Merge branch 'master' of github.com:kimiyoung/transformer-xl

README.md

@@ -7,18 +7,18 @@ This repository contains the code in both **PyTorch** and **TensorFlow** for our
 
 >Preprint 2018
 
-#### TensorFlow
+## 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
+## 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
+## Results
 
 Transformer-XL achieves new state-of-the-art results on multipole 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.
 
@@ -31,4 +31,4 @@ 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 :)
+A large portion of the `getdata.sh` script comes from the [awd-lstm](https://github.com/salesforce/awd-lstm-lm/) repo. Happy Language Modeling :)
\ No newline at end of file

pytorch/README.md

-#### Introduction
+## Introduction
 
 This directory contains our pytorch implementation of Transformer-XL. Note that our state-of-the-art results reported in the paper were obtained by training the model on a large-scale TPU cluster, and our pytorch codebase currently does not support distributed training. Here we provide two sets of hyperparameters and scripts:
 - `*large.sh` are for the SoTA setting with large models which might not be directly runnable on a local GPU machine.
@@ -7,16 +7,16 @@ This directory contains our pytorch implementation of Transformer-XL. Note that
 The pytorch implementation produces similar results to the TF codebase under the same settings in our preliminary experiments.
 
 
-#### Prerequisite
+## Prerequisite
 
 - Pytorch 0.4: `conda install pytorch torchvision -c pytorch`
 
 
-#### Data Prepration
+## Data Prepration
 
 `bash getdata.sh`
 
-
+## Training and Evaluation
 
 #### Replicate the "bpc = 1.06" result on `enwik8` with a 12-layer Transformer-XL
 
@@ -24,11 +24,11 @@ The pytorch implementation produces similar results to the TF codebase under the
 
 - Training
 
-  `bash run_enwik8.sh train --work_dir PATH_TO_WORK_DIR`
+  `bash run_enwik8_base.sh train --work_dir PATH_TO_WORK_DIR`
 
 - Evaluation
 
-  `bash run_enwik8.sh eval --work_dir PATH_TO_WORK_DIR`
+  `bash run_enwik8_base.sh eval --work_dir PATH_TO_WORK_DIR`
 
 
 
@@ -38,11 +38,11 @@ The pytorch implementation produces similar results to the TF codebase under the
 
 - Evaluation
 
-  `bash run_wt103.sh train --work_dir PATH_TO_WORK_DIR`
+  `bash run_wt103_base.sh train --work_dir PATH_TO_WORK_DIR`
 
 - Testing
 
-  `bash run_wt103.sh eval --work_dir PATH_TO_WORK_DIR`
+  `bash run_wt103_base.sh eval --work_dir PATH_TO_WORK_DIR`
 
 
 
@@ -53,10 +53,10 @@ The pytorch implementation produces similar results to the TF codebase under the
 - `--fp16` and `--dynamic-loss-scale`: Run in pseudo-fp16 mode (fp16 storage fp32 math) with dynamic loss scaling. 
   - Note: to explore the `--fp16` option, please make sure the `apex` package is installed (https://github.com/NVIDIA/apex/).
 - To see performance without the recurrence mechanism, simply use `mem_len=0` in all your scripts.
-- To see performance with a standard Transformer without relative positional encodings and recurrence mechanisms, use `attn_type=2` and `mem_len=0`.
+- To see performance of a standard Transformer without relative positional encodings or recurrence mechanisms, use `attn_type=2` and `mem_len=0`.
 
 
 #### Other datasets:
 
-- `Text8` character-level language modeling: check out `run_text8.sh`
-- `lm1b` word-level language modeling: check out `run_lm1b.sh`
+- `Text8` character-level language modeling: check out `run_text8_base.sh`
+- `lm1b` word-level language modeling: check out `run_lm1b_base.sh`

tf/README.md

@@ -80,8 +80,8 @@ We used 32, 32, 64, and 512 TPU cores for training our best models on enwik8, te
 
 For `dataset` in `[enwik8, lm1b, wt103, text8]`:
 
-- check out `scripts/dataset_gpu.sh` for GPU training and evaluation
-- check out `scripts/dataset_tpu.sh` for TPU training and evaluation
+- check out `scripts/dataset_base_gpu.sh` for GPU training and evaluation
+- check out `scripts/dataset_large_tpu.sh` for TPU training and evaluation