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4 changes: 4 additions & 0 deletions .gitignore
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*build
*install
.nfs*
toolchain*
102 changes: 102 additions & 0 deletions CMakeLists.txt
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cmake_minimum_required(VERSION 3.7)

if (NOT CMAKE_BUILD_TYPE OR CMAKE_BUILD_TYPE STREQUAL "")
set(CMAKE_BUILD_TYPE "Release" CACHE STRING "" FORCE)
endif()

set(CMAKE_C_STANDARD 99)
if(CMAKE_BUILD_TYPE STREQUAL "Release" AND NOT CMAKE_C_FLAGS)
set(CMAKE_C_FLAGS "-O2" CACHE STRING "" FORCE)
endif()

option(EML_ARMV7A "build for armv7a architecture instead of armv8a" OFF)

if(ANDROID)
#Android build
#ANDROID_NDK must be provided
#ANDROID_PLATFORM is optional
if(NOT DEFINED ANDROID_PLATFORM)
set(ANDROID_PLATFORM 27)
endif()
if(EML_ARMV7A)
set(ANDROID_ABI "armeabi-v7a")
set(ANDROID_ARM_MODE arm) #not to use thumb
set(ANDROID_ARM_NEON ON) #enable NEON on armv7a
else() #armv8a
set(ANDROID_ABI "arm64-v8a")
endif()
include(${ANDROID_NDK}/build/cmake/android.toolchain.cmake)
if(CMAKE_BUILD_TYPE STREQUAL "Release")
add_compile_options(-g0) #disable NDK debug info generation
endif()
set(RUNTIME_LIB dl log)
else()
#Linux build
#CMAKE_C_COMPILER must be provided
#CMAKE_SYSROOT is optional
set(CMAKE_SYSTEM_NAME Linux)
if(EML_ARMV7A)
set(CMAKE_SYSTEM_PROCESSOR arm)
add_compile_options(-marm -march=armv7ve)
add_compile_options(-mfpu=neon-vfpv4 -mfp16-format=ieee)
else()
set(CMAKE_SYSTEM_PROCESSOR aarch64)
endif()
set(RUNTIME_LIB pthread)
endif()

project(emll
VERSION 1.0
LANGUAGES C)

try_compile(EMLL_COMPILER_OPENMP_SUPPORTED ${CMAKE_BINARY_DIR}
"${PROJECT_SOURCE_DIR}/test/TestCompilerOpenMP.c"
COMPILE_DEFINITIONS -fopenmp
LINK_LIBRARIES -fopenmp)

if (EMLL_COMPILER_OPENMP_SUPPORTED)
add_compile_options(-fopenmp)
list(APPEND RUNTIME_LIB -fopenmp)
else()
message(STATUS "The compiler doesn't support OpenMP. Build serial version only.")
add_definitions(-DEMLL_SERIAL_ONLY)
endif()

include_directories("${PROJECT_SOURCE_DIR}/include")

file(GLOB interface_header "${PROJECT_SOURCE_DIR}/include/*.h")
file(GLOB arm_src "${PROJECT_SOURCE_DIR}/src/arm_neon/*.c")
if(EML_ARMV7A)
file(GLOB_RECURSE neon_src "${PROJECT_SOURCE_DIR}/src/neon_armv7a/*.c")
add_library(eml-armneon ${arm_src} ${neon_src})
else()
file(GLOB neon_src "${PROJECT_SOURCE_DIR}/src/neon_armv8a/*.c")
file(GLOB skinny_dot_src
"${PROJECT_SOURCE_DIR}/src/neon_armv8a/sgemm_skinny_dot_kernel/*.c")
file(GLOB ext_src "${PROJECT_SOURCE_DIR}/src/neon_armv8a/extension/*.c")
set_source_files_properties(${arm_src} ${ext_src}
PROPERTIES COMPILE_FLAGS "-march=armv8.2-a+dotprod+fp16")
add_library(eml-armneon ${arm_src} ${ext_src} ${skinny_dot_src} ${neon_src})
endif()

option(EML_TEST "build test programs for the library" ON)

if(EML_TEST)
message(STATUS "Build testing executables for EML")
set(EML_TEST_EXECUTABLES test_emll_gemm test_emll_bias test_emll_quant)
add_executable(test_emll_gemm "${PROJECT_SOURCE_DIR}/test/TestGemm.c")
add_executable(test_emll_bias "${PROJECT_SOURCE_DIR}/test/TestBias.c")
add_executable(test_emll_quant "${PROJECT_SOURCE_DIR}/test/TestQuant.c")
target_link_libraries(test_emll_gemm eml-armneon ${RUNTIME_LIB})
target_link_libraries(test_emll_bias eml-armneon ${RUNTIME_LIB})
target_link_libraries(test_emll_quant eml-armneon ${RUNTIME_LIB})
endif()

set_target_properties(eml-armneon PROPERTIES PUBLIC_HEADER "${interface_header}")
install(TARGETS eml-armneon ${EML_TEST_EXECUTABLES}
EXPORT EMLLTargets
LIBRARY DESTINATION lib
ARCHIVE DESTINATION lib
RUNTIME DESTINATION bin
PUBLIC_HEADER DESTINATION include)

201 changes: 201 additions & 0 deletions LICENSE
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70 changes: 70 additions & 0 deletions ReadMe.md
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![logo](doc/EMLL.png)

[中文介绍](ReadMe_ZH.md)

# Edge ML Library - High-performance Compute Library for On-device Machine Learning Inference

Edge ML Library (EMLL) offers optimized basic routines like general matrix multiplications (GEMM) and quantizations, to speed up machine learning (ML) inference on ARM-based devices. EMLL supports fp32, fp16 and int8 data types. EMLL accelerates on-device NMT, ASR and OCR engines of Youdao, Inc.

## Features

### Performance-Oriented Design

The matrix-multiplication routines are heavily-optimized for matrix shapes common in on-device ML tasks, including "skinny" ones. The matrix-multiplication kernels are tuned for specific CPUs with a large portion of inline assembly codes.

Here are benchmarks of SGEMM on 2 machines<sup>[1]</sup>:

| armv8a cortex-A35 4-thread | armv8a cortex-A53 4-thread |
| -------------------------- | -------------------------- |
| ![test1](bench/test_sgemm_en1.png) | ![test2](bench/test_sgemm_en2.png) |

[1].The fomular of GEMM: C[MxN] = A[MxK] B[KxN]; For each test case, the better performance in all-row-major and all-column-major situations is selected.

### Facile Interface

The data and parameters are passed straightforward without wrappings. Matrices and arrays are passed with base address + dimensions. GEMM parameters seldom used in on-device inference like LDA-LDC are excluded from the interface. There is no dependency on any third-party compute libraries.


### Extensibility

EMLL abstracts the core structures of CPU-based high-performance matrix multiplication algorithms and also bias/quant functions to general macros (see files under include/common), which can be applied to a variety of processors. When developing for a new architecture, a lot of coding works can be saved with these macros.

## EMLL APIs

EMLL provides a series of C functions. See [Usage_EN.md](doc/Usage_EN.md) for details.

| Type | Name | Parameters |
| ---- | ---- | ---------- |
| Matrix Multiplication | data_type + "gemm" | matrix_orders, addresses of matrices, M, N, K, beta, number of threads |
| Fully-connect Layer (fp32) | "fc" | addresses of src/weight/bias/output, dimensions M/K/N, orders of source matrices, (number of threads) |
| Quantization | "quantize_" + "symmetric"/"asymmetric" + input_type + output_type | input array, output array, (zero point), scale, size of array, input range |
| Requantization | "requantize_" + "symmetric/asymmetric" + "_XtoY" | input array, output array, (zero point), output scale, size of array, input range |
| Bias | "bias" + data_type | the matrix to be biased, scalar bias to all elements, vector bias along major direction, vector bias along minor direction, dimensions of the matrix |

## Supported Architectures and Data Types

| Target CPU | Matrix Multiplication | Bias | Quantization | Requantization |
| -------------- | ------------------------------------------------ | ----------------- | ------------------------ | ------------------------------------------- |
| ARMv7a 32-bit | fp32 -> fp32, (u)int8 -> (u)int32 | fp32, int32 | fp32 -> (u)int8/(u)int16 | int32 -> (u)int8/(u)int16, int16 -> (u)int8 |
| ARMv8a 64-bit | fp32 -> fp32, (u)int8 -> (u)int32, fp16 -> fp16 | fp32, fp16, int32 | fp32 -> (u)int8/(u)int16 | int32 -> (u)int8/(u)int16, int16 -> (u)int8 |

Supported OS: Linux & Android

Supported Compilers: GCC & Clang

## Future Plan

EMLL may support on-device GPUs and NPUs in the future, with the expansion of available functions, according to business requirements.

## License

Apache 2.0

## Reference

Eigen: [https://eigen.tuxfamily.org]

OpenBLAS: [https://github.com/xianyi/OpenBLAS]



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