build : on Mac OS enable Metal by default (#2901)
* build : on Mac OS enable Metal by default * make : try to fix build on Linux * make : move targets back to the top * make : fix target clean * llama : enable GPU inference by default with Metal * llama : fix vocab_only logic when GPU is enabled * common : better `n_gpu_layers` assignment * readme : update Metal instructions * make : fix merge conflict remnants * gitignore : metal
This commit is contained in:
Georgi Gerganov
GitHub
parent
bd33e5ab92
commit
e36ecdccc8
29
.gitignore
vendored
29
.gitignore
vendored
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@ -31,28 +31,29 @@ tmp/
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models/*
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models-mnt
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/main
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/quantize
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/quantize-stats
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/result
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/perplexity
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/embedding
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/train-text-from-scratch
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/convert-llama2c-to-ggml
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/simple
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/benchmark-matmult
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/vdot
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/server
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/Pipfile
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/baby-llama
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/beam-search
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/benchmark-matmult
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/convert-llama2c-to-ggml
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/embd-input-test
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/embedding
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/gguf
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/gguf-llama-simple
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/libllama.so
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/llama-bench
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/baby-llama
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/beam-search
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/main
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/metal
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/perplexity
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/quantize
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/quantize-stats
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/result
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/save-load-state
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/server
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/simple
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/speculative
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/train-text-from-scratch
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/vdot
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build-info.h
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arm_neon.h
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compile_commands.json
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|
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@ -36,6 +36,12 @@ endif()
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# Option list
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#
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if (APPLE)
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set(LLAMA_METAL_DEFAULT ON)
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else()
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set(LLAMA_METAL_DEFAULT OFF)
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endif()
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# general
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option(LLAMA_STATIC "llama: static link libraries" OFF)
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option(LLAMA_NATIVE "llama: enable -march=native flag" OFF)
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@ -76,7 +82,7 @@ option(LLAMA_CUDA_F16 "llama: use 16 bit floats for some
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set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K")
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option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF)
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option(LLAMA_CLBLAST "llama: use CLBlast" OFF)
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option(LLAMA_METAL "llama: use Metal" OFF)
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option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT})
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option(LLAMA_MPI "llama: use MPI" OFF)
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option(LLAMA_K_QUANTS "llama: use k-quants" ON)
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option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF)
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@ -158,6 +164,31 @@ if (APPLE AND LLAMA_ACCELERATE)
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endif()
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endif()
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if (LLAMA_METAL)
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find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
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find_library(METAL_FRAMEWORK Metal REQUIRED)
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find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
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message(STATUS "Metal framework found")
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set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
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add_compile_definitions(GGML_USE_METAL)
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#add_compile_definitions(GGML_METAL_NDEBUG)
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# get full path to the file
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#add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
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# copy ggml-metal.metal to bin directory
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configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
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set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
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${FOUNDATION_LIBRARY}
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${METAL_FRAMEWORK}
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${METALKIT_FRAMEWORK}
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)
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endif()
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if (LLAMA_BLAS)
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if (LLAMA_STATIC)
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set(BLA_STATIC ON)
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@ -293,29 +324,6 @@ if (LLAMA_CUBLAS)
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endif()
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endif()
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if (LLAMA_METAL)
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find_library(FOUNDATION_LIBRARY Foundation REQUIRED)
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find_library(METAL_FRAMEWORK Metal REQUIRED)
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find_library(METALKIT_FRAMEWORK MetalKit REQUIRED)
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set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h)
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add_compile_definitions(GGML_USE_METAL)
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#add_compile_definitions(GGML_METAL_NDEBUG)
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# get full path to the file
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#add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/")
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# copy ggml-metal.metal to bin directory
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configure_file(ggml-metal.metal bin/ggml-metal.metal COPYONLY)
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set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS}
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${FOUNDATION_LIBRARY}
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${METAL_FRAMEWORK}
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${METALKIT_FRAMEWORK}
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)
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endif()
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if (LLAMA_MPI)
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cmake_minimum_required(VERSION 3.10)
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find_package(MPI)
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|
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76
Makefile
76
Makefile
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@ -7,6 +7,39 @@ TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-dou
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# Code coverage output files
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COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report
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ifndef UNAME_S
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UNAME_S := $(shell uname -s)
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endif
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ifndef UNAME_P
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UNAME_P := $(shell uname -p)
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endif
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ifndef UNAME_M
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UNAME_M := $(shell uname -m)
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endif
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# Mac OS + Arm can report x86_64
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# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
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ifeq ($(UNAME_S),Darwin)
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ifndef LLAMA_NO_METAL
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LLAMA_METAL := 1
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endif
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ifneq ($(UNAME_P),arm)
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SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null)
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ifeq ($(SYSCTL_M),1)
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# UNAME_P := arm
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# UNAME_M := arm64
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warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789)
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endif
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endif
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endif
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ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))'
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BUILD_TARGETS += metal
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endif
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default: $(BUILD_TARGETS)
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test:
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@ -38,18 +71,6 @@ gcovr-report: coverage ## Generate gcovr report
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mkdir -p gcovr-report
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gcovr --root . --html --html-details --output gcovr-report/coverage.html
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ifndef UNAME_S
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UNAME_S := $(shell uname -s)
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endif
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ifndef UNAME_P
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UNAME_P := $(shell uname -p)
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endif
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ifndef UNAME_M
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UNAME_M := $(shell uname -m)
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endif
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ifdef RISCV_CROSS_COMPILE
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CC := riscv64-unknown-linux-gnu-gcc
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CXX := riscv64-unknown-linux-gnu-g++
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@ -58,19 +79,6 @@ endif
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CCV := $(shell $(CC) --version | head -n 1)
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CXXV := $(shell $(CXX) --version | head -n 1)
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# Mac OS + Arm can report x86_64
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# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
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ifeq ($(UNAME_S),Darwin)
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ifneq ($(UNAME_P),arm)
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SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null)
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ifeq ($(SYSCTL_M),1)
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# UNAME_P := arm
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# UNAME_M := arm64
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warn := $(warning Your arch is announced as x86_64, but it seems to actually be ARM64. Not fixing that can lead to bad performance. For more info see: https://github.com/ggerganov/whisper.cpp/issues/66\#issuecomment-1282546789)
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endif
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endif
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endif
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#
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# Compile flags
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#
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@ -231,14 +239,24 @@ endif
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endif
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ifndef LLAMA_NO_ACCELERATE
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# Mac M1 - include Accelerate framework.
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# `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time).
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# Mac OS - include Accelerate framework.
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# `-framework Accelerate` works both with Apple Silicon and Mac Intel
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ifeq ($(UNAME_S),Darwin)
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MK_CPPFLAGS += -DGGML_USE_ACCELERATE
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MK_LDFLAGS += -framework Accelerate
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endif
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endif # LLAMA_NO_ACCELERATE
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ifdef LLAMA_METAL
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# By default - use GPU acceleration on Mac OS
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ifeq ($(UNAME_S),Darwin)
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CFLAGS += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG
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CXXFLAGS += -DGGML_USE_METAL
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LDFLAGS += -framework Foundation -framework Metal -framework MetalKit
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OBJS += ggml-metal.o
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endif
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endif # LLAMA_METAL
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ifdef LLAMA_MPI
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MK_CPPFLAGS += -DGGML_USE_MPI
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MK_CFLAGS += -Wno-cast-qual
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@ -480,10 +498,6 @@ beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o co
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speculative: examples/speculative/speculative.cpp build-info.h ggml.o llama.o common.o $(OBJS)
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$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
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ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))'
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BUILD_TARGETS += metal
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endif
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ifdef LLAMA_METAL
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metal: examples/metal/metal.cpp ggml.o $(OBJS)
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$(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS)
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|
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26
README.md
26
README.md
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@ -280,29 +280,11 @@ In order to build llama.cpp you have three different options.
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### Metal Build
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Using Metal allows the computation to be executed on the GPU for Apple devices:
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On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU.
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To disable the Metal build at compile time use the `LLAMA_NO_METAL=1` flag or the `LLAMA_METAL=OFF` cmake option.
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- Using `make`:
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```bash
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LLAMA_METAL=1 make
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```
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- Using `CMake`:
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```bash
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mkdir build-metal
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cd build-metal
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cmake -DLLAMA_METAL=ON ..
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cmake --build . --config Release
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```
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When built with Metal support, you can enable GPU inference with the `--gpu-layers|-ngl` command-line argument.
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Any value larger than 0 will offload the computation to the GPU. For example:
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```bash
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./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 -ngl 1
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```
|
||||
When built with Metal support, you can explicitly disable GPU inference with the `--gpu-layers|-ngl 0` command-line
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argument.
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|
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### MPI Build
|
||||
|
||||
|
|
|
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|
|
@ -717,7 +717,9 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param
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|||
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lparams.n_ctx = params.n_ctx;
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lparams.n_batch = params.n_batch;
|
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lparams.n_gpu_layers = params.n_gpu_layers;
|
||||
if (params.n_gpu_layers != -1) {
|
||||
lparams.n_gpu_layers = params.n_gpu_layers;
|
||||
}
|
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lparams.main_gpu = params.main_gpu;
|
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lparams.tensor_split = params.tensor_split;
|
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lparams.low_vram = params.low_vram;
|
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|
|
@ -1212,7 +1214,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l
|
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fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str());
|
||||
fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false");
|
||||
fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false");
|
||||
fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers);
|
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fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers);
|
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fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict);
|
||||
fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs);
|
||||
fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false");
|
||||
|
|
|
|||
|
|
@ -34,7 +34,7 @@ struct gpt_params {
|
|||
int32_t n_keep = 0; // number of tokens to keep from initial prompt
|
||||
int32_t n_draft = 16; // number of tokens to draft during speculative decoding
|
||||
int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited)
|
||||
int32_t n_gpu_layers = 0; // number of layers to store in VRAM
|
||||
int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default)
|
||||
int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors
|
||||
float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs
|
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int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens.
|
||||
|
|
|
|||
|
|
@ -151,14 +151,6 @@ int main(int argc, char ** argv) {
|
|||
LOG_TEE("%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale);
|
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}
|
||||
|
||||
if (params.n_ctx > 2048) {
|
||||
// TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048
|
||||
LOG_TEE("%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx);
|
||||
} else if (params.n_ctx < 8) {
|
||||
LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
|
||||
params.n_ctx = 8;
|
||||
}
|
||||
|
||||
LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
|
||||
|
||||
if (params.seed == LLAMA_DEFAULT_SEED) {
|
||||
|
|
@ -194,6 +186,13 @@ int main(int argc, char ** argv) {
|
|||
return 1;
|
||||
}
|
||||
|
||||
if (params.n_ctx > llama_n_ctx(ctx)) {
|
||||
LOG_TEE("%s: warning: base model only supports context sizes no greater than %d tokens (%d specified)\n", __func__, llama_n_ctx(ctx), params.n_ctx);
|
||||
} else if (params.n_ctx < 8) {
|
||||
LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__);
|
||||
params.n_ctx = 8;
|
||||
}
|
||||
|
||||
// print system information
|
||||
{
|
||||
LOG_TEE("\n");
|
||||
|
|
|
|||
|
|
@ -368,7 +368,7 @@ results_perplexity perplexity(llama_context * ctx, const gpt_params & params) {
|
|||
// Example, we have a context window of 512, we will compute perplexity for each of the
|
||||
// last 256 tokens. Then, we split the input up into context window size chunks to
|
||||
// process the entire prompt.
|
||||
const int first = std::min(512, params.n_ctx/2);
|
||||
const int first = params.n_ctx/2;
|
||||
process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first,
|
||||
workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
|
||||
count += params.n_ctx - first - 1;
|
||||
|
|
@ -668,11 +668,6 @@ int main(int argc, char ** argv) {
|
|||
params.n_ctx += params.ppl_stride/2;
|
||||
}
|
||||
|
||||
if (params.n_ctx > 2048) {
|
||||
fprintf(stderr, "%s: warning: model might not support context sizes greater than 2048 tokens (%d specified);"
|
||||
"expect poor results\n", __func__, params.n_ctx);
|
||||
}
|
||||
|
||||
fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT);
|
||||
|
||||
if (params.seed == LLAMA_DEFAULT_SEED) {
|
||||
|
|
@ -698,6 +693,11 @@ int main(int argc, char ** argv) {
|
|||
return 1;
|
||||
}
|
||||
|
||||
if (params.n_ctx > llama_n_ctx(ctx)) {
|
||||
fprintf(stderr, "%s: warning: model might not support context sizes greater than %d tokens (%d specified);"
|
||||
"expect poor results\n", __func__, llama_n_ctx(ctx), params.n_ctx);
|
||||
}
|
||||
|
||||
// print system information
|
||||
{
|
||||
fprintf(stderr, "\n");
|
||||
|
|
|
|||
54
llama.cpp
54
llama.cpp
|
|
@ -5340,7 +5340,7 @@ struct llama_context_params llama_context_default_params() {
|
|||
/*.seed =*/ LLAMA_DEFAULT_SEED,
|
||||
/*.n_ctx =*/ 512,
|
||||
/*.n_batch =*/ 512,
|
||||
/*.gpu_layers =*/ 0,
|
||||
/*.n_gpu_layers =*/ 0,
|
||||
/*.main_gpu =*/ 0,
|
||||
/*.tensor_split =*/ nullptr,
|
||||
/*.rope_freq_base =*/ 10000.0f,
|
||||
|
|
@ -5357,6 +5357,10 @@ struct llama_context_params llama_context_default_params() {
|
|||
/*.embedding =*/ false,
|
||||
};
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
result.n_gpu_layers = 1;
|
||||
#endif
|
||||
|
||||
return result;
|
||||
}
|
||||
|
||||
|
|
@ -5549,43 +5553,43 @@ struct llama_context * llama_new_context_with_model(
|
|||
}
|
||||
#endif
|
||||
}
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_METAL
|
||||
if (params.n_gpu_layers > 0) {
|
||||
// this allocates all Metal resources and memory buffers
|
||||
if (params.n_gpu_layers > 0) {
|
||||
// this allocates all Metal resources and memory buffers
|
||||
|
||||
void * data_ptr = NULL;
|
||||
size_t data_size = 0;
|
||||
void * data_ptr = NULL;
|
||||
size_t data_size = 0;
|
||||
|
||||
if (params.use_mmap) {
|
||||
data_ptr = ctx->model.mapping->addr;
|
||||
data_size = ctx->model.mapping->size;
|
||||
} else {
|
||||
data_ptr = ggml_get_mem_buffer(ctx->model.ctx);
|
||||
data_size = ggml_get_mem_size (ctx->model.ctx);
|
||||
}
|
||||
if (params.use_mmap) {
|
||||
data_ptr = ctx->model.mapping->addr;
|
||||
data_size = ctx->model.mapping->size;
|
||||
} else {
|
||||
data_ptr = ggml_get_mem_buffer(ctx->model.ctx);
|
||||
data_size = ggml_get_mem_size (ctx->model.ctx);
|
||||
}
|
||||
|
||||
const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx);
|
||||
const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx);
|
||||
|
||||
LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);
|
||||
LLAMA_LOG_INFO("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0);
|
||||
|
||||
#define LLAMA_METAL_CHECK_BUF(result) \
|
||||
if (!(result)) { \
|
||||
LLAMA_LOG_ERROR("%s: failed to add buffer\n", __func__); \
|
||||
llama_free(ctx); \
|
||||
return NULL; \
|
||||
}
|
||||
if (!(result)) { \
|
||||
LLAMA_LOG_ERROR("%s: failed to add buffer\n", __func__); \
|
||||
llama_free(ctx); \
|
||||
return NULL; \
|
||||
}
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size));
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.data, ctx->buf_compute.size, 0));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.data, ctx->kv_self.buf.size, 0));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.data, ctx->buf_compute.size, 0));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.data, ctx->kv_self.buf.size, 0));
|
||||
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "alloc", ctx->buf_alloc.data, ctx->buf_alloc.size, 0));
|
||||
LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "alloc", ctx->buf_alloc.data, ctx->buf_alloc.size, 0));
|
||||
#undef LLAMA_METAL_CHECK_BUF
|
||||
}
|
||||
}
|
||||
#endif
|
||||
}
|
||||
|
||||
#ifdef GGML_USE_MPI
|
||||
ctx->ctx_mpi = ggml_mpi_init();
|
||||
|
|
|
|||
Loading…
Reference in a new issue