# Word-level language modeling RNN

This example is based on [https://github.com/pytorch/examples/tree/master/word_language_model](https://github.com/pytorch/examples/tree/master/word_language_model).
It trains a multi-layer RNN (Elman, GRU, or LSTM) on a language modeling task.
By default, the training script uses the Wikitext-2 dataset, provided.
The trained model can then be used by the generate script to generate new text.

`main.py` with the `--fp16` argument demonstrates mixed precision training with manual management of master parameters and loss scaling.

`main_fp16_optimizer.py` with `--fp16` demonstrates use of `apex.fp16_utils.FP16_Optimizer` to automatically manage master parameters and loss scaling.

This example is intended as an illustration of the mixed precision recipe, not necessarily as a performance showcase.  However, it does demonstrate certain best practices.  With `--fp16`, to enable Tensor Core use and achieve best performance, dimensions that participate in GEMMs in the model should be multiples of 8.  Specifically, these are
* dictionary length (ntokens in `main.py`),
* embedding size (`--emsize`),
* hidden size (`--nhid`), and
* batch size (`--batch_size`).

The dictionary length is a property of the dataset, and is not controlled by a command line argument. In `main.py`, `corpus = data.Corpus(args.data, pad_to_multiple_of=8)` and the `Corpus` constructor in
`data.py` ensure that the dictionary length is a multiple of 8.

Also, for mixed precision performance, a good general rule is: the more work you give the GPU, the better.  Bigger models and larger batch sizes supply the cores with more work and do a better job saturating the device.  A (very rough) way to check if you're saturating the device is to run nvidia-smi from another terminal, and see what fraction of device memory you're using.  This will tell you how much leeway you have to increase model or batch size.

```bash
python main.py --cuda --epochs 6        # Train a LSTM on Wikitext-2 with CUDA
python main.py --cuda --epochs 6 --fp16 # Train a LSTM on Wikitext-2 with CUDA and mixed precision
python main.py --cuda --epochs 6 --tied # Train a tied LSTM on Wikitext-2 with CUDA
python main.py --cuda --tied            # Train a tied LSTM on Wikitext-2 with CUDA for 40 epochs
python generate.py                      # Generate samples from the trained LSTM model.
```

The model uses the `nn.RNN` module (and its sister modules `nn.GRU` and `nn.LSTM`)
which will automatically use the cuDNN backend if run on CUDA with cuDNN installed.

During training, if a keyboard interrupt (Ctrl-C) is received,
training is stopped and the current model is evaluated against the test dataset.

## Usage for `main.py` and `main_fp16_optimizer.py`

```bash
usage: main.py [-h] [--data DATA] [--model MODEL] [--emsize EMSIZE]
               [--nhid NHID] [--nlayers NLAYERS] [--lr LR] [--clip CLIP]
               [--epochs EPOCHS] [--batch_size N] [--bptt BPTT]
               [--dropout DROPOUT] [--tied] [--seed SEED] [--cuda]
               [--log-interval N] [--save SAVE] [--fp16]
               [--static-loss-scale STATIC_LOSS_SCALE]

PyTorch Wikitext-2 RNN/LSTM Language Model

optional arguments:
  -h, --help            show this help message and exit
  --data DATA           location of the data corpus
  --model MODEL         type of recurrent net (RNN_TANH, RNN_RELU, LSTM, GRU)
  --emsize EMSIZE       size of word embeddings
  --nhid NHID           number of hidden units per layer
  --nlayers NLAYERS     number of layers
  --lr LR               initial learning rate
  --clip CLIP           gradient clipping
  --epochs EPOCHS       upper epoch limit
  --batch_size N        batch size
  --bptt BPTT           sequence length
  --dropout DROPOUT     dropout applied to layers (0 = no dropout)
  --tied                tie the word embedding and softmax weights
  --seed SEED           random seed
  --cuda                use CUDA
  --log-interval N      report interval
  --save SAVE           path to save the final model
  --fp16                Run model in pseudo-fp16 mode (fp16 storage fp32
                        math).
  --static-loss-scale STATIC_LOSS_SCALE
                        Static loss scale, positive power of 2 values can
                        improve fp16 convergence.

```

`main_fp16_optimizer` also accepts the optional flag
```bash
  --dynamic-loss-scale  Use dynamic loss scaling. If supplied, this argument
                        supersedes --static-loss-scale.
```
which triggers the use of dynamic loss scaling.  Supplying `--dynamic-loss-scale` will override the `--loss_scale` argument, if any.

With these arguments, a variety of models can be tested.  For example

```bash
python main.py --cuda --emsize 656 --nhid 656 --dropout 0.5 --epochs 40
python main.py --cuda --emsize 656 --nhid 656 --dropout 0.5 --epochs 40 --tied
python main.py --cuda --emsize 1504 --nhid 1504 --dropout 0.65 --epochs 40
python main.py --cuda --emsize 1504 --nhid 1504 --dropout 0.65 --epochs 40 --tied
```
