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Unverified Commit 5e83bc0c authored by UnicornChan's avatar UnicornChan Committed by GitHub
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Merge pull request #17 from UnicornChan/feature-support-multi-instruct 

Feature support multi instruct
parents 25620829 86ba1336
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Showing with 272 additions and 25 deletions
Dockerfile 0 → 100644
View file @ 5e83bc0c
FROM node:20.16.0 as web_compile
WORKDIR /home
RUN <<EOF
git clone https://github.com/kvcache-ai/ktransformers.git &&
cd ktransformers/ktransformers/website/ &&
npm install @vue/cli &&
npm run build &&
rm -rf node_modules
EOF
FROM pytorch/pytorch:2.3.1-cuda12.1-cudnn8-devel as compile_server
WORKDIR /workspace
COPY --from=web_compile /home/ktransformers /workspace/ktransformers
RUN <<EOF
apt update -y && apt install -y --no-install-recommends \
git \
wget \
vim \
gcc \
g++ \
cmake &&
rm -rf /var/lib/apt/lists/* &&
cd ktransformers &&
git submodule init &&
git submodule update &&
pip install ninja pyproject numpy &&
pip install flash-attn &&
CPU_INSTRUCT=NATIVE KTRANSFORMERS_FORCE_BUILD=TRUE TORCH_CUDA_ARCH_LIST="8.0;8.6;8.7;8.9" pip install . --no-build-isolation --verbose &&
pip cache purge
EOF
ENTRYPOINT [ "/opt/conda/bin/ktransformers" ]
\ No newline at end of file
README.md
View file @ 5e83bc0c
......@@ -80,13 +80,15 @@ Some preparation:
```
<h3>Installation</h3>
You can install using Pypi:
```
pip install ktransformers --no-build-isolation
```
1. Use a Docker image, see [documentation for Docker](./doc/en/docker.md)
2. You can install using Pypi:
```
pip install ktransformers --no-build-isolation
```
Or download source code and compile:
3. Or you can download source code and compile:
- init source code
```sh
git clone https://github.com/kvcache-ai/ktransformers.git
......@@ -103,7 +105,7 @@ Or download source code and compile:
<h3>Local Chat</h3>
We provide a simple command-line local chat Python script that you can run for testing.
> Note that this is a very simple test tool only support one round chat without any memory about last input, if you want to try full ability of the model, you may go to [RESTful API and Web UI](#id_666). We use the DeepSeek-V2-Lite-Chat-GGUF model as an example here. But we alse support other models, you can replace it with any other model that you want to test.
> Note that this is a very simple test tool only support one round chat without any memory about last input, if you want to try full ability of the model, you may go to [RESTful API and Web UI](#id_666). We use the DeepSeek-V2-Lite-Chat-GGUF model as an example here. But we also support other models, you can replace it with any other model that you want to test.
<h4>Run Example</h4>
......
doc/en/Docker.md 0 → 100644
View file @ 5e83bc0c
# Docker
## Prerequisites
* Docker must be installed and running on your system.
* Create a folder to store big models & intermediate files (ex. /mnt/models)
## Images
There are Docker images available for our project：
**Uploading**
## Building docker image locally
- Download Dockerfile in [there](../../Dockerfile)
- finish, execute
```bash
docker build -t approachingai/ktransformers:v0.1.1 .
```
## Usage
Assuming you have the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) that you can use the GPU in a Docker container.
```
docker run --gpus all -v /path/to/models:/models -p 10002:10002 approachingai/ktransformers:v0.1.1 --port 10002 --gguf_path /models/path/to/gguf_path --model_path /models/path/to/model_path --web True
```
More operators you can see in the [readme](../../README.md)
\ No newline at end of file
doc/en/deepseek-v2-injection.md
View file @ 5e83bc0c
......@@ -43,7 +43,11 @@ In the current version of KTransformers, we utilize Marlin for GPU kernels and l
<img alt="CPUInfer Performance" src="../assets/cpuinfer.png" width=80%>
</picture>
</p>
<p align="center">
<picture>
<img alt="marlin performance" src="https://github.com/IST-DASLab/marlin/blob/master/assets/sustained.png?raw=true" width=80%>
</picture>
</p>
### Arithmetic Intensity Guided Offloading
......
ktransformers/__init__.py
View file @ 5e83bc0c
__version__ = "0.1.0"
\ No newline at end of file
__version__ = "0.1.1"
\ No newline at end of file
ktransformers/ktransformers_ext/CMakeLists.txt
View file @ 5e83bc0c
......@@ -2,7 +2,7 @@ cmake_minimum_required(VERSION 3.16)
project(cpuinfer_ext VERSION 0.1.0)
set(CMAKE_CXX_STANDARD 17)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3")
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3 -ffast-math")
set(CMAKE_BUILD_TYPE "Release")
include(CheckCXXCompilerFlag)
set(CMAKE_POSITION_INDEPENDENT_CODE ON)
......@@ -10,6 +10,27 @@ set(CMAKE_POSITION_INDEPENDENT_CODE ON)
option(LLAMA_NATIVE "llama: enable -march=native flag" ON)
# instruction set specific
if (LLAMA_NATIVE)
set(INS_ENB OFF)
else()
set(INS_ENB ON)
endif()
option(LLAMA_AVX "llama: enable AVX" OFF)
option(LLAMA_AVX2 "llama: enable AVX2" OFF)
option(LLAMA_AVX512 "llama: enable AVX512" OFF)
option(LLAMA_AVX512_VBMI "llama: enable AVX512-VBMI" OFF)
option(LLAMA_AVX512_VNNI "llama: enable AVX512-VNNI" OFF)
option(LLAMA_FMA "llama: enable FMA" OFF)
# in MSVC F16C is implied with AVX2/AVX512
if (NOT MSVC)
option(LLAMA_F16C "llama: enable F16C" OFF)
endif()
option(LLAMA_AVX512_FANCY_SIMD "llama: enable AVX512-VL, AVX512-BW, AVX512-DQ, AVX512-VNNI" OFF)
option(LLAMA_AVX512_BF16 "llama: enable AVX512-BF16" OFF)
# 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
......@@ -102,6 +123,20 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512VNNI__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VNNI__>)
endif()
if (LLAMA_AVX512_FANCY_SIMD)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512VL__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VL__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512BW__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512BW__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512DQ__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512DQ__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512VNNI__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512VNNI__>)
endif()
if (LLAMA_AVX512_BF16)
add_compile_definitions($<$<COMPILE_LANGUAGE:C>:__AVX512BF16__>)
add_compile_definitions($<$<COMPILE_LANGUAGE:CXX>:__AVX512BF16__>)
endif()
elseif (LLAMA_AVX2)
list(APPEND ARCH_FLAGS /arch:AVX2)
elseif (LLAMA_AVX)
......@@ -133,6 +168,15 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW
if (LLAMA_AVX512_VNNI)
list(APPEND ARCH_FLAGS -mavx512vnni)
endif()
if (LLAMA_AVX512_FANCY_SIMD)
list(APPEND ARCH_FLAGS -mavx512vl)
list(APPEND ARCH_FLAGS -mavx512bw)
list(APPEND ARCH_FLAGS -mavx512dq)
list(APPEND ARCH_FLAGS -mavx512vnni)
endif()
if (LLAMA_AVX512_BF16)
list(APPEND ARCH_FLAGS -mavx512bf16)
endif()
endif()
elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64")
message(STATUS "PowerPC detected")
......
ktransformers/ktransformers_ext/cmake/FindSIMD.cmake 0 → 100644
View file @ 5e83bc0c
include(CheckCSourceRuns)
set(AVX_CODE "
#include <immintrin.h>
int main()
{
__m256 a;
a = _mm256_set1_ps(0);
return 0;
}
")
set(AVX512_CODE "
#include <immintrin.h>
int main()
{
__m512i a = _mm512_set_epi8(0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0);
__m512i b = a;
__mmask64 equality_mask = _mm512_cmp_epi8_mask(a, b, _MM_CMPINT_EQ);
return 0;
}
")
set(AVX2_CODE "
#include <immintrin.h>
int main()
{
__m256i a = {0};
a = _mm256_abs_epi16(a);
__m256i x;
_mm256_extract_epi64(x, 0); // we rely on this in our AVX2 code
return 0;
}
")
set(FMA_CODE "
#include <immintrin.h>
int main()
{
__m256 acc = _mm256_setzero_ps();
const __m256 d = _mm256_setzero_ps();
const __m256 p = _mm256_setzero_ps();
acc = _mm256_fmadd_ps( d, p, acc );
return 0;
}
")
macro(check_sse type flags)
set(__FLAG_I 1)
set(CMAKE_REQUIRED_FLAGS_SAVE ${CMAKE_REQUIRED_FLAGS})
foreach (__FLAG ${flags})
if (NOT ${type}_FOUND)
set(CMAKE_REQUIRED_FLAGS ${__FLAG})
check_c_source_runs("${${type}_CODE}" HAS_${type}_${__FLAG_I})
if (HAS_${type}_${__FLAG_I})
set(${type}_FOUND TRUE CACHE BOOL "${type} support")
set(${type}_FLAGS "${__FLAG}" CACHE STRING "${type} flags")
endif()
math(EXPR __FLAG_I "${__FLAG_I}+1")
endif()
endforeach()
set(CMAKE_REQUIRED_FLAGS ${CMAKE_REQUIRED_FLAGS_SAVE})
if (NOT ${type}_FOUND)
set(${type}_FOUND FALSE CACHE BOOL "${type} support")
set(${type}_FLAGS "" CACHE STRING "${type} flags")
endif()
mark_as_advanced(${type}_FOUND ${type}_FLAGS)
endmacro()
# flags are for MSVC only!
check_sse("AVX" " ;/arch:AVX")
if (NOT ${AVX_FOUND})
set(LLAMA_AVX OFF)
else()
set(LLAMA_AVX ON)
endif()
check_sse("AVX2" " ;/arch:AVX2")
check_sse("FMA" " ;/arch:AVX2")
if ((NOT ${AVX2_FOUND}) OR (NOT ${FMA_FOUND}))
set(LLAMA_AVX2 OFF)
else()
set(LLAMA_AVX2 ON)
endif()
check_sse("AVX512" " ;/arch:AVX512")
if (NOT ${AVX512_FOUND})
set(LLAMA_AVX512 OFF)
else()
set(LLAMA_AVX512 ON)
endif()
pyproject.toml
View file @ 5e83bc0c
[build-system]
requires = [
"setuptools",
"torch == 2.3.1",
"torch >= 2.3.0",
"ninja",
"packaging"
]
......@@ -29,7 +29,7 @@ dependencies = [
"fire"
]
requires-python = ">=3.11"
requires-python = ">=3.10"
authors = [
{name = "KVCache.AI", email = "zhang.mingxing@outlook.com"}
......@@ -50,6 +50,7 @@ keywords = ["ktransformers", "llm"]
classifiers = [
"Development Status :: 4 - Beta",
"Programming Language :: Python :: 3.10",
"Programming Language :: Python :: 3.11",
"Programming Language :: Python :: 3.12"
]
......
setup.py
View file @ 5e83bc0c
......@@ -6,7 +6,7 @@ Author : chenxl
Date : 2024-07-27 16:15:27
Version : 1.0.0
LastEditors : chenxl
LastEditTime : 2024-07-29 09:40:24
LastEditTime : 2024-07-31 09:44:46
Adapted from:
https://github.com/Dao-AILab/flash-attention/blob/v2.6.3/setup.py
Copyright (c) 2023, Tri Dao.
......@@ -19,6 +19,7 @@ import re
import ast
import subprocess
import platform
import http.client
import urllib.request
import urllib.error
from pathlib import Path
......@@ -28,6 +29,15 @@ from wheel.bdist_wheel import bdist_wheel as _bdist_wheel
from setuptools import setup, Extension
from torch.utils.cpp_extension import BuildExtension, CUDAExtension, CUDA_HOME
class CpuInstructInfo:
CPU_INSTRUCT = os.getenv("CPU_INSTRUCT", "NATIVE")
FANCY = "FANCY"
AVX512 = "AVX512"
AVX2 = "AVX2"
CMAKE_NATIVE = "-DLLAMA_NATIVE=ON"
CMAKE_FANCY = "-DLLAMA_NATIVE=OFF -DLLAMA_FMA=ON -DLLAMA_F16C=ON -DLLAMA_AVX=ON -DLLAMA_AVX2=ON -DLLAMA_AVX512=ON -DLLAMA_AVX512_FANCY_SIMD=ON"
CMAKE_AVX512 = "-DLLAMA_NATIVE=OFF -DLLAMA_FMA=ON -DLLAMA_F16C=ON -DLLAMA_AVX=ON -DLLAMA_AVX2=ON -DLLAMA_AVX512=ON"
CMAKE_AVX2 = "-DLLAMA_NATIVE=OFF -DLLAMA_FMA=ON -DLLAMA_F16C=ON -DLLAMA_AVX=ON -DLLAMA_AVX2=ON"
class VersionInfo:
THIS_DIR = os.path.dirname(os.path.abspath(__file__))
......@@ -61,12 +71,24 @@ class VersionInfo:
raise ValueError("Unsupported platform: {}".format(sys.platform))
def get_cpu_instruct(self,):
if CpuInstructInfo.CPU_INSTRUCT == CpuInstructInfo.FANCY:
return "fancy"
elif CpuInstructInfo.CPU_INSTRUCT == CpuInstructInfo.AVX512:
return "avx512"
elif CpuInstructInfo.CPU_INSTRUCT == CpuInstructInfo.AVX2:
return "avx2"
else:
print("Using native cpu instruct")
if sys.platform.startswith("linux"):
with open('/proc/cpuinfo', 'r', encoding="utf-8") as cpu_f:
cpuinfo = cpu_f.read()
flags_line = [line for line in cpuinfo.split(
'\n') if line.startswith('flags')][0]
flags = flags_line.split(':')[1].strip().split(' ')
# fancy with AVX512-VL, AVX512-BW, AVX512-DQ, AVX512-VNNI
for flag in flags:
if 'avx512bw' in flag:
return 'fancy'
for flag in flags:
if 'avx512' in flag:
return 'avx512'
......@@ -116,6 +138,7 @@ class BuildWheelsCommand(_bdist_wheel):
def run(self):
if VersionInfo.FORCE_BUILD:
super().run()
return
wheel_filename, wheel_url = self.get_wheel_name()
print("Guessing wheel URL: ", wheel_url)
try:
......@@ -132,7 +155,7 @@ class BuildWheelsCommand(_bdist_wheel):
wheel_path = os.path.join(self.dist_dir, archive_basename + ".whl")
print("Raw wheel path", wheel_path)
os.rename(wheel_filename, wheel_path)
except (urllib.error.HTTPError, urllib.error.URLError):
except (urllib.error.HTTPError, urllib.error.URLError, http.client.RemoteDisconnected):
print("Precompiled wheel not found. Building from source...")
# If the wheel could not be downloaded, build from source
super().run()
......@@ -187,6 +210,18 @@ class CMakeBuild(BuildExtension):
cmake_args += [
item for item in os.environ["CMAKE_ARGS"].split(" ") if item]
if CpuInstructInfo.CPU_INSTRUCT == CpuInstructInfo.FANCY:
cpu_args = CpuInstructInfo.CMAKE_FANCY
elif CpuInstructInfo.CPU_INSTRUCT == CpuInstructInfo.AVX512:
cpu_args = CpuInstructInfo.CMAKE_AVX512
elif CpuInstructInfo.CPU_INSTRUCT == CpuInstructInfo.AVX2:
cpu_args = CpuInstructInfo.CMAKE_AVX2
else:
cpu_args = CpuInstructInfo.CMAKE_NATIVE
cmake_args += [
item for item in cpu_args.split(" ") if item
]
# In this example, we pass in the version to C++. You might not need to.
cmake_args += [
f"-DEXAMPLE_VERSION_INFO={self.distribution.get_version()}"]
......
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