_PATH%\bin;%PATH%
|
||||
cmake -S . -B build -G Ninja -DGPU_TARGETS=gfx1100 -DGGML_HIP=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release
|
||||
cmake --build build
|
||||
```
|
||||
If necessary, adapt `GPU_TARGETS` to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors)
|
||||
Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`.
|
||||
|
||||
|
||||
The environment variable [`HIP_VISIBLE_DEVICES`](https://rocm.docs.amd.com/en/latest/understand/gpu_isolation.html#hip-visible-devices) can be used to specify which GPU(s) will be used.
|
||||
If your GPU is not officially supported you can use the environment variable [`HSA_OVERRIDE_GFX_VERSION`] set to a similar GPU, for example 10.3.0 on RDNA2 (e.g. gfx1030, gfx1031, or gfx1035) or 11.0.0 on RDNA3.
|
||||
|
||||
### Unified Memory
|
||||
|
||||
On Linux it is possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting environment variable `GGML_CUDA_ENABLE_UNIFIED_MEMORY=1`. However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs).
|
||||
|
||||
## Vulkan
|
||||
|
||||
### For Windows Users:
|
||||
**w64devkit**
|
||||
|
||||
Download and extract [`w64devkit`](https://github.com/skeeto/w64devkit/releases).
|
||||
|
||||
Download and install the [`Vulkan SDK`](https://vulkan.lunarg.com/sdk/home#windows) with the default settings.
|
||||
|
||||
Launch `w64devkit.exe` and run the following commands to copy Vulkan dependencies:
|
||||
```sh
|
||||
SDK_VERSION=1.3.283.0
|
||||
cp /VulkanSDK/$SDK_VERSION/Bin/glslc.exe $W64DEVKIT_HOME/bin/
|
||||
cp /VulkanSDK/$SDK_VERSION/Lib/vulkan-1.lib $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/
|
||||
cp -r /VulkanSDK/$SDK_VERSION/Include/* $W64DEVKIT_HOME/x86_64-w64-mingw32/include/
|
||||
cat > $W64DEVKIT_HOME/x86_64-w64-mingw32/lib/pkgconfig/vulkan.pc <<EOF
|
||||
Name: Vulkan-Loader
|
||||
Description: Vulkan Loader
|
||||
Version: $SDK_VERSION
|
||||
Libs: -lvulkan-1
|
||||
EOF
|
||||
|
||||
```
|
||||
|
||||
Switch into the `llama.cpp` directory and build using CMake.
|
||||
```sh
|
||||
cmake -B build -DGGML_VULKAN=ON
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
**Git Bash MINGW64**
|
||||
|
||||
Download and install [`Git-SCM`](https://git-scm.com/downloads/win) with the default settings
|
||||
|
||||
Download and install [`Visual Studio Community Edition`](https://visualstudio.microsoft.com/) and make sure you select `C++`
|
||||
|
||||
Download and install [`CMake`](https://cmake.org/download/) with the default settings
|
||||
|
||||
Download and install the [`Vulkan SDK`](https://vulkan.lunarg.com/sdk/home#windows) with the default settings.
|
||||
|
||||
Go into your `llama.cpp` directory and right click, select `Open Git Bash Here` and then run the following commands
|
||||
|
||||
```
|
||||
cmake -B build -DGGML_VULKAN=ON
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
Now you can load the model in conversation mode using `Vulkan`
|
||||
|
||||
```sh
|
||||
build/bin/Release/llama-cli -m "[PATH TO MODEL]" -ngl 100 -c 16384 -t 10 -n -2 -cnv
|
||||
```
|
||||
|
||||
**MSYS2**
|
||||
|
||||
Install [MSYS2](https://www.msys2.org/) and then run the following commands in a UCRT terminal to install dependencies.
|
||||
```sh
|
||||
pacman -S git \
|
||||
mingw-w64-ucrt-x86_64-gcc \
|
||||
mingw-w64-ucrt-x86_64-cmake \
|
||||
mingw-w64-ucrt-x86_64-vulkan-devel \
|
||||
mingw-w64-ucrt-x86_64-shaderc
|
||||
```
|
||||
|
||||
Switch into the `llama.cpp` directory and build using CMake.
|
||||
```sh
|
||||
cmake -B build -DGGML_VULKAN=ON
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
### For Docker users:
|
||||
|
||||
You don't need to install the Vulkan SDK. It will be installed inside the container.
|
||||
|
||||
```sh
|
||||
# Build the image
|
||||
docker build -t llama-cpp-vulkan --target light -f .devops/vulkan.Dockerfile .
|
||||
|
||||
# Then, use it:
|
||||
docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33
|
||||
```
|
||||
|
||||
### For Linux users:
|
||||
|
||||
#### Using the LunarG Vulkan SDK
|
||||
|
||||
First, follow the official LunarG instructions for the installation and setup of the Vulkan SDK in the [Getting Started with the Linux Tarball Vulkan SDK](https://vulkan.lunarg.com/doc/sdk/latest/linux/getting_started.html) guide.
|
||||
|
||||
> [!IMPORTANT]
|
||||
> After completing the first step, ensure that you have used the `source` command on the `setup_env.sh` file inside of the Vulkan SDK in your current terminal session. Otherwise, the build won't work. Additionally, if you close out of your terminal, you must perform this step again if you intend to perform a build. However, there are ways to make this persistent. Refer to the Vulkan SDK guide linked in the first step for more information about any of this.
|
||||
|
||||
#### Using system packages
|
||||
|
||||
On Debian / Ubuntu, you can install the required dependencies using:
|
||||
```sh
|
||||
sudo apt-get install libvulkan-dev glslc
|
||||
```
|
||||
|
||||
#### Common steps
|
||||
|
||||
Second, after verifying that you have followed all of the SDK installation/setup steps, use this command to make sure before proceeding:
|
||||
```bash
|
||||
vulkaninfo
|
||||
```
|
||||
|
||||
Then, assuming you have `cd` into your llama.cpp folder and there are no errors with running `vulkaninfo`, you can proceed to build llama.cpp using the CMake commands below:
|
||||
```bash
|
||||
cmake -B build -DGGML_VULKAN=1
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
Finally, after finishing your build, you should be able to do something like this:
|
||||
```bash
|
||||
# Test the output binary
|
||||
# "-ngl 99" should offload all of the layers to GPU for most (if not all) models.
|
||||
./build/bin/llama-cli -m "PATH_TO_MODEL" -p "Hi you how are you" -ngl 99
|
||||
|
||||
# You should see in the output, ggml_vulkan detected your GPU. For example:
|
||||
# ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32
|
||||
```
|
||||
|
||||
## CANN
|
||||
This provides NPU acceleration using the AI cores of your Ascend NPU. And [CANN](https://www.hiascend.com/en/software/cann) is a hierarchical APIs to help you to quickly build AI applications and service based on Ascend NPU.
|
||||
|
||||
For more information about Ascend NPU in [Ascend Community](https://www.hiascend.com/en/).
|
||||
|
||||
Make sure to have the CANN toolkit installed. You can download it from here: [CANN Toolkit](https://www.hiascend.com/developer/download/community/result?module=cann)
|
||||
|
||||
Go to `llama.cpp` directory and build using CMake.
|
||||
```bash
|
||||
cmake -B build -DGGML_CANN=on -DCMAKE_BUILD_TYPE=release
|
||||
cmake --build build --config release
|
||||
```
|
||||
|
||||
You can test with:
|
||||
|
||||
```bash
|
||||
./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -ngl 32
|
||||
```
|
||||
|
||||
If the following info is output on screen, you are using `llama.cpp` with the CANN backend:
|
||||
```bash
|
||||
llm_load_tensors: CANN model buffer size = 13313.00 MiB
|
||||
llama_new_context_with_model: CANN compute buffer size = 1260.81 MiB
|
||||
```
|
||||
|
||||
For detailed info, such as model/device supports, CANN install, please refer to [llama.cpp for CANN](./backend/CANN.md).
|
||||
|
||||
## ZenDNN
|
||||
|
||||
ZenDNN provides optimized deep learning primitives for AMD EPYC™ CPUs. It accelerates matrix multiplication operations for inference workloads.
|
||||
|
||||
### Compilation
|
||||
|
||||
- Using `CMake` on Linux (automatic build):
|
||||
|
||||
```bash
|
||||
cmake -B build -DGGML_ZENDNN=ON
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
The first build will automatically download and build ZenDNN, which may take 5-10 minutes. Subsequent builds will be much faster.
|
||||
|
||||
- Using `CMake` with custom ZenDNN installation:
|
||||
|
||||
```bash
|
||||
cmake -B build -DGGML_ZENDNN=ON -DZENDNN_ROOT=/path/to/zendnn/install
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
### Testing
|
||||
|
||||
You can test with:
|
||||
|
||||
```bash
|
||||
./build/bin/llama-cli -m PATH_TO_MODEL -p "Building a website can be done in 10 steps:" -n 50
|
||||
```
|
||||
|
||||
For detailed information about hardware support, setup instructions, and performance optimization, refer to [llama.cpp for ZenDNN](./backend/ZenDNN.md).
|
||||
|
||||
## Arm® KleidiAI™
|
||||
KleidiAI is a library of optimized microkernels for AI workloads, specifically designed for Arm CPUs. These microkernels enhance performance and can be enabled for use by the CPU backend.
|
||||
|
||||
To enable KleidiAI, go to the llama.cpp directory and build using CMake
|
||||
```bash
|
||||
cmake -B build -DGGML_CPU_KLEIDIAI=ON
|
||||
cmake --build build --config Release
|
||||
```
|
||||
You can verify that KleidiAI is being used by running
|
||||
```bash
|
||||
./build/bin/llama-cli -m PATH_TO_MODEL -p "What is a car?"
|
||||
```
|
||||
If KleidiAI is enabled, the ouput will contain a line similar to:
|
||||
```
|
||||
load_tensors: CPU_KLEIDIAI model buffer size = 3474.00 MiB
|
||||
```
|
||||
KleidiAI's microkernels implement optimized tensor operations using Arm CPU features such as dotprod, int8mm and SME. llama.cpp selects the most efficient kernel based on runtime CPU feature detection. However, on platforms that support SME, you must manually enable SME microkernels by setting the environment variable `GGML_KLEIDIAI_SME=1`.
|
||||
|
||||
Depending on your build target, other higher priority backends may be enabled by default. To ensure the CPU backend is used, you must disable the higher priority backends either at compile time, e.g. -DGGML_METAL=OFF, or during run-time using the command line option `--device none`.
|
||||
|
||||
## OpenCL
|
||||
|
||||
This provides GPU acceleration through OpenCL on recent Adreno GPU.
|
||||
More information about OpenCL backend can be found in [OPENCL.md](./backend/OPENCL.md) for more information.
|
||||
|
||||
### Android
|
||||
|
||||
Assume NDK is available in `$ANDROID_NDK`. First, install OpenCL headers and ICD loader library if not available,
|
||||
|
||||
```sh
|
||||
mkdir -p ~/dev/llm
|
||||
cd ~/dev/llm
|
||||
|
||||
git clone https://github.com/KhronosGroup/OpenCL-Headers && \
|
||||
cd OpenCL-Headers && \
|
||||
cp -r CL $ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include
|
||||
|
||||
cd ~/dev/llm
|
||||
|
||||
git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && \
|
||||
cd OpenCL-ICD-Loader && \
|
||||
mkdir build_ndk && cd build_ndk && \
|
||||
cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release \
|
||||
-DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
|
||||
-DOPENCL_ICD_LOADER_HEADERS_DIR=$ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/include \
|
||||
-DANDROID_ABI=arm64-v8a \
|
||||
-DANDROID_PLATFORM=24 \
|
||||
-DANDROID_STL=c++_shared && \
|
||||
ninja && \
|
||||
cp libOpenCL.so $ANDROID_NDK/toolchains/llvm/prebuilt/linux-x86_64/sysroot/usr/lib/aarch64-linux-android
|
||||
```
|
||||
|
||||
Then build llama.cpp with OpenCL enabled,
|
||||
|
||||
```sh
|
||||
cd ~/dev/llm
|
||||
|
||||
git clone https://github.com/ggml-org/llama.cpp && \
|
||||
cd llama.cpp && \
|
||||
mkdir build-android && cd build-android
|
||||
|
||||
cmake .. -G Ninja \
|
||||
-DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \
|
||||
-DANDROID_ABI=arm64-v8a \
|
||||
-DANDROID_PLATFORM=android-28 \
|
||||
-DBUILD_SHARED_LIBS=OFF \
|
||||
-DGGML_OPENCL=ON
|
||||
|
||||
ninja
|
||||
```
|
||||
|
||||
### Windows Arm64
|
||||
|
||||
First, install OpenCL headers and ICD loader library if not available,
|
||||
|
||||
```powershell
|
||||
mkdir -p ~/dev/llm
|
||||
|
||||
cd ~/dev/llm
|
||||
git clone https://github.com/KhronosGroup/OpenCL-Headers && cd OpenCL-Headers
|
||||
mkdir build && cd build
|
||||
cmake .. -G Ninja `
|
||||
-DBUILD_TESTING=OFF `
|
||||
-DOPENCL_HEADERS_BUILD_TESTING=OFF `
|
||||
-DOPENCL_HEADERS_BUILD_CXX_TESTS=OFF `
|
||||
-DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl"
|
||||
cmake --build . --target install
|
||||
|
||||
cd ~/dev/llm
|
||||
git clone https://github.com/KhronosGroup/OpenCL-ICD-Loader && cd OpenCL-ICD-Loader
|
||||
mkdir build && cd build
|
||||
cmake .. -G Ninja `
|
||||
-DCMAKE_BUILD_TYPE=Release `
|
||||
-DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" `
|
||||
-DCMAKE_INSTALL_PREFIX="$HOME/dev/llm/opencl"
|
||||
cmake --build . --target install
|
||||
```
|
||||
|
||||
Then build llama.cpp with OpenCL enabled,
|
||||
|
||||
```powershell
|
||||
cmake .. -G Ninja `
|
||||
-DCMAKE_TOOLCHAIN_FILE="$HOME/dev/llm/llama.cpp/cmake/arm64-windows-llvm.cmake" `
|
||||
-DCMAKE_BUILD_TYPE=Release `
|
||||
-DCMAKE_PREFIX_PATH="$HOME/dev/llm/opencl" `
|
||||
-DBUILD_SHARED_LIBS=OFF `
|
||||
-DGGML_OPENCL=ON
|
||||
ninja
|
||||
```
|
||||
|
||||
## Android
|
||||
|
||||
To read documentation for how to build on Android, [click here](./android.md)
|
||||
|
||||
## WebGPU [In Progress]
|
||||
|
||||
The WebGPU backend relies on [Dawn](https://dawn.googlesource.com/dawn). Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/docs/quickstart-cmake.md) to install Dawn locally so that llama.cpp can find it using CMake. The currrent implementation is up-to-date with Dawn commit `bed1a61`.
|
||||
|
||||
In the llama.cpp directory, build with CMake:
|
||||
|
||||
```
|
||||
cmake -B build -DGGML_WEBGPU=ON
|
||||
cmake --build build --config Release
|
||||
```
|
||||
|
||||
### Browser Support
|
||||
|
||||
WebGPU allows cross-platform access to the GPU from supported browsers. We utilize [Emscripten](https://emscripten.org/) to compile ggml's WebGPU backend to WebAssembly. Emscripten does not officially support WebGPU bindings yet, but Dawn currently maintains its own WebGPU bindings called emdawnwebgpu.
|
||||
|
||||
Follow the instructions [here](https://dawn.googlesource.com/dawn/+/refs/heads/main/src/emdawnwebgpu/) to download or build the emdawnwebgpu package (Note that it might be safer to build the emdawbwebgpu package locally, so that it stays in sync with the version of Dawn you have installed above). When building using CMake, the path to the emdawnwebgpu port file needs to be set with the flag `EMDAWNWEBGPU_DIR`.
|
||||
|
||||
## IBM Z & LinuxONE
|
||||
|
||||
To read documentation for how to build on IBM Z & LinuxONE, [click here](./build-s390x.md)
|
||||
|
||||
## Notes about GPU-accelerated backends
|
||||
|
||||
The GPU may still be used to accelerate some parts of the computation even when using the `-ngl 0` option. You can fully disable GPU acceleration by using `--device none`.
|
||||
|
||||
In most cases, it is possible to build and use multiple backends at the same time. For example, you can build llama.cpp with both CUDA and Vulkan support by using the `-DGGML_CUDA=ON -DGGML_VULKAN=ON` options with CMake. At runtime, you can specify which backend devices to use with the `--device` option. To see a list of available devices, use the `--list-devices` option.
|
||||
|
||||
Backends can be built as dynamic libraries that can be loaded dynamically at runtime. This allows you to use the same llama.cpp binary on different machines with different GPUs. To enable this feature, use the `GGML_BACKEND_DL` option when building.
|
||||
131
docs/development/HOWTO-add-model.md
Normal file
131
docs/development/HOWTO-add-model.md
Normal file
@@ -0,0 +1,131 @@
|
||||
# Add a new model architecture to `llama.cpp`
|
||||
|
||||
Adding a model requires few steps:
|
||||
|
||||
1. Convert the model to GGUF
|
||||
2. Define the model architecture in `llama.cpp`
|
||||
3. Build the GGML graph implementation
|
||||
|
||||
After following these steps, you can open PR.
|
||||
|
||||
Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially:
|
||||
- [cli](/tools/cli/)
|
||||
- [completion](/tools/completion/)
|
||||
- [imatrix](/tools/imatrix/)
|
||||
- [quantize](/tools/quantize/)
|
||||
- [server](/tools/server/)
|
||||
|
||||
### 1. Convert the model to GGUF
|
||||
|
||||
This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library.
|
||||
Depending on the model architecture, you can use either [convert_hf_to_gguf.py](/convert_hf_to_gguf.py) or [examples/convert_legacy_llama.py](/examples/convert_legacy_llama.py) (for `llama/llama2` models in `.pth` format).
|
||||
|
||||
The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors.
|
||||
|
||||
The required steps to implement for an HF model are:
|
||||
|
||||
1. Define the model `ModelBase.register` annotation in a new `TextModel` or `MmprojModel` subclass, example:
|
||||
|
||||
```python
|
||||
@ModelBase.register("MyModelForCausalLM")
|
||||
class MyModel(TextModel):
|
||||
model_arch = gguf.MODEL_ARCH.MYMODEL
|
||||
```
|
||||
|
||||
or
|
||||
|
||||
```python
|
||||
@ModelBase.register("MyModelForConditionalGeneration")
|
||||
class MyModel(MmprojModel):
|
||||
model_arch = gguf.MODEL_ARCH.MYMODEL
|
||||
```
|
||||
|
||||
2. Define the layout of the GGUF tensors in [constants.py](/gguf-py/gguf/constants.py)
|
||||
|
||||
Add an enum entry in `MODEL_ARCH`, the model human friendly name in `MODEL_ARCH_NAMES` and the GGUF tensor names in `MODEL_TENSORS`.
|
||||
|
||||
Example for `falcon` model:
|
||||
```python
|
||||
MODEL_ARCH.FALCON: [
|
||||
MODEL_TENSOR.TOKEN_EMBD,
|
||||
MODEL_TENSOR.OUTPUT_NORM,
|
||||
MODEL_TENSOR.OUTPUT,
|
||||
MODEL_TENSOR.ATTN_NORM,
|
||||
MODEL_TENSOR.ATTN_NORM_2,
|
||||
MODEL_TENSOR.ATTN_QKV,
|
||||
MODEL_TENSOR.ATTN_OUT,
|
||||
MODEL_TENSOR.FFN_DOWN,
|
||||
MODEL_TENSOR.FFN_UP,
|
||||
]
|
||||
```
|
||||
|
||||
3. Map the original tensor names to the standardize equivalent in GGUF
|
||||
|
||||
As a general rule, before adding a new tensor name to GGUF, be sure the equivalent naming does not already exist.
|
||||
|
||||
Once you have found the GGUF tensor name equivalent, add it to the [tensor_mapping.py](/gguf-py/gguf/tensor_mapping.py) file.
|
||||
|
||||
If the tensor name is part of a repetitive layer/block, the key word `bid` substitutes it.
|
||||
|
||||
Example for the normalization tensor in attention layers:
|
||||
|
||||
```python
|
||||
block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
|
||||
# Attention norm
|
||||
MODEL_TENSOR.ATTN_NORM: (
|
||||
"gpt_neox.layers.{bid}.input_layernorm", # gptneox
|
||||
"transformer.h.{bid}.ln_1", # gpt2 gpt-j refact qwen
|
||||
"transformer.blocks.{bid}.norm_1", # mpt
|
||||
...
|
||||
)
|
||||
}
|
||||
```
|
||||
|
||||
`transformer.blocks.{bid}.norm_1` will be mapped to `blk.{bid}.attn_norm` in GGUF.
|
||||
|
||||
Depending on the model configuration, tokenizer, code and tensors layout, you will have to override:
|
||||
- `TextModel#set_gguf_parameters`
|
||||
- `MmprojModel#set_gguf_parameters`
|
||||
- `ModelBase#set_vocab`
|
||||
- `ModelBase#modify_tensors`
|
||||
|
||||
NOTE: Tensor names must end with `.weight` or `.bias` suffixes, that is the convention and several tools like `quantize` expect this to proceed the weights.
|
||||
|
||||
### 2. Define the model architecture in `llama.cpp`
|
||||
|
||||
The model params and tensors layout must be defined in `llama.cpp` source files:
|
||||
1. Define a new `llm_arch` enum value in `src/llama-arch.h`.
|
||||
2. In `src/llama-arch.cpp`:
|
||||
- Add the architecture name to the `LLM_ARCH_NAMES` map.
|
||||
- Add the list of model tensors to `llm_get_tensor_names` (you may also need to update `LLM_TENSOR_NAMES`)
|
||||
3. Add any non-standard metadata loading in the `llama_model_loader` constructor in `src/llama-model-loader.cpp`.
|
||||
4. If the model has a RoPE operation, add a case for the architecture in `llama_model_rope_type` function in `src/llama-model.cpp`.
|
||||
|
||||
NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorch` dimensions.
|
||||
|
||||
### 3. Build the GGML graph implementation
|
||||
|
||||
This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `src/llama-model.cpp`.
|
||||
Create a new struct that inherits from `llm_graph_context` and implement the graph-building logic in its constructor.
|
||||
Have a look at existing implementations like `llm_build_llama`, `llm_build_dbrx` or `llm_build_bert`.
|
||||
Then, in the `llama_model::build_graph` method, add a case for your architecture to instantiate your new graph-building struct.
|
||||
|
||||
Some `ggml` backends do not support all operations. Backend implementations can be added in a separate PR.
|
||||
|
||||
Note: to debug the inference graph: you can use [llama-eval-callback](/examples/eval-callback/).
|
||||
|
||||
## GGUF specification
|
||||
|
||||
https://github.com/ggml-org/ggml/blob/master/docs/gguf.md
|
||||
|
||||
## Resources
|
||||
|
||||
- YaRN RoPE scaling https://github.com/ggml-org/llama.cpp/pull/2268
|
||||
- support Baichuan serial models https://github.com/ggml-org/llama.cpp/pull/3009
|
||||
- support attention bias https://github.com/ggml-org/llama.cpp/pull/4283
|
||||
- Mixtral support https://github.com/ggml-org/llama.cpp/pull/4406
|
||||
- BERT embeddings https://github.com/ggml-org/llama.cpp/pull/5423
|
||||
- Grok-1 support https://github.com/ggml-org/llama.cpp/pull/6204
|
||||
- Command R Plus support https://github.com/ggml-org/llama.cpp/pull/6491
|
||||
- support arch DBRX https://github.com/ggml-org/llama.cpp/pull/6515
|
||||
- How to convert HuggingFace model to GGUF format https://github.com/ggml-org/llama.cpp/discussions/2948
|
||||
104
docs/development/debugging-tests.md
Normal file
104
docs/development/debugging-tests.md
Normal file
@@ -0,0 +1,104 @@
|
||||
# Debugging Tests Tips
|
||||
|
||||
## How to run & execute or debug a specific test without anything else to keep the feedback loop short?
|
||||
|
||||
There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number.
|
||||
|
||||
For example, running the following command will output an interactive list from which you can select a test. It takes this form:
|
||||
|
||||
`debug-test.sh [OPTION]... <test_regex> <test_number>`
|
||||
|
||||
It will then build & run in the debugger for you.
|
||||
|
||||
To just execute a test and get back a PASS or FAIL message run:
|
||||
|
||||
```bash
|
||||
./scripts/debug-test.sh test-tokenizer
|
||||
```
|
||||
|
||||
To test in GDB use the `-g` flag to enable gdb test mode.
|
||||
|
||||
```bash
|
||||
./scripts/debug-test.sh -g test-tokenizer
|
||||
|
||||
# Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows:
|
||||
>>> b main
|
||||
```
|
||||
|
||||
To speed up the testing loop, if you know your test number you can just run it similar to below:
|
||||
|
||||
```bash
|
||||
./scripts/debug-test.sh test 23
|
||||
```
|
||||
|
||||
For further reference use `debug-test.sh -h` to print help.
|
||||
|
||||
|
||||
|
||||
### How does the script work?
|
||||
If you want to be able to use the concepts contained in the script separately, the important ones are briefly outlined below.
|
||||
|
||||
#### Step 1: Reset and Setup folder context
|
||||
|
||||
From base of this repository, let's create `build-ci-debug` as our build context.
|
||||
|
||||
```bash
|
||||
rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug
|
||||
```
|
||||
|
||||
#### Step 2: Setup Build Environment and Compile Test Binaries
|
||||
|
||||
Setup and trigger a build under debug mode. You may adapt the arguments as needed, but in this case these are sane defaults.
|
||||
|
||||
```bash
|
||||
cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON ..
|
||||
make -j
|
||||
```
|
||||
|
||||
#### Step 3: Find all tests available that matches REGEX
|
||||
|
||||
The output of this command will give you the command & arguments needed to run GDB.
|
||||
|
||||
* `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex)
|
||||
* `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB.
|
||||
* `-V` : Verbose Mode
|
||||
|
||||
```bash
|
||||
ctest -R "test-tokenizer" -V -N
|
||||
```
|
||||
|
||||
This may return output similar to below (focusing on key lines to pay attention to):
|
||||
|
||||
```bash
|
||||
...
|
||||
1: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"
|
||||
1: Working Directory: .
|
||||
Labels: main
|
||||
Test #1: test-tokenizer-0-llama-spm
|
||||
...
|
||||
4: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-falcon.gguf"
|
||||
4: Working Directory: .
|
||||
Labels: main
|
||||
Test #4: test-tokenizer-0-falcon
|
||||
...
|
||||
```
|
||||
|
||||
#### Step 4: Identify Test Command for Debugging
|
||||
|
||||
So for test #1 above we can tell these two pieces of relevant information:
|
||||
* Test Binary: `~/llama.cpp/build-ci-debug/bin/test-tokenizer-0`
|
||||
* Test GGUF Model: `~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf`
|
||||
|
||||
#### Step 5: Run GDB on test command
|
||||
|
||||
Based on the ctest 'test command' report above we can then run a gdb session via this command below:
|
||||
|
||||
```bash
|
||||
gdb --args ${Test Binary} ${Test GGUF Model}
|
||||
```
|
||||
|
||||
Example:
|
||||
|
||||
```bash
|
||||
gdb --args ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf"
|
||||
```
|
||||
BIN
docs/development/llama-star/idea-arch.key
Executable file
BIN
docs/development/llama-star/idea-arch.key
Executable file
Binary file not shown.
BIN
docs/development/llama-star/idea-arch.pdf
Normal file
BIN
docs/development/llama-star/idea-arch.pdf
Normal file
Binary file not shown.
288
docs/development/parsing.md
Normal file
288
docs/development/parsing.md
Normal file
@@ -0,0 +1,288 @@
|
||||
# Parsing Model Output
|
||||
|
||||
The `common` library contains a PEG parser implementation suitable for parsing
|
||||
model output.
|
||||
|
||||
Types with the prefix `common_peg_*` are intended for general use and may have
|
||||
applications beyond parsing model output, such as parsing user-provided regex
|
||||
patterns.
|
||||
|
||||
Types with the prefix `common_chat_peg_*` are specialized helpers for model
|
||||
output.
|
||||
|
||||
The parser features:
|
||||
|
||||
- Partial parsing of streaming input
|
||||
- Built-in JSON parsers
|
||||
- AST generation with semantics via "tagged" nodes
|
||||
|
||||
## Example
|
||||
|
||||
Below is a contrived example demonstrating how to use the PEG parser to parse
|
||||
output from a model that emits arguments as JSON.
|
||||
|
||||
```cpp
|
||||
auto parser = build_chat_peg_native_parser([&](common_chat_peg_native_builder & p) {
|
||||
// Build a choice of all available tools
|
||||
auto tool_choice = p.choice();
|
||||
for (const auto & tool : tools) {
|
||||
const auto & function = tool.at("function");
|
||||
std::string name = function.at("name");
|
||||
const auto & schema = function.at("parameters");
|
||||
|
||||
auto tool_name = p.json_member("name", "\"" + p.literal(name) + "\"");
|
||||
auto tool_args = p.json_member("arguments", p.schema(p.json(), "tool-" + name + "-schema", schema));
|
||||
|
||||
tool_choice |= p.rule("tool-" + name, "{" << tool_name << "," << tool_args << "}");
|
||||
}
|
||||
|
||||
// Define the tool call structure: <tool_call>[{tool}]</tool_call>
|
||||
auto tool_call = p.trigger_rule("tool-call",
|
||||
p.sequence({
|
||||
p.literal("<tool_call>["),
|
||||
tool_choice,
|
||||
p.literal("]</tool_call>")
|
||||
})
|
||||
);
|
||||
|
||||
// Parser accepts content, optionally followed by a tool call
|
||||
return p.sequence({
|
||||
p.content(p.until("<tool_call>")),
|
||||
p.optional(tool_call),
|
||||
p.end()
|
||||
});
|
||||
});
|
||||
```
|
||||
|
||||
For a more complete example, see `test_example_native()` in
|
||||
[tests/test-chat-peg-parser.cpp](/tests/test-chat-peg-parser.cpp).
|
||||
|
||||
## Parsers/Combinators
|
||||
|
||||
### Basic Matchers
|
||||
|
||||
- **`eps()`** - Matches nothing and always succeeds (epsilon/empty match)
|
||||
- **`start()`** - Matches the start of input (anchor `^`)
|
||||
- **`end()`** - Matches the end of input (anchor `$`)
|
||||
- **`literal(string)`** - Matches an exact literal string
|
||||
- **`any()`** - Matches any single character (`.`)
|
||||
|
||||
### Combinators
|
||||
|
||||
- **`sequence(...)`** - Matches parsers in order; all must succeed
|
||||
- **`choice(...)`** - Matches the first parser that succeeds from alternatives (ordered choice)
|
||||
- **`one_or_more(p)`** - Matches one or more repetitions (`+`)
|
||||
- **`zero_or_more(p)`** - Matches zero or more repetitions (`*`)
|
||||
- **`optional(p)`** - Matches zero or one occurrence (`?`)
|
||||
- **`repeat(p, min, max)`** - Matches between min and max repetitions (use `-1` for unbounded)
|
||||
- **`repeat(p, n)`** - Matches exactly n repetitions
|
||||
|
||||
### Lookahead
|
||||
|
||||
- **`peek(p)`** - Positive lookahead: succeeds if parser succeeds without consuming input (`&`)
|
||||
- **`negate(p)`** - Negative lookahead: succeeds if parser fails without consuming input (`!`)
|
||||
|
||||
### Character Classes & Utilities
|
||||
|
||||
- **`chars(classes, min, max)`** - Matches repetitions of characters from a character class
|
||||
- **`space()`** - Matches zero or more whitespace characters (space, tab, newline)
|
||||
- **`until(delimiter)`** - Matches characters until delimiter is found (delimiter not consumed)
|
||||
- **`until_one_of(delimiters)`** - Matches characters until any delimiter in the list is found
|
||||
- **`rest()`** - Matches everything remaining (`.*`)
|
||||
|
||||
### JSON Parsers
|
||||
|
||||
- **`json()`** - Complete JSON parser (objects, arrays, strings, numbers, booleans, null)
|
||||
- **`json_object()`** - JSON object parser
|
||||
- **`json_array()`** - JSON array parser
|
||||
- **`json_string()`** - JSON string parser
|
||||
- **`json_number()`** - JSON number parser
|
||||
- **`json_bool()`** - JSON boolean parser
|
||||
- **`json_null()`** - JSON null parser
|
||||
- **`json_string_content()`** - JSON string content without surrounding quotes
|
||||
- **`json_member(key, p)`** - JSON object member with specific key and value parser
|
||||
|
||||
### Grammar Building
|
||||
|
||||
- **`ref(name)`** - Creates a lightweight reference to a named rule (for recursive grammars)
|
||||
- **`rule(name, p, trigger)`** - Creates a named rule and returns a reference
|
||||
- **`trigger_rule(name, p)`** - Creates a trigger rule (entry point for lazy grammar generation)
|
||||
- **`schema(p, name, schema, raw)`** - Wraps parser with JSON schema metadata for grammar generation
|
||||
|
||||
### AST Control
|
||||
|
||||
- **`atomic(p)`** - Prevents AST node creation for partial parses
|
||||
- **`tag(tag, p)`** - Creates AST nodes with semantic tags (multiple nodes can share tags)
|
||||
|
||||
## GBNF Grammar Generation
|
||||
|
||||
The PEG parser also acts as a convenient DSL for generating GBNF grammars, with
|
||||
some exceptions.
|
||||
|
||||
```cpp
|
||||
data.grammar = build_grammar([&](const common_grammar_builder & builder) {
|
||||
foreach_function(params.tools, [&](const json & fn) {
|
||||
builder.resolve_refs(fn.at("parameters"));
|
||||
});
|
||||
parser.build_grammar(builder, data.grammar_lazy);
|
||||
});
|
||||
```
|
||||
|
||||
The notable exception is the `negate(p)` lookahead parser, which cannot be
|
||||
defined as a CFG grammar and therefore does not produce a rule. Its usage
|
||||
should be limited and preferably hidden behind a `schema()` parser. In many
|
||||
cases, `until(delimiter)` or `until_one_of(delimiters)` is a better choice.
|
||||
|
||||
Another limitation is that the PEG parser requires an unambiguous grammar. In
|
||||
contrast, the `llama-grammar` implementation can support ambiguous grammars,
|
||||
though they are difficult to parse.
|
||||
|
||||
### Lazy Grammars
|
||||
|
||||
During lazy grammar generation, only rules reachable from a `trigger_rule(p)`
|
||||
are emitted in the grammar. All trigger rules are added as alternations in the
|
||||
root rule. It is still necessary to define trigger patterns, as the parser has
|
||||
no interaction with the grammar sampling.
|
||||
|
||||
### JSON Schema
|
||||
|
||||
The `schema(p, name, schema, raw)` parser will use the `json-schema-to-grammar`
|
||||
implementation to generate the grammar instead of the underlying parser.
|
||||
|
||||
The `raw` option emits a grammar suitable for a raw string instead of a JSON
|
||||
string. In other words, it won't be wrapped in quotes or require escaping
|
||||
quotes. It should only be used when `type == "string"`.
|
||||
|
||||
The downside is that it can potentially lead to ambiguous grammars. For
|
||||
example, if a user provides the pattern `^.*$`, the following grammar may be
|
||||
generated:
|
||||
|
||||
```
|
||||
root ::= "<arg>" .* "</arg>"
|
||||
```
|
||||
|
||||
This creates an ambiguous grammar that cannot be parsed by the PEG parser. To
|
||||
help mitigate this, if `.*` is found in the pattern, the grammar from the
|
||||
underlying parser will be emitted instead.
|
||||
|
||||
## Common AST Shapes for Chat Parsing
|
||||
|
||||
Most model output can be placed in one of the following categories:
|
||||
|
||||
- Content only
|
||||
- Tool calling with arguments emitted as a single JSON object
|
||||
- Tool calling with arguments emitted as separate entities, either XML
|
||||
(Qwen3-Coder, MiniMax M2) or pseudo-function calls (LFM2)
|
||||
|
||||
To provide broad coverage,
|
||||
[`common/chat-peg-parser.h`](/common/chat-peg-parser.h) contains builders and
|
||||
mappers that help create parsers and visitors/extractors for these types. They
|
||||
require parsers to tag nodes to conform to an AST "shape". This normalization
|
||||
makes it easy to extract information and generalize parsing.
|
||||
|
||||
### Simple
|
||||
|
||||
The `common_chat_peg_builder` builds a `simple` parser that supports
|
||||
content-only models with optional reasoning.
|
||||
|
||||
- **`reasoning(p)`** - Tag node for extracting `reasoning_content`
|
||||
- **`content(p)`** - Tag node for extracting `content`
|
||||
|
||||
```cpp
|
||||
build_chat_peg_parser([&](common_chat_peg_parser & p) {
|
||||
return p.sequence({
|
||||
p.optional("<think>" + p.reasoning(p.until("</think>")) + "</think>"),
|
||||
p.content(p.until("<tool_call>")),
|
||||
p.end()
|
||||
});
|
||||
});
|
||||
```
|
||||
|
||||
Use `common_chat_peg_mapper` to extract the content. Note that this is already
|
||||
done for you in `common_chat_peg_parser` when
|
||||
`chat_format == COMMON_CHAT_FORMAT_PEG_SIMPLE`.
|
||||
|
||||
```cpp
|
||||
auto result = parser.parse(ctx);
|
||||
|
||||
common_chat_msg msg;
|
||||
auto mapper = common_chat_peg_mapper(msg);
|
||||
mapper.from_ast(ctx.ast, result);
|
||||
```
|
||||
|
||||
### Native
|
||||
|
||||
The `common_chat_peg_native_builder` builds a `native` parser suitable for
|
||||
models that emit tool arguments as a direct JSON object.
|
||||
|
||||
- **`reasoning(p)`** - Tag node for `reasoning_content`
|
||||
- **`content(p)`** - Tag node for `content`
|
||||
- **`tool(p)`** - Tag entirety of a single tool call
|
||||
- **`tool_open(p)`** - Tag start of a tool call
|
||||
- **`tool_close(p)`** - Tag end of a tool call
|
||||
- **`tool_id(p)`** - Tag the tool call ID (optional)
|
||||
- **`tool_name(p)`** - Tag the tool name
|
||||
- **`tool_args(p)`** - Tag the tool arguments
|
||||
|
||||
```cpp
|
||||
build_chat_peg_native_parser([&](common_chat_peg_native_parser & p) {
|
||||
auto get_weather_tool = p.tool(p.sequence({
|
||||
p.tool_open(p.literal("{")),
|
||||
p.json_member("name", "\"" + p.tool_name(p.literal("get_weather")) + "\""),
|
||||
p.literal(","),
|
||||
p.json_member("arguments", p.tool_args(p.json())),
|
||||
p.tool_close(p.literal("}"))
|
||||
}));
|
||||
|
||||
return p.sequence({
|
||||
p.content(p.until("<tool_call>")),
|
||||
p.literal("<tool_call>"),
|
||||
get_weather_tool,
|
||||
p.literal("</tool_call>"),
|
||||
p.end()
|
||||
});
|
||||
});
|
||||
```
|
||||
|
||||
### Constructed
|
||||
|
||||
The `common_chat_peg_constructed_builder` builds a `constructed` parser
|
||||
suitable for models that emit tool arguments as separate entities, such as XML
|
||||
tags.
|
||||
|
||||
- **`reasoning(p)`** - Tag node for `reasoning_content`
|
||||
- **`content(p)`** - Tag node for `content`
|
||||
- **`tool(p)`** - Tag entirety of a single tool call
|
||||
- **`tool_open(p)`** - Tag start of a tool call
|
||||
- **`tool_close(p)`** - Tag end of a tool call
|
||||
- **`tool_name(p)`** - Tag the tool name
|
||||
- **`tool_arg(p)`** - Tag a complete tool argument (name + value)
|
||||
- **`tool_arg_open(p)`** - Tag start of a tool argument
|
||||
- **`tool_arg_close(p)`** - Tag end of a tool argument
|
||||
- **`tool_arg_name(p)`** - Tag the argument name
|
||||
- **`tool_arg_string_value(p)`** - Tag string value for the argument
|
||||
- **`tool_arg_json_value(p)`** - Tag JSON value for the argument
|
||||
|
||||
```cpp
|
||||
build_chat_peg_constructed_parser([&](common_chat_peg_constructed_builder & p) {
|
||||
auto location_arg = p.tool_arg(
|
||||
p.tool_arg_open("<parameter name=\"" + p.tool_arg_name(p.literal("location")) + "\">"),
|
||||
p.tool_arg_string_value(p.until("</parameter>")),
|
||||
p.tool_arg_close(p.literal("</parameter>"))
|
||||
);
|
||||
|
||||
auto get_weather_tool = p.tool(p.sequence({
|
||||
p.tool_open("<function name=\"" + p.tool_name(p.literal("get_weather")) + "\">"),
|
||||
location_arg,
|
||||
p.tool_close(p.literal("</function>"))
|
||||
}));
|
||||
|
||||
return p.sequence({
|
||||
p.content(p.until("<tool_call>")),
|
||||
p.literal("<tool_call>"),
|
||||
get_weather_tool,
|
||||
p.literal("</tool_call>"),
|
||||
p.end()
|
||||
});
|
||||
});
|
||||
```
|
||||
40
docs/development/token_generation_performance_tips.md
Normal file
40
docs/development/token_generation_performance_tips.md
Normal file
@@ -0,0 +1,40 @@
|
||||
# Token generation performance troubleshooting
|
||||
|
||||
## Verifying that the model is running on the GPU with CUDA
|
||||
Make sure you compiled llama with the correct env variables according to [this guide](/docs/build.md#cuda), so that llama accepts the `-ngl N` (or `--n-gpu-layers N`) flag. When running llama, you may configure `N` to be very large, and llama will offload the maximum possible number of layers to the GPU, even if it's less than the number you configured. For example:
|
||||
```shell
|
||||
./llama-cli -m "path/to/model.gguf" -ngl 200000 -p "Please sir, may I have some "
|
||||
```
|
||||
|
||||
When running llama, before it starts the inference work, it will output diagnostic information that shows whether cuBLAS is offloading work to the GPU. Look for these lines:
|
||||
```shell
|
||||
llama_model_load_internal: [cublas] offloading 60 layers to GPU
|
||||
llama_model_load_internal: [cublas] offloading output layer to GPU
|
||||
llama_model_load_internal: [cublas] total VRAM used: 17223 MB
|
||||
... rest of inference
|
||||
```
|
||||
|
||||
If you see these lines, then the GPU is being used.
|
||||
|
||||
## Verifying that the CPU is not oversaturated
|
||||
llama accepts a `-t N` (or `--threads N`) parameter. It's extremely important that this parameter is not too large. If your token generation is extremely slow, try setting this number to 1. If this significantly improves your token generation speed, then your CPU is being oversaturated and you need to explicitly set this parameter to the number of the physical CPU cores on your machine (even if you utilize a GPU). If in doubt, start with 1 and double the amount until you hit a performance bottleneck, then scale the number down.
|
||||
|
||||
# Example of runtime flags effect on inference speed benchmark
|
||||
These runs were tested on the following machine:
|
||||
GPU: A6000 (48GB VRAM)
|
||||
CPU: 7 physical cores
|
||||
RAM: 32GB
|
||||
|
||||
Model: `TheBloke_Wizard-Vicuna-30B-Uncensored-GGML/Wizard-Vicuna-30B-Uncensored.q4_0.gguf` (30B parameters, 4bit quantization, GGML)
|
||||
|
||||
Run command: `./llama-cli -m "path/to/model.gguf" -p "An extremely detailed description of the 10 best ethnic dishes will follow, with recipes: " -n 1000 [additional benchmark flags]`
|
||||
|
||||
Result:
|
||||
|
||||
| command | tokens/second (higher is better) |
|
||||
| - | - |
|
||||
| -ngl 2000000 | N/A (less than 0.1) |
|
||||
| -t 7 | 1.7 |
|
||||
| -t 1 -ngl 2000000 | 5.5 |
|
||||
| -t 7 -ngl 2000000 | 8.7 |
|
||||
| -t 4 -ngl 2000000 | 9.1 |
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user