FBGEMM | General Matrix-Matrix Multiplication | Math library

by pytorch C++ Version: v0.4.1 License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | FBGEMM Summary

FBGEMM is a C++ library typically used in Utilities, Math, Pytorch applications. FBGEMM has no bugs, it has no vulnerabilities and it has medium support. However FBGEMM has a Non-SPDX License. You can download it from GitHub.

FBGEMM (Facebook GEneral Matrix Multiplication) is a low-precision, high-performance matrix-matrix multiplications and convolution library for server-side inference. The library provides efficient low-precision general matrix multiplication for small batch sizes and support for accuracy-loss minimizing techniques such as row-wise quantization and outlier-aware quantization. FBGEMM also exploits fusion opportunities in order to overcome the unique challenges of matrix multiplication at lower precision with bandwidth-bound operations.

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Support

FBGEMM has a medium active ecosystem.

It has 943 star(s) with 348 fork(s). There are 48 watchers for this library.

It had no major release in the last 12 months.

There are 11 open issues and 97 have been closed. On average issues are closed in 69 days. There are 186 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of FBGEMM is v0.4.1

Quality

FBGEMM has 0 bugs and 0 code smells.

Security

FBGEMM has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

FBGEMM code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

FBGEMM has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

FBGEMM releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

It has 15234 lines of code, 373 functions and 35 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

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FBGEMM Key Features

No Key Features are available at this moment for FBGEMM.

FBGEMM Examples and Code Snippets

No Code Snippets are available at this moment for FBGEMM.

Community Discussions

Trending Discussions on FBGEMM

Reproducing arithmetic with pytorch's quantized tensors with numpy operations

Getting linker error LNK2001: unresolved external symbol PyInit_your_module_name when building a Python extension using Pybind11

cmake - linking static library pytorch cannot find its internal functions during build

Why does the parameters saved in the checkpoint are different from the ones in the fused model?

QUESTION

Reproducing arithmetic with pytorch's quantized tensors with numpy operations

Asked 2022-Jan-26 at 12:37

I would like to know what exact arithmetic operations I have to do to reproduce results of quantized operations in pytorch.

This is almost duplicate question to: I want to use Numpy to simulate the inference process of a quantized MobileNet V2 network, but the outcome is different with pytorch realized one

But I would even simplify it with the example of adding two quantized tensors. For example for addition of two quantized tensors in Resnet architecture I use nn.quantized.FloatFunctional().

...

ANSWER

Answered 2022-Jan-26 at 12:37

The answer is twofold:

Integer operations are implemented taking into account that int8 number refer to different domain. Convolution (or matrix-matrix multiplication in general) is implemented with respect to this fact and my answer here I want to use Numpy to simulate the inference process of a quantized MobileNet V2 network, but the outcome is different with pytorch realized one worked for me.
Addition in pytorch is implemented in floats. You need to convert from int to float, make an addition and then convert back to int.

Source https://stackoverflow.com/questions/70651229

QUESTION

Getting linker error LNK2001: unresolved external symbol PyInit_your_module_name when building a Python extension using Pybind11

Asked 2020-Sep-14 at 13:33

I'm trying to build a Python extension using Pybind11, and I believe I set up all libs, linker related objects correctly. However I get this weird linker error! This is my example input

...

ANSWER

Answered 2020-Sep-14 at 13:33

OK, I made a silly mistake! it seems when defining the PYBIND11_MODULE, the first name, and the name used in setup() need to be the same as the source file, i.e. PythonManager_Pybind11.cpp in my case. This why the linker was complaining about the actual object which was the main source file.
Making these changes, now everything builds just fine.

This is how it looks after these minor changes:

Source https://stackoverflow.com/questions/63885285

QUESTION

cmake - linking static library pytorch cannot find its internal functions during build

Asked 2020-Mar-24 at 20:42

I'm trying to build a program using cmake. For several reasons, the program must be built using static libraries rather than dynamic libraries, and I need to use PyTorch so this is what I've done:

Downloaded and installed PyTorch static library (I've found libtorch.a in the proper path, in /home/me/pytorch/torch/lib)
Made CMakeLists.txt with the following contents:

...

ANSWER

Answered 2020-Mar-24 at 20:42

Lately went through similar process with static linking of PyTorch and to be honest it wasn't too pretty.

I will outline the steps I have undertaken (you can find exact source code in torchlambda, here is CMakeLists.txt (it also includes AWS SDK and AWS Lambda static builds), here is a script building pytorch from source ( cloning and building via /scripts/build_mobile.sh with only CPU support)), though it's only with CPU support (though similar steps should be fine if you need CUDA, it will get you started at least).

Pytorch static library Pre-built static PyTorch

First of all, you need pre-built static library files (all of them need to be static, hence no .so, only those with .a extension are suitable).

Tbh I've been looking for those provided by PyTorch on installation page, yet there is only shared version. In one GitHub issue I've found a way to download them as follows:

Instead of downloading (here via wget) shared libraries:

Source https://stackoverflow.com/questions/60629537

QUESTION

Why does the parameters saved in the checkpoint are different from the ones in the fused model?

Asked 2020-Feb-16 at 20:54

I trained a QAT (Quantization Aware Training) based model in Pytorch, the training went on smoothly. However when I tried to load the weights into the fused model and run a test on widerface dataset I faced lots of errors:

...

ANSWER

Answered 2020-Feb-16 at 20:54

I finally found out the cause. The error messages with the form of :

While copying the parameter named "xxx.weight", whose dimensions in the model are torch.Size([yyy]) and whose dimensions in the checkpoint are torch.Size([yyy]).

are actually generic messages, only returned when an exception has occured while copying the parameters in question.

Pytorch developers could easily add the actual exception args into this spurious yet unhelpful message, so it could actually help better debug the issue at hand. Anyway, looking at the exception which was by the way :

Source https://stackoverflow.com/questions/59941776

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install FBGEMM

FBGEMM uses the standard CMAKE-based build flow.

Support

We have extensively used comments in our source files. The best and up-do-date documentation is available in the source files. You can also turn on the option to generate the documentation (using Doxygen and Sphinx by setting FBGEMM_BUILD_DOCS to ON, and then follow the above cmake build process.

Find more information at: