mirror of
https://github.com/nomic-ai/gpt4all.git
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Nomic vulkan backend licensed under the Software for Open Models License (SOM), version 1.0.
This commit is contained in:
Adam Treat
parent
d55cbbee32
commit
987546c63b
30
LICENSE_SOM.txt
Normal file
30
LICENSE_SOM.txt
Normal file
@ -0,0 +1,30 @@
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Software for Open Models License (SOM)
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Version 1.0 dated August 30th, 2023
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This license governs use of the accompanying Software. If you use the Software, you accept this license. If you do not accept the license, do not use the Software.
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This license is intended to encourage open release of models created, modified, processed, or otherwise used via the Software under open licensing terms, and should be interpreted in light of that intent.
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1. Definitions
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The “Licensor” is the person or entity who is making the Software available under this license. “Software” is the software made available by Licensor under this license.
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A “Model” is the output of a machine learning algorithm, and excludes the Software.
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“Model Source Materials” must include the Model and model weights, and may include any input data, input data descriptions, documentation or training descriptions for the Model.
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“Open Licensing Terms” means: (a) any open source license approved by the Open Source Initiative, or (b) any other terms that make the Model Source Materials publicly available free of charge, and allow recipients to use, modify and distribute the Model Source Materials. Terms described in (b) may include reasonable restrictions such as non-commercial or non-production limitations, or require use in compliance with law.
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2. Grant of Rights. Subject to the conditions and limitations in section 3:
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(A) Copyright Grant. Licensor grants you a non-exclusive, worldwide, royalty-free copyright license to copy, modify, and distribute the Software and any modifications of the Software you create under this license. The foregoing license includes without limitation the right to create, modify, and use Models using this Software.
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(B) Patent Grant. Licensor grants you a non-exclusive, worldwide, royalty-free license, under any patents owned or controlled by Licensor, to make, have made, use, sell, offer for sale, import, or otherwise exploit the Software. No license is granted to patent rights that are not embodied in the operation of the Software in the form provided by Licensor.
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3. Conditions and Limitations
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(A) Model Licensing and Access. If you use the Software to create, modify, process, or otherwise use any Model, including usage to create inferences with a Model, whether or not you make the Model available to others, you must make that Model Source Materials publicly available under Open Licensing Terms.
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(B) No Re-Licensing. If you redistribute the Software, or modifications to the Software made under the license granted above, you must make it available only under the terms of this license. You may offer additional terms such as warranties, maintenance and support, but You, and not Licensor, are responsible for performing such terms.
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(C) No Trademark License. This license does not grant you rights to use the Licensor’s name, logo, or trademarks.
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(D) If you assert in writing a claim against any person or entity alleging that the use of the Software infringes any patent, all of your licenses to the Software under Section 2 end automatically as of the date you asserted the claim.
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(E) If you distribute any portion of the Software, you must retain all copyright, patent, trademark, and attribution notices that are present in the Software, and you must include a copy of this license.
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(F) The Software is licensed “as-is.” You bear the entire risk of using it. Licensor gives You no express warranties, guarantees or conditions. You may have additional consumer rights under your local laws that this license cannot change. To the extent permitted under your local laws, the Licensor disclaims and excludes the implied warranties of merchantability, fitness for a particular purpose and non-infringement. To the extent this disclaimer is unlawful, you, and not Licensor, are responsible for any liability.
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@ -20,7 +20,7 @@ endif()
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include_directories("${CMAKE_CURRENT_BINARY_DIR}")
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set(LLMODEL_VERSION_MAJOR 0)
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set(LLMODEL_VERSION_MINOR 3)
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set(LLMODEL_VERSION_MINOR 4)
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set(LLMODEL_VERSION_PATCH 0)
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set(LLMODEL_VERSION "${LLMODEL_VERSION_MAJOR}.${LLMODEL_VERSION_MINOR}.${LLMODEL_VERSION_PATCH}")
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project(llmodel VERSION ${LLMODEL_VERSION} LANGUAGES CXX C)
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@ -39,6 +39,8 @@ else()
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message(STATUS "Interprocedural optimization support detected")
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endif()
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set(LLAMA_KOMPUTE YES)
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include(llama.cpp.cmake)
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set(BUILD_VARIANTS default avxonly)
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@ -1 +1 @@
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Subproject commit acfc5478ff3446ca3b54553967a3dea09b7c771a
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Subproject commit 4cdaa3c9cb2d649f45a928d65d900d6b6bb5be3a
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@ -1,3 +1,11 @@
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#
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# Copyright (c) 2023 Nomic, Inc. All rights reserved.
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#
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# This software is licensed under the terms of the Software for Open Models License (SOM),
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# version 1.0, as detailed in the LICENSE_SOM.txt file. A copy of this license should accompany
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# this software. Except as expressly granted in the SOM license, all rights are reserved by Nomic, Inc.
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#
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cmake_minimum_required(VERSION 3.12) # Don't bump this version for no reason
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set(CMAKE_EXPORT_COMPILE_COMMANDS ON)
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@ -145,6 +153,129 @@ if (LLAMA_OPENBLAS)
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endif()
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endif()
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if (LLAMA_KOMPUTE)
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find_package(Vulkan COMPONENTS glslc REQUIRED)
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find_program(glslc_executable NAMES glslc HINTS Vulkan::glslc)
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if (NOT glslc_executable)
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message(FATAL_ERROR "glslc not found")
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endif()
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set(LLAMA_DIR ${CMAKE_CURRENT_SOURCE_DIR}/llama.cpp-mainline)
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function(compile_shader)
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set(options)
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set(oneValueArgs)
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set(multiValueArgs SOURCES)
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cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})
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foreach(source ${compile_shader_SOURCES})
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get_filename_component(OP_FILE ${source} NAME)
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set(spv_file ${CMAKE_CURRENT_BINARY_DIR}/${OP_FILE}.spv)
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add_custom_command(
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OUTPUT ${spv_file}
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DEPENDS ${LLAMA_DIR}/${source}
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COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${LLAMA_DIR}/${source}
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COMMENT "Compiling ${source} to ${source}.spv"
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)
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get_filename_component(RAW_FILE_NAME ${spv_file} NAME)
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set(FILE_NAME "shader${RAW_FILE_NAME}")
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string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME})
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string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE)
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string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}")
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set(OUTPUT_HEADER_FILE "${HEADER_FILE}")
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message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}")
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add_custom_command(
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OUTPUT ${OUTPUT_HEADER_FILE}
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COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE}
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COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
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COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
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COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE}
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COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE}
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COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${spv_file} >> ${OUTPUT_HEADER_FILE}
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COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE}
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COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE}
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DEPENDS ${spv_file} xxd
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COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd"
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)
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endforeach()
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endfunction()
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if (EXISTS "${LLAMA_DIR}/kompute/CMakeLists.txt")
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message(STATUS "Kompute found")
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add_subdirectory(${LLAMA_DIR}/kompute)
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# Compile our shaders
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compile_shader(SOURCES
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kompute/op_scale.comp
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kompute/op_add.comp
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kompute/op_addrow.comp
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kompute/op_mul.comp
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kompute/op_mulrow.comp
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kompute/op_silu.comp
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kompute/op_relu.comp
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kompute/op_gelu.comp
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kompute/op_softmax.comp
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kompute/op_norm.comp
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kompute/op_rmsnorm.comp
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kompute/op_diagmask.comp
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kompute/op_mul_mat_f16.comp
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kompute/op_mul_mat_q4_0.comp
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kompute/op_mul_mat_q4_1.comp
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kompute/op_getrows_f16.comp
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kompute/op_getrows_q4_0.comp
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kompute/op_getrows_q4_1.comp
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kompute/op_rope.comp
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kompute/op_cpy_f16_f16.comp
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kompute/op_cpy_f16_f32.comp
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kompute/op_cpy_f32_f16.comp
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kompute/op_cpy_f32_f32.comp
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)
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# Create a custom target for our generated shaders
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add_custom_target(generated_shaders DEPENDS
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shaderop_scale.h
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shaderop_add.h
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shaderop_addrow.h
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shaderop_mul.h
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shaderop_mulrow.h
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shaderop_silu.h
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shaderop_relu.h
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shaderop_gelu.h
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shaderop_softmax.h
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shaderop_norm.h
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shaderop_rmsnorm.h
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shaderop_diagmask.h
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shaderop_mul_mat_f16.h
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shaderop_mul_mat_q4_0.h
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shaderop_mul_mat_q4_1.h
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shaderop_getrows_f16.h
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shaderop_getrows_q4_0.h
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shaderop_getrows_q4_1.h
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shaderop_rope.h
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shaderop_cpy_f16_f16.h
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shaderop_cpy_f16_f32.h
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shaderop_cpy_f32_f16.h
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shaderop_cpy_f32_f32.h
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)
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# Create a custom command that depends on the generated_shaders
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add_custom_command(
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OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan.stamp
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COMMAND ${CMAKE_COMMAND} -E touch ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan.stamp
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DEPENDS generated_shaders
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COMMENT "Ensuring shaders are generated before compiling ggml-vulkan.cpp"
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)
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# Add the stamp to the main sources to ensure dependency tracking
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set(GGML_SOURCES_KOMPUTE ${LLAMA_DIR}/ggml-vulkan.cpp ${LLAMA_DIR}/ggml-vulkan.h ${CMAKE_CURRENT_BINARY_DIR}/ggml-vulkan.stamp)
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add_compile_definitions(GGML_USE_KOMPUTE)
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set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} kompute)
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set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${CMAKE_BINARY_DIR})
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else()
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message(WARNING "Kompute not found")
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endif()
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endif()
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if (LLAMA_ALL_WARNINGS)
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if (NOT MSVC)
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set(c_flags
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@ -301,7 +432,8 @@ function(include_ggml DIRECTORY SUFFIX WITH_LLAMA)
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${GGML_SOURCES_QUANT_K}
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${GGML_SOURCES_CUDA}
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${GGML_METAL_SOURCES}
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${GGML_OPENCL_SOURCES})
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${GGML_OPENCL_SOURCES}
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${GGML_SOURCES_KOMPUTE})
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if (LLAMA_K_QUANTS)
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target_compile_definitions(ggml${SUFFIX} PUBLIC GGML_USE_K_QUANTS)
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@ -28,6 +28,9 @@
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#include <llama.h>
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#include <ggml.h>
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#ifdef GGML_USE_KOMPUTE
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#include "ggml-vulkan.h"
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#endif
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namespace {
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const char *modelType_ = "LLaMA";
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@ -155,6 +158,13 @@ bool LLamaModel::loadModel(const std::string &modelPath)
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// currently
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d_ptr->params.n_gpu_layers = 1;
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#endif
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#ifdef GGML_USE_KOMPUTE
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if (ggml_vk_has_device()) {
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// vulkan always runs the whole model if n_gpu_layers is not 0, at least
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// currently
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d_ptr->params.n_gpu_layers = 1;
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}
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#endif
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d_ptr->ctx = llama_init_from_file(modelPath.c_str(), d_ptr->params);
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if (!d_ptr->ctx) {
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@ -162,6 +172,12 @@ bool LLamaModel::loadModel(const std::string &modelPath)
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return false;
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}
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#ifdef GGML_USE_KOMPUTE
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if (ggml_vk_has_device()) {
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std::cerr << "llama.cpp: using Vulkan on " << ggml_vk_current_device().name << std::endl;
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}
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#endif
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d_ptr->n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
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d_ptr->modelLoaded = true;
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fflush(stderr);
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@ -252,6 +268,75 @@ const std::vector<LLModel::Token> &LLamaModel::endTokens() const
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return fres;
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}
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#if defined(GGML_USE_KOMPUTE)
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#include "ggml-vulkan.h"
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#endif
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std::vector<LLModel::GPUDevice> LLamaModel::availableGPUDevices(size_t memoryRequired)
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{
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#if defined(GGML_USE_KOMPUTE)
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std::vector<ggml_vk_device> vkDevices = ggml_vk_available_devices(memoryRequired);
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std::vector<LLModel::GPUDevice> devices;
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for(const auto& vkDevice : vkDevices) {
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LLModel::GPUDevice device;
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device.index = vkDevice.index;
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device.type = vkDevice.type;
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device.heapSize = vkDevice.heapSize;
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device.name = vkDevice.name;
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device.vendor = vkDevice.vendor;
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devices.push_back(device);
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}
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return devices;
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#else
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return std::vector<LLModel::GPUDevice>();
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#endif
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}
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bool LLamaModel::initializeGPUDevice(size_t memoryRequired, const std::string& device)
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{
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#if defined(GGML_USE_KOMPUTE)
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return ggml_vk_init_device(memoryRequired, device);
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#else
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return false;
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#endif
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}
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bool LLamaModel::initializeGPUDevice(const LLModel::GPUDevice &device)
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{
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#if defined(GGML_USE_KOMPUTE)
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ggml_vk_device vkDevice;
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vkDevice.index = device.index;
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vkDevice.type = device.type;
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vkDevice.heapSize = device.heapSize;
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vkDevice.name = device.name;
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vkDevice.vendor = device.vendor;
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return ggml_vk_init_device(vkDevice);
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#else
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return false;
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#endif
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}
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bool LLamaModel::initializeGPUDevice(int device)
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{
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#if defined(GGML_USE_KOMPUTE)
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return ggml_vk_init_device(device);
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#else
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return false;
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#endif
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}
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bool LLamaModel::hasGPUDevice()
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{
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#if defined(GGML_USE_KOMPUTE)
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return ggml_vk_has_device();
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#else
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return false;
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#endif
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||||
}
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||||
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||||
#if defined(_WIN32)
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#define DLL_EXPORT __declspec(dllexport)
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#else
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@ -25,6 +25,11 @@ public:
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size_t restoreState(const uint8_t *src) override;
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void setThreadCount(int32_t n_threads) override;
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int32_t threadCount() const override;
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||||
std::vector<GPUDevice> availableGPUDevices(size_t memoryRequired) override;
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||||
bool initializeGPUDevice(size_t memoryRequired, const std::string& device) override;
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||||
bool initializeGPUDevice(const GPUDevice &device) override;
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||||
bool initializeGPUDevice(int device) override;
|
||||
bool hasGPUDevice() override;
|
||||
|
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private:
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LLamaPrivate *d_ptr;
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||||
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||||
@ -58,6 +58,14 @@ public:
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||||
// window
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||||
};
|
||||
|
||||
struct GPUDevice {
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||||
int index = 0;
|
||||
int type = 0;
|
||||
size_t heapSize = 0;
|
||||
std::string name;
|
||||
std::string vendor;
|
||||
};
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||||
|
||||
explicit LLModel() {}
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||||
virtual ~LLModel() {}
|
||||
|
||||
@ -87,6 +95,12 @@ public:
|
||||
return *m_implementation;
|
||||
}
|
||||
|
||||
virtual std::vector<GPUDevice> availableGPUDevices(size_t /*memoryRequired*/) { return std::vector<GPUDevice>(); }
|
||||
virtual bool initializeGPUDevice(size_t /*memoryRequired*/, const std::string& /*device*/) { return false; }
|
||||
virtual bool initializeGPUDevice(const GPUDevice &/*device*/) { return false; }
|
||||
virtual bool initializeGPUDevice(int /*device*/) { return false; }
|
||||
virtual bool hasGPUDevice() { return false; }
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||||
|
||||
protected:
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||||
// These are pure virtual because subclasses need to implement as the default implementation of
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||||
// 'prompt' above calls these functions
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||||
|
||||
@ -5,7 +5,6 @@
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||||
#include <cerrno>
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||||
#include <utility>
|
||||
|
||||
|
||||
struct LLModelWrapper {
|
||||
LLModel *llModel = nullptr;
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||||
LLModel::PromptContext promptContext;
|
||||
@ -210,3 +209,57 @@ const char *llmodel_get_implementation_search_path()
|
||||
{
|
||||
return LLModel::Implementation::implementationsSearchPath().c_str();
|
||||
}
|
||||
|
||||
struct llmodel_gpu_device* llmodel_available_gpu_devices(llmodel_model model, size_t memoryRequired, int* num_devices)
|
||||
{
|
||||
LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
|
||||
std::vector<LLModel::GPUDevice> devices = wrapper->llModel->availableGPUDevices(memoryRequired);
|
||||
|
||||
// Set the num_devices
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||||
*num_devices = devices.size();
|
||||
|
||||
if (*num_devices == 0) return nullptr; // Return nullptr if no devices are found
|
||||
|
||||
// Allocate memory for the output array
|
||||
struct llmodel_gpu_device* output = (struct llmodel_gpu_device*) malloc(*num_devices * sizeof(struct llmodel_gpu_device));
|
||||
|
||||
for (int i = 0; i < *num_devices; i++) {
|
||||
output[i].index = devices[i].index;
|
||||
output[i].type = devices[i].type;
|
||||
output[i].heapSize = devices[i].heapSize;
|
||||
output[i].name = strdup(devices[i].name.c_str()); // Convert std::string to char* and allocate memory
|
||||
output[i].vendor = strdup(devices[i].vendor.c_str()); // Convert std::string to char* and allocate memory
|
||||
}
|
||||
|
||||
return output;
|
||||
}
|
||||
|
||||
bool llmodel_gpu_init_gpu_device_by_string(llmodel_model model, size_t memoryRequired, const char *device)
|
||||
{
|
||||
LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
|
||||
return wrapper->llModel->initializeGPUDevice(memoryRequired, std::string(device));
|
||||
}
|
||||
|
||||
bool llmodel_gpu_init_gpu_device_by_struct(llmodel_model model, const llmodel_gpu_device *device)
|
||||
{
|
||||
LLModel::GPUDevice d;
|
||||
d.index = device->index;
|
||||
d.type = device->type;
|
||||
d.heapSize = device->heapSize;
|
||||
d.name = device->name;
|
||||
d.vendor = device->vendor;
|
||||
LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
|
||||
return wrapper->llModel->initializeGPUDevice(d);
|
||||
}
|
||||
|
||||
bool llmodel_gpu_init_gpu_device_by_int(llmodel_model model, int device)
|
||||
{
|
||||
LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
|
||||
return wrapper->llModel->initializeGPUDevice(device);
|
||||
}
|
||||
|
||||
bool llmodel_has_gpu_device(llmodel_model model)
|
||||
{
|
||||
LLModelWrapper *wrapper = reinterpret_cast<LLModelWrapper*>(model);
|
||||
return wrapper->llModel->hasGPUDevice();
|
||||
}
|
||||
|
||||
@ -56,8 +56,18 @@ struct llmodel_prompt_context {
|
||||
int32_t repeat_last_n; // last n tokens to penalize
|
||||
float context_erase; // percent of context to erase if we exceed the context window
|
||||
};
|
||||
|
||||
struct llmodel_gpu_device {
|
||||
int index = 0;
|
||||
int type = 0; // same as VkPhysicalDeviceType
|
||||
size_t heapSize = 0;
|
||||
const char * name;
|
||||
const char * vendor;
|
||||
};
|
||||
|
||||
#ifndef __cplusplus
|
||||
typedef struct llmodel_prompt_context llmodel_prompt_context;
|
||||
typedef struct llmodel_gpu_device llmodel_gpu_device;
|
||||
#endif
|
||||
|
||||
/**
|
||||
@ -218,6 +228,50 @@ void llmodel_set_implementation_search_path(const char *path);
|
||||
*/
|
||||
const char *llmodel_get_implementation_search_path();
|
||||
|
||||
/**
|
||||
* Get a list of available GPU devices given the memory required.
|
||||
* @return A pointer to an array of llmodel_gpu_device's whose number is given by num_devices.
|
||||
*/
|
||||
struct llmodel_gpu_device* llmodel_available_gpu_devices(llmodel_model model, size_t memoryRequired, int* num_devices);
|
||||
|
||||
/**
|
||||
* Initializes a GPU device based on a specified string criterion.
|
||||
*
|
||||
* This function initializes a GPU device based on a string identifier provided. The function
|
||||
* allows initialization based on general device type ("gpu"), vendor name ("amd", "nvidia", "intel"),
|
||||
* or any specific device name.
|
||||
*
|
||||
* @param memoryRequired The amount of memory (in bytes) required by the application or task
|
||||
* that will utilize the GPU device.
|
||||
* @param device A string specifying the desired criterion for GPU device selection. It can be:
|
||||
* - "gpu": To initialize the best available GPU.
|
||||
* - "amd", "nvidia", or "intel": To initialize the best available GPU from that vendor.
|
||||
* - A specific GPU device name: To initialize a GPU with that exact name.
|
||||
*
|
||||
* @return True if the GPU device is successfully initialized based on the provided string
|
||||
* criterion. Returns false if the desired GPU device could not be initialized.
|
||||
*/
|
||||
bool llmodel_gpu_init_gpu_device_by_string(llmodel_model model, size_t memoryRequired, const char *device);
|
||||
|
||||
/**
|
||||
* Initializes a GPU device by specifying a valid gpu device pointer.
|
||||
* @param device A gpu device pointer.
|
||||
* @return True if the GPU device is successfully initialized, false otherwise.
|
||||
*/
|
||||
bool llmodel_gpu_init_gpu_device_by_struct(llmodel_model model, const llmodel_gpu_device *device);
|
||||
|
||||
/**
|
||||
* Initializes a GPU device by its index.
|
||||
* @param device An integer representing the index of the GPU device to be initialized.
|
||||
* @return True if the GPU device is successfully initialized, false otherwise.
|
||||
*/
|
||||
bool llmodel_gpu_init_gpu_device_by_int(llmodel_model model, int device);
|
||||
|
||||
/**
|
||||
* @return True if a GPU device is successfully initialized, false otherwise.
|
||||
*/
|
||||
bool llmodel_has_gpu_device(llmodel_model model);
|
||||
|
||||
#ifdef __cplusplus
|
||||
}
|
||||
#endif
|
||||
|
||||
@ -4,6 +4,49 @@
|
||||
#include <vector>
|
||||
#include <ggml.h>
|
||||
|
||||
#if defined(GGML_USE_KOMPUTE)
|
||||
#include "ggml-vulkan.h"
|
||||
struct llm_buffer {
|
||||
uint8_t * addr = NULL;
|
||||
size_t size = 0;
|
||||
ggml_vk_memory memory;
|
||||
|
||||
llm_buffer() = default;
|
||||
|
||||
void resize(size_t size) {
|
||||
free();
|
||||
|
||||
if (!ggml_vk_has_device()) {
|
||||
this->addr = new uint8_t[size];
|
||||
this->size = size;
|
||||
} else {
|
||||
this->memory = ggml_vk_allocate(size);
|
||||
this->addr = (uint8_t*)memory.data;
|
||||
this->size = size;
|
||||
}
|
||||
}
|
||||
|
||||
void free() {
|
||||
if (!memory.primaryMemory) {
|
||||
delete[] addr;
|
||||
} else if (memory.data) {
|
||||
ggml_vk_free_memory(memory);
|
||||
}
|
||||
this->addr = NULL;
|
||||
this->size = 0;
|
||||
}
|
||||
|
||||
~llm_buffer() {
|
||||
free();
|
||||
}
|
||||
|
||||
// disable copy and move
|
||||
llm_buffer(const llm_buffer&) = delete;
|
||||
llm_buffer(llm_buffer&&) = delete;
|
||||
llm_buffer& operator=(const llm_buffer&) = delete;
|
||||
llm_buffer& operator=(llm_buffer&&) = delete;
|
||||
};
|
||||
#else
|
||||
struct llm_buffer {
|
||||
uint8_t * addr = NULL;
|
||||
size_t size = 0;
|
||||
@ -18,6 +61,7 @@ struct llm_buffer {
|
||||
delete[] addr;
|
||||
}
|
||||
};
|
||||
#endif
|
||||
|
||||
struct llm_kv_cache {
|
||||
struct ggml_tensor * k;
|
||||
|
||||
@ -66,6 +66,7 @@ class GPT4All:
|
||||
model_type: Optional[str] = None,
|
||||
allow_download: bool = True,
|
||||
n_threads: Optional[int] = None,
|
||||
device: Optional[str] = "cpu",
|
||||
):
|
||||
"""
|
||||
Constructor
|
||||
@ -78,11 +79,22 @@ class GPT4All:
|
||||
descriptive identifier for user. Default is None.
|
||||
allow_download: Allow API to download models from gpt4all.io. Default is True.
|
||||
n_threads: number of CPU threads used by GPT4All. Default is None, then the number of threads are determined automatically.
|
||||
device: The processing unit on which the GPT4All model will run. It can be set to:
|
||||
- "cpu": Model will run on the central processing unit.
|
||||
- "gpu": Model will run on the best available graphics processing unit, irrespective of its vendor.
|
||||
- "amd", "nvidia", "intel": Model will run on the best available GPU from the specified vendor.
|
||||
Alternatively, a specific GPU name can also be provided, and the model will run on the GPU that matches the name if it's available.
|
||||
Default is "cpu".
|
||||
|
||||
Note: If a selected GPU device does not have sufficient RAM to accommodate the model, an error will be thrown, and the GPT4All instance will be rendered invalid. It's advised to ensure the device has enough memory before initiating the model.
|
||||
"""
|
||||
self.model_type = model_type
|
||||
self.model = pyllmodel.LLModel()
|
||||
# Retrieve model and download if allowed
|
||||
self.config: ConfigType = self.retrieve_model(model_name, model_path=model_path, allow_download=allow_download)
|
||||
if device is not None:
|
||||
if device != "cpu":
|
||||
self.model.init_gpu(model_path=self.config["path"], device=device)
|
||||
self.model.load_model(self.config["path"])
|
||||
# Set n_threads
|
||||
if n_threads is not None:
|
||||
|
||||
@ -70,6 +70,14 @@ class LLModelPromptContext(ctypes.Structure):
|
||||
("context_erase", ctypes.c_float),
|
||||
]
|
||||
|
||||
class LLModelGPUDevice(ctypes.Structure):
|
||||
_fields_ = [
|
||||
("index", ctypes.c_int32),
|
||||
("type", ctypes.c_int32),
|
||||
("heapSize", ctypes.c_size_t),
|
||||
("name", ctypes.c_char_p),
|
||||
("vendor", ctypes.c_char_p),
|
||||
]
|
||||
|
||||
# Define C function signatures using ctypes
|
||||
llmodel.llmodel_model_create.argtypes = [ctypes.c_char_p]
|
||||
@ -125,6 +133,20 @@ llmodel.llmodel_threadCount.restype = ctypes.c_int32
|
||||
|
||||
llmodel.llmodel_set_implementation_search_path(MODEL_LIB_PATH.encode("utf-8"))
|
||||
|
||||
llmodel.llmodel_available_gpu_devices.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.POINTER(ctypes.c_int32)]
|
||||
llmodel.llmodel_available_gpu_devices.restype = ctypes.POINTER(LLModelGPUDevice)
|
||||
|
||||
llmodel.llmodel_gpu_init_gpu_device_by_string.argtypes = [ctypes.c_void_p, ctypes.c_size_t, ctypes.c_char_p]
|
||||
llmodel.llmodel_gpu_init_gpu_device_by_string.restype = ctypes.c_bool
|
||||
|
||||
llmodel.llmodel_gpu_init_gpu_device_by_struct.argtypes = [ctypes.c_void_p, ctypes.POINTER(LLModelGPUDevice)]
|
||||
llmodel.llmodel_gpu_init_gpu_device_by_struct.restype = ctypes.c_bool
|
||||
|
||||
llmodel.llmodel_gpu_init_gpu_device_by_int.argtypes = [ctypes.c_void_p, ctypes.c_int32]
|
||||
llmodel.llmodel_gpu_init_gpu_device_by_int.restype = ctypes.c_bool
|
||||
|
||||
llmodel.llmodel_has_gpu_device.argtypes = [ctypes.c_void_p]
|
||||
llmodel.llmodel_has_gpu_device.restype = ctypes.c_bool
|
||||
|
||||
ResponseCallbackType = Callable[[int, str], bool]
|
||||
RawResponseCallbackType = Callable[[int, bytes], bool]
|
||||
@ -169,6 +191,60 @@ class LLModel:
|
||||
else:
|
||||
raise ValueError("Unable to instantiate model")
|
||||
|
||||
def list_gpu(self, model_path: str) -> list:
|
||||
"""
|
||||
Lists available GPU devices that satisfy the model's memory requirements.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model_path : str
|
||||
Path to the model.
|
||||
|
||||
Returns
|
||||
-------
|
||||
list
|
||||
A list of LLModelGPUDevice structures representing available GPU devices.
|
||||
"""
|
||||
if self.model is not None:
|
||||
model_path_enc = model_path.encode("utf-8")
|
||||
mem_required = llmodel.llmodel_required_mem(self.model, model_path_enc)
|
||||
else:
|
||||
mem_required = self.memory_needed(model_path)
|
||||
num_devices = ctypes.c_int32(0)
|
||||
devices_ptr = self.llmodel_lib.llmodel_available_gpu_devices(self.model, mem_required, ctypes.byref(num_devices))
|
||||
if not devices_ptr:
|
||||
raise ValueError("Unable to retrieve available GPU devices")
|
||||
devices = [devices_ptr[i] for i in range(num_devices.value)]
|
||||
return devices
|
||||
|
||||
def init_gpu(self, model_path: str, device: str):
|
||||
if self.model is not None:
|
||||
model_path_enc = model_path.encode("utf-8")
|
||||
mem_required = llmodel.llmodel_required_mem(self.model, model_path_enc)
|
||||
else:
|
||||
mem_required = self.memory_needed(model_path)
|
||||
device_enc = device.encode("utf-8")
|
||||
success = self.llmodel_lib.llmodel_gpu_init_gpu_device_by_string(self.model, mem_required, device_enc)
|
||||
if not success:
|
||||
# Retrieve all GPUs without considering memory requirements.
|
||||
num_devices = ctypes.c_int32(0)
|
||||
all_devices_ptr = self.llmodel_lib.llmodel_available_gpu_devices(self.model, 0, ctypes.byref(num_devices))
|
||||
if not all_devices_ptr:
|
||||
raise ValueError("Unable to retrieve list of all GPU devices")
|
||||
all_gpus = [all_devices_ptr[i].name.decode('utf-8') for i in range(num_devices.value)]
|
||||
|
||||
# Retrieve GPUs that meet the memory requirements using list_gpu
|
||||
available_gpus = [device.name.decode('utf-8') for device in self.list_gpu(model_path)]
|
||||
|
||||
# Identify GPUs that are unavailable due to insufficient memory or features
|
||||
unavailable_gpus = set(all_gpus) - set(available_gpus)
|
||||
|
||||
# Formulate the error message
|
||||
error_msg = "Unable to initialize model on GPU: '{}'.".format(device)
|
||||
error_msg += "\nAvailable GPUs: {}.".format(available_gpus)
|
||||
error_msg += "\nUnavailable GPUs due to insufficient memory or features: {}.".format(unavailable_gpus)
|
||||
raise ValueError(error_msg)
|
||||
|
||||
def load_model(self, model_path: str) -> bool:
|
||||
"""
|
||||
Load model from a file.
|
||||
|
||||
@ -401,7 +401,7 @@ bool ChatLLM::handlePrompt(int32_t token)
|
||||
#endif
|
||||
++m_promptTokens;
|
||||
++m_promptResponseTokens;
|
||||
m_timer->inc();
|
||||
m_timer->start();
|
||||
return !m_stopGenerating;
|
||||
}
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user