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README.md
View file @ 0c47afbb
# Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context Fast MoE
===
This repository contains the code in both **PyTorch** and **TensorFlow** for our paper An easy-to-use but efficient implementation of the Mixture of Experts (MoE)
>[Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context](http://arxiv.org/abs/1901.02860) model for PyTorch
>Zihang Dai\*, Zhilin Yang\*, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov (*: equal contribution)
>Preprint 2018
## TensorFlow
- The source code is in the `tf/` folder, supporting (1) single-node multi-gpu training, and (2) multi-host TPU training.
- Besides the source code, we also provide pretrained "TensorFlow" models with state-of-the-art (SoTA) performances reported in the paper.
- Please refer to `tf/README.md` for details.
## PyTorch
- The source code is in the `pytorch/` folder, supporting single-node multi-gpu training via the module `nn.DataParallel`.
- Please refer to `pytorch/README.md` for details.
## Results
Transformer-XL achieves new state-of-the-art results on multiple language modeling benchmarks. Transformer-XL is also the first to break through the 1.0 barrier on char-level language modeling. Below is a summary.
Method | enwiki8 | text8 | One Billion Word | WT-103 | PTB (w/o finetuning)
-- | -- | -- | -- | -- | --
Previous Best | 1.06 | 1.13 | 23.7 | 20.5 | 55.5
Transformer-XL | **0.99** | **1.08** | **21.8** | **18.3** | **54.5**
## Acknowledgement
A large portion of the `getdata.sh` script comes from the [awd-lstm](https://github.com/salesforce/awd-lstm-lm/) repo. Happy Language Modeling :)
pytorch/cuda/cuda_stream_manager.cpp → cuda/cuda_stream_manager.cpp
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...@@ -6,7 +6,7 @@ ...@@ -6,7 +6,7 @@
#include "cuda_stream_manager.h" #include "cuda_stream_manager.h"
#include <helper_cuda.h> #include <helper_cuda.h>
#define SMGR_N_STREAMS 4 #define SMGR_N_STREAMS 16
cudaStream_t CudaStreamManager::stream(size_t idx) { cudaStream_t CudaStreamManager::stream(size_t idx) {
return this->streams[idx % SMGR_N_STREAMS]; return this->streams[idx % SMGR_N_STREAMS];
......
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