Metadata-Version: 2.1
Name: fastertransformer
Version: 5.0.0.113
Summary: fastertransformer: fastertransformer tf op
Home-page: https://github.com/ssbuild
Author: ssbuild
Author-email: 9727464@qq.com
License: Apache 2.0
Keywords: fastertransformer,bert
Platform: linux_x86_64
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: C++
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3, <4
Description-Content-Type: text/markdown

fastertransformer: fastertransformer tf op

```py

https://github.com/NVIDIA/FasterTransformer <br>

libtf_fastertransformer.so build for linux os

In NLP, encoder and decoder are two important components, with the transformer layer becoming a popular architecture for both components. 
FasterTransformer implements a highly optimized transformer layer for both the encoder and decoder for inference. On Volta, Turing and Ampere GPUs, 
the computing power of Tensor Cores are used automatically when the precision of the data and weights are FP16.

FasterTransformer is built on top of CUDA, cuBLAS, cuBLASLt and C++. We provide at least one API of the following frameworks: TensorFlow, PyTorch and Triton backend. 
Users can integrate FasterTransformer into these frameworks directly.
For supporting frameworks, we also provide example codes to demonstrate how to use, and show the performance on these frameworks.

```
