merge + working embeddings
This commit is contained in:
strikingLoo
commit
e4524da1a1
26 changed files with 1821 additions and 539 deletions
|
|
@ -6,7 +6,7 @@ RUN apt-get update && \
|
|||
apt-get install -y build-essential python3 python3-pip
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||||
|
||||
RUN pip install --upgrade pip setuptools wheel \
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&& pip install torch torchvision torchaudio sentencepiece numpy
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&& pip install numpy requests sentencepiece torch tqdm
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||||
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||||
WORKDIR /app
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||||
|
||||
|
|
@ -14,4 +14,4 @@ COPY . .
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|||
|
||||
RUN make
|
||||
|
||||
ENTRYPOINT ["/app/.devops/tools.sh"]
|
||||
ENTRYPOINT ["/app/.devops/tools.sh"]
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||||
|
|
|
|||
198
.github/ISSUE_TEMPLATE/custom.md
vendored
Normal file
198
.github/ISSUE_TEMPLATE/custom.md
vendored
Normal file
|
|
@ -0,0 +1,198 @@
|
|||
---
|
||||
name: Custom issue template
|
||||
about: Used to report user-related issues with the software
|
||||
title: "[User] I encountered a problem .."
|
||||
labels: ''
|
||||
assignees: ''
|
||||
|
||||
---
|
||||
|
||||
# Prerequisites
|
||||
|
||||
Please answer the following questions for yourself before submitting an issue.
|
||||
|
||||
- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now.
|
||||
- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md).
|
||||
- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed).
|
||||
- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share.
|
||||
|
||||
# Expected Behavior
|
||||
|
||||
Please provide a detailed written description of what you were trying to do, and what you expected `lamma.cpp` to do.
|
||||
|
||||
# Current Behavior
|
||||
|
||||
Please provide a detailed written description of what `lamma.cpp` did, instead.
|
||||
|
||||
# Environment and Context
|
||||
|
||||
Please provide detailed information about your computer setup. This is important in case the issue is not reproducible except for under certain specific conditions.
|
||||
|
||||
* Physical (or virtual) hardware you are using, e.g. for Linux:
|
||||
|
||||
`$ lscpu`
|
||||
|
||||
* Operating System, e.g. for Linux:
|
||||
|
||||
`$ uname -a`
|
||||
|
||||
* SDK version, e.g. for Linux:
|
||||
|
||||
```
|
||||
$ python3 --version
|
||||
$ make --version
|
||||
$ g++ --version
|
||||
```
|
||||
|
||||
# Models
|
||||
|
||||
* The LLaMA models are officially distributed by Facebook and will never be provided through this repository. See this [pull request in Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to obtain access to the model data.
|
||||
* If your issue is with model conversion please verify the `sha256sum` of each of your `consolidated*.pth` and `ggml-model-XXX.bin` files to confirm that you have the correct model data files before logging an issue. [Latest sha256 sums for your reference](https://github.com/ggerganov/llama.cpp/issues/238).
|
||||
* If your issue is with model generation quality then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
|
||||
* LLaMA:
|
||||
* [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
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||||
* [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
|
||||
* GPT-3
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||||
* [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
|
||||
* GPT-3.5 / InstructGPT / ChatGPT:
|
||||
* [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
|
||||
* [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
|
||||
|
||||
# Failure Information (for bugs)
|
||||
|
||||
Please help provide information about the failure if this is a bug. If it is not a bug, please remove the rest of this template.
|
||||
|
||||
# Steps to Reproduce
|
||||
|
||||
Please provide detailed steps for reproducing the issue. We are not sitting in front of your screen, so the more detail the better.
|
||||
|
||||
1. step 1
|
||||
2. step 2
|
||||
3. step 3
|
||||
4. etc.
|
||||
|
||||
# Failure Logs
|
||||
|
||||
Please include any relevant log snippets or files. If it works under one configuration but not under another, please provide logs for both configurations and their corresponding outputs so it is easy to see where behavior changes.
|
||||
|
||||
Also, please try to **avoid using screenshots** if at all possible. Instead, copy/paste the console output and use [Github's markdown](https://docs.github.com/en/get-started/writing-on-github/getting-started-with-writing-and-formatting-on-github/basic-writing-and-formatting-syntax) to cleanly format your logs for easy readability. e.g.
|
||||
|
||||
```
|
||||
llama.cpp$ git log | head -1
|
||||
commit 2af23d30434a677c6416812eea52ccc0af65119c
|
||||
|
||||
llama.cpp$ lscpu | egrep "AMD|Flags"
|
||||
Vendor ID: AuthenticAMD
|
||||
Model name: AMD Ryzen Threadripper 1950X 16-Core Processor
|
||||
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid amd_dcm aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb hw_pstate ssbd ibpb vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt sha_ni xsaveopt xsavec xgetbv1 xsaves clzero irperf xsaveerptr arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif overflow_recov succor smca sme sev
|
||||
Virtualization: AMD-V
|
||||
|
||||
llama.cpp$ python3 --version
|
||||
Python 3.10.9
|
||||
|
||||
llama.cpp$ pip list | egrep "torch|numpy|sentencepiece"
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||||
numpy 1.24.2
|
||||
numpydoc 1.5.0
|
||||
sentencepiece 0.1.97
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||||
torch 1.13.1
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||||
torchvision 0.14.1
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||||
|
||||
llama.cpp$ make --version | head -1
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||||
GNU Make 4.3
|
||||
|
||||
$ md5sum ./models/65B/ggml-model-q4_0.bin
|
||||
dbdd682cce80e2d6e93cefc7449df487 ./models/65B/ggml-model-q4_0.bin
|
||||
```
|
||||
Here's a run with the Linux command [perf](https://www.brendangregg.com/perf.html)
|
||||
|
||||
```
|
||||
llama.cpp$ perf stat ./main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p "Please close your issue when it has been answered."
|
||||
main: seed = 1679149377
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||||
llama_model_load: loading model from './models/65B/ggml-model-q4_0.bin' - please wait ...
|
||||
llama_model_load: n_vocab = 32000
|
||||
llama_model_load: n_ctx = 512
|
||||
llama_model_load: n_embd = 8192
|
||||
llama_model_load: n_mult = 256
|
||||
llama_model_load: n_head = 64
|
||||
llama_model_load: n_layer = 80
|
||||
llama_model_load: n_rot = 128
|
||||
llama_model_load: f16 = 2
|
||||
llama_model_load: n_ff = 22016
|
||||
llama_model_load: n_parts = 8
|
||||
llama_model_load: ggml ctx size = 41477.73 MB
|
||||
llama_model_load: memory_size = 2560.00 MB, n_mem = 40960
|
||||
llama_model_load: loading model part 1/8 from './models/65B/ggml-model-q4_0.bin'
|
||||
llama_model_load: .......................................................................................... done
|
||||
llama_model_load: model size = 4869.09 MB / num tensors = 723
|
||||
llama_model_load: loading model part 2/8 from './models/65B/ggml-model-q4_0.bin.1'
|
||||
llama_model_load: .......................................................................................... done
|
||||
llama_model_load: model size = 4869.09 MB / num tensors = 723
|
||||
llama_model_load: loading model part 3/8 from './models/65B/ggml-model-q4_0.bin.2'
|
||||
llama_model_load: .......................................................................................... done
|
||||
llama_model_load: model size = 4869.09 MB / num tensors = 723
|
||||
llama_model_load: loading model part 4/8 from './models/65B/ggml-model-q4_0.bin.3'
|
||||
llama_model_load: .......................................................................................... done
|
||||
llama_model_load: model size = 4869.09 MB / num tensors = 723
|
||||
llama_model_load: loading model part 5/8 from './models/65B/ggml-model-q4_0.bin.4'
|
||||
llama_model_load: .......................................................................................... done
|
||||
llama_model_load: model size = 4869.09 MB / num tensors = 723
|
||||
llama_model_load: loading model part 6/8 from './models/65B/ggml-model-q4_0.bin.5'
|
||||
llama_model_load: .......................................................................................... done
|
||||
llama_model_load: model size = 4869.09 MB / num tensors = 723
|
||||
llama_model_load: loading model part 7/8 from './models/65B/ggml-model-q4_0.bin.6'
|
||||
llama_model_load: .......................................................................................... done
|
||||
llama_model_load: model size = 4869.09 MB / num tensors = 723
|
||||
llama_model_load: loading model part 8/8 from './models/65B/ggml-model-q4_0.bin.7'
|
||||
llama_model_load: .......................................................................................... done
|
||||
llama_model_load: model size = 4869.09 MB / num tensors = 723
|
||||
|
||||
system_info: n_threads = 16 / 32 | AVX = 1 | AVX2 = 1 | AVX512 = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | VSX = 0 |
|
||||
|
||||
main: prompt: 'Please close your issue when it has been answered.'
|
||||
main: number of tokens in prompt = 11
|
||||
1 -> ''
|
||||
12148 -> 'Please'
|
||||
3802 -> ' close'
|
||||
596 -> ' your'
|
||||
2228 -> ' issue'
|
||||
746 -> ' when'
|
||||
372 -> ' it'
|
||||
756 -> ' has'
|
||||
1063 -> ' been'
|
||||
7699 -> ' answered'
|
||||
29889 -> '.'
|
||||
|
||||
sampling parameters: temp = 0.800000, top_k = 40, top_p = 0.950000, repeat_last_n = 64, repeat_penalty = 1.300000
|
||||
|
||||
|
||||
Please close your issue when it has been answered.
|
||||
@duncan-donut: I'm trying to figure out what kind of "support" you need for this script and why, exactly? Is there a question about how the code works that hasn't already been addressed in one or more comments below this ticket, or are we talking something else entirely like some sorta bugfixing job because your server setup is different from mine??
|
||||
I can understand if your site needs to be running smoothly and you need help with a fix of sorts but there should really be nothing wrong here that the code itself could not handle. And given that I'm getting reports about how it works perfectly well on some other servers, what exactly are we talking? A detailed report will do wonders in helping us get this resolved for ya quickly so please take your time and describe the issue(s) you see as clearly & concisely as possible!!
|
||||
@duncan-donut: I'm not sure if you have access to cPanel but you could try these instructions. It is worth a shot! Let me know how it goes (or what error message, exactly!) when/if ya give that code a go? [end of text]
|
||||
|
||||
|
||||
main: mem per token = 71159620 bytes
|
||||
main: load time = 19309.95 ms
|
||||
main: sample time = 168.62 ms
|
||||
main: predict time = 223895.61 ms / 888.47 ms per token
|
||||
main: total time = 246406.42 ms
|
||||
|
||||
Performance counter stats for './main -m ./models/65B/ggml-model-q4_0.bin -t 16 -n 1024 -p Please close your issue when it has been answered.':
|
||||
|
||||
3636882.89 msec task-clock # 14.677 CPUs utilized
|
||||
13509 context-switches # 3.714 /sec
|
||||
2436 cpu-migrations # 0.670 /sec
|
||||
10476679 page-faults # 2.881 K/sec
|
||||
13133115082869 cycles # 3.611 GHz (16.77%)
|
||||
29314462753 stalled-cycles-frontend # 0.22% frontend cycles idle (16.76%)
|
||||
10294402631459 stalled-cycles-backend # 78.39% backend cycles idle (16.74%)
|
||||
23479217109614 instructions # 1.79 insn per cycle
|
||||
# 0.44 stalled cycles per insn (16.76%)
|
||||
2353072268027 branches # 647.002 M/sec (16.77%)
|
||||
1998682780 branch-misses # 0.08% of all branches (16.76%)
|
||||
|
||||
247.802177522 seconds time elapsed
|
||||
|
||||
3618.573072000 seconds user
|
||||
18.491698000 seconds sys
|
||||
```
|
||||
5
.github/workflows/build.yml
vendored
5
.github/workflows/build.yml
vendored
|
|
@ -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 -C Release --output-on-failure
|
||||
|
||||
- name: Get commit hash
|
||||
id: commit
|
||||
|
|
@ -116,7 +119,7 @@ jobs:
|
|||
id: pack_artifacts
|
||||
if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }}
|
||||
run: |
|
||||
7z a llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-x64.zip .\build\Release\*
|
||||
7z a llama-${{ env.BRANCH_NAME }}-${{ steps.commit.outputs.short }}-bin-win-x64.zip .\build\bin\Release\*
|
||||
|
||||
- name: Create release
|
||||
id: create_release
|
||||
|
|
|
|||
2
.github/workflows/docker.yml
vendored
2
.github/workflows/docker.yml
vendored
|
|
@ -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
287
CMakeLists.txt
|
|
@ -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
63
Makefile
|
|
@ -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)
|
||||
|
|
|
|||
79
README.md
79
README.md
|
|
@ -5,9 +5,18 @@
|
|||
|
||||
Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
|
||||
|
||||
---
|
||||
|
||||
**TEMPORARY NOTICE:**
|
||||
Big code change incoming: https://github.com/ggerganov/llama.cpp/pull/370
|
||||
|
||||
Do not merge stuff until we merge this. Probably merge will happen on March 22 ~6:00am UTC
|
||||
|
||||
---
|
||||
|
||||
**Hot topics:**
|
||||
|
||||
- RMSNorm implementation / fixes: https://github.com/ggerganov/llama.cpp/issues/173
|
||||
- [Added Alpaca support](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca)
|
||||
- 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
|
||||
|
||||
|
|
@ -147,7 +156,7 @@ python3 -m pip install torch numpy sentencepiece
|
|||
python3 convert-pth-to-ggml.py models/7B/ 1
|
||||
|
||||
# quantize the model to 4-bits
|
||||
./quantize.sh 7B
|
||||
python3 quantize.py 7B
|
||||
|
||||
# run the inference
|
||||
./main -m ./models/7B/ggml-model-q4_0.bin -n 128
|
||||
|
|
@ -175,22 +184,70 @@ 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:" \
|
||||
-p \
|
||||
"Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
|
||||
|
||||
User: Hello, Bob.
|
||||
Bob: Hello. How may I help you today?
|
||||
User: Please tell me the largest city in Europe.
|
||||
Bob: Sure. The largest city in Europe is Moscow, the capital of Russia.
|
||||
User:"
|
||||
```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.
|
||||
|
||||
|
||||
|
||||
### Instruction mode with Alpaca
|
||||
|
||||
First, download the `ggml` Alpaca model into the `./models` folder:
|
||||
|
||||
```
|
||||
# use one of these
|
||||
# TODO: add a script to simplify the download
|
||||
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:
|
||||
|
||||
```
|
||||
./main -m ./models/ggml-alpaca-7b-q4.bin --color -f ./prompts/alpaca.txt -ins
|
||||
```
|
||||
|
||||
Sample run:
|
||||
|
||||
```
|
||||
== Running in interactive mode. ==
|
||||
- Press Ctrl+C to interject at any time.
|
||||
- Press Return to return control to LLaMa.
|
||||
- If you want to submit another line, end your input in '\'.
|
||||
|
||||
Below is an instruction that describes a task. Write a response that appropriately completes the request.
|
||||
|
||||
> How many letters are there in the English alphabet?
|
||||
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".
|
||||
cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach.
|
||||
>
|
||||
```
|
||||
|
||||
### Obtaining and verifying the Facebook LLaMA original model and Stanford Alpaca model data
|
||||
|
||||
* The LLaMA models are officially distributed by Facebook and will never be provided through this repository. See this [Pull Request in Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to obtain access to the model data.
|
||||
|
||||
* Please verify the sha256 checksums of all of your `consolidated*.pth` and corresponding converted `ggml-model-*.bin` model files to confirm that you have the correct model data files before creating an issue relating to your model files.
|
||||
|
||||
The following command will verify if you have all possible latest files in your self-installed `./models` subdirectory:
|
||||
|
||||
`sha256sum --ignore-missing -c SHA256SUMS` on Linux
|
||||
|
||||
or
|
||||
|
||||
`shasum -a 256 --ignore-missing -c SHA256SUMS` on macOS
|
||||
|
||||
### Android
|
||||
|
||||
You can easily run `llama.cpp` on Android device with [termux](https://play.google.com/store/apps/details?id=com.termux).
|
||||
|
|
|
|||
53
SHA256SUMS
Normal file
53
SHA256SUMS
Normal file
|
|
@ -0,0 +1,53 @@
|
|||
700df0d3013b703a806d2ae7f1bfb8e59814e3d06ae78be0c66368a50059f33d models/7B/consolidated.00.pth
|
||||
abe4aec2cdc297e2916011f66c7efd6fb4424e0e84315503005b5c118358cc22 models/7B/ggml-model-f16.bin
|
||||
f495fa02a0b5ef265e1864d9680eede7fd23a60b0a2f93edba8091e2a4ca68b9 models/7B/ggml-model-q4_0.bin
|
||||
7e89e242ddc0dd6f060b43ca219ce8b3e8f08959a72cb3c0855df8bb04d46265 models/7B/params.json
|
||||
745bf4e29a4dd6f411e72976d92b452da1b49168a4f41c951cfcc8051823cf08 models/13B/consolidated.00.pth
|
||||
d5ccbcc465c71c0de439a5aeffebe8344c68a519bce70bc7f9f92654ee567085 models/13B/consolidated.01.pth
|
||||
a6bd0537c6873f36c47292df0b6f794e1135f5aafb89c3343bcc9e93264bf167 models/13B/ggml-model-f16.bin
|
||||
0fb0951b90f2ec46c1f2f2372af5dacb4614b27e9fb6c10c69fbec58d7dd0e36 models/13B/ggml-model-f16.bin.1
|
||||
1c218ba37ae61e15e35efd9949c78d6edf553b6280824c263cad56ae0b9d5a8f models/13B/ggml-model-q4_0.bin
|
||||
c37a20c2ab9fa74b006b389085660269ee06110d1e45a494eb57d4602c9bcdb2 models/13B/ggml-model-q4_0.bin.1
|
||||
4ab77bec4d4405ccb66a97b282574c89a94417e3c32e5f68f37e2876fc21322f models/13B/params.json
|
||||
e23294a58552d8cdec5b7e8abb87993b97ea6eced4178ff2697c02472539d067 models/30B/consolidated.00.pth
|
||||
4e077b7136c7ae2302e954860cf64930458d3076fcde9443f4d0e939e95903ff models/30B/consolidated.01.pth
|
||||
24a87f01028cbd3a12de551dcedb712346c0b5cbdeff1454e0ddf2df9b675378 models/30B/consolidated.02.pth
|
||||
1adfcef71420886119544949767f6a56cb6339b4d5fcde755d80fe68b49de93b models/30B/consolidated.03.pth
|
||||
def20ea508f4e36793719f857471e85b85f96e497a2cbffbbaa1b60e2b18202c models/30B/ggml-model-f16.bin
|
||||
b37040aa67fa8608cb2d8e0719132cf3e267fd35ec1e2f0d37dbc9fa43d674f1 models/30B/ggml-model-f16.bin.1
|
||||
e7f263557e99069fe29003262ea5fa9ed885dbe79069083e6eb569b328cf30d3 models/30B/ggml-model-f16.bin.2
|
||||
2ad6a23af05eb720f202f63d130f4fc5de9b6d2efc95b921be003209a56695aa models/30B/ggml-model-f16.bin.3
|
||||
7de31d005e6d02ebd9603b2cf5329ad2f832b65d08873a098c5cafc4046cb9ed models/30B/ggml-model-q4_0.bin
|
||||
f91feef9f30f9a023616db2e91297ca6d5d5d7b9eb351e452a82115c46f7da9e models/30B/ggml-model-q4_0.bin.1
|
||||
66f3a0916ac7a81839153eb061fa861030ed1892477c2f7af2ce4f98d2f6d06f models/30B/ggml-model-q4_0.bin.2
|
||||
e3c587ba97f83d2088b001bcda3026571065649ee3090bef6743a51390b01d3b models/30B/ggml-model-q4_0.bin.3
|
||||
2c07118ea98d69dbe7810d88520e30288fa994751b337f8fca02b171955f44cb models/30B/params.json
|
||||
135c563f6b3938114458183afb01adc9a63bef3d8ff7cccc3977e5d3664ecafe models/65B/consolidated.00.pth
|
||||
9a600b37b19d38c7e43809485f70d17d1dc12206c07efa83bc72bb498a568bde models/65B/consolidated.01.pth
|
||||
e7babf7c5606f165a3756f527cb0fedc4f83e67ef1290391e52fb1cce5f26770 models/65B/consolidated.02.pth
|
||||
73176ffb426b40482f2aa67ae1217ef79fbbd1fff5482bae5060cdc5a24ab70e models/65B/consolidated.03.pth
|
||||
882e6431d0b08a8bc66261a0d3607da21cbaeafa96a24e7e59777632dbdac225 models/65B/consolidated.04.pth
|
||||
a287c0dfe49081626567c7fe87f74cce5831f58e459b427b5e05567641f47b78 models/65B/consolidated.05.pth
|
||||
72b4eba67a1a3b18cb67a85b70f8f1640caae9b40033ea943fb166bd80a7b36b models/65B/consolidated.06.pth
|
||||
d27f5b0677d7ff129ceacd73fd461c4d06910ad7787cf217b249948c3f3bc638 models/65B/consolidated.07.pth
|
||||
7eba2625260cd91f8de901fd9704a1aa39448425514a335a0d3878de4ab9dc77 models/65B/ggml-model-f16.bin
|
||||
f6aa886575df0785d4231f30cc776d499ccde18857818effc0378c65b178e0b5 models/65B/ggml-model-f16.bin.1
|
||||
076037141682f5d7537955058c4740ab27f285aa4588915f830874a589c0693d models/65B/ggml-model-f16.bin.2
|
||||
7853d96d2903ad7de2b2a89c4acf5a33a2f8e3c24ac39c9df6b44cdb42bf530a models/65B/ggml-model-f16.bin.3
|
||||
b16b7b941abb3bc03a14df1656140855e9360a5371c83e919b9da83a72362314 models/65B/ggml-model-f16.bin.4
|
||||
5291270216f888697695acb78ef28df0c080f9e85d3245c92fb9992d1fde6678 models/65B/ggml-model-f16.bin.5
|
||||
0685ee77715f34686841006f8f94d3e7eaf148b97cecc9d3eee72808b0f7989c models/65B/ggml-model-f16.bin.6
|
||||
00d993d73bb21d7c29388ffe0dced008cbaa0d391831dea77d7eb8f0b5c404b9 models/65B/ggml-model-f16.bin.7
|
||||
4e398f05842206e08cdc5e7bb4f6c7c34b9dc373435ece6f261b14b7b4fe9b89 models/65B/ggml-model-q4_0.bin
|
||||
4c4e899e3b12d9f57c9dcea5a1fb41bbc72023323535551f6273582ca7d7294b models/65B/ggml-model-q4_0.bin.1
|
||||
d7b4594bbbd192043b3db0e5acc2561c42e6944e1cb91cc6e61510eee89dbcd8 models/65B/ggml-model-q4_0.bin.2
|
||||
9a099d271648863d923d0d097391ea0bc75591f27a2ca3a327760f42e6b69af2 models/65B/ggml-model-q4_0.bin.3
|
||||
5ee474051e418c5732b7949190b084d9d679db447f83c1de0d2a82daaa1a0cfa models/65B/ggml-model-q4_0.bin.4
|
||||
a45aa05e7212bd6782790722d68056c5419667ea6b564ccc94bbcb8111d79b8b models/65B/ggml-model-q4_0.bin.5
|
||||
a58fda714b759c28ad5e4c1d8bf8fda7b158fd5e4c4a49f851f36342fa97a105 models/65B/ggml-model-q4_0.bin.6
|
||||
a3540cfcbcda33c223c6b0d606034adbd78f17e0e5de1582b78795e78754f7a8 models/65B/ggml-model-q4_0.bin.7
|
||||
999ed1659b469ccc2a941714c0a9656fa571d17c9f7c8c7589817ca90edef51b models/65B/params.json
|
||||
1f582babc2bd56bb63b33141898748657d369fd110c4358b2bc280907882bf13 models/alpaca-7B/ggml-model-q4_0.bin
|
||||
e17730c6b62b565b098af023ca446dcb9e3535d4222ead6369c7aae67207eb3d models/alpaca-13B/ggml-model-q4_0.bin
|
||||
9bcd1bb30e679c939f367be11b030fe20b3eb9a3606b9bc4106420f1827b6ae4 models/alpaca-30B/ggml-model-q4_0.bin
|
||||
36079249f53c292a4c2302d7784005dcae94c865f0bedfdbfa51d9ddad402935 models/alpaca-30B/params.json
|
||||
6
alpaca.sh
Executable file
6
alpaca.sh
Executable file
|
|
@ -0,0 +1,6 @@
|
|||
#!/bin/bash
|
||||
#
|
||||
# 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 0.2 --repeat_penalty 1 -t 7
|
||||
6
chat.sh
Executable file
6
chat.sh
Executable 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
172
convert-gptq-to-ggml.py
Normal 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("")
|
||||
|
|
@ -10,150 +10,104 @@
|
|||
# - 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
|
||||
|
||||
if len(sys.argv) < 3:
|
||||
print("Usage: convert-ckpt-to-ggml.py dir-model ftype\n")
|
||||
print(" ftype == 0 -> float32")
|
||||
print(" ftype == 1 -> float16")
|
||||
sys.exit(1)
|
||||
def parse_args():
|
||||
|
||||
# output in the same directory as the model
|
||||
dir_model = sys.argv[1]
|
||||
|
||||
fname_hparams = sys.argv[1] + "/params.json"
|
||||
fname_tokenizer = sys.argv[1] + "/../tokenizer.model"
|
||||
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', 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):
|
||||
if dim == 4096:
|
||||
return 1
|
||||
elif dim == 5120:
|
||||
return 2
|
||||
elif dim == 6656:
|
||||
return 4
|
||||
elif dim == 8192:
|
||||
return 8
|
||||
else:
|
||||
print("Invalid dim: " + str(dim))
|
||||
|
||||
mappings = {4096: 1, 5120: 2, 6656: 4, 8192: 8}
|
||||
n_parts = mappings.get(dim)
|
||||
if n_parts is None:
|
||||
print(f"Invalid dim: {dim}")
|
||||
sys.exit(1)
|
||||
|
||||
# possible data types
|
||||
# ftype == 0 -> float32
|
||||
# ftype == 1 -> float16
|
||||
#
|
||||
# map from ftype to string
|
||||
ftype_str = ["f32", "f16"]
|
||||
print(f"n_parts = {n_parts}\n")
|
||||
return n_parts
|
||||
|
||||
ftype = 1
|
||||
if len(sys.argv) > 2:
|
||||
ftype = int(sys.argv[2])
|
||||
if ftype < 0 or ftype > 1:
|
||||
print("Invalid ftype: " + str(ftype))
|
||||
sys.exit(1)
|
||||
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
|
||||
def load_hparams_and_tokenizer(dir_model):
|
||||
|
||||
if os.path.exists(fname_out):
|
||||
print(f"Skip conversion, it already exists: {fname_out}")
|
||||
sys.exit(0)
|
||||
# `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))
|
||||
|
||||
with open(fname_hparams, "r") as f:
|
||||
hparams = json.load(f)
|
||||
fname_hparams = f"{dir_model}/params.json"
|
||||
fname_tokenizer = f"{model_parent_dir}/tokenizer.model"
|
||||
|
||||
tokenizer = SentencePieceProcessor(fname_tokenizer)
|
||||
with open(fname_hparams, "r") as f:
|
||||
hparams = json.load(f)
|
||||
print(hparams)
|
||||
|
||||
hparams.update({"vocab_size": tokenizer.vocab_size()})
|
||||
tokenizer = SentencePieceProcessor(fname_tokenizer)
|
||||
hparams.update({"vocab_size": tokenizer.vocab_size()})
|
||||
|
||||
n_parts = get_n_parts(hparams["dim"])
|
||||
return hparams, tokenizer
|
||||
|
||||
print(hparams)
|
||||
print('n_parts = ', n_parts)
|
||||
def write_header(fout, hparams, ftype):
|
||||
|
||||
for p in range(n_parts):
|
||||
print('Processing part ', p)
|
||||
keys = ["vocab_size", "dim", "multiple_of", "n_heads", "n_layers"]
|
||||
values = [
|
||||
0x67676d66, # magic: ggmf in hex
|
||||
1, # file version
|
||||
*[hparams[key] for key in keys],
|
||||
hparams["dim"] // hparams["n_heads"], # rot (obsolete)
|
||||
ftype
|
||||
]
|
||||
fout.write(struct.pack("i" * len(values), *values))
|
||||
|
||||
#fname_model = sys.argv[1] + "/consolidated.00.pth"
|
||||
fname_model = sys.argv[1] + "/consolidated.0" + str(p) + ".pth"
|
||||
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin"
|
||||
if (p > 0):
|
||||
fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".bin" + "." + str(p)
|
||||
def write_tokens(fout, tokenizer):
|
||||
|
||||
model = torch.load(fname_model, map_location="cpu")
|
||||
|
||||
fout = open(fname_out, "wb")
|
||||
|
||||
fout.write(struct.pack("i", 0x67676d6c)) # magic: ggml in hex
|
||||
fout.write(struct.pack("i", hparams["vocab_size"]))
|
||||
fout.write(struct.pack("i", hparams["dim"]))
|
||||
fout.write(struct.pack("i", hparams["multiple_of"]))
|
||||
fout.write(struct.pack("i", hparams["n_heads"]))
|
||||
fout.write(struct.pack("i", hparams["n_layers"]))
|
||||
fout.write(struct.pack("i", hparams["dim"] // hparams["n_heads"])) # rot (obsolete)
|
||||
fout.write(struct.pack("i", ftype))
|
||||
|
||||
# Is this correct??
|
||||
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))
|
||||
text = b""
|
||||
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)
|
||||
print(f"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))
|
||||
text = 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)
|
||||
fout.write(struct.pack("i", len(text)))
|
||||
fout.write(text)
|
||||
fout.write(struct.pack("f", tokenizer.get_score(i)))
|
||||
|
||||
for k, v in model.items():
|
||||
name = k
|
||||
shape = v.shape
|
||||
def process_and_write_variables(fout, model, ftype):
|
||||
|
||||
# skip layers.X.attention.inner_attention.rope.freqs
|
||||
if name[-5:] == "freqs":
|
||||
for name, datao in model.items():
|
||||
|
||||
if name.endswith("freqs"):
|
||||
continue
|
||||
|
||||
print("Processing variable: " + name + " with shape: ", shape, " and type: ", v.dtype)
|
||||
shape = datao.shape
|
||||
|
||||
#data = tf.train.load_variable(dir_model, name).squeeze()
|
||||
data = v.numpy().squeeze()
|
||||
n_dims = len(data.shape);
|
||||
print(f"Processing variable: {name} with shape: {shape} and type: {datao.dtype}")
|
||||
|
||||
# for efficiency - transpose some matrices
|
||||
# "model/h.*/attn/c_attn/w"
|
||||
# "model/h.*/attn/c_proj/w"
|
||||
# "model/h.*/mlp/c_fc/w"
|
||||
# "model/h.*/mlp/c_proj/w"
|
||||
#if name[-14:] == "/attn/c_attn/w" or \
|
||||
# name[-14:] == "/attn/c_proj/w" or \
|
||||
# name[-11:] == "/mlp/c_fc/w" or \
|
||||
# name[-13:] == "/mlp/c_proj/w":
|
||||
# print(" Transposing")
|
||||
# data = data.transpose()
|
||||
|
||||
dshape = data.shape
|
||||
data = datao.numpy().squeeze()
|
||||
n_dims = len(shape)
|
||||
|
||||
# default type is fp16
|
||||
ftype_cur = 1
|
||||
|
|
@ -164,18 +118,64 @@ for p in range(n_parts):
|
|||
|
||||
# header
|
||||
sname = name.encode('utf-8')
|
||||
fout.write(struct.pack("iii", n_dims, len(sname), ftype_cur))
|
||||
for i in range(n_dims):
|
||||
fout.write(struct.pack("i", dshape[n_dims - 1 - i]))
|
||||
fout.write(sname);
|
||||
fout.write(struct.pack("iii", len(data.shape), len(sname), ftype_cur))
|
||||
for dim in reversed(data.shape):
|
||||
fout.write(struct.pack("i", dim))
|
||||
fout.write(sname)
|
||||
|
||||
# data
|
||||
# data output to file
|
||||
data.tofile(fout)
|
||||
|
||||
# I hope this deallocates the memory ..
|
||||
model = None
|
||||
def main():
|
||||
|
||||
fout.close()
|
||||
args = parse_args()
|
||||
dir_model = args.dir_model
|
||||
ftype = args.ftype
|
||||
ftype_str = ["f32", "f16"]
|
||||
|
||||
print("Done. Output file: " + fname_out + ", (part ", p, ")")
|
||||
print("")
|
||||
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):
|
||||
|
||||
print(f"Processing part {p}\n")
|
||||
|
||||
fname_model = f"{dir_model}/consolidated.0{p}.pth"
|
||||
fname_out = f"{dir_model}/ggml-model-{ftype_str[ftype]}.bin{'' if p == 0 else '.' + str(p)}"
|
||||
|
||||
model = torch.load(fname_model, map_location="cpu")
|
||||
|
||||
with open(fname_out, "wb") as fout:
|
||||
write_header(fout, hparams, ftype)
|
||||
write_tokens(fout, tokenizer)
|
||||
process_and_write_variables(fout, model, ftype)
|
||||
|
||||
del model
|
||||
|
||||
print(f"Done. Output file: {fname_out}, (part {p})\n")
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
|
|||
53
examples/chatLLaMa
Executable file
53
examples/chatLLaMa
Executable 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 aloud 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
|
||||
$USER_NAME:" "$@"
|
||||
|
|
@ -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; [
|
||||
|
|
|
|||
86
ggml.c
86
ggml.c
|
|
@ -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++) {
|
||||
|
|
@ -5556,7 +5630,7 @@ static void ggml_compute_forward_rms_norm_f32(
|
|||
const size_t nb2 = dst->nb[2];
|
||||
const size_t nb3 = dst->nb[3];
|
||||
|
||||
const ggml_float eps = 1e-5f; // TODO: make this a parameter
|
||||
const ggml_float eps = 1e-6f; // TODO: make this a parameter
|
||||
|
||||
// TODO: optimize
|
||||
for (int i03 = 0; i03 < ne03; i03++) {
|
||||
|
|
@ -5572,7 +5646,7 @@ static void ggml_compute_forward_rms_norm_f32(
|
|||
mean /= ne00;
|
||||
|
||||
float * y = (float *) ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3);
|
||||
|
||||
|
||||
memcpy(y, x, ne00 * sizeof(float));
|
||||
// for (int i00 = 0; i00 < ne00; i00++) {
|
||||
// y[i00] = x[i00];
|
||||
|
|
|
|||
455
main.cpp
455
main.cpp
|
|
@ -3,11 +3,12 @@
|
|||
#include "utils.h"
|
||||
|
||||
#include <cassert>
|
||||
#include <cinttypes>
|
||||
#include <cmath>
|
||||
#include <cstdio>
|
||||
#include <cstring>
|
||||
#include <fstream>
|
||||
#include <map>
|
||||
#include <iostream>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
|
||||
|
|
@ -18,6 +19,13 @@
|
|||
#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"
|
||||
|
|
@ -27,8 +35,40 @@
|
|||
#define ANSI_COLOR_RESET "\x1b[0m"
|
||||
#define ANSI_BOLD "\x1b[1m"
|
||||
|
||||
/* Keep track of current color of output, and emit ANSI code if it changes. */
|
||||
enum console_state {
|
||||
CONSOLE_STATE_DEFAULT=0,
|
||||
CONSOLE_STATE_PROMPT,
|
||||
CONSOLE_STATE_USER_INPUT
|
||||
};
|
||||
|
||||
static console_state con_st = CONSOLE_STATE_DEFAULT;
|
||||
static bool con_use_color = false;
|
||||
|
||||
void set_console_state(console_state new_st)
|
||||
{
|
||||
if (!con_use_color) return;
|
||||
// only emit color code if state changed
|
||||
if (new_st != con_st) {
|
||||
con_st = new_st;
|
||||
switch(con_st) {
|
||||
case CONSOLE_STATE_DEFAULT:
|
||||
printf(ANSI_COLOR_RESET);
|
||||
return;
|
||||
case CONSOLE_STATE_PROMPT:
|
||||
printf(ANSI_COLOR_YELLOW);
|
||||
return;
|
||||
case CONSOLE_STATE_USER_INPUT:
|
||||
printf(ANSI_BOLD ANSI_COLOR_GREEN);
|
||||
return;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
static const int EOS_TOKEN_ID = 2;
|
||||
|
||||
// determine number of model parts based on the dimension
|
||||
static const std::map<int, int> LLAMA_N_PARTS = {
|
||||
static const std::unordered_map<int, int> LLAMA_N_PARTS = {
|
||||
{ 4096, 1 },
|
||||
{ 5120, 2 },
|
||||
{ 6656, 4 },
|
||||
|
|
@ -82,11 +122,12 @@ struct llama_model {
|
|||
|
||||
//
|
||||
struct ggml_context * ctx;
|
||||
std::map<std::string, struct ggml_tensor *> tensors;
|
||||
std::unordered_map<std::string, struct ggml_tensor *> tensors;
|
||||
};
|
||||
|
||||
// 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) {
|
||||
|
||||
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);
|
||||
|
|
@ -102,14 +143,27 @@ 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
|
||||
{
|
||||
|
|
@ -127,7 +181,16 @@ 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);
|
||||
|
|
@ -144,30 +207,43 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
|
|||
// load vocab
|
||||
{
|
||||
std::string word;
|
||||
vocab.id_to_token.resize(model.hparams.n_vocab);
|
||||
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());
|
||||
//}
|
||||
auto &tok_score = vocab.id_to_token[i];
|
||||
tok_score.tok = word;
|
||||
tok_score.score = 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",
|
||||
|
|
@ -176,8 +252,6 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
|
|||
}
|
||||
}
|
||||
|
||||
const ggml_type wtype2 = GGML_TYPE_F32;
|
||||
|
||||
auto & ctx = model.ctx;
|
||||
|
||||
size_t ctx_size = 0;
|
||||
|
|
@ -190,11 +264,11 @@ 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
|
||||
|
||||
|
|
@ -209,8 +283,8 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
|
|||
ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w2
|
||||
ctx_size += n_layer*(n_ff*n_embd*ggml_type_sizef(wtype)); // w3
|
||||
|
||||
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_k
|
||||
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(GGML_TYPE_F32); // memory_v
|
||||
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_k
|
||||
ctx_size += n_ctx*n_layer*n_embd*ggml_type_sizef(memory_type); // memory_v
|
||||
|
||||
ctx_size += (5 + 10*n_layer)*256; // object overhead
|
||||
|
||||
|
|
@ -237,15 +311,14 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
|
|||
|
||||
const int n_embd = hparams.n_embd;
|
||||
const int n_layer = hparams.n_layer;
|
||||
const int n_ctx = hparams.n_ctx;
|
||||
const int n_vocab = hparams.n_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;
|
||||
|
|
@ -296,8 +369,8 @@ bool llama_model_load(const std::string & fname, llama_model & model, gpt_vocab
|
|||
const int n_mem = n_layer*n_ctx;
|
||||
const int n_elements = n_embd*n_mem;
|
||||
|
||||
model.memory_k = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
|
||||
model.memory_v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_elements);
|
||||
model.memory_k = ggml_new_tensor_1d(ctx, memory_type, n_elements);
|
||||
model.memory_v = ggml_new_tensor_1d(ctx, memory_type, n_elements);
|
||||
|
||||
const size_t memory_size = ggml_nbytes(model.memory_k) + ggml_nbytes(model.memory_v);
|
||||
|
||||
|
|
@ -536,10 +609,12 @@ 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 bool embedding_mode) {
|
||||
const std::vector<llama_vocab::id> & embd_inp,
|
||||
std::vector<float> & embd_w,
|
||||
size_t & mem_per_token,
|
||||
const bool embedding_mode,
|
||||
bool return_all_logits = false
|
||||
) {
|
||||
const int N = embd_inp.size();
|
||||
|
||||
const auto & hparams = model.hparams;
|
||||
|
|
@ -551,15 +626,13 @@ bool llama_eval(
|
|||
const int n_vocab = hparams.n_vocab;
|
||||
const int n_rot = hparams.n_embd/hparams.n_head;
|
||||
|
||||
const int d_key = n_embd/n_head;
|
||||
|
||||
// TODO: check if this size scales with n_ctx linearly and remove constant. somehow I feel it wasn't the case
|
||||
// TODO: check if this size scales with n_ctx linearly and remove constant. somehow I feel it wasn't the case
|
||||
// static size_t buf_size = hparams.n_ctx*1024*1024;
|
||||
static size_t buf_size = 512u*1024*1024;
|
||||
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
|
||||
|
|
@ -730,13 +803,14 @@ bool llama_eval(
|
|||
{
|
||||
inpL = ggml_mul_mat(ctx0, model.output, inpL);
|
||||
}
|
||||
|
||||
// logits -> probs
|
||||
//inpL = ggml_soft_max(ctx0, inpL);
|
||||
|
||||
// run the computation
|
||||
ggml_build_forward_expand(&gf, inpL);
|
||||
ggml_graph_compute (ctx0, &gf);
|
||||
|
||||
|
||||
//if (n_past%100 == 0) {
|
||||
// ggml_graph_print (&gf);
|
||||
// ggml_graph_dump_dot(&gf, NULL, "gpt-2.dot");
|
||||
|
|
@ -745,18 +819,22 @@ 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;
|
||||
}
|
||||
//fprintf(stderr, "used_mem = %zu\n", ggml_used_mem(ctx0));
|
||||
|
||||
ggml_free(ctx0);
|
||||
return true;
|
||||
} else {
|
||||
// capture input sentence embedding
|
||||
// capture input sentence embedding
|
||||
ggml_build_forward_expand(&gf, inpL);
|
||||
ggml_graph_compute (ctx0, &gf);
|
||||
std::vector<float> embedding_representation;
|
||||
|
|
@ -765,15 +843,87 @@ bool llama_eval(
|
|||
display_embedding(embedding_representation);
|
||||
ggml_free(ctx0);
|
||||
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 ¶ms, 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, false, 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)
|
||||
void sigint_handler(int signo) {
|
||||
printf(ANSI_COLOR_RESET);
|
||||
set_console_state(CONSOLE_STATE_DEFAULT);
|
||||
printf("\n"); // this also force flush stdout.
|
||||
if (signo == SIGINT) {
|
||||
if (!is_interacting) {
|
||||
is_interacting=true;
|
||||
|
|
@ -814,7 +964,7 @@ int main(int argc, char ** argv) {
|
|||
if (gpt_params_parse(argc, argv, params) == false) {
|
||||
return 1;
|
||||
}
|
||||
|
||||
|
||||
if (params.n_ctx > 2048) {
|
||||
fprintf(stderr, "%s: warning: model does not support context sizes greater than 2048 tokens (%d specified);"
|
||||
"expect poor results\n", __func__, params.n_ctx);
|
||||
|
|
@ -827,22 +977,27 @@ int main(int argc, char ** argv) {
|
|||
fprintf(stderr, "%s: seed = %d\n", __func__, params.seed);
|
||||
|
||||
std::mt19937 rng(params.seed);
|
||||
if (params.prompt.empty()) {
|
||||
if (params.random_prompt) {
|
||||
params.prompt = gpt_random_prompt(rng);
|
||||
}
|
||||
|
||||
// save choice to use color for later
|
||||
// (note for later: this is a slightly awkward choice)
|
||||
con_use_color = params.use_color;
|
||||
|
||||
// params.prompt = R"(// this function checks if the number n is prime
|
||||
//bool is_prime(int n) {)";
|
||||
|
||||
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)) {
|
||||
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;
|
||||
}
|
||||
|
|
@ -857,28 +1012,49 @@ 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, false);
|
||||
|
||||
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());
|
||||
|
||||
// tokenize the reverse prompt
|
||||
std::vector<gpt_vocab::id> antiprompt_inp = ::llama_tokenize(vocab, params.antiprompt, false);
|
||||
// prefix & suffix for instruct mode
|
||||
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) {
|
||||
params.interactive = true;
|
||||
params.antiprompt.push_back("### Instruction:\n\n");
|
||||
}
|
||||
|
||||
// enable interactive mode if reverse prompt is specified
|
||||
if (params.antiprompt.size() != 0) {
|
||||
params.interactive = true;
|
||||
}
|
||||
|
||||
fprintf(stderr, "\n");
|
||||
fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
|
||||
fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size());
|
||||
for (int i = 0; i < (int) embd_inp.size(); i++) {
|
||||
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).c_str());
|
||||
fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], vocab.id_to_token.at(embd_inp[i]).tok.c_str());
|
||||
}
|
||||
fprintf(stderr, "\n");
|
||||
if (params.interactive) {
|
||||
|
|
@ -894,26 +1070,20 @@ int main(int argc, char ** argv) {
|
|||
|
||||
fprintf(stderr, "%s: interactive mode on.\n", __func__);
|
||||
|
||||
if(antiprompt_inp.size()) {
|
||||
fprintf(stderr, "%s: reverse prompt: '%s'\n", __func__, params.antiprompt.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());
|
||||
if(params.antiprompt.size()) {
|
||||
for (auto antiprompt : params.antiprompt) {
|
||||
fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str());
|
||||
}
|
||||
fprintf(stderr, "\n");
|
||||
}
|
||||
}
|
||||
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;
|
||||
std::vector<llama_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, false);
|
||||
|
||||
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) {
|
||||
|
|
@ -922,22 +1092,27 @@ int main(int argc, char ** argv) {
|
|||
" - Press Ctrl+C to interject at any time.\n"
|
||||
#endif
|
||||
" - Press Return to return control to LLaMa.\n"
|
||||
" - If you want to submit another line, end your input in '\\'.\n");
|
||||
}
|
||||
|
||||
int remaining_tokens = params.n_predict;
|
||||
int input_consumed = 0;
|
||||
bool input_noecho = false;
|
||||
|
||||
// prompt user immediately after the starting prompt has been loaded
|
||||
if (params.interactive_start) {
|
||||
" - If you want to submit another line, end your input in '\\'.\n\n");
|
||||
is_interacting = true;
|
||||
}
|
||||
|
||||
// set the color for the prompt which will be output initially
|
||||
if (params.use_color) {
|
||||
printf(ANSI_COLOR_YELLOW);
|
||||
int input_consumed = 0;
|
||||
bool input_noecho = false;
|
||||
|
||||
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);
|
||||
|
||||
if (params.embedding){
|
||||
embd = embd_inp;
|
||||
|
|
@ -955,7 +1130,7 @@ int main(int argc, char ** argv) {
|
|||
return 0;
|
||||
}
|
||||
|
||||
while (remaining_tokens > 0) {
|
||||
while (remaining_tokens > 0 || params.interactive) {
|
||||
// predict
|
||||
if (embd.size() > 0) {
|
||||
const int64_t t_start_us = ggml_time_us();
|
||||
|
|
@ -971,7 +1146,7 @@ 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;
|
||||
|
|
@ -980,11 +1155,16 @@ 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();
|
||||
|
||||
if (params.ignore_eos) {
|
||||
// set the logit of the eos token to zero to avoid sampling it
|
||||
logits[logits.size() - n_vocab + EOS_TOKEN_ID] = 0;
|
||||
}
|
||||
|
||||
id = llama_sample_top_p_top_k(vocab, logits.data() + (logits.size() - n_vocab), last_n_tokens, repeat_penalty, top_k, top_p, temp, rng);
|
||||
|
||||
last_n_tokens.erase(last_n_tokens.begin());
|
||||
|
|
@ -1003,79 +1183,100 @@ 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]);
|
||||
++input_consumed;
|
||||
if (embd.size() > params.n_batch) {
|
||||
if ((int) embd.size() >= params.n_batch) {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// reset color to default if we there is no pending user input
|
||||
if (!input_noecho && params.use_color && embd_inp.size() == input_consumed) {
|
||||
printf(ANSI_COLOR_RESET);
|
||||
}
|
||||
}
|
||||
|
||||
// display text
|
||||
if (!input_noecho) {
|
||||
for (auto id : embd) {
|
||||
printf("%s", vocab.id_to_token[id].c_str());
|
||||
printf("%s", vocab.id_to_token[id].tok.c_str());
|
||||
}
|
||||
fflush(stdout);
|
||||
}
|
||||
// reset color to default if we there is no pending user input
|
||||
if (!input_noecho && (int)embd_inp.size() == input_consumed) {
|
||||
set_console_state(CONSOLE_STATE_DEFAULT);
|
||||
}
|
||||
|
||||
// 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
|
||||
if (antiprompt_inp.size() && std::equal(antiprompt_inp.rbegin(), antiprompt_inp.rend(), last_n_tokens.rbegin())) {
|
||||
// reverse prompt found
|
||||
is_interacting = true;
|
||||
std::string last_output;
|
||||
for (auto id : last_n_tokens) {
|
||||
last_output += vocab.id_to_token[id].tok;
|
||||
}
|
||||
|
||||
// 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;
|
||||
}
|
||||
}
|
||||
if (is_interacting) {
|
||||
// currently being interactive
|
||||
bool another_line=true;
|
||||
while (another_line) {
|
||||
fflush(stdout);
|
||||
char buf[256] = {0};
|
||||
int n_read;
|
||||
if(params.use_color) printf(ANSI_BOLD ANSI_COLOR_GREEN);
|
||||
if (scanf("%255[^\n]%n%*c", buf, &n_read) <= 0) {
|
||||
// presumable empty line, consume the newline
|
||||
std::ignore = scanf("%*c");
|
||||
n_read=0;
|
||||
}
|
||||
if(params.use_color) printf(ANSI_COLOR_RESET);
|
||||
// potentially set color to indicate we are taking user input
|
||||
set_console_state(CONSOLE_STATE_USER_INPUT);
|
||||
|
||||
if (n_read > 0 && buf[n_read-1]=='\\') {
|
||||
another_line = true;
|
||||
buf[n_read-1] = '\n';
|
||||
buf[n_read] = 0;
|
||||
} else {
|
||||
another_line = false;
|
||||
buf[n_read] = '\n';
|
||||
buf[n_read+1] = 0;
|
||||
}
|
||||
if (params.instruct) {
|
||||
input_consumed = embd_inp.size();
|
||||
embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
|
||||
|
||||
std::vector<gpt_vocab::id> line_inp = ::llama_tokenize(vocab, buf, false);
|
||||
embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());
|
||||
|
||||
remaining_tokens -= line_inp.size();
|
||||
|
||||
input_noecho = true; // do not echo this again
|
||||
printf("\n> ");
|
||||
}
|
||||
|
||||
is_interacting = false;
|
||||
std::string buffer;
|
||||
std::string line;
|
||||
bool another_line = true;
|
||||
do {
|
||||
std::getline(std::cin, line);
|
||||
if (line.empty() || line.back() != '\\') {
|
||||
another_line = false;
|
||||
} else {
|
||||
line.pop_back(); // Remove the continue character
|
||||
}
|
||||
buffer += line + '\n'; // Append the line to the result
|
||||
} while (another_line);
|
||||
|
||||
// done taking input, reset color
|
||||
set_console_state(CONSOLE_STATE_DEFAULT);
|
||||
|
||||
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) {
|
||||
embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
|
||||
}
|
||||
|
||||
remaining_tokens -= line_inp.size();
|
||||
|
||||
input_noecho = true; // do not echo this again
|
||||
}
|
||||
is_interacting = false;
|
||||
}
|
||||
|
||||
// end of text token
|
||||
if (embd.back() == 2) {
|
||||
fprintf(stderr, " [end of text]\n");
|
||||
break;
|
||||
if (embd.back() == EOS_TOKEN_ID) {
|
||||
if (params.interactive) {
|
||||
is_interacting = true;
|
||||
} else {
|
||||
fprintf(stderr, " [end of text]\n");
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// In interactive mode, respect the maximum number of tokens and drop back to user input when reached.
|
||||
if (params.interactive && remaining_tokens <= 0) {
|
||||
remaining_tokens = params.n_predict;
|
||||
is_interacting = true;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -1097,9 +1298,7 @@ int main(int argc, char ** argv) {
|
|||
|
||||
ggml_free(model.ctx);
|
||||
|
||||
if (params.use_color) {
|
||||
printf(ANSI_COLOR_RESET);
|
||||
}
|
||||
set_console_state(CONSOLE_STATE_DEFAULT);
|
||||
|
||||
return 0;
|
||||
}
|
||||
|
|
|
|||
BIN
models/ggml-vocab.bin
Normal file
BIN
models/ggml-vocab.bin
Normal file
Binary file not shown.
1
prompts/alpaca.txt
Normal file
1
prompts/alpaca.txt
Normal file
|
|
@ -0,0 +1 @@
|
|||
Below is an instruction that describes a task. Write a response that appropriately completes the request.
|
||||
7
prompts/chat-with-bob.txt
Normal file
7
prompts/chat-with-bob.txt
Normal file
|
|
@ -0,0 +1,7 @@
|
|||
Transcript of a dialog, where the User interacts with an Assistant named Bob. Bob is helpful, kind, honest, good at writing, and never fails to answer the User's requests immediately and with precision.
|
||||
|
||||
User: Hello, Bob.
|
||||
Bob: Hello. How may I help you today?
|
||||
User: Please tell me the largest city in Europe.
|
||||
Bob: Sure. The largest city in Europe is Moscow, the capital of Russia.
|
||||
User:
|
||||
32
quantize.cpp
32
quantize.cpp
|
|
@ -3,11 +3,11 @@
|
|||
#include "utils.h"
|
||||
|
||||
#include <cassert>
|
||||
#include <cinttypes>
|
||||
#include <cmath>
|
||||
#include <cstdio>
|
||||
#include <cstring>
|
||||
#include <fstream>
|
||||
#include <map>
|
||||
#include <string>
|
||||
#include <vector>
|
||||
#include <regex>
|
||||
|
|
@ -43,7 +43,7 @@ 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 +63,28 @@ 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;
|
||||
|
|
@ -113,6 +129,7 @@ bool llama_model_quantize(const std::string & fname_inp, const std::string & fna
|
|||
}
|
||||
|
||||
std::string word;
|
||||
vocab.id_to_token.resize(n_vocab);
|
||||
for (int i = 0; i < n_vocab; i++) {
|
||||
uint32_t len;
|
||||
finp.read ((char *) &len, sizeof(len));
|
||||
|
|
@ -122,8 +139,15 @@ 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;
|
||||
|
||||
auto &tok_score = vocab.id_to_token[i];
|
||||
tok_score.tok = word;
|
||||
tok_score.score = score;
|
||||
}
|
||||
}
|
||||
|
||||
|
|
|
|||
126
quantize.py
Normal file
126
quantize.py
Normal file
|
|
@ -0,0 +1,126 @@
|
|||
#!/usr/bin/env python3
|
||||
|
||||
"""Script to execute the "quantize" script on a given set of models."""
|
||||
|
||||
import subprocess
|
||||
import argparse
|
||||
import glob
|
||||
import sys
|
||||
import os
|
||||
|
||||
|
||||
def main():
|
||||
"""Update the quantize binary name depending on the platform and parse
|
||||
the command line arguments and execute the script.
|
||||
"""
|
||||
|
||||
if "linux" in sys.platform or "darwin" in sys.platform:
|
||||
quantize_script_binary = "quantize"
|
||||
|
||||
elif "win32" in sys.platform or "cygwin" in sys.platform:
|
||||
quantize_script_binary = "quantize.exe"
|
||||
|
||||
else:
|
||||
print("WARNING: Unknown platform. Assuming a UNIX-like OS.\n")
|
||||
quantize_script_binary = "quantize"
|
||||
|
||||
parser = argparse.ArgumentParser(
|
||||
prog='python3 quantize.py',
|
||||
description='This script quantizes the given models by applying the '
|
||||
f'"{quantize_script_binary}" script on them.'
|
||||
)
|
||||
parser.add_argument(
|
||||
'models', nargs='+', choices=('7B', '13B', '30B', '65B'),
|
||||
help='The models to quantize.'
|
||||
)
|
||||
parser.add_argument(
|
||||
'-r', '--remove-16', action='store_true', dest='remove_f16',
|
||||
help='Remove the f16 model after quantizing it.'
|
||||
)
|
||||
parser.add_argument(
|
||||
'-m', '--models-path', dest='models_path',
|
||||
default=os.path.join(os.getcwd(), "models"),
|
||||
help='Specify the directory where the models are located.'
|
||||
)
|
||||
parser.add_argument(
|
||||
'-q', '--quantize-script-path', dest='quantize_script_path',
|
||||
default=os.path.join(os.getcwd(), quantize_script_binary),
|
||||
help='Specify the path to the "quantize" script.'
|
||||
)
|
||||
|
||||
# TODO: Revise this code
|
||||
# parser.add_argument(
|
||||
# '-t', '--threads', dest='threads', type='int',
|
||||
# default=os.cpu_count(),
|
||||
# help='Specify the number of threads to use to quantize many models at '
|
||||
# 'once. Defaults to os.cpu_count().'
|
||||
# )
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
if not os.path.isfile(args.quantize_script_path):
|
||||
print(
|
||||
f'The "{quantize_script_binary}" script was not found in the '
|
||||
"current location.\nIf you want to use it from another location, "
|
||||
"set the --quantize-script-path argument from the command line."
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
for model in args.models:
|
||||
# The model is separated in various parts
|
||||
# (ggml-model-f16.bin, ggml-model-f16.bin.0, ggml-model-f16.bin.1...)
|
||||
f16_model_path_base = os.path.join(
|
||||
args.models_path, model, "ggml-model-f16.bin"
|
||||
)
|
||||
|
||||
f16_model_parts_paths = map(
|
||||
lambda filename: os.path.join(f16_model_path_base, filename),
|
||||
glob.glob(f"{f16_model_path_base}*")
|
||||
)
|
||||
|
||||
for f16_model_part_path in f16_model_parts_paths:
|
||||
if not os.path.isfile(f16_model_part_path):
|
||||
print(
|
||||
f"The f16 model {os.path.basename(f16_model_part_path)} "
|
||||
f"was not found in {args.models_path}{os.path.sep}{model}"
|
||||
". If you want to use it from another location, set the "
|
||||
"--models-path argument from the command line."
|
||||
)
|
||||
sys.exit(1)
|
||||
|
||||
__run_quantize_script(
|
||||
args.quantize_script_path, f16_model_part_path
|
||||
)
|
||||
|
||||
if args.remove_f16:
|
||||
os.remove(f16_model_part_path)
|
||||
|
||||
|
||||
# This was extracted to a top-level function for parallelization, if
|
||||
# implemented. See https://github.com/ggerganov/llama.cpp/pull/222/commits/f8db3d6cd91bf1a1342db9d29e3092bc12dd783c#r1140496406
|
||||
|
||||
def __run_quantize_script(script_path, f16_model_part_path):
|
||||
"""Run the quantize script specifying the path to it and the path to the
|
||||
f16 model to quantize.
|
||||
"""
|
||||
|
||||
new_quantized_model_path = f16_model_part_path.replace("f16", "q4_0")
|
||||
subprocess.run(
|
||||
[script_path, f16_model_part_path, new_quantized_model_path, "2"],
|
||||
check=True
|
||||
)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
try:
|
||||
main()
|
||||
|
||||
except subprocess.CalledProcessError:
|
||||
print("\nAn error ocurred while trying to quantize the models.")
|
||||
sys.exit(1)
|
||||
|
||||
except KeyboardInterrupt:
|
||||
sys.exit(0)
|
||||
|
||||
else:
|
||||
print("\nSuccesfully quantized all models.")
|
||||
15
quantize.sh
15
quantize.sh
|
|
@ -1,15 +0,0 @@
|
|||
#!/usr/bin/env bash
|
||||
|
||||
if ! [[ "$1" =~ ^[0-9]{1,2}B$ ]]; then
|
||||
echo
|
||||
echo "Usage: quantize.sh 7B|13B|30B|65B [--remove-f16]"
|
||||
echo
|
||||
exit 1
|
||||
fi
|
||||
|
||||
for i in `ls models/$1/ggml-model-f16.bin*`; do
|
||||
./quantize "$i" "${i/f16/q4_0}" 2
|
||||
if [[ "$2" == "--remove-f16" ]]; then
|
||||
rm "$i"
|
||||
fi
|
||||
done
|
||||
4
tests/CMakeLists.txt
Normal file
4
tests/CMakeLists.txt
Normal 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
69
tests/test-tokenizer-0.cpp
Normal 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;
|
||||
}
|
||||
335
utils.cpp
335
utils.cpp
|
|
@ -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
|
||||
|
||||
|
|
@ -38,19 +39,19 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
} else if (arg == "-p" || arg == "--prompt") {
|
||||
params.prompt = argv[++i];
|
||||
} else if (arg == "-f" || arg == "--file") {
|
||||
|
||||
std::ifstream file(argv[++i]);
|
||||
|
||||
std::copy(std::istreambuf_iterator<char>(file),
|
||||
std::istreambuf_iterator<char>(),
|
||||
back_inserter(params.prompt));
|
||||
|
||||
std::copy(std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>(), back_inserter(params.prompt));
|
||||
if (params.prompt.back() == '\n') {
|
||||
params.prompt.pop_back();
|
||||
}
|
||||
} else if (arg == "-n" || arg == "--n_predict") {
|
||||
params.n_predict = std::stoi(argv[++i]);
|
||||
} else if (arg == "--top_k") {
|
||||
params.top_k = std::stoi(argv[++i]);
|
||||
} else if (arg == "-c" || arg == "--ctx_size") {
|
||||
params.n_ctx = std::stoi(argv[++i]);
|
||||
} else if (arg == "--memory_f16") {
|
||||
params.memory_f16 = true;
|
||||
} else if (arg == "--top_p") {
|
||||
params.top_p = std::stof(argv[++i]);
|
||||
} else if (arg == "--temp") {
|
||||
|
|
@ -70,13 +71,23 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
} else if (arg == "--interactive-start") {
|
||||
params.interactive = true;
|
||||
params.interactive_start = true;
|
||||
} else if (arg == "-ins" || arg == "--instruct") {
|
||||
params.instruct = true;
|
||||
} else if (arg == "--color") {
|
||||
params.use_color = true;
|
||||
} else if (arg == "-r" || arg == "--reverse-prompt") {
|
||||
params.antiprompt = argv[++i];
|
||||
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);
|
||||
} else if (arg == "--random-prompt") {
|
||||
params.random_prompt = true;
|
||||
} else {
|
||||
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
|
||||
gpt_print_usage(argc, argv, params);
|
||||
|
|
@ -87,20 +98,22 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) {
|
|||
return true;
|
||||
}
|
||||
|
||||
void gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
|
||||
void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) {
|
||||
fprintf(stderr, "usage: %s [options]\n", argv[0]);
|
||||
fprintf(stderr, "\n");
|
||||
fprintf(stderr, "options:\n");
|
||||
fprintf(stderr, " -h, --help show this help message and exit\n");
|
||||
fprintf(stderr, " -i, --interactive run in interactive mode\n");
|
||||
fprintf(stderr, " --interactive-start run in interactive mode and poll user input at startup\n");
|
||||
fprintf(stderr, " -ins, --instruct run in instruction mode (use with Alpaca models)\n");
|
||||
fprintf(stderr, " -r PROMPT, --reverse-prompt PROMPT\n");
|
||||
fprintf(stderr, " in interactive mode, poll user input upon seeing PROMPT\n");
|
||||
fprintf(stderr, " in interactive mode, poll user input upon seeing PROMPT (can be\n");
|
||||
fprintf(stderr, " specified more than once for multiple prompts).\n");
|
||||
fprintf(stderr, " --color colorise output to distinguish prompt and user input from generations\n");
|
||||
fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1)\n");
|
||||
fprintf(stderr, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads);
|
||||
fprintf(stderr, " -p PROMPT, --prompt PROMPT\n");
|
||||
fprintf(stderr, " prompt to start generation with (default: random)\n");
|
||||
fprintf(stderr, " prompt to start generation with (default: empty)\n");
|
||||
fprintf(stderr, " --random-prompt start with a randomized prompt.\n");
|
||||
fprintf(stderr, " -f FNAME, --file FNAME\n");
|
||||
fprintf(stderr, " prompt file to start generation.\n");
|
||||
fprintf(stderr, " -n N, --n_predict N number of tokens to predict (default: %d)\n", params.n_predict);
|
||||
|
|
@ -109,8 +122,12 @@ void gpt_print_usage(int argc, char ** argv, const gpt_params & params) {
|
|||
fprintf(stderr, " --repeat_last_n N last n tokens to consider for penalize (default: %d)\n", params.repeat_last_n);
|
||||
fprintf(stderr, " --repeat_penalty N penalize repeat sequence of tokens (default: %.1f)\n", params.repeat_penalty);
|
||||
fprintf(stderr, " -c N, --ctx_size N size of the prompt context (default: %d)\n", params.n_ctx);
|
||||
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");
|
||||
|
|
@ -143,8 +160,8 @@ void replace(std::string & str, const std::string & needle, const std::string &
|
|||
}
|
||||
}
|
||||
|
||||
std::map<std::string, int32_t> json_parse(const std::string & fname) {
|
||||
std::map<std::string, int32_t> result;
|
||||
std::unordered_map<std::string, int32_t> json_parse(const std::string & fname) {
|
||||
std::unordered_map<std::string, int32_t> result;
|
||||
|
||||
// read file into string
|
||||
std::string json;
|
||||
|
|
@ -234,151 +251,213 @@ 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;
|
||||
}
|
||||
|
||||
if (static_cast<size_t>((*token).second) >= vocab_.id_to_token.size()) {
|
||||
return;
|
||||
}
|
||||
|
||||
const auto &tok_score = vocab_.id_to_token[(*token).second];
|
||||
|
||||
llama_sp_bigram bigram;
|
||||
bigram.left = left;
|
||||
bigram.right = right;
|
||||
bigram.score = tok_score.score;
|
||||
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());
|
||||
vocab.id_to_token.resize(n_vocab);
|
||||
|
||||
vocab.token_to_id = ::json_parse(fname);
|
||||
for (int i = 0; i < n_vocab; i++) {
|
||||
uint32_t len;
|
||||
fin.read((char *) &len, sizeof(len));
|
||||
|
||||
for (const auto & kv : vocab.token_to_id) {
|
||||
vocab.id_to_token[kv.second] = kv.first;
|
||||
word.resize(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;
|
||||
|
||||
auto &tok_score = vocab.id_to_token[i];
|
||||
tok_score.tok = word;
|
||||
tok_score.score = 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,
|
||||
|
|
@ -386,7 +465,7 @@ 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);
|
||||
|
||||
{
|
||||
|
|
@ -400,7 +479,7 @@ gpt_vocab::id llama_sample_top_p_top_k(
|
|||
logits_id.push_back(std::make_pair(logits[i]*scale*repeat_penalty, i));
|
||||
} else {
|
||||
logits_id.push_back(std::make_pair(logits[i]*scale/repeat_penalty, i));
|
||||
}
|
||||
}
|
||||
} else {
|
||||
logits_id.push_back(std::make_pair(logits[i]*scale, i));
|
||||
}
|
||||
|
|
@ -529,7 +608,7 @@ 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));
|
||||
|
|
@ -552,7 +631,7 @@ 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) {
|
||||
|
|
|
|||
79
utils.h
79
utils.h
|
|
@ -3,7 +3,7 @@
|
|||
#pragma once
|
||||
|
||||
#include <string>
|
||||
#include <map>
|
||||
#include <unordered_map>
|
||||
#include <vector>
|
||||
#include <random>
|
||||
#include <thread>
|
||||
|
|
@ -13,28 +13,36 @@
|
|||
//
|
||||
|
||||
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
|
||||
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 use_color = false; // use color to distinguish generations and inputs
|
||||
bool embedding = false; // get only sentence embedding
|
||||
bool interactive = false; // interactive mode
|
||||
|
||||
std::vector<std::string> antiprompt; // string upon seeing which more user input is prompted
|
||||
|
||||
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 embedding = false; // get only sentence embedding
|
||||
bool interactive_start = false; // reverse prompt immediately
|
||||
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 perplexity = false; // compute perplexity over the prompt
|
||||
};
|
||||
|
||||
bool gpt_params_parse(int argc, char ** argv, gpt_params & params);
|
||||
|
|
@ -43,51 +51,52 @@ 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;
|
||||
struct token_score {
|
||||
token tok;
|
||||
float score;
|
||||
};
|
||||
|
||||
std::unordered_map<token, id> token_to_id;
|
||||
std::vector<token_score> id_to_token;
|
||||
};
|
||||
|
||||
void replace(std::string & str, const std::string & needle, const std::string & replacement);
|
||||
|
||||
// poor-man's JSON parsing
|
||||
std::map<std::string, int32_t> json_parse(const std::string & fname);
|
||||
std::unordered_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,
|
||||
|
|
@ -95,7 +104,7 @@ 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
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue