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Matvey Soloviev 2023-03-21 17:45:01 +01:00 • committed by GitHub
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4
.devops/full.Dockerfile
View file

@ -6,7 +6,7 @@ RUN apt-get update && \
apt-get install -y build-essential python3 python3-pip
RUN pip install --upgrade pip setuptools wheel \
&& pip install torch torchvision torchaudio sentencepiece numpy
&& pip install numpy requests sentencepiece torch tqdm
WORKDIR /app
@ -14,4 +14,4 @@ COPY . .
RUN make
ENTRYPOINT ["/app/.devops/tools.sh"]
ENTRYPOINT ["/app/.devops/tools.sh"]

3
.github/workflows/build.yml vendored
View file

@ -54,6 +54,7 @@ jobs:
cd build
cmake ..
cmake --build . --config Release
ctest --output-on-failure
macOS-latest-make:
runs-on: macos-latest
@ -90,6 +91,7 @@ jobs:
cd build
cmake ..
cmake --build . --config Release
ctest --output-on-failure
windows-latest-cmake:
runs-on: windows-latest
@ -106,6 +108,7 @@ jobs:
cd build
cmake ..
cmake --build . --config Release
ctest --output-on-failure
- name: Get commit hash
id: commit

2
.github/workflows/docker.yml vendored
View file

@ -40,7 +40,7 @@ jobs:
uses: docker/login-action@v2
with:
registry: ghcr.io
username: ${{ github.actor }}
username: ${{ github.repository_owner }}
password: ${{ secrets.GITHUB_TOKEN }}
- name: Build and push Docker image (versioned)

287
CMakeLists.txt
View file

@ -1,131 +1,252 @@
cmake_minimum_required(VERSION 3.8)
project("llama.cpp")
cmake_minimum_required(VERSION 3.12) # Don't bump this version for no reason
project("llama.cpp" C CXX)
set(CMAKE_CXX_STANDARD 20)
set(CMAKE_CXX_STANDARD_REQUIRED true)
set(CMAKE_C_STANDARD 11)
set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads REQUIRED)
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()
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)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin)
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)
if(CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR)
set(LLAMA_STANDALONE ON)
if (APPLE)
option(LLAMA_NO_ACCELERATE "llama: disable Accelerate framework" OFF)
option(LLAMA_NO_AVX "llama: disable AVX" OFF)
option(LLAMA_NO_AVX2 "llama: disable AVX2" OFF)
option(LLAMA_NO_FMA "llama: disable FMA" OFF)
# configure project version
# TODO
else()
set(LLAMA_STANDALONE OFF)
endif()
if (EMSCRIPTEN)
set(BUILD_SHARED_LIBS_DEFAULT OFF)
option(LLAMA_WASM_SINGLE_FILE "llama: embed WASM inside the generated llama.js" ON)
else()
if (MINGW)
set(BUILD_SHARED_LIBS_DEFAULT OFF)
else()
set(BUILD_SHARED_LIBS_DEFAULT ON)
endif()
endif()
#
# Option list
#
# general
option(LLAMA_STATIC "llama: static link libraries" OFF)
option(LLAMA_NATIVE "llama: enable -march=native flag" OFF)
option(LLAMA_LTO "llama: enable link time optimization" OFF)
# 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)
option(LLAMA_GPROF "llama: enable gprof" 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)
# instruction set specific
option(LLAMA_AVX "llama: enable AVX" ON)
option(LLAMA_AVX2 "llama: enable AVX2" ON)
option(LLAMA_FMA "llama: enable FMA" ON)
# 3rd party libs
option(LLAMA_ACCELERATE "llama: enable Accelerate framework" ON)
option(LLAMA_OPENBLAS "llama: use OpenBLAS" OFF)
option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE})
option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE})
#
# Compile flags
#
set(CMAKE_CXX_STANDARD_REQUIRED true)
set(CMAKE_C_STANDARD_REQUIRED true)
set(THREADS_PREFER_PTHREAD_FLAG ON)
find_package(Threads REQUIRED)
if (NOT MSVC)
if (LLAMA_SANITIZE_THREAD)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fsanitize=thread")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=thread")
add_compile_options(-fsanitize=thread)
endif()
if (LLAMA_SANITIZE_ADDRESS)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fsanitize=address -fno-omit-frame-pointer")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=address -fno-omit-frame-pointer")
add_compile_options(-fsanitize=address -fno-omit-frame-pointer)
endif()
if (LLAMA_SANITIZE_UNDEFINED)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -fsanitize=undefined")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsanitize=undefined")
add_compile_options(-fsanitize=undefined)
endif()
endif()
if (APPLE AND NOT LLAMA_NO_ACCELERATE)
if (APPLE AND LLAMA_ACCELERATE)
find_library(ACCELERATE_FRAMEWORK Accelerate)
if (ACCELERATE_FRAMEWORK)
message(STATUS "Accelerate framework found")
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK})
set(LLAMA_EXTRA_FLAGS ${LLAMA_EXTRA_FLAGS} -DGGML_USE_ACCELERATE)
add_compile_definitions(GGML_USE_ACCELERATE)
set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${ACCELERATE_FRAMEWORK})
else()
message(WARNING "Accelerate framework not found")
endif()
endif()
if (LLAMA_OPENBLAS)
if (LLAMA_STATIC)
set(BLA_STATIC ON)
endif()
set(BLA_VENDOR OpenBLAS)
find_package(BLAS)
if (BLAS_FOUND)
message(STATUS "OpenBLAS found")
add_compile_definitions(GGML_USE_OPENBLAS)
add_link_options(${BLAS_LIBRARIES})
else()
message(WARNING "OpenBLAS not found")
endif()
endif()
if (LLAMA_ALL_WARNINGS)
if (NOT MSVC)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} \
-Wall \
-Wextra \
-Wpedantic \
-Wshadow \
-Wcast-qual \
-Wstrict-prototypes \
-Wpointer-arith \
-Wno-unused-function \
")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} \
-Wall \
-Wextra \
-Wpedantic \
-Wcast-qual \
")
set(c_flags
-Wall
-Wextra
-Wpedantic
-Wshadow
-Wcast-qual
-Wstrict-prototypes
-Wpointer-arith
-Wno-unused-function
)
set(cxx_flags
-Wall
-Wextra
-Wpedantic
-Wcast-qual
)
else()
# todo : msvc
endif()
add_compile_options(
"$<$<COMPILE_LANGUAGE:C>:${c_flags}>"
"$<$<COMPILE_LANGUAGE:CXX>:${cxx_flags}>"
)
endif()
message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
message(STATUS "ARM detected")
else()
message(STATUS "x86 detected")
if (MSVC)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} /arch:AVX2")
set(CMAKE_CXX_FLAGS_RELEASE "${CMAKE_CXX_FLAGS_RELEASE} /arch:AVX2")
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} /arch:AVX2")
if (LLAMA_LTO)
include(CheckIPOSupported)
check_ipo_supported(RESULT result OUTPUT output)
if (result)
set(CMAKE_INTERPROCEDURAL_OPTIMIZATION TRUE)
else()
if(NOT LLAMA_NO_AVX)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx")
endif()
if(NOT LLAMA_NO_AVX2)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mavx2")
endif()
if(NOT LLAMA_NO_FMA)
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mfma")
endif()
set(CMAKE_C_FLAGS "${CMAKE_C_FLAGS} -mf16c")
message(WARNING "IPO is not supported: ${output}")
endif()
endif()
# if (LLAMA_PERF)
# set(LLAMA_EXTRA_FLAGS ${LLAMA_EXTRA_FLAGS} -DGGML_PERF)
# endif()
# Architecture specific
# TODO: probably these flags need to be tweaked on some architectures
# feel free to update the Makefile for your architecture and send a pull request or issue
message(STATUS "CMAKE_SYSTEM_PROCESSOR: ${CMAKE_SYSTEM_PROCESSOR}")
if (NOT MSVC)
if (LLAMA_STATIC)
add_link_options(-static)
if (MINGW)
add_link_options(-static-libgcc -static-libstdc++)
endif()
endif()
if (LLAMA_GPROF)
add_compile_options(-pg)
endif()
if (LLAMA_NATIVE)
add_compile_options(-march=native)
endif()
endif()
add_executable(llama
main.cpp
utils.cpp
utils.h)
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
message(STATUS "ARM detected")
if (MSVC)
# TODO: arm msvc?
else()
if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64")
add_compile_options(-mcpu=native)
endif()
# TODO: armv6,7,8 version specific flags
endif()
elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$")
message(STATUS "x86 detected")
if (MSVC)
if (LLAMA_AVX2)
add_compile_options(/arch:AVX2)
elseif (LLAMA_AVX)
add_compile_options(/arch:AVX)
endif()
else()
add_compile_options(-mf16c)
if (LLAMA_FMA)
add_compile_options(-mfma)
endif()
if (LLAMA_AVX)
add_compile_options(-mavx)
endif()
if (LLAMA_AVX2)
add_compile_options(-mavx2)
endif()
endif()
else()
# TODO: support PowerPC
message(STATUS "Unknown architecture")
endif()
add_executable(quantize
quantize.cpp
utils.cpp
utils.h)
add_library(ggml
ggml.c
ggml.h)
#
# Build library
#
target_compile_definitions(ggml PUBLIC ${LLAMA_EXTRA_FLAGS})
target_compile_definitions(llama PUBLIC ${LLAMA_EXTRA_FLAGS})
target_compile_definitions(quantize PUBLIC ${LLAMA_EXTRA_FLAGS})
add_executable(llama main.cpp)
add_executable(quantize quantize.cpp)
add_library(utils OBJECT
utils.cpp
utils.h)
target_include_directories(utils PUBLIC .)
target_compile_features(utils PUBLIC cxx_std_11) # don't bump
add_library(ggml OBJECT
ggml.c
ggml.h)
target_link_libraries(ggml PRIVATE ${LLAMA_EXTRA_LIBS})
target_include_directories(ggml PUBLIC .)
target_link_libraries(quantize PRIVATE ggml)
target_link_libraries(llama PRIVATE ggml)
target_link_libraries(ggml PRIVATE Threads::Threads)
target_compile_features(ggml PUBLIC c_std_11) # don't bump
#
# Linking
#
target_link_libraries(ggml PRIVATE Threads::Threads ${LLAMA_EXTRA_LIBS})
target_link_libraries(llama PRIVATE ggml utils)
target_link_libraries(quantize PRIVATE ggml utils)
#
# programs, examples and tests
#
if (LLAMA_BUILD_TESTS AND NOT CMAKE_JS_VERSION)
enable_testing()
add_subdirectory(tests)
endif ()
#if (LLAMA_BUILD_EXAMPLES)
# add_subdirectory(examples)
#endif()

63
Makefile
View file

@ -17,7 +17,7 @@ CXXV := $(shell $(CXX) --version | head -n 1)
# ref: https://github.com/ggerganov/whisper.cpp/issues/66#issuecomment-1282546789
ifeq ($(UNAME_S),Darwin)
ifneq ($(UNAME_P),arm)
SYSCTL_M := $(shell sysctl -n hw.optional.arm64)
SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null)
ifeq ($(SYSCTL_M),1)
# UNAME_P := arm
# UNAME_M := arm64
@ -30,6 +30,7 @@ endif
# Compile flags
#
# keep standard at C11 and C++11
CFLAGS = -I. -O3 -DNDEBUG -std=c11 -fPIC
CXXFLAGS = -I. -I./examples -O3 -DNDEBUG -std=c++11 -fPIC
LDFLAGS =
@ -52,6 +53,10 @@ ifeq ($(UNAME_S),NetBSD)
CFLAGS += -pthread
CXXFLAGS += -pthread
endif
ifeq ($(UNAME_S),OpenBSD)
CFLAGS += -pthread
CXXFLAGS += -pthread
endif
ifeq ($(UNAME_S),Haiku)
CFLAGS += -pthread
CXXFLAGS += -pthread
@ -95,30 +100,59 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686))
ifneq (,$(findstring sse3,$(SSE3_M)))
CFLAGS += -msse3
endif
AVX512F_M := $(shell grep "avx512f " /proc/cpuinfo)
ifneq (,$(findstring avx512f,$(AVX512F_M)))
CFLAGS += -mavx512f
endif
AVX512BW_M := $(shell grep "avx512bw " /proc/cpuinfo)
ifneq (,$(findstring avx512bw,$(AVX512BW_M)))
CFLAGS += -mavx512bw
endif
AVX512DQ_M := $(shell grep "avx512dq " /proc/cpuinfo)
ifneq (,$(findstring avx512dq,$(AVX512DQ_M)))
CFLAGS += -mavx512dq
endif
AVX512VL_M := $(shell grep "avx512vl " /proc/cpuinfo)
ifneq (,$(findstring avx512vl,$(AVX512VL_M)))
CFLAGS += -mavx512vl
endif
AVX512CD_M := $(shell grep "avx512cd " /proc/cpuinfo)
ifneq (,$(findstring avx512cd,$(AVX512CD_M)))
CFLAGS += -mavx512cd
endif
AVX512ER_M := $(shell grep "avx512er " /proc/cpuinfo)
ifneq (,$(findstring avx512er,$(AVX512ER_M)))
CFLAGS += -mavx512er
endif
AVX512IFMA_M := $(shell grep "avx512ifma " /proc/cpuinfo)
ifneq (,$(findstring avx512ifma,$(AVX512IFMA_M)))
CFLAGS += -mavx512ifma
endif
AVX512PF_M := $(shell grep "avx512pf " /proc/cpuinfo)
ifneq (,$(findstring avx512pf,$(AVX512PF_M)))
CFLAGS += -mavx512pf
endif
else ifeq ($(UNAME_S),Haiku)
AVX1_M := $(shell sysinfo -cpu | grep "AVX ")
ifneq (,$(findstring avx,$(AVX1_M)))
AVX1_M := $(shell sysinfo -cpu | grep -w "AVX")
ifneq (,$(findstring AVX,$(AVX1_M)))
CFLAGS += -mavx
endif
AVX2_M := $(shell sysinfo -cpu | grep "AVX2 ")
ifneq (,$(findstring avx2,$(AVX2_M)))
AVX2_M := $(shell sysinfo -cpu | grep -w "AVX2")
ifneq (,$(findstring AVX2,$(AVX2_M)))
CFLAGS += -mavx2
endif
FMA_M := $(shell sysinfo -cpu | grep "FMA ")
ifneq (,$(findstring fma,$(FMA_M)))
FMA_M := $(shell sysinfo -cpu | grep -w "FMA")
ifneq (,$(findstring FMA,$(FMA_M)))
CFLAGS += -mfma
endif
F16C_M := $(shell sysinfo -cpu | grep "F16C ")
ifneq (,$(findstring f16c,$(F16C_M)))
F16C_M := $(shell sysinfo -cpu | grep -w "F16C")
ifneq (,$(findstring F16C,$(F16C_M)))
CFLAGS += -mf16c
endif
else
CFLAGS += -mfma -mf16c -mavx -mavx2
endif
endif
ifeq ($(UNAME_M),amd64)
CFLAGS += -mavx -mavx2 -mfma -mf16c
endif
ifneq ($(filter ppc64%,$(UNAME_M)),)
POWER9_M := $(shell grep "POWER9" /proc/cpuinfo)
ifneq (,$(findstring POWER9,$(POWER9_M)))
@ -130,7 +164,8 @@ ifneq ($(filter ppc64%,$(UNAME_M)),)
endif
endif
ifndef LLAMA_NO_ACCELERATE
# Mac M1 - include Accelerate framework
# Mac M1 - include Accelerate framework.
# `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time).
ifeq ($(UNAME_S),Darwin)
CFLAGS += -DGGML_USE_ACCELERATE
LDFLAGS += -framework Accelerate
@ -193,7 +228,7 @@ clean:
main: main.cpp ggml.o utils.o
$(CXX) $(CXXFLAGS) main.cpp ggml.o utils.o -o main $(LDFLAGS)
./main -h
@echo "\x1b[36mrun ./main -h for help\x1b[0m"
quantize: quantize.cpp ggml.o utils.o
$(CXX) $(CXXFLAGS) quantize.cpp ggml.o utils.o -o quantize $(LDFLAGS)

21
README.md
View file

@ -11,6 +11,9 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
- Cache input prompts for faster initialization: https://github.com/ggerganov/llama.cpp/issues/64
- Create a `llama.cpp` logo: https://github.com/ggerganov/llama.cpp/issues/105
**TEMPORARY NOTICE:**
If you're updating to the latest master, you will need to regenerate your model files as the format has changed.
## Description
The main goal is to run the model using 4-bit quantization on a MacBook
@ -175,10 +178,15 @@ If you want a more ChatGPT-like experience, you can run in interactive mode by p
In this mode, you can always interrupt generation by pressing Ctrl+C and enter one or more lines of text which will be converted into tokens and appended to the current context. You can also specify a *reverse prompt* with the parameter `-r "reverse prompt string"`. This will result in user input being prompted whenever the exact tokens of the reverse prompt string are encountered in the generation. A typical use is to use a prompt which makes LLaMa emulate a chat between multiple users, say Alice and Bob, and pass `-r "Alice:"`.
Here is an example few-shot interaction, invoked with the command
```
./main -m ./models/13B/ggml-model-q4_0.bin -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
```bash
# default arguments using 7B model
./chat.sh
# custom arguments using 13B model
./main -m ./models/13B/ggml-model-q4_0.bin -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt
```
Note the use of `--color` to distinguish between user input and generated text.
![image](https://user-images.githubusercontent.com/1991296/224575029-2af3c7dc-5a65-4f64-a6bb-517a532aea38.png)
@ -189,11 +197,10 @@ First, download the `ggml` Alpaca model into the `./models` folder:
```
# use one of these
# NOTE: these are copied from the alpaca.cpp repo - not sure how long these will work
# TODO: add a script to simplify the download
curl -o ggml-alpaca-7b-q4.bin -C - https://gateway.estuary.tech/gw/ipfs/QmQ1bf2BTnYxq73MFJWu1B7bQ2UD6qG7D7YDCxhTndVkPC
curl -o ggml-alpaca-7b-q4.bin -C - https://ipfs.io/ipfs/QmQ1bf2BTnYxq73MFJWu1B7bQ2UD6qG7D7YDCxhTndVkPC
curl -o ggml-alpaca-7b-q4.bin -C - https://cloudflare-ipfs.com/ipfs/QmQ1bf2BTnYxq73MFJWu1B7bQ2UD6qG7D7YDCxhTndVkPC
curl -o ./models/ggml-alpaca-7b-q4.bin -C - https://gateway.estuary.tech/gw/ipfs/QmUp1UGeQFDqJKvtjbSYPBiZZKRjLp8shVP9hT8ZB9Ynv1
curl -o ./models/ggml-alpaca-7b-q4.bin -C - https://ipfs.io/ipfs/QmUp1UGeQFDqJKvtjbSYPBiZZKRjLp8shVP9hT8ZB9Ynv1
curl -o ./models/ggml-alpaca-7b-q4.bin -C - https://cloudflare-ipfs.com/ipfs/QmUp1UGeQFDqJKvtjbSYPBiZZKRjLp8shVP9hT8ZB9Ynv1
```
Now run the `main` tool like this:
@ -216,7 +223,7 @@ Sample run:
There 26 letters in the English Alphabet
> What is the most common way of transportation in Amsterdam?
The majority (54%) are using public transit. This includes buses, trams and metros with over 100 lines throughout the city which make it very accessible for tourists to navigate around town as well as locals who commute by tram or metro on a daily basis
> List 5 words that start with "ca".
> List 5 words that start with "ca".
cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
>
```

2
alpaca.sh
View file

@ -3,4 +3,4 @@
# Temporary script - will be removed in the future
#
./main -m ./models/ggml-alpaca-7b-q4.bin --color -f ./prompts/alpaca.txt -ins --top_k 10000 --temp 0.96 --repeat_penalty 1 -t 7
./main -m ./models/ggml-alpaca-7b-q4.bin --color -f ./prompts/alpaca.txt -ins --top_k 10000 --temp 0.2 --repeat_penalty 1 -t 7

6
chat.sh Executable file
View file

@ -0,0 +1,6 @@
#!/bin/bash
#
# Temporary script - will be removed in the future
#
./main -m ./models/7B/ggml-model-q4_0.bin -n 256 --repeat_penalty 1.0 --color -i -r "User:" -f prompts/chat-with-bob.txt

172
convert-gptq-to-ggml.py Normal file
View file

@ -0,0 +1,172 @@
# Convert a GPTQ quantized LLaMA model to a ggml compatible file
# Based on: https://github.com/qwopqwop200/GPTQ-for-LLaMa
#
import os
import re
import sys
import json
import struct
import numpy as np
import torch
from sentencepiece import SentencePieceProcessor
if len(sys.argv) != 4:
print("Usage: convert-gptq-to-ggml.py llamaXXb-4bit.pt tokenizer.model out.bin\n")
sys.exit(1)
fname_model = sys.argv[1]
fname_tokenizer = sys.argv[2]
dir_out = sys.argv[3]
model = torch.load(fname_model, map_location="cpu")
n_vocab, n_embd = model['model.embed_tokens.weight'].shape
n_layer = 1 + max(int(m.group(1)) for name in model
if (m := re.match(r'model\.layers\.([0-9]+)', name)))
# hardcoded:
n_mult = 256
n_head = {32: 32, 40: 40, 60: 52, 80: 64}[n_layer]
tokenizer = SentencePieceProcessor(fname_tokenizer)
assert tokenizer.vocab_size() == n_vocab
fname_out = sys.argv[3]
fout = open(fname_out, "wb")
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
fout.write(struct.pack("i", n_vocab))
fout.write(struct.pack("i", n_embd))
fout.write(struct.pack("i", n_mult))
fout.write(struct.pack("i", n_head))
fout.write(struct.pack("i", n_layer))
fout.write(struct.pack("i", n_embd // n_head)) # rot (obsolete)
fout.write(struct.pack("i", 4))
# This loop unchanged from convert-pth-to-ggml.py:
for i in range(tokenizer.vocab_size()):
if tokenizer.is_unknown(i):
# "<unk>" token (translated as ??)
text = " \u2047 ".encode("utf-8")
fout.write(struct.pack("i", len(text)))
fout.write(text)
elif tokenizer.is_control(i):
# "<s>"/"</s>" tokens
fout.write(struct.pack("i", 0))
elif tokenizer.is_byte(i):
# "<U+XX>" tokens (which may be invalid UTF-8)
piece = tokenizer.id_to_piece(i)
if len(piece) != 6:
print("Invalid token: " + piece)
sys.exit(1)
byte_value = int(piece[3:-1], 16)
fout.write(struct.pack("i", 1))
fout.write(struct.pack("B", byte_value))
else:
# normal token. Uses U+2581 (LOWER ONE EIGHTH BLOCK) to represent spaces.
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
fout.write(struct.pack("i", len(text)))
fout.write(text)
def write_header(shape, dst_name, ftype_cur):
sname = dst_name.encode('utf-8')
fout.write(struct.pack("iii", len(shape), len(sname), ftype_cur))
fout.write(struct.pack("i" * len(shape), *shape[::-1]))
fout.write(sname)
def convert_non_q4(src_name, dst_name):
v = model[src_name]
shape = v.shape
print("Processing non-Q4 variable: " + src_name + " with shape: ", shape, " and type: ", v.dtype)
if len(shape) == 1:
print(" Converting to float32")
v = v.to(torch.float32)
ftype_cur = {torch.float16: 1, torch.float32: 0}[v.dtype]
# header
write_header(shape, dst_name, ftype_cur)
# data
v.numpy().tofile(fout)
def convert_q4(src_name, dst_name, permute=False):
zeros = model[f"{src_name}.zeros"].numpy()
scales = model[f"{src_name}.scales"].numpy()
bias = model[f"{src_name}.bias"].numpy()
qweight = model[f"{src_name}.qweight"].numpy().T # transpose
# Q4_1 does not support bias; good thing the bias is always all zeros.
assert not np.any(bias)
# Each int32 item is actually 8 int4 items packed together, and it's transposed.
shape = (qweight.shape[0], qweight.shape[1] * 8)
print("Processing Q4 variable: " + src_name + " with shape: ", shape)
# The output format has the int4 weights in groups of 32 rather than 8.
# It looks like this:
# For each row:
# For each group of 32 columns:
# - addend (float32, 4 bytes)
# - scale (float32, 4 bytes)
# - weights (int4 * 32, 16 bytes)
# Note that in the input, the scales and addends are shared between all
# the columns in a row, so we end up wasting quite a bit of memory with
# repeated scales and addends.
addends = -zeros # flip sign
# Since the output format is mixed between integers and floats, we have
# to hackily view the floats as int32s just so numpy will let us
# concatenate them.
addends_view = addends.view(dtype=np.int32)
scales_view = scales.view(dtype=np.int32)
# Split into groups of 4 columns (i.e. 32 columns of quantized data):
grouped = qweight.reshape([qweight.shape[0], qweight.shape[1] // 4, 4])
# Repeat addends and scales:
addends_rep = np.atleast_3d(addends_view).repeat(grouped.shape[1], axis=1)
scales_rep = np.atleast_3d(scales_view).repeat(grouped.shape[1], axis=1)
blob = np.concatenate([scales_rep, addends_rep, grouped], axis=2, casting='no')
if permute:
# Permute some rows to undo the permutation done by convert_llama_weights_to_hf.py.
# This can be done after the above conversion because it doesn't affect column order/layout.
blob = (blob.reshape(n_head, 2, shape[0] // n_head // 2, *blob.shape[1:])
.swapaxes(1, 2)
.reshape(blob.shape))
# header
write_header(shape, dst_name, 3) # ftype = Q4_1
# data
blob.tofile(fout)
convert_non_q4("model.embed_tokens.weight", "tok_embeddings.weight")
convert_non_q4("model.norm.weight", "norm.weight")
convert_non_q4("lm_head.weight", "output.weight")
for i in range(n_layer):
convert_q4(f"model.layers.{i}.self_attn.q_proj", f"layers.{i}.attention.wq.weight", permute=True)
convert_q4(f"model.layers.{i}.self_attn.k_proj", f"layers.{i}.attention.wk.weight", permute=True)
convert_q4(f"model.layers.{i}.self_attn.v_proj", f"layers.{i}.attention.wv.weight")
convert_q4(f"model.layers.{i}.self_attn.o_proj", f"layers.{i}.attention.wo.weight")
convert_q4(f"model.layers.{i}.mlp.gate_proj", f"layers.{i}.feed_forward.w1.weight")
convert_q4(f"model.layers.{i}.mlp.down_proj", f"layers.{i}.feed_forward.w2.weight")
convert_q4(f"model.layers.{i}.mlp.up_proj", f"layers.{i}.feed_forward.w3.weight")
convert_non_q4(f"model.layers.{i}.input_layernorm.weight", f"layers.{i}.attention_norm.weight")
convert_non_q4(f"model.layers.{i}.post_attention_layernorm.weight", f"layers.{i}.ffn_norm.weight")
fout.close()
print("Done. Output file: " + fname_out)
print("")

47
convert-pth-to-ggml.py
View file

@ -10,25 +10,26 @@
# - Name (char[name_length])
# - Data (float[n_dims])
#
# By default, the bigger matrices are converted to 16-bit floats.
# This can be disabled by adding the "use-f32" CLI argument.
#
# At the start of the ggml file we write the model parameters
# and vocabulary.
#
import argparse
import os
import sys
import json
import struct
import numpy as np
import torch
from sentencepiece import SentencePieceProcessor
def parse_args():
parser = argparse.ArgumentParser(description='Convert a LLaMA model checkpoint to a ggml compatible file')
parser.add_argument('dir_model', help='directory containing the model checkpoint')
parser.add_argument('ftype', type=int, choices=[0, 1], default=1, help='file type (0: float32, 1: float16)')
parser.add_argument('dir_model', help='directory containing the model checkpoint')
parser.add_argument('ftype', help='file type (0: float32, 1: float16)', type=int, choices=[0, 1], default=1)
parser.add_argument('vocab_only', help='only write vocab to file', type=int, default=0, nargs='?')
return parser.parse_args()
def get_n_parts(dim):
@ -44,8 +45,14 @@ def get_n_parts(dim):
def load_hparams_and_tokenizer(dir_model):
# `dir_model` is something like `models/7B` or `models/7B/`.
# "tokenizer.model" is expected under model's parent dir.
# When `dir_model` is a symlink, f"{dir_model}/../tokenizer.model" would not be found.
# Let's use the model's parent dir directly.
model_parent_dir = os.path.dirname(os.path.normpath(dir_model))
fname_hparams = f"{dir_model}/params.json"
fname_tokenizer = f"{dir_model}/../tokenizer.model"
fname_tokenizer = f"{model_parent_dir}/tokenizer.model"
with open(fname_hparams, "r") as f:
hparams = json.load(f)
@ -60,7 +67,8 @@ def write_header(fout, hparams, ftype):
keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"]
values = [
0x67676d6c, # magic: ggml in hex
0x67676d66, # magic: ggmf in hex
1, # file version
*[hparams[key] for key in keys],
hparams["dim"] // hparams["n_heads"], # rot (obsolete)
ftype
@ -85,6 +93,7 @@ def write_tokens(fout, tokenizer):
text = tokenizer.id_to_piece(i).replace("\u2581", " ").encode("utf-8")
fout.write(struct.pack("i", len(text)))
fout.write(text)
fout.write(struct.pack("f", tokenizer.get_score(i)))
def process_and_write_variables(fout, model, ftype):
@ -125,6 +134,29 @@ def main():
ftype_str = ["f32", "f16"]
hparams, tokenizer = load_hparams_and_tokenizer(dir_model)
print(args)
# if only writing vocab to file
if args.vocab_only:
fname_model = f"{dir_model}/consolidated.00.pth"
fname_out = f"{dir_model}/ggml-vocab.bin"
print(f"Extracting only the vocab from '{fname_model}'\n")
model = torch.load(fname_model, map_location="cpu")
with open(fname_out, "wb") as fout:
fout.write(struct.pack("i", hparams["vocab_size"]))
write_tokens(fout, tokenizer)
del model
print(f"Done. Output file: {fname_out}\n")
return
n_parts = get_n_parts(hparams["dim"])
for p in range(n_parts):
@ -142,6 +174,7 @@ def main():
process_and_write_variables(fout, model, ftype)
del model
print(f"Done. Output file: {fname_out}, (part {p})\n")
if __name__ == "__main__":

53
examples/chatLLaMa Executable file
View file

@ -0,0 +1,53 @@
#!/bin/bash
cd "$(dirname "$0")/.." || exit
MODEL="${MODEL:-./models/13B/ggml-model-q4_0.bin}"
USER_NAME="${USER_NAME:-User}"
AI_NAME="${AI_NAME:-ChatLLaMa}"
# Adjust to the number of CPU cores you want to use.
N_THREAD="${N_THREAD:-8}"
# Number of tokens to predict (made it larger than default because we want a long interaction)
N_PREDICTS="${N_PREDICTS:-2048}"
# Note: you can also override the generation options by specifying them on the command line:
# For example, override the context size by doing: ./chatLLaMa --ctx_size 1024
GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --repeat_penalty 1.17647}"
# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
./main $GEN_OPTIONS \
--model "$MODEL" \
--threads "$N_THREAD" \
--n_predict "$N_PREDICTS" \
--color --interactive \
--reverse-prompt "${USER_NAME}:" \
--prompt "
Text transcript of a never ending dialog, where ${USER_NAME} interacts with an AI assistant named ${AI_NAME}.
${AI_NAME} is helpful, kind, honest, friendly, good at writing and never fails to answer ${USER_NAME}’s requests immediately and with details and precision.
There are no annotations like (30 seconds passed...) or (to himself), just what ${USER_NAME} and ${AI_NAME} say alound to each other.
The dialog lasts for years, the entirety of it is shared below. It's 10000 pages long.
The transcript only includes text, it does not include markup like HTML and Markdown.
$USER_NAME: Hello, $AI_NAME!
$AI_NAME: Hello $USER_NAME! How may I help you today?
$USER_NAME: What time is it?
$AI_NAME: It is $(date +%H:%M).
$USER_NAME: What year is it?
$AI_NAME: We are in $(date +%Y).
$USER_NAME: Please tell me the largest city in Europe.
$AI_NAME: The largest city in Europe is Moscow, the capital of Russia.
$USER_NAME: What can you tell me about Moscow?
$AI_NAME: Moscow, on the Moskva River in western Russia, is the nation’s cosmopolitan capital. In its historic core is the Kremlin, a complex that’s home to the president and tsarist treasures in the Armoury. Outside its walls is Red Square, Russia’s symbolic center.
$USER_NAME: What is a cat?
$AI_NAME: A cat is a domestic species of small carnivorous mammal. It is the only domesticated species in the family Felidae.
$USER_NAME: How do I pass command line arguments to a Node.js program?
$AI_NAME: The arguments are stored in process.argv.
argv[0] is the path to the Node. js executable.
argv[1] is the path to the script file.
argv[2] is the first argument passed to the script.
argv[3] is the second argument passed to the script and so on.
$USER_NAME: Name a color.
$AI_NAME: Blue
$USER_NAME:" "$@"

1
flake.nix
View file

@ -34,6 +34,7 @@
cat ${./convert-pth-to-ggml.py} >> $out/bin/convert-pth-to-ggml
chmod +x $out/bin/convert-pth-to-ggml
'';
meta.mainProgram = "llama";
};
devShells.default = pkgs.mkShell {
packages = with pkgs; [

82
ggml.c
View file

@ -2,7 +2,7 @@
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <malloc.h> // using malloc.h with MSC/MINGW
#elif !defined(__FreeBSD__) && !defined(__NetBSD__)
#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
#include <alloca.h>
#endif
@ -361,7 +361,7 @@ static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
// AVX routines provided by GH user Const-me
// ref: https://github.com/ggerganov/ggml/pull/27#issuecomment-1464934600
#if __AVX2__
#if __AVX2__ || __AVX512F__
// Unpack 32 4-bit fields into 32 bytes
// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval
static inline __m256i bytesFromNibbles( const uint8_t* rsi )
@ -397,7 +397,6 @@ static inline __m128i packNibbles( __m256i bytes )
}
#endif
// method 5
// blocks of QK elements
// represented with a single float (delta) and QK/2 8-bit ints (i.e QK 4-bit signed integer factors)
@ -1262,6 +1261,47 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float
*s = sumf;
}
#if __AVX512F__ && QK == 32
static inline __m512 dot_q4_0_oneblock_avx512(
__m512 acc,
const uint8_t * pd0,
const uint8_t * pd1,
const uint8_t * pb0,
const uint8_t * pb1,
size_t bs,
int i
) {
const float * d0_0 = (const float *) (pd0 + i*bs);
const float * d1_0 = (const float *) (pd1 + i*bs);
const uint8_t * restrict p0 = pb0 + (i+0)*bs;
const uint8_t * restrict p1 = pb1 + (i+0)*bs;
// Compute combined scale for the block
float scaleScalar = d0_0[0] * d1_0[0];
__m512 scale = _mm512_set1_ps( scaleScalar );
__m256i bx = bytesFromNibbles( p0 );
__m256i by = bytesFromNibbles( p1 );
// Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval.
const __m256i off = _mm256_set1_epi8( 8 );
bx = _mm256_sub_epi8( bx, off );
by = _mm256_sub_epi8( by, off );
// Sign-extend 16 signed bytes into int16_t
__m512i x32 = _mm512_cvtepi8_epi16( bx );
__m512i y32 = _mm512_cvtepi8_epi16( by );
// Compute products of int16_t integers, add pairwise
__m512i i64 = _mm512_madd_epi16( x32, y32 );
// Convert int32_t to float
__m512 p = _mm512_cvtepi32_ps( i64 );
// Apply the scale, and accumulate
return _mm512_fmadd_ps( scale, p, acc );
}
#endif
inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) {
ggml_float sumf = 0.0;
@ -1417,6 +1457,40 @@ inline static void ggml_vec_dot_q4_0(const int n, float * restrict s, const void
#else
#error "not implemented for QK"
#endif
#elif defined(__AVX512F__)
#if QK == 32
// Initialize accumulator with zeros
__m512 acc0 = _mm512_setzero_ps();
__m512 acc1 = _mm512_setzero_ps();
const int superblock_size = 8;
const int superblock_count = nb / superblock_size;
const int remainder = nb % superblock_size;
for (int superblock_ix = 0; superblock_ix < superblock_count; superblock_ix += 1) {
int i = superblock_ix * superblock_size;
acc0 = dot_q4_0_oneblock_avx512( acc0, pd0, pd1, pb0, pb1, bs, i+0 );
acc1 = dot_q4_0_oneblock_avx512( acc1, pd0, pd1, pb0, pb1, bs, i+1 );
acc0 = dot_q4_0_oneblock_avx512( acc0, pd0, pd1, pb0, pb1, bs, i+2 );
acc1 = dot_q4_0_oneblock_avx512( acc1, pd0, pd1, pb0, pb1, bs, i+3 );
acc0 = dot_q4_0_oneblock_avx512( acc0, pd0, pd1, pb0, pb1, bs, i+4 );
acc1 = dot_q4_0_oneblock_avx512( acc1, pd0, pd1, pb0, pb1, bs, i+5 );
acc0 = dot_q4_0_oneblock_avx512( acc0, pd0, pd1, pb0, pb1, bs, i+6 );
acc1 = dot_q4_0_oneblock_avx512( acc1, pd0, pd1, pb0, pb1, bs, i+7 );
}
// Remainders
for (int i = superblock_count * superblock_size; i < nb; ++i) {
acc0 = dot_q4_0_oneblock_avx512( acc0, pd0, pd1, pb0, pb1, bs, i );
}
// Horizontal sum of all lanes of the accumulator
sumf = _mm512_reduce_add_ps( acc0 ) + _mm512_reduce_add_ps( acc1 );
#else
#error "not implemented for QK"
#endif
#elif defined(__AVX2__)
#if QK == 32
const size_t countBlocks = nb;
@ -1928,7 +2002,7 @@ inline static void ggml_vec_mad_q4_1(const int n, float * restrict y, void * res
const size_t bs = 2*sizeof(float) + QK/2;
const uint8_t * restrict pd = ((const uint8_t *)x + 0*bs);
const uint8_t * restrict pm = ((const uint8_t *)x + 0*bs + sizeof(float));
const uint8_t * restrict pm = ((const uint8_t *)x + 0*bs + sizeof(float));
const uint8_t * restrict pb = ((const uint8_t *)x + 0*bs + 2*sizeof(float));
for (int i = 0; i < nb; i++) {

250
main.cpp
View file

@ -3,6 +3,7 @@
#include "utils.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
@ -19,6 +20,13 @@
#include <signal.h>
#endif
#if defined (_WIN32)
#pragma comment(lib,"kernel32.lib")
extern "C" __declspec(dllimport) void* __stdcall GetStdHandle(unsigned long nStdHandle);
extern "C" __declspec(dllimport) int __stdcall GetConsoleMode(void* hConsoleHandle, unsigned long* lpMode);
extern "C" __declspec(dllimport) int __stdcall SetConsoleMode(void* hConsoleHandle, unsigned long dwMode);
#endif
#define ANSI_COLOR_RED "\x1b[31m"
#define ANSI_COLOR_GREEN "\x1b[32m"
#define ANSI_COLOR_YELLOW "\x1b[33m"
@ -119,7 +127,8 @@ struct llama_model {
};
// load the model's weights from a file
bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab & vocab, int n_ctx, ggml_type memory_type = GGML_TYPE_F32) {
bool llama_model_load(const std::string & fname, llama_model & model, llama_vocab & vocab, int n_ctx, int n_parts, ggml_type memory_type = GGML_TYPE_F32) {
fprintf(stderr, "%s: loading model from '%s' - please wait ...\n", __func__, fname.c_str());
std::vector<char> f_buf(1024*1024);
@ -135,14 +144,27 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
{
uint32_t magic;
fin.read((char *) &magic, sizeof(magic));
if (magic != 0x67676d6c) {
if (magic == FILE_MAGIC_UNVERSIONED) {
fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
__func__, fname.c_str());
return false;
}
if (magic != FILE_MAGIC) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname.c_str());
return false;
}
uint32_t format_version;
fin.read((char *) &format_version, sizeof(format_version));
if (format_version != FILE_VERSION) {
fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
__func__, fname.c_str(), format_version, FILE_VERSION);
return false;
}
}
int n_ff = 0;
int n_parts = 0;
// load hparams
{
@ -160,7 +182,16 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
hparams.n_ctx = n_ctx;
n_ff = ((2*(4*hparams.n_embd)/3 + hparams.n_mult - 1)/hparams.n_mult)*hparams.n_mult;
n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
if (n_parts < 1) {
n_parts = LLAMA_N_PARTS.at(hparams.n_embd);
}
// temp warning to tell the user to use "--n_parts"
if (hparams.f16 == 4 && n_parts != 1) {
fprintf(stderr, "%s: GPTQ model detected - are you sure n_parts should be %d? we normally expect it to be 1\n", __func__, n_parts);
fprintf(stderr, "%s: use '--n_parts 1' if necessary\n", __func__);
}
fprintf(stderr, "%s: n_vocab = %d\n", __func__, hparams.n_vocab);
fprintf(stderr, "%s: n_ctx = %d\n", __func__, hparams.n_ctx);
@ -177,30 +208,40 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
// load vocab
{
std::string word;
std::vector<char> tmp(64);
for (int i = 0; i < model.hparams.n_vocab; i++) {
uint32_t len;
fin.read((char *) &len, sizeof(len));
word.resize(len);
fin.read((char *) word.data(), len);
if (len > 0) {
tmp.resize(len);
fin.read(tmp.data(), len);
word.assign(tmp.data(), len);
} else {
word.clear();
}
float score;
fin.read((char *) &score, sizeof(score));
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
//if (i < 30000) {
// fprintf(stderr, "%s: vocab[%d] = '%s'\n", __func__, i, word.c_str());
//}
vocab.score[i] = score;
}
}
// for the big tensors, we have the option to store the data in 16-bit floats or quantized
// in order to save memory and also to speed up the computation
ggml_type wtype = GGML_TYPE_COUNT;
// wtype is for per-layer weights, while vtype is for other weights
ggml_type wtype, vtype;
switch (model.hparams.f16) {
case 0: wtype = GGML_TYPE_F32; break;
case 1: wtype = GGML_TYPE_F16; break;
case 2: wtype = GGML_TYPE_Q4_0; break;
case 3: wtype = GGML_TYPE_Q4_1; break;
case 0: wtype = vtype = GGML_TYPE_F32; break;
case 1: wtype = vtype = GGML_TYPE_F16; break;
case 2: wtype = vtype = GGML_TYPE_Q4_0; break;
case 3: wtype = vtype = GGML_TYPE_Q4_1; break;
case 4: wtype = GGML_TYPE_Q4_1; vtype = GGML_TYPE_F16; break;
default:
{
fprintf(stderr, "%s: invalid model file '%s' (bad f16 value %d)\n",
@ -221,11 +262,11 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
const int n_ctx = hparams.n_ctx;
const int n_vocab = hparams.n_vocab;
ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // tok_embeddings
ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // tok_embeddings
ctx_size += n_embd*ggml_type_sizef(GGML_TYPE_F32); // norm
ctx_size += n_embd*n_vocab*ggml_type_sizef(wtype); // output
ctx_size += n_embd*n_vocab*ggml_type_sizef(vtype); // output
ctx_size += n_layer*(n_embd*ggml_type_sizef(GGML_TYPE_F32)); // attention_norm
@ -272,10 +313,10 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
model.layers.resize(n_layer);
model.tok_embeddings = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
model.tok_embeddings = ggml_new_tensor_2d(ctx, vtype, n_embd, n_vocab);
model.norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd);
model.output = ggml_new_tensor_2d(ctx, wtype, n_embd, n_vocab);
model.output = ggml_new_tensor_2d(ctx, vtype, n_embd, n_vocab);
// map by name
model.tensors["tok_embeddings.weight"] = model.tok_embeddings;
@ -555,9 +596,10 @@ bool llama_eval(
const llama_model & model,
const int n_threads,
const int n_past,
const std::vector<gpt_vocab::id> & embd_inp,
std::vector<float> & embd_w,
size_t & mem_per_token) {
const std::vector<llama_vocab::id> & embd_inp,
std::vector<float> & embd_w,
size_t & mem_per_token,
bool return_all_logits = false) {
const int N = embd_inp.size();
const auto & hparams = model.hparams;
@ -575,7 +617,7 @@ bool llama_eval(
static void * buf = malloc(buf_size);
if (mem_per_token > 0 && mem_per_token*N > buf_size) {
const size_t buf_size_new = 1.1*(mem_per_token*N); // add 10% to account for ggml object overhead
const size_t buf_size_new = 1.3*(mem_per_token*N); // add 30% to account for ggml object overhead
//fprintf(stderr, "\n%s: reallocating buffer from %zu to %zu bytes\n", __func__, buf_size, buf_size_new);
// reallocate
@ -761,9 +803,14 @@ bool llama_eval(
//embd_w.resize(n_vocab*N);
//memcpy(embd_w.data(), ggml_get_data(inpL), sizeof(float)*n_vocab*N);
// return result for just the last token
embd_w.resize(n_vocab);
memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
if (return_all_logits) {
embd_w.resize(n_vocab * N);
memcpy(embd_w.data(), (float *) ggml_get_data(inpL), sizeof(float)*n_vocab*N);
} else {
// return result for just the last token
embd_w.resize(n_vocab);
memcpy(embd_w.data(), (float *) ggml_get_data(inpL) + (n_vocab*(N-1)), sizeof(float)*n_vocab);
}
if (mem_per_token == 0) {
mem_per_token = ggml_used_mem(ctx0)/N;
@ -775,6 +822,76 @@ bool llama_eval(
return true;
}
std::vector<double> softmax(const std::vector<float>& logits) {
std::vector<double> probs(logits.size());
float max_logit = logits[0];
for (float v : logits) max_logit = std::max(max_logit, v);
double sum_exp = 0.0;
for (size_t i = 0; i < logits.size(); i++) {
// Subtract the maximum logit value from the current logit value for numerical stability
float logit = logits[i] - max_logit;
double exp_logit = std::exp(logit);
sum_exp += exp_logit;
probs[i] = exp_logit;
}
for (size_t i = 0; i < probs.size(); i++) probs[i] /= sum_exp;
return probs;
}
void perplexity(const llama_vocab &vocab, const llama_model &model, const gpt_params &params, size_t mem_per_token) {
// Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research
// Run `./main --perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw`
// Output: `perplexity: 13.5106 [114/114]`
std::vector<llama_vocab::id> tokens = ::llama_tokenize(vocab, params.prompt, true);
int count = 0;
double nll = 0.0;
int seq_count = tokens.size() / params.n_ctx;
printf("Calculating perplexity over %d chunks\n", seq_count);
for (int i = 0; i < seq_count; ++i) {
int start = i * params.n_ctx;
int end = start + params.n_ctx - 1;
std::vector<llama_vocab::id> embd(tokens.begin() + start, tokens.begin() + end);
std::vector<float> logits;
auto start_t = std::chrono::high_resolution_clock::now();
if (!llama_eval(model, params.n_threads, 0, embd, logits, mem_per_token, true)) {
fprintf(stderr, "Failed to predict\n");
return;
}
auto end_t = std::chrono::high_resolution_clock::now();
if (i == 0) {
double seconds = std::chrono::duration<double>(end_t - start_t).count();
printf("%.2f seconds per pass - ETA %.2f hours\n", seconds, (seconds * seq_count) / (60.0*60.0));
}
// We get the logits for all the tokens in the context window (params.n_ctx)
// from llama_eval above. Now, based on https://huggingface.co/docs/transformers/perplexity,
// calculate the perplexity over the last half the window (so the model always has
// some context to predict the token).
//
// We rely on the fact that attention in the forward pass only looks at previous
// tokens here, so the logits returned for each token are an accurate representation
// of what the model would have predicted at that point.
//
// 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.
for (int j = params.n_ctx / 2; j < params.n_ctx - 1; ++j) {
// Calculate probability of next token, given the previous ones.
int n_vocab = model.hparams.n_vocab;
std::vector<float> tok_logits(
logits.begin() + j * n_vocab,
logits.begin() + (j + 1) * n_vocab);
double prob = softmax(tok_logits)[tokens[start + j + 1]];
nll += -std::log(prob);
++count;
}
// perplexity is e^(average negative log-likelihood)
printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
fflush(stdout);
}
printf("\n");
}
static bool is_interacting = false;
#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32)
@ -847,14 +964,14 @@ int main(int argc, char ** argv) {
int64_t t_load_us = 0;
gpt_vocab vocab;
llama_vocab vocab;
llama_model model;
// load the model
{
const ggml_type memory_type = params.memory_f16 ? GGML_TYPE_F16 : GGML_TYPE_F32;
const int64_t t_start_us = ggml_time_us();
if (!llama_model_load(params.model, model, vocab, params.n_ctx, memory_type)) {
if (!llama_model_load(params.model, model, vocab, params.n_ctx, params.n_parts, memory_type)) {
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, params.model.c_str());
return 1;
}
@ -869,23 +986,32 @@ int main(int argc, char ** argv) {
params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info());
}
std::vector<float> logits;
// determine the required inference memory per token:
size_t mem_per_token = 0;
llama_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
if (params.perplexity) {
perplexity(vocab, model, params, mem_per_token);
exit(0);
}
int n_past = 0;
int64_t t_sample_us = 0;
int64_t t_predict_us = 0;
std::vector<float> logits;
// Add a space in front of the first character to match OG llama tokenizer behavior
params.prompt.insert(0, 1, ' ');
// tokenize the prompt
std::vector<gpt_vocab::id> embd_inp = ::llama_tokenize(vocab, params.prompt, true);
std::vector<llama_vocab::id> embd_inp = ::llama_tokenize(vocab, params.prompt, true);
params.n_predict = std::min(params.n_predict, model.hparams.n_ctx - (int) embd_inp.size());
// prefix & suffix for instruct mode
const std::vector<gpt_vocab::id> inp_pfx = ::llama_tokenize(vocab, "\n\n### Instruction:\n\n", true);
const std::vector<gpt_vocab::id> inp_sfx = ::llama_tokenize(vocab, "\n\n### Response:\n\n", false);
const std::vector<llama_vocab::id> inp_pfx = ::llama_tokenize(vocab, "\n\n### Instruction:\n\n", true);
const std::vector<llama_vocab::id> inp_sfx = ::llama_tokenize(vocab, "\n\n### Response:\n\n", false);
// in instruct mode, we inject a prefix and a suffix to each input by the user
if (params.instruct) {
@ -893,15 +1019,8 @@ int main(int argc, char ** argv) {
params.antiprompt.push_back("### Instruction:\n\n");
}
// tokenize the reverse prompt
std::vector<std::vector<gpt_vocab::id>> antipromptv_inp;
for (auto antiprompt : params.antiprompt) {
antipromptv_inp.push_back(::llama_tokenize(vocab, antiprompt, false));
}
// enable interactive mode if reverse prompt is specified
if (antipromptv_inp.size() != 0) {
if (params.antiprompt.size() != 0) {
params.interactive = true;
}
@ -925,29 +1044,19 @@ int main(int argc, char ** argv) {
fprintf(stderr, "%s: interactive mode on.\n", __func__);
if(antipromptv_inp.size()) {
for (size_t apindex = 0; apindex < antipromptv_inp.size(); ++apindex) {
auto antiprompt_inp = antipromptv_inp.at(apindex);
fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.at(apindex).c_str());
fprintf(stderr, "%s: number of tokens in reverse prompt = %zu\n", __func__, antiprompt_inp.size());
for (int i = 0; i < (int) antiprompt_inp.size(); i++) {
fprintf(stderr, "%6d -> '%s'\n", antiprompt_inp[i], vocab.id_to_token.at(antiprompt_inp[i]).c_str());
}
fprintf(stderr, "\n");
if(params.antiprompt.size()) {
for (auto antiprompt : params.antiprompt) {
fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str());
}
}
}
fprintf(stderr, "sampling parameters: temp = %f, top_k = %d, top_p = %f, repeat_last_n = %i, repeat_penalty = %f\n", params.temp, params.top_k, params.top_p, params.repeat_last_n, params.repeat_penalty);
fprintf(stderr, "\n\n");
std::vector<gpt_vocab::id> embd;
// determine the required inference memory per token:
size_t mem_per_token = 0;
llama_eval(model, params.n_threads, 0, { 0, 1, 2, 3 }, logits, mem_per_token);
std::vector<llama_vocab::id> embd;
int last_n_size = params.repeat_last_n;
std::vector<gpt_vocab::id> last_n_tokens(last_n_size);
std::vector<llama_vocab::id> last_n_tokens(last_n_size);
std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);
if (params.interactive) {
@ -965,6 +1074,16 @@ int main(int argc, char ** argv) {
int remaining_tokens = params.n_predict;
#if defined (_WIN32)
if (params.use_color) {
// Enable ANSI colors on Windows 10+
unsigned long dwMode = 0;
void* hConOut = GetStdHandle((unsigned long)-11); // STD_OUTPUT_HANDLE (-11)
if (hConOut && hConOut != (void*)-1 && GetConsoleMode(hConOut, &dwMode) && !(dwMode & 0x4)) {
SetConsoleMode(hConOut, dwMode | 0x4); // ENABLE_VIRTUAL_TERMINAL_PROCESSING (0x4)
}
}
#endif
// the first thing we will do is to output the prompt, so set color accordingly
set_console_state(CONSOLE_STATE_PROMPT);
@ -984,7 +1103,7 @@ int main(int argc, char ** argv) {
n_past += embd.size();
embd.clear();
if (embd_inp.size() <= input_consumed) {
if ((int) embd_inp.size() <= input_consumed) {
// out of user input, sample next token
const float top_k = params.top_k;
const float top_p = params.top_p;
@ -993,7 +1112,7 @@ int main(int argc, char ** argv) {
const int n_vocab = model.hparams.n_vocab;
gpt_vocab::id id = 0;
llama_vocab::id id = 0;
{
const int64_t t_start_sample_us = ggml_time_us();
@ -1021,7 +1140,7 @@ int main(int argc, char ** argv) {
--remaining_tokens;
} else {
// some user input remains from prompt or interaction, forward it to processing
while (embd_inp.size() > input_consumed) {
while ((int) embd_inp.size() > input_consumed) {
embd.push_back(embd_inp[input_consumed]);
last_n_tokens.erase(last_n_tokens.begin());
last_n_tokens.push_back(embd_inp[input_consumed]);
@ -1046,11 +1165,16 @@ int main(int argc, char ** argv) {
// in interactive mode, and not currently processing queued inputs;
// check if we should prompt the user for more
if (params.interactive && embd_inp.size() <= input_consumed) {
if (params.interactive && (int) embd_inp.size() <= input_consumed) {
// check for reverse prompt
for (auto antiprompt_inp : antipromptv_inp) {
if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
// reverse prompt found
std::string last_output;
for (auto id : last_n_tokens) {
last_output += vocab.id_to_token[id];
}
// Check if each of the reverse prompts appears at the end of the output.
for (std::string antiprompt : params.antiprompt) {
if (last_output.find(antiprompt.c_str(), last_output.length() - antiprompt.length(), antiprompt.length()) != std::string::npos) {
is_interacting = true;
break;
}
@ -1082,7 +1206,7 @@ int main(int argc, char ** argv) {
// done taking input, reset color
set_console_state(CONSOLE_STATE_DEFAULT);
std::vector<gpt_vocab::id> line_inp = ::llama_tokenize(vocab, buffer, false);
std::vector<llama_vocab::id> line_inp = ::llama_tokenize(vocab, buffer, false);
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
if (params.instruct) {

BIN
models/ggml-vocab.bin Normal file
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26
quantize.cpp
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@ -3,6 +3,7 @@
#include "utils.h"
#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
@ -43,7 +44,7 @@ bool llama_model_quantize(const std::string & fname_inp, const std::string & fna
return false;
}
gpt_vocab vocab;
llama_vocab vocab;
printf("%s: loading model from '%s'\n", __func__, fname_inp.c_str());
@ -63,12 +64,28 @@ bool llama_model_quantize(const std::string & fname_inp, const std::string & fna
{
uint32_t magic;
finp.read((char *) &magic, sizeof(magic));
if (magic != 0x67676d6c) {
if (magic == FILE_MAGIC_UNVERSIONED) {
fprintf(stderr, "%s: invalid model file '%s' (too old, regenerate your model files!)\n",
__func__, fname_inp.c_str());
return false;
}
if (magic != FILE_MAGIC) {
fprintf(stderr, "%s: invalid model file '%s' (bad magic)\n", __func__, fname_inp.c_str());
return false;
}
fout.write((char *) &magic, sizeof(magic));
uint32_t format_version;
finp.read((char *) &format_version, sizeof(format_version));
if (format_version != FILE_VERSION) {
fprintf(stderr, "%s: invalid model file '%s' (unsupported format version %" PRIu32 ", expected %d)\n",
__func__, fname_inp.c_str(), format_version, FILE_VERSION);
return false;
}
fout.write((char *) &format_version, sizeof(format_version));
}
llama_hparams hparams;
@ -122,8 +139,13 @@ bool llama_model_quantize(const std::string & fname_inp, const std::string & fna
finp.read ((char *) word.data(), len);
fout.write((char *) word.data(), len);
float score;
finp.read ((char *) &score, sizeof(score));
fout.write((char *) &score, sizeof(score));
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
vocab.score[i] = score;
}
}

4
tests/CMakeLists.txt Normal file
View file

@ -0,0 +1,4 @@
set(TEST_TARGET test-tokenizer-0)
add_executable(${TEST_TARGET} ${TEST_TARGET}.cpp)
target_link_libraries(${TEST_TARGET} PRIVATE utils)
add_test(NAME ${TEST_TARGET} COMMAND $<TARGET_FILE:${TEST_TARGET}> ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab.bin)

69
tests/test-tokenizer-0.cpp Normal file
View file

@ -0,0 +1,69 @@
#include "utils.h"
#include <cstdio>
#include <string>
#include <map>
static const std::map<std::string, std::vector<llama_vocab::id>> k_tests = {
{ "Hello World", { 1, 10994, 2787, }, },
{ " Hello World", { 1, 15043, 2787, }, },
{ " Hello World!", { 1, 15043, 2787, 29991, }, },
{ " this is 🦙.cpp", { 1, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, },
{ "w048 7tuijk dsdfhu", { 1, 29893, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, },
{ "нещо на Български", { 1, 821, 4851, 665, 1386, 29713, 1305, }, },
};
int main(int argc, char **argv) {
if (argc < 2) {
fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]);
return 1;
}
const std::string fname = argv[1];
fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str());
llama_vocab vocab;
if (!llama_vocab_load(fname, vocab)) {
fprintf(stderr, "%s : failed to load vocab from: '%s'\n", __func__, fname.c_str());
return 1;
}
const int n_vocab = vocab.id_to_token.size();
if (n_vocab != 32000) {
fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab);
return 2;
}
for (const auto & test_kv : k_tests) {
const auto res = llama_tokenize(vocab, test_kv.first, true);
bool correct = res.size() == test_kv.second.size();
for (int i = 0; i < (int) res.size() && correct; ++i) {
if (res[i] != test_kv.second[i]) {
correct = false;
}
}
if (!correct) {
fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str());
fprintf(stderr, "%s : expected tokens: ", __func__);
for (const auto & t : test_kv.second) {
fprintf(stderr, "%6d, ", t);
}
fprintf(stderr, "\n");
fprintf(stderr, "%s : got tokens: ", __func__);
for (const auto & t : res) {
fprintf(stderr, "%6d, ", t);
}
fprintf(stderr, "\n");
return 3;
}
}
return 0;
}

293
utils.cpp
View file

@ -6,12 +6,13 @@
#include <regex>
#include <iostream>
#include <iterator>
#include <queue>
#include <string>
#include <math.h>
#if defined(_MSC_VER) || defined(__MINGW32__)
#include <malloc.h> // using malloc.h with MSC/MINGW
#elif !defined(__FreeBSD__) && !defined(__NetBSD__)
#elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__)
#include <alloca.h>
#endif
@ -71,8 +72,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
params.use_color = true;
} else if (arg == "-r" || arg == "--reverse-prompt") {
params.antiprompt.push_back(argv[++i]);
} else if (arg == "--perplexity") {
params.perplexity = true;
} else if (arg == "--ignore-eos") {
params.ignore_eos = true;
} else if (arg == "--n_parts") {
params.n_parts = std::stoi(argv[++i]);
} else if (arg == "-h" || arg == "--help") {
gpt_print_usage(argc, argv, params);
exit(0);
@ -115,7 +120,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
fprintf(stderr, " --ignore-eos ignore end of stream token and continue generating\n");
fprintf(stderr, " --memory_f16 use f16 instead of f32 for memory key+value\n");
fprintf(stderr, " --temp N temperature (default: %.1f)\n", params.temp);
fprintf(stderr, " --n_parts N number of model parts (default: -1 = determine from dimensions)\n");
fprintf(stderr, " -b N, --batch_size N batch size for prompt processing (default: %d)\n", params.n_batch);
fprintf(stderr, " --perplexity compute perplexity over the prompt\n");
fprintf(stderr, " -m FNAME, --model FNAME\n");
fprintf(stderr, " model path (default: %s)\n", params.model.c_str());
fprintf(stderr, "\n");
@ -239,151 +246,209 @@ std::map<std::string, int32_t> json_parse(const std::string & fname) {
return result;
}
std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text) {
std::vector<std::string> words;
// first split the text into words
{
std::string str = text;
std::string pat = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)";
std::regex re(pat);
std::smatch m;
while (std::regex_search(str, m, re)) {
for (auto x : m) {
words.push_back(x);
}
str = m.suffix();
}
}
// find the longest tokens that form the words:
std::vector<gpt_vocab::id> tokens;
for (const auto & word : words) {
if (word.size() == 0) continue;
int i = 0;
int n = word.size();
while (i < n) {
int j = n;
while (j > i) {
auto it = vocab.token_to_id.find(word.substr(i, j-i));
if (it != vocab.token_to_id.end()) {
tokens.push_back(it->second);
i = j;
break;
}
--j;
}
if (i == n) {
break;
}
if (j == i) {
auto sub = word.substr(i, 1);
if (vocab.token_to_id.find(sub) != vocab.token_to_id.end()) {
tokens.push_back(vocab.token_to_id.at(sub));
} else {
fprintf(stderr, "%s: unknown token '%s'\n", __func__, sub.data());
}
++i;
}
}
}
return tokens;
static size_t utf8_len(char src) {
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>(src) >> 4;
return lookup[highbits];
}
// TODO: Calculate this constant from the vocabulary
#define MAX_TOKEN_LEN 18
// SentencePiece implementation after https://guillaume-be.github.io/2020-05-30/sentence_piece
std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, const std::string & text, bool bos) {
std::vector<gpt_vocab::id> res;
std::vector<int> score;
std::vector<gpt_vocab::id> prev;
int len = text.length();
struct llama_sp_symbol {
using index = int;
index prev;
index next;
const char * text;
size_t n;
};
score.resize(len + 1);
prev.resize(len + 1);
struct llama_sp_bigram {
struct comparator {
bool operator()(llama_sp_bigram & l, llama_sp_bigram & r) {
return (l.score < r.score) || (l.score == r.score && l.left > r.left);
}
};
using queue_storage = std::vector<llama_sp_bigram>;
using queue = std::priority_queue<llama_sp_bigram, queue_storage, comparator>;
llama_sp_symbol::index left;
llama_sp_symbol::index right;
float score;
size_t size;
};
// Forward pass
for (int i = 0; i < len; i++) {
int max_len = std::min(len - i, MAX_TOKEN_LEN);
for (int sub_len = 1; sub_len <= max_len; sub_len++) {
auto sub = text.substr(i, sub_len);
auto token = vocab.token_to_id.find(sub);
if (token != vocab.token_to_id.end()) {
int token_score = sub.length() * sub.length();
int local_score = score[i] + token_score;
int next = i + sub_len;
if (score[next] < local_score) {
score[next] = local_score;
prev[next] = (*token).second;
// original implementation:
// https://github.com/ggerganov/llama.cpp/commit/074bea2eb1f1349a0118239c4152914aecaa1be4
struct llama_tokenizer {
llama_tokenizer(const llama_vocab & vocab): vocab_(vocab) {}
void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) {
// split string into utf8 chars
int index = 0;
size_t offs = 0;
while (offs < text.size()) {
llama_sp_symbol sym;
size_t char_len = std::min(text.size() - offs, utf8_len(text[offs]));
sym.text = text.c_str() + offs;
sym.n = char_len;
offs += char_len;
sym.prev = index - 1;
sym.next = offs == text.size() ? -1 : index + 1;
index++;
symbols_.emplace_back(std::move(sym));
}
// seed the work queue with all possible 2-character tokens.
for (size_t i = 1; i < symbols_.size(); ++i) {
try_add_bigram(i - 1, i);
}
// keep substituting the highest frequency pairs for as long as we can.
while (!work_queue_.empty()) {
auto bigram = work_queue_.top();
work_queue_.pop();
auto & left_sym = symbols_[bigram.left];
auto & right_sym = symbols_[bigram.right];
// if one of the symbols already got merged, skip it.
if (left_sym.n == 0 || right_sym.n == 0 ||
left_sym.n + right_sym.n != bigram.size) {
continue;
}
// merge the right sym into the left one
left_sym.n += right_sym.n;
right_sym.n = 0;
//printf("left = '%*s' size = %zu\n", (int) left_sym.n, left_sym.text, bigram.size);
// remove the right sym from the chain
left_sym.next = right_sym.next;
if (right_sym.next >= 0) {
symbols_[right_sym.next].prev = bigram.left;
}
// find more substitutions
try_add_bigram(left_sym.prev, bigram.left);
try_add_bigram(bigram.left, left_sym.next);
}
for (int i = 0; i != -1; i = symbols_[i].next) {
auto & symbol = symbols_[i];
auto token = vocab_.token_to_id.find(std::string(symbol.text, symbol.n));
if (token == vocab_.token_to_id.end()) {
// output any symbols that did not form tokens as bytes.
for (int j = 0; j < (int) symbol.n; ++j) {
llama_vocab::id token_id = static_cast<uint8_t>(symbol.text[j]) + 3;
output.push_back(token_id);
}
} else {
output.push_back((*token).second);
}
}
}
// Backward pass
int i = len;
while (i > 0) {
gpt_vocab::id token_id = prev[i];
if (token_id == 0) {
// TODO: Return error or something more meaningful
printf("failed to tokenize string!\n");
break;
private:
void try_add_bigram(int left, int right) {
if (left == -1 || right == -1) {
return;
}
res.push_back(token_id);
auto token = (*vocab.id_to_token.find(token_id)).second;
i -= token.length();
const std::string text = std::string(symbols_[left].text, symbols_[left].n + symbols_[right].n);
auto token = vocab_.token_to_id.find(text);
if (token == vocab_.token_to_id.end()) {
return;
}
auto score = vocab_.score.find((*token).second);
if (score == vocab_.score.end()) {
return;
}
llama_sp_bigram bigram;
bigram.left = left;
bigram.right = right;
bigram.score = (*score).second;
bigram.size = text.size();
work_queue_.push(bigram);
}
if (bos) {
res.push_back(1); // TODO: replace with vocab.bos
const llama_vocab & vocab_;
std::vector<llama_sp_symbol> symbols_;
llama_sp_bigram::queue work_queue_;
};
// TODO: temporary code duplication with llama.cpp
// will resolve after #77 is merged
bool llama_vocab_load(const std::string & fname, llama_vocab & vocab) {
std::ifstream fin(fname, std::ios::binary);
if (!fin.is_open()) {
return false;
}
// Pieces are in reverse order so correct that
std::reverse(res.begin(), res.end());
int n_vocab = 0;
fin.read((char *) &n_vocab, sizeof(n_vocab));
return res;
}
std::string word;
std::vector<char> tmp(64);
bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab) {
printf("%s: loading vocab from '%s'\n", __func__, fname.c_str());
for (int i = 0; i < n_vocab; i++) {
uint32_t len;
fin.read((char *) &len, sizeof(len));
vocab.token_to_id = ::json_parse(fname);
word.resize(len);
if (len > 0) {
tmp.resize(len);
fin.read(tmp.data(), len);
word.assign(tmp.data(), len);
} else {
word.clear();
}
for (const auto & kv : vocab.token_to_id) {
vocab.id_to_token[kv.second] = kv.first;
float score;
fin.read((char *) &score, sizeof(score));
vocab.token_to_id[word] = i;
vocab.id_to_token[i] = word;
vocab.score[i] = score;
}
printf("%s: vocab size = %d\n", __func__, (int) vocab.token_to_id.size());
// print the vocabulary
//for (auto kv : vocab.token_to_id) {
// printf("'%s' -> %d\n", kv.first.data(), kv.second);
//}
return true;
}
std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos) {
llama_tokenizer tokenizer(vocab);
std::vector<llama_vocab::id> output;
void sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k) {
if (text.size() == 0) {
return output;
}
if (bos) {
output.push_back(1);
}
tokenizer.tokenize(text, output);
return output;
}
void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k) {
// find the top K tokens
std::partial_sort(
logits_id.begin(),
logits_id.begin() + top_k, logits_id.end(),
[](const std::pair<double, gpt_vocab::id> & a, const std::pair<double, gpt_vocab::id> & b) {
[](const std::pair<double, llama_vocab::id> & a, const std::pair<double, llama_vocab::id> & b) {
return a.first > b.first;
});
logits_id.resize(top_k);
}
gpt_vocab::id llama_sample_top_p_top_k(
const gpt_vocab & vocab,
llama_vocab::id llama_sample_top_p_top_k(
const llama_vocab & vocab,
const float * logits,
std::vector<gpt_vocab::id> & last_n_tokens,
std::vector<llama_vocab::id> & last_n_tokens,
double repeat_penalty,
int top_k,
double top_p,
@ -391,7 +456,7 @@ gpt_vocab::id llama_sample_top_p_top_k(
std::mt19937 & rng) {
int n_logits = vocab.id_to_token.size();
std::vector<std::pair<double, gpt_vocab::id>> logits_id;
std::vector<std::pair<double, llama_vocab::id>> logits_id;
logits_id.reserve(n_logits);
{
@ -534,7 +599,7 @@ size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t
char * pdst = (char *) dst;
for (int j = 0; j < n; j += k) {
for (int j = 0; j < n; j += k) {
uint8_t * pd = (uint8_t *) (pdst + (j/k)*row_size + 0*bs);
uint8_t * pm = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + sizeof(float));
uint8_t * pb = (uint8_t *) (pdst + (j/k)*row_size + 0*bs + 2*sizeof(float));
@ -557,7 +622,7 @@ size_t ggml_quantize_q4_1(float * src, void * dst, int n, int k, int qk, int64_t
*(float *) pd = d;
*(float *) pm = min;
pd += bs;
pd += bs;
pm += bs;
for (int l = 0; l < qk; l += 2) {

70
utils.h
View file

@ -13,33 +13,34 @@
//
struct gpt_params {
int32_t seed = -1; // RNG seed
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t n_predict = 128; // new tokens to predict
int32_t seed = -1; // RNG seed
int32_t n_threads = std::min(4, (int32_t) std::thread::hardware_concurrency());
int32_t n_predict = 128; // new tokens to predict
int32_t repeat_last_n = 64; // last n tokens to penalize
int32_t n_ctx = 512; //context size
bool memory_f16 = false; // use f16 instead of f32 for memory kv
int32_t n_parts = -1; // amount of model parts (-1 = determine from model dimensions)
int32_t n_ctx = 512; //context size
// sampling parameters
int32_t top_k = 40;
float top_p = 0.95f;
float temp = 0.80f;
float repeat_penalty = 1.30f;
float repeat_penalty = 1.10f;
int32_t n_batch = 8; // batch size for prompt processing
std::string model = "models/lamma-7B/ggml-model.bin"; // model path
std::string prompt = "";
std::string model = "models/lamma-7B/ggml-model.bin"; // model path
std::string prompt = "";
bool random_prompt = false;
bool use_color = false; // use color to distinguish generations and inputs
bool interactive = false; // interactive mode
bool interactive_start = false; // reverse prompt immediately
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
bool instruct = false; // instruction mode (used for Alpaca models)
bool ignore_eos = false; // do not stop generating after eos
bool memory_f16 = false; // use f16 instead of f32 for memory kv
bool random_prompt = false; // do not randomize prompt if none provided
bool use_color = false; // use color to distinguish generations and inputs
bool interactive = false; // interactive mode
bool interactive_start = false; // reverse prompt immediately
bool instruct = false; // instruction mode (used for Alpaca models)
bool ignore_eos = false; // do not stop generating after eos
bool perplexity = false; // compute perplexity over the prompt
};
bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
@ -48,16 +49,25 @@ void gpt_print_usage(int argc, char ** argv, const gpt_params & params);
std::string gpt_random_prompt(std::mt19937 & rng);
//
// Model file parsing
//
#define FILE_MAGIC_UNVERSIONED 0x67676d6c // pre-versioned files
#define FILE_MAGIC 0x67676d66 // 'ggmf' in hex
#define FILE_VERSION 1
//
// Vocab utils
//
struct gpt_vocab {
struct llama_vocab {
using id = int32_t;
using token = std::string;
std::map<token, id> token_to_id;
std::map<id, token> id_to_token;
std::map<id, float> score;
};
void replace(std::string & str, const std::string & needle, const std::string & replacement);
@ -65,34 +75,22 @@ void replace(std::string & str, const std::string & needle, const std::string &
// poor-man's JSON parsing
std::map<std::string, int32_t> json_parse(const std::string & fname);
// split text into tokens
//
// ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53
//
// Regex (Python):
// r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"""
//
// Regex (C++):
// R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"
//
std::vector<gpt_vocab::id> gpt_tokenize(const gpt_vocab & vocab, const std::string & text);
// TODO: temporary until #77 is merged, need this now for some tokenizer tests
bool llama_vocab_load(const std::string & fname, llama_vocab & vocab);
// TODO: this is probably wrong, but I cannot figure out how this tokenizer works ..
// ref: https://github.com/google/sentencepiece
std::vector<gpt_vocab::id> llama_tokenize(const gpt_vocab & vocab, const std::string & text, bool bos);
// load the tokens from encoder.json
bool gpt_vocab_init(const std::string & fname, gpt_vocab & vocab);
std::vector<llama_vocab::id> llama_tokenize(const llama_vocab & vocab, const std::string & text, bool bos);
// sample next token given probabilities for each embedding
//
// - consider only the top K tokens
// - from them, consider only the top tokens with cumulative probability > P
//
gpt_vocab::id llama_sample_top_p_top_k(
const gpt_vocab & vocab,
llama_vocab::id llama_sample_top_p_top_k(
const llama_vocab & vocab,
const float * logits,
std::vector<gpt_vocab::id> & last_n_tokens,
std::vector<llama_vocab::id> & last_n_tokens,
double repeat_penalty,
int top_k,
double top_p,
@ -100,7 +98,7 @@ gpt_vocab::id llama_sample_top_p_top_k(
std::mt19937 & rng);
// filer to top K tokens from list of logits
void sample_top_k(std::vector<std::pair<double, gpt_vocab::id>> & logits_id, int top_k);
void sample_top_k(std::vector<std::pair<double, llama_vocab::id>> & logits_id, int top_k);
//
// Quantization