### Running fairseq-train with Custom Module

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/overview.rst

Command line example showing how to invoke the fairseq-train script and load a custom module containing new plug-ins using the --user-dir flag, specifying a custom architecture.

```Shell
fairseq-train ... --user-dir /home/user/my-module -a my_transformer --task translation
```

--------------------------------

### Custom Module __init__.py for Fairseq

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/overview.rst

Example content for an __init__.py file within a custom user module, showing how to import necessary components and register a new model architecture based on an existing one.

```Python
from fairseq.models import register_model_architecture
from fairseq.models.transformer import transformer_vaswani_wmt_en_de_big

@register_model_architecture('transformer', 'my_transformer')
def transformer_mmt_big(args):
    transformer_vaswani_wmt_en_de_big(args)
```

--------------------------------

### Setup Simple Classification Task (Python)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Loads the input and label dictionaries required for the task from specified file paths. Prints the size of each loaded dictionary and returns an instance of the SimpleClassificationTask.

```Python
# loading Dictionaries, initializing shared Embedding layers, etc.
# In this case we'll just load the Dictionaries.
input_vocab = Dictionary.load(os.path.join(args.data, 'dict.input.txt'))
label_vocab = Dictionary.load(os.path.join(args.data, 'dict.label.txt'))
print('| [input] dictionary: {} types'.format(len(input_vocab)))
print('| [label] dictionary: {} types'.format(len(label_vocab)))

return SimpleClassificationTask(args, input_vocab, label_vocab)
```

--------------------------------

### Training Fairseq Model with Custom Architecture Console

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

Example command-line instruction to train the Fairseq model using the `fairseq-train` tool. It specifies the data directory, the custom named architecture (`tutorial_simple_lstm`), dropout rates, optimizer settings, learning rate, and batch size parameters.

```console
> fairseq-train data-bin/iwslt14.tokenized.de-en \
    --arch tutorial_simple_lstm \
    --encoder-dropout 0.2 --decoder-dropout 0.2 \
    --optimizer adam --lr 0.005 --lr-shrink 0.5 \
    --max-tokens 12000
```

--------------------------------

### Pre-process and Binarize IWSLT 2014 Dataset

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/getting_started.rst

Navigates to the examples directory, runs a script to prepare the IWSLT 2014 German-English dataset, sets a text variable, and then uses fairseq-preprocess to tokenize and binarize the training, validation, and test sets, saving the output to a specified directory.

```console
cd examples/translation/
bash prepare-iwslt14.sh
cd ../..
TEXT=examples/translation/iwslt14.tokenized.de-en
fairseq-preprocess --source-lang de --target-lang en \
    --trainpref $TEXT/train --validpref $TEXT/valid --testpref $TEXT/test \
    --destdir data-bin/iwslt14.tokenized.de-en
```

--------------------------------

### Launch Distributed Training with torch.distributed.launch (Console)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/getting_started.rst

Command to launch distributed training for a fairseq model across multiple nodes and GPUs using the `torch.distributed.launch` utility. This example demonstrates training a large Transformer model on 2 nodes with 8 GPUs each, configuring node rank, master address, and various training parameters.

```console
> python -m torch.distributed.launch --nproc_per_node=8 \
    --nnodes=2 --node_rank=0 --master_addr=\"192.168.1.1\" \
    --master_port=1234 \
    $(which fairseq-train) data-bin/wmt16_en_de_bpe32k \
    --arch transformer_vaswani_wmt_en_de_big --share-all-embeddings \
    --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0 \
    --lr-scheduler inverse_sqrt --warmup-init-lr 1e-07 --warmup-updates 4000 \
    --lr 0.0005 --min-lr 1e-09 \
    --dropout 0.3 --weight-decay 0.0 --criterion label_smoothed_cross_entropy --label-smoothing 0.1 \
    --max-tokens 3584 \
    --fp16  --distributed-no-spawn
```

--------------------------------

### Registering a Fairseq Model Plug-in

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/overview.rst

Example demonstrating how to register a new model plug-in in Fairseq using the @register_model decorator, making it available for use with command-line tools.

```Python
@register_model('my_lstm')
class MyLSTM(FairseqEncoderDecoderModel):
    (...)
```

--------------------------------

### Generating Translations with fairseq Console Command

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

Example command to generate translations using a trained fairseq model. It specifies the data binary, model path, beam size, and removes BPE tokens.

```console
> fairseq-generate data-bin/iwslt14.tokenized.de-en \
  --path checkpoints/checkpoint_best.pt \
  --beam 5 \
  --remove-bpe
```

--------------------------------

### Run T-Fixup Training Script (Shell)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/README.md

Executes the example shell script to build the IWSLT'14 De-En dataset and run the T-Fixup training process for the small Transformer model. This script removes layer normalization and learning rate warmup, setting a fixed starting learning rate.

```Shell
./Train_IWSLT_TFixup_example.sh
```

--------------------------------

### Example Batch Iterator Override (Python)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Commented-out example showing the signature of the `get_batch_iterator` method, which could be overridden for custom batch construction if needed, although it's not required for this simple task.

```Python
# We could override this method if we wanted more control over how batches
# are constructed, but it's not necessary for this tutorial since we can
# reuse the batching provided by LanguagePairDataset.
#
# def get_batch_iterator(
#     self, dataset, max_tokens=None, max_sentences=None, max_positions=None,
#     ignore_invalid_inputs=False, required_batch_size_multiple=1,
#     seed=1, num_shards=1, shard_id=0,
# ):
#     (...)
```

--------------------------------

### Registering Simple Classification Task in Fairseq

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Shows how to define and register a custom Fairseq task named 'simple_classification' using the `@register_task` decorator. It includes the `add_args` method to define command-line arguments for data location and max input length, and the `setup_task` method for task-specific setup.

```Python
import os
  import torch

  from fairseq.data import Dictionary, LanguagePairDataset
  from fairseq.tasks import FairseqTask, register_task


  @register_task('simple_classification')
  class SimpleClassificationTask(FairseqTask):

      @staticmethod
      def add_args(parser):
          # Add some command-line arguments for specifying where the data is
          # located and the maximum supported input length.
          parser.add_argument('data', metavar='FILE',
                              help='file prefix for data')
          parser.add_argument('--max-positions', default=1024, type=int,
                              help='max input length')

      @classmethod
      def setup_task(cls, args, **kwargs):
          # Here we can perform any setup required for the task. This may include
```

--------------------------------

### Get Source Dictionary Property (Python)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

A property that provides access to the source dictionary (input vocabulary) associated with the task.

```Python
@property
def source_dictionary(self):
    """Return the source :class:`~fairseq.data.Dictionary`."""
    return self.input_vocab
```

--------------------------------

### Train with Large Effective Batch Size (Console)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/getting_started.rst

Command to simulate a larger batch size on a single GPU by accumulating gradients over multiple mini-batches before updating. This is achieved using the `--update-freq` option. The example shows how to achieve an effective batch size equivalent to 8 GPUs on a single device.

```console
> CUDA_VISIBLE_DEVICES=0 fairseq-train --update-freq 8 (...)
```

--------------------------------

### Get Target Dictionary Property (Python)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

A property that provides access to the target dictionary (label vocabulary) associated with the task.

```Python
@property
def target_dictionary(self):
    """Return the target :class:`~fairseq.data.Dictionary`."""
    return self.label_vocab
```

--------------------------------

### Running Fairseq Evaluation Script - Console

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

This console command demonstrates how to execute the `eval_classifier.py` script. It specifies the data directory (`names-bin`) for loading dictionaries and the path to the best model checkpoint (`checkpoints/checkpoint_best.pt`). The output shows the script loading dictionaries and the model, followed by interactive input prompts and model predictions for sample inputs ("Satoshi" and "Sinbad").

```Console
> python eval_classifier.py names-bin --path checkpoints/checkpoint_best.pt
| [input] dictionary: 64 types
| [label] dictionary: 24 types
| loading model from checkpoints/checkpoint_best.pt

Input: Satoshi
(-0.61)	Japanese
(-1.20)	Arabic
(-2.86)	Italian

Input: Sinbad
(-0.30)	Arabic
(-1.76)	English
(-4.08)	Russian
```

--------------------------------

### Launch Tensorboard for Monitoring (Shell)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/README.md

Launches Tensorboard to visualize and monitor the training progress. Replace `<log_path>` with the directory where training logs are saved.

```Shell
tensorboard --logdir=<log_path>
```

--------------------------------

### Preprocessing Data with fairseq-preprocess (Console)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Demonstrates how to use the `fairseq-preprocess` command-line tool to prepare data for a classification task. It specifies input, validation, and test prefixes, source and target languages, destination directory, and dataset implementation format.

```Console
fairseq-preprocess \
  --trainpref names/train --validpref names/valid --testpref names/test \
  --source-lang input --target-lang label \
  --destdir names-bin --dataset-impl raw
```

--------------------------------

### Fairseq High-Level Training Loop

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/overview.rst

Illustrates the high-level training flow implemented by fairseq, showing the iteration over epochs and batches, calling task.train_step, gradient handling, and optimizer/scheduler steps.

```Python
for epoch in range(num_epochs):
    itr = task.get_batch_iterator(task.dataset('train'))
    for num_updates, batch in enumerate(itr):
        task.train_step(batch, model, criterion, optimizer)
        average_and_clip_gradients()
        optimizer.step()
        lr_scheduler.step_update(num_updates)
    lr_scheduler.step(epoch)
```

--------------------------------

### Train Simple Classification Model (Console)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Command-line instruction using `fairseq-train` to train the model. It specifies the custom task, architecture, optimizer, learning rate settings, and maximum tokens per batch.

```Console
fairseq-train names-bin \
  --task simple_classification \
  --arch pytorch_tutorial_rnn \
  --optimizer adam --lr 0.001 --lr-shrink 0.5 \
  --max-tokens 1000
```

--------------------------------

### Load Dataset for Simple Classification Task (Python)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Loads a specific dataset split (e.g., train, valid, test) by reading input sentences and corresponding labels from files. It tokenizes inputs, converts labels to numeric IDs, and constructs a LanguagePairDataset suitable for classification.

```Python
def load_dataset(self, split, **kwargs):
    """Load a given dataset split (e.g., train, valid, test)."""

    prefix = os.path.join(self.args.data, '{}.input-label'.format(split))

    # Read input sentences.
    sentences, lengths = [], []
    with open(prefix + '.input', encoding='utf-8') as file:
        for line in file:
            sentence = line.strip()

            # Tokenize the sentence, splitting on spaces
            tokens = self.input_vocab.encode_line(
                sentence, add_if_not_exist=False,
            )

            sentences.append(tokens)
            lengths.append(tokens.numel())

    # Read labels.
    labels = []
    with open(prefix + '.label', encoding='utf-8') as file:
        for line in file:
            label = line.strip()
            labels.append(
                # Convert label to a numeric ID.
                torch.LongTensor([self.label_vocab.add_symbol(label)])
            )

    assert len(sentences) == len(labels)
    print('| {} {} {} examples'.format(self.args.data, split, len(sentences)))

    # We reuse LanguagePairDataset since classification can be modeled as a
    # sequence-to-sequence task where the target sequence has length 1.
    self.datasets[split] = LanguagePairDataset(
        src=sentences,
        src_sizes=lengths,
        src_dict=self.input_vocab,
        tgt=labels,
        tgt_sizes=torch.ones(len(labels)),  # targets have length 1
        tgt_dict=self.label_vocab,
        left_pad_source=False,
        max_source_positions=self.args.max_positions,
        max_target_positions=1,
        # Since our target is a single class label, there's no need for
        # teacher forcing. If we set this to ``True`` then our Model's
        # ``forward()`` method would receive an additional argument called
        # *prev_output_tokens* that would contain a shifted version of the
        # target sequence.
        input_feeding=False,
    )
```

--------------------------------

### Using Fairseq Tasks in Python

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tasks.rst

This snippet demonstrates the typical workflow for using a Fairseq Task. It covers setting up the task, building the model and criterion, loading datasets, iterating over data batches, and computing the loss for a batch.

```Python
# setup the task (e.g., load dictionaries)
task = fairseq.tasks.setup_task(args)

# build model and criterion
model = task.build_model(args)
criterion = task.build_criterion(args)

# load datasets
task.load_dataset('train')
task.load_dataset('valid')

# iterate over mini-batches of data
batch_itr = task.get_batch_iterator(
    task.dataset('train'), max_tokens=4096,
)
for batch in batch_itr:
    # compute the loss
    loss, sample_size, logging_output = task.get_loss(
        model, criterion, batch,
    )
    loss.backward()
```

--------------------------------

### Enable FP16 Training (Console)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/getting_started.rst

Command to enable half-precision floating-point (FP16) training in fairseq. This feature requires a compatible GPU (e.g., Nvidia Volta) and CUDA 9.1 or greater to leverage efficient half-precision computation.

```console
> fairseq-train --fp16 (...)
```

--------------------------------

### Initialize Simple Classification Task (Python)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Constructor for the SimpleClassificationTask class. It calls the constructor of the parent class and stores the provided input and label vocabulary dictionaries as instance attributes.

```Python
def __init__(self, args, input_vocab, label_vocab):
    super().__init__(args)
    self.input_vocab = input_vocab
    self.label_vocab = label_vocab
```

--------------------------------

### Download and Extract Pre-trained Fairseq Model

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/getting_started.rst

Downloads a pre-trained Fairseq model (WMT14 English-French fconv-py) and extracts its contents using curl and tar. This provides the model weights and associated vocabulary files.

```console
curl https://dl.fbaipublicfiles.com/fairseq/models/wmt14.v2.en-fr.fconv-py.tar.bz2 | tar xvjf -
```

--------------------------------

### Interactive Translation with fairseq-interactive

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/getting_started.rst

Uses the fairseq-interactive tool to perform interactive machine translation. It loads a specified model, sets beam size, source/target languages, and applies Moses tokenization and Subword NMT BPE using the provided codes file. It automatically handles BPE removal and detokenization.

```console
MODEL_DIR=wmt14.en-fr.fconv-py
fairseq-interactive \
    --path $MODEL_DIR/model.pt $MODEL_DIR \
    --beam 5 --source-lang en --target-lang fr \
    --tokenizer moses \
    --bpe subword_nmt --bpe-codes $MODEL_DIR/bpecodes
```

--------------------------------

### Demonstrating fairseq-generate Speedup in Console

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

This console output snippet shows the results of running the `fairseq-generate` command before and after an optimization. It compares the time taken to translate a set of sentences, clearly demonstrating a significant reduction in generation time (from 17.3s to 5.5s) and a corresponding increase in sentences/second and tokens/second throughput, while maintaining the same BLEU score.

```Console
# Before

> fairseq-generate data-bin/iwslt14.tokenized.de-en \
  --path checkpoints/checkpoint_best.pt \
  --beam 5 \
  --remove-bpe
(...)
| Translated 6750 sentences (153132 tokens) in 17.3s (389.12 sentences/s, 8827.68 tokens/s)
| Generate test with beam=5: BLEU4 = 8.18, 38.8/12.1/4.7/2.0 (BP=1.000, ratio=1.066, syslen=139865, reflen=131146)

# After

> fairseq-generate data-bin/iwslt14.tokenized.de-en \
  --path checkpoints/checkpoint_best.pt \
  --beam 5 \
  --remove-bpe
(...)
| Translated 6750 sentences (153132 tokens) in 5.5s (1225.54 sentences/s, 27802.94 tokens/s)
| Generate test with beam=5: BLEU4 = 8.18, 38.8/12.1/4.7/2.0 (BP=1.000, ratio=1.066, syslen=139865, reflen=131146)
```

--------------------------------

### Building Fairseq Simple LSTM Model Python

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

Implements the `build_model` class method required by Fairseq. It initializes the `SimpleLSTMEncoder` and `SimpleLSTMDecoder` instances with the provided arguments and dictionaries, then combines them into a `SimpleLSTMModel` instance.

```Python
@classmethod
      def build_model(cls, args, task):
          # Fairseq initializes models by calling the ``build_model()``
          # function. This provides more flexibility, since the returned model
          # instance can be of a different type than the one that was called.
          # In this case we'll just return a SimpleLSTMModel instance.

          # Initialize our Encoder and Decoder.
          encoder = SimpleLSTMEncoder(
              args=args,
              dictionary=task.source_dictionary,
              embed_dim=args.encoder_embed_dim,
              hidden_dim=args.encoder_hidden_dim,
              dropout=args.encoder_dropout,
          )
          decoder = SimpleLSTMDecoder(
              dictionary=task.target_dictionary,
              encoder_hidden_dim=args.encoder_hidden_dim,
              embed_dim=args.decoder_embed_dim,
              hidden_dim=args.decoder_hidden_dim,
              dropout=args.decoder_dropout,
          )
          model = SimpleLSTMModel(encoder, decoder)

          # Print the model architecture.
          print(model)

          return model
```

--------------------------------

### Evaluating Fairseq Simple Classification Model - Python

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

This Python script uses the Fairseq library to load a pre-trained simple classification model and evaluate it interactively. It takes model path and data path as arguments, tokenizes user input character by character, builds a batch, feeds it to the model, and prints the top 3 predicted labels with their scores.

```Python
from fairseq import checkpoint_utils, data, options, tasks

# Parse command-line arguments for generation
parser = options.get_generation_parser(default_task='simple_classification')
args = options.parse_args_and_arch(parser)

# Setup task
task = tasks.setup_task(args)

# Load model
print('| loading model from {}'.format(args.path))
models, _model_args = checkpoint_utils.load_model_ensemble([args.path], task=task)
model = models[0]

while True:
    sentence = input('\nInput: ')

    # Tokenize into characters
    chars = ' '.join(list(sentence.strip()))
    tokens = task.source_dictionary.encode_line(
        chars, add_if_not_exist=False,
    )

    # Build mini-batch to feed to the model
    batch = data.language_pair_dataset.collate(
        samples=[{'id': -1, 'source': tokens}],  # bsz = 1
        pad_idx=task.source_dictionary.pad(),
        eos_idx=task.source_dictionary.eos(),
        left_pad_source=False,
        input_feeding=False,
    )

    # Feed batch to the model and get predictions
    preds = model(**batch['net_input'])

    # Print top 3 predictions and their log-probabilities
    top_scores, top_labels = preds[0].topk(k=3)
    for score, label_idx in zip(top_scores, top_labels):
        label_name = task.target_dictionary.string([label_idx])
        print('({:.2f})\t{}'.format(score, label_name))
```

--------------------------------

### Train a New Fairseq Model

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/getting_started.rst

Creates a directory for checkpoints, sets the CUDA_VISIBLE_DEVICES environment variable to use a specific GPU, and runs fairseq-train. It trains a model on the binarized IWSLT 2014 data using specified hyperparameters (learning rate, clip norm, dropout, max tokens) and architecture (fconv_iwslt_de_en), saving checkpoints to the specified directory.

```console
mkdir -p checkpoints/fconv
CUDA_VISIBLE_DEVICES=0 fairseq-train data-bin/iwslt14.tokenized.de-en \
    --lr 0.25 --clip-norm 0.1 --dropout 0.2 --max-tokens 4000 \
    --arch fconv_iwslt_de_en --save-dir checkpoints/fconv
```

--------------------------------

### Generate Translations from Trained Model

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/getting_started.rst

Uses the fairseq-generate tool to produce translations for a binarized test set using a trained model. It specifies the data directory, the path to the best checkpoint file, batch size, and beam size.

```console
fairseq-generate data-bin/iwslt14.tokenized.de-en \
    --path checkpoints/fconv/checkpoint_best.pt \
    --batch-size 128 --beam 5
```

--------------------------------

### Fairseq Default task.train_step Implementation

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/overview.rst

Shows the default implementation of the task.train_step method, which calculates the loss using the criterion and performs the backward pass on the optimizer.

```Python
def train_step(self, batch, model, criterion, optimizer):
    loss = criterion(model, batch)
    optimizer.backward(loss)
    return loss
```

--------------------------------

### Default Fairseq EncoderDecoderModel Forward Method Python

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

Shows the default implementation of the `forward` method in the `FairseqEncoderDecoderModel` base class. It demonstrates how the encoder output is passed to the decoder. This method can be overridden for custom behavior.

```Python
# We could override the ``forward()`` if we wanted more control over how
      # the encoder and decoder interact, but it's not necessary for this
      # tutorial since we can inherit the default implementation provided by
      # the FairseqEncoderDecoderModel base class, which looks like:
      #
      # def forward(self, src_tokens, src_lengths, prev_output_tokens):
      #     encoder_out = self.encoder(src_tokens, src_lengths)
      #     decoder_out = self.decoder(prev_output_tokens, encoder_out)
      #     return decoder_out
```

--------------------------------

### Registering RNN as Fairseq Model (Python)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Defines a wrapper class `FairseqRNNClassifier` that inherits from `BaseFairseqModel` and is registered with Fairseq using the `@register_model` decorator. It includes methods `add_args` for adding command-line arguments and `build_model` for model initialization within the Fairseq framework. Note: The provided code snippet is incomplete.

```Python
from fairseq.models import BaseFairseqModel, register_model

# Note: the register_model "decorator" should immediately precede the
# definition of the Model class.

@register_model('rnn_classifier')
class FairseqRNNClassifier(BaseFairseqModel):

    @staticmethod
    def add_args(parser):
        # Models can override this method to add new command-line arguments.
        # Here we'll add a new command-line argument to configure the
        # dimensionality of the hidden state.
        parser.add_argument(
            '--hidden-dim', type=int, metavar='N',
            help='dimensionality of the hidden state',
        )

    @classmethod
    def build_model(cls, args, task):
        # Fairseq initializes models by calling the ``build_model()``
        # function. This provides more flexibility, since the returned model
        # instance can be of a different type than the one that was called.
        # In this case we'll just return a FairseqRNNClassifier instance.

        # Initialize our RNN module
        rnn = RNN(
```

--------------------------------

### Registering Simple LSTM Model in Fairseq (Python)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

This class wraps the encoder and decoder components and registers the complete model with the fairseq framework using the @register_model decorator. It inherits from FairseqEncoderDecoderModel and includes a static add_args method to define command-line arguments for model configuration like dropout and dimensionality.

```Python
from fairseq.models import FairseqEncoderDecoderModel, register_model

# Note: the register_model "decorator" should immediately precede the
# definition of the Model class.

@register_model('simple_lstm')
class SimpleLSTMModel(FairseqEncoderDecoderModel):

    @staticmethod
    def add_args(parser):
        # Models can override this method to add new command-line arguments.
        # Here we'll add some new command-line arguments to configure dropout
        # and the dimensionality of the embeddings and hidden states.
        parser.add_argument(
```

--------------------------------

### Defining PyTorch Character-Level RNN Module (Python)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Implements a basic RNN module for character-level processing, based on the PyTorch tutorial. It includes linear layers for input-to-hidden and input-to-output transformations and a LogSoftmax layer for output probabilities. It also provides a method to initialize the hidden state.

```Python
import torch
import torch.nn as nn

class RNN(nn.Module):

    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()

        self.hidden_size = hidden_size

        self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
        self.i2o = nn.Linear(input_size + hidden_size, output_size)
        self.softmax = nn.LogSoftmax(dim=1)

    def forward(self, input, hidden):
        combined = torch.cat((input, hidden), 1)
        hidden = self.i2h(combined)
        output = self.i2o(combined)
        output = self.softmax(output)
        return output, hidden

    def initHidden(self):
        return torch.zeros(1, self.hidden_size)
```

--------------------------------

### Implementing Fairseq Incremental Decoder in Python

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

Defines a SimpleLSTMDecoder class that inherits from FairseqIncrementalDecoder. It includes an __init__ method for initialization and a forward method that handles incremental decoding by caching LSTM states using incremental_state. It also mentions the requirement for reorder_incremental_state.

```python
import torch
from fairseq.models import FairseqIncrementalDecoder

class SimpleLSTMDecoder(FairseqIncrementalDecoder):

    def __init__(
        self, dictionary, encoder_hidden_dim=128, embed_dim=128, hidden_dim=128,
        dropout=0.1,
    ):
        # This remains the same as before.
        super().__init__(dictionary)
        self.embed_tokens = nn.Embedding(
            num_embeddings=len(dictionary),
            embedding_dim=embed_dim,
            padding_idx=dictionary.pad(),
        )
        self.dropout = nn.Dropout(p=dropout)
        self.lstm = nn.LSTM(
            input_size=encoder_hidden_dim + embed_dim,
            hidden_size=hidden_dim,
            num_layers=1,
            bidirectional=False,
        )
        self.output_projection = nn.Linear(hidden_dim, len(dictionary))

    # We now take an additional kwarg (*incremental_state*) for caching the
    # previous hidden and cell states.
    def forward(self, prev_output_tokens, encoder_out, incremental_state=None):
        if incremental_state is not None:
            # If the *incremental_state* argument is not ``None`` then we are
            # in incremental inference mode. While *prev_output_tokens* will
            # still contain the entire decoded prefix, we will only use the
            # last step and assume that the rest of the state is cached.
            prev_output_tokens = prev_output_tokens[:, -1:]

        # This remains the same as before.
        bsz, tgt_len = prev_output_tokens.size()
        final_encoder_hidden = encoder_out['final_hidden']
        x = self.embed_tokens(prev_output_tokens)
        x = self.dropout(x)
        x = torch.cat(
            [x, final_encoder_hidden.unsqueeze(1).expand(bsz, tgt_len, -1)],
            dim=2,
        )

        # We will now check the cache and load the cached previous hidden and
        # cell states, if they exist, otherwise we will initialize them to
        # zeros (as before). We will use the ``utils.get_incremental_state()``
        # and ``utils.set_incremental_state()`` helpers.
        initial_state = utils.get_incremental_state(
            self, incremental_state, 'prev_state',
        )
        if initial_state is None:
            # first time initialization, same as the original version
            initial_state = (
                final_encoder_hidden.unsqueeze(0),  # hidden
                torch.zeros_like(final_encoder_hidden).unsqueeze(0),  # cell
            )

        # Run one step of our LSTM.
        output, latest_state = self.lstm(x.transpose(0, 1), initial_state)

        # Update the cache with the latest hidden and cell states.
        utils.set_incremental_state(
            self, incremental_state, 'prev_state', latest_state,
        )

        # This remains the same as before
        x = output.transpose(0, 1)
        x = self.output_projection(x)
        return x, None

    # The ``FairseqIncrementalDecoder`` interface also requires implementing a
    # ``reorder_incremental_state()`` method, which is used during beam search
    # to select and reorder the incremental state.
    def reorder_incremental_state(self, incremental_state, new_order):
        # Load the cached state.
        pass # The original snippet was incomplete, adding pass to make it valid Python
```

--------------------------------

### Defining Model Arguments with Fairseq Python

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

Adds command-line arguments for configuring the encoder and decoder dimensions and dropout rates using Fairseq's argument parsing system. These arguments are typically added within a `add_args` class method.

```Python
'--encoder-embed-dim', type=int, metavar='N',
              help='dimensionality of the encoder embeddings',
          )
          parser.add_argument(
              '--encoder-hidden-dim', type=int, metavar='N',
              help='dimensionality of the encoder hidden state',
          )
          parser.add_argument(
              '--encoder-dropout', type=float, default=0.1,
              help='encoder dropout probability',
          )
          parser.add_argument(
              '--decoder-embed-dim', type=int, metavar='N',
              help='dimensionality of the decoder embeddings',
          )
          parser.add_argument(
              '--decoder-hidden-dim', type=int, metavar='N',
              help='dimensionality of the decoder hidden state',
          )
          parser.add_argument(
              '--decoder-dropout', type=float, default=0.1,
              help='decoder dropout probability',
          )
```

--------------------------------

### Fairseq RNN Classifier Model Definition

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Defines the `FairseqRNNClassifier` class, including its initialization (`__init__`) and forward pass (`forward`) methods. It wraps a basic RNN module, handles one-hot encoding of inputs, manages batched hidden states, and processes sequential input tokens.

```Python
input_size=len(task.source_dictionary),
                hidden_size=args.hidden_dim,
                output_size=len(task.target_dictionary),
            )

            # Return the wrapped version of the module
            return FairseqRNNClassifier(
                rnn=rnn,
                input_vocab=task.source_dictionary,
            )

        def __init__(self, rnn, input_vocab):
            super(FairseqRNNClassifier, self).__init__()

            self.rnn = rnn
            self.input_vocab = input_vocab

            # The RNN module in the tutorial expects one-hot inputs, so we can
            # precompute the identity matrix to help convert from indices to
            # one-hot vectors. We register it as a buffer so that it is moved to
            # the GPU when ``cuda()`` is called.
            self.register_buffer('one_hot_inputs', torch.eye(len(input_vocab)))

        def forward(self, src_tokens, src_lengths):
            # The inputs to the ``forward()`` function are determined by the
            # Task, and in particular the ``'net_input'`` key in each
            # mini-batch. We'll define the Task in the next section, but for
            # now just know that *src_tokens* has shape `(batch, src_len)` and
            # *src_lengths* has shape `(batch)`.
            bsz, max_src_len = src_tokens.size()

            # Initialize the RNN hidden state. Compared to the original PyTorch
            # tutorial we'll also handle batched inputs and work on the GPU.
            hidden = self.rnn.initHidden()
            hidden = hidden.repeat(bsz, 1)  # expand for batched inputs
            hidden = hidden.to(src_tokens.device)  # move to GPU

            for i in range(max_src_len):
                # WARNING: The inputs have padding, so we should mask those
                # elements here so that padding doesn't affect the results.
                # This is left as an exercise for the reader. The padding symbol
                # is given by ``self.input_vocab.pad()`` and the unpadded length
                # of each input is given by *src_lengths*.

                # One-hot encode a batch of input characters.
                input = self.one_hot_inputs[src_tokens[:, i].long()]

                # Feed the input to our RNN.
                output, hidden = self.rnn(input, hidden)

            # Return the final output state for making a prediction
            return output
```

--------------------------------

### Implementing Simple LSTM Decoder in Fairseq (Python)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

This class defines a simple LSTM-based decoder for sequence-to-sequence models within the fairseq framework. It inherits from FairseqDecoder, embeds input tokens, uses a single-layer LSTM, and projects the output to the vocabulary size. It takes previous output tokens and encoder output as input and returns logits and attention weights (None in this case).

```Python
import torch
from fairseq.models import FairseqDecoder

class SimpleLSTMDecoder(FairseqDecoder):

    def __init__(
        self, dictionary, encoder_hidden_dim=128, embed_dim=128, hidden_dim=128,
        dropout=0.1,
    ):
        super().__init__(dictionary)

        # Our decoder will embed the inputs before feeding them to the LSTM.
        self.embed_tokens = nn.Embedding(
            num_embeddings=len(dictionary),
            embedding_dim=embed_dim,
            padding_idx=dictionary.pad(),
        )
        self.dropout = nn.Dropout(p=dropout)

        # We'll use a single-layer, unidirectional LSTM for simplicity.
        self.lstm = nn.LSTM(
            # For the first layer we'll concatenate the Encoder's final hidden
            # state with the embedded target tokens.
            input_size=encoder_hidden_dim + embed_dim,
            hidden_size=hidden_dim,
            num_layers=1,
            bidirectional=False,
        )

        # Define the output projection.
        self.output_projection = nn.Linear(hidden_dim, len(dictionary))

    # During training Decoders are expected to take the entire target sequence
    # (shifted right by one position) and produce logits over the vocabulary.
    # The *prev_output_tokens* tensor begins with the end-of-sentence symbol,
    # ``dictionary.eos()``, followed by the target sequence.
    def forward(self, prev_output_tokens, encoder_out):
        """
        Args:
            prev_output_tokens (LongTensor): previous decoder outputs of shape
                `(batch, tgt_len)`, for teacher forcing
            encoder_out (Tensor, optional): output from the encoder, used for
                encoder-side attention

        Returns:
            tuple:
                - the last decoder layer's output of shape
                  `(batch, tgt_len, vocab)`
                - the last decoder layer's attention weights of shape
                  `(batch, tgt_len, src_len)`
        """
        bsz, tgt_len = prev_output_tokens.size()

        # Extract the final hidden state from the Encoder.
        final_encoder_hidden = encoder_out['final_hidden']

        # Embed the target sequence, which has been shifted right by one
        # position and now starts with the end-of-sentence symbol.
        x = self.embed_tokens(prev_output_tokens)

        # Apply dropout.
        x = self.dropout(x)

        # Concatenate the Encoder's final hidden state to *every* embedded
        # target token.
        x = torch.cat(
            [x, final_encoder_hidden.unsqueeze(1).expand(bsz, tgt_len, -1)],
            dim=2,
        )

        # Using PackedSequence objects in the Decoder is harder than in the
        # Encoder, since the targets are not sorted in descending length order,
        # which is a requirement of ``pack_padded_sequence()``. Instead we'll
        # feed nn.LSTM directly.
        initial_state = (
            final_encoder_hidden.unsqueeze(0),  # hidden
            torch.zeros_like(final_encoder_hidden).unsqueeze(0),  # cell
        )
        output, _ = self.lstm(
            x.transpose(0, 1),  # convert to shape `(tgt_len, bsz, dim)`
            initial_state,
        )
        x = output.transpose(0, 1)  # convert to shape `(bsz, tgt_len, hidden)`

        # Project the outputs to the size of the vocabulary.
        x = self.output_projection(x)

        # Return the logits and ``None`` for the attention weights
        return x, None
```

--------------------------------

### Registering Fairseq Named Architecture Python

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

Registers a named model architecture ('tutorial_simple_lstm') for the 'simple_lstm' model using the `@register_model_architecture` decorator. The decorated function `tutorial_simple_lstm` modifies the input `args` object in-place to set default values for model dimensions if not provided via command line.

```Python
from fairseq.models import register_model_architecture

  # The first argument to ``register_model_architecture()`` should be the name
  # of the model we registered above (i.e., 'simple_lstm'). The function we
  # register here should take a single argument *args* and modify it in-place
  # to match the desired architecture.

  @register_model_architecture('simple_lstm', 'tutorial_simple_lstm')
  def tutorial_simple_lstm(args):
      # We use ``getattr()`` to prioritize arguments that are explicitly given
      # on the command-line, so that the defaults defined below are only used
      # when no other value has been specified.
      args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 256)
      args.encoder_hidden_dim = getattr(args, 'encoder_hidden_dim', 256)
      args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 256)
      args.decoder_hidden_dim = getattr(args, 'decoder_hidden_dim', 256)
```

--------------------------------

### Registering Fairseq Model Architecture

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Demonstrates how to register a named model architecture ('pytorch_tutorial_rnn') for the 'rnn_classifier' model using the `@register_model_architecture` decorator. The function sets default values for model arguments like `hidden_dim`.

```Python
from fairseq.models import register_model_architecture

    # The first argument to ``register_model_architecture()`` should be the name
    # of the model we registered above (i.e., 'rnn_classifier'). The function we
    # register here should take a single argument *args* and modify it in-place
    # to match the desired architecture.

    @register_model_architecture('rnn_classifier', 'pytorch_tutorial_rnn')
    def pytorch_tutorial_rnn(args):
        # We use ``getattr()`` to prioritize arguments that are explicitly given
        # on the command-line, so that the defaults defined below are only used
        # when no other value has been specified.
        args.hidden_dim = getattr(args, 'hidden_dim', 128)
```

--------------------------------

### Implementing Simple LSTM Encoder in Fairseq

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

Defines a custom encoder class `SimpleLSTMEncoder` that inherits from `FairseqEncoder`. It uses an embedding layer and a single-layer, unidirectional LSTM to process source tokens and returns the final hidden state. Includes methods for initialization, forward pass, and reordering encoder output for inference.

```Python
import torch.nn as nn
from fairseq import utils
from fairseq.models import FairseqEncoder

class SimpleLSTMEncoder(FairseqEncoder):

    def __init__(
        self, args, dictionary, embed_dim=128, hidden_dim=128, dropout=0.1,
    ):
        super().__init__(dictionary)
        self.args = args

        # Our encoder will embed the inputs before feeding them to the LSTM.
        self.embed_tokens = nn.Embedding(
            num_embeddings=len(dictionary),
            embedding_dim=embed_dim,
            padding_idx=dictionary.pad(),
        )
        self.dropout = nn.Dropout(p=dropout)

        # We'll use a single-layer, unidirectional LSTM for simplicity.
        self.lstm = nn.LSTM(
            input_size=embed_dim,
            hidden_size=hidden_dim,
            num_layers=1,
            bidirectional=False,
        )

    def forward(self, src_tokens, src_lengths):
        # The inputs to the ``forward()`` function are determined by the
        # Task, and in particular the ``'net_input'`` key in each
        # mini-batch. We discuss Tasks in the next tutorial, but for now just
        # know that *src_tokens* has shape `(batch, src_len)` and *src_lengths*
        # has shape `(batch)`.

        # Note that the source is typically padded on the left. This can be
        # configured by adding the `--left-pad-source "False"` command-line
        # argument, but here we'll make the Encoder handle either kind of
        # padding by converting everything to be right-padded.
        if self.args.left_pad_source:
            # Convert left-padding to right-padding.
            src_tokens = utils.convert_padding_direction(
                src_tokens,
                padding_idx=self.dictionary.pad(),
                left_to_right=True
            )

        # Embed the source.
        x = self.embed_tokens(src_tokens)

        # Apply dropout.
        x = self.dropout(x)

        # Pack the sequence into a PackedSequence object to feed to the LSTM.
        x = nn.utils.rnn.pack_padded_sequence(x, src_lengths, batch_first=True)

        # Get the output from the LSTM.
        _outputs, (final_hidden, _final_cell) = self.lstm(x)

        # Return the Encoder's output. This can be any object and will be
        # passed directly to the Decoder.
        return {
            # this will have shape `(bsz, hidden_dim)`
            'final_hidden': final_hidden.squeeze(0),
        }

    # Encoders are required to implement this method so that we can rearrange
    # the order of the batch elements during inference (e.g., beam search).
    def reorder_encoder_out(self, encoder_out, new_order):
        """
        Reorder encoder output according to `new_order`.

        Args:
            encoder_out: output from the ``forward()`` method
            new_order (LongTensor): desired order

        Returns:
            `encoder_out` rearranged according to `new_order`
        """
        final_hidden = encoder_out['final_hidden']
        return {
            'final_hidden': final_hidden.index_select(0, new_order),
        }

```

--------------------------------

### Define Max Positions for Simple Classification Task (Python)

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_classifying_names.rst

Returns a tuple specifying the maximum allowed length for the input sequence (source) and the target sequence. For this classification task, the target length is fixed at 1.

```Python
def max_positions(self):
    """Return the max input length allowed by the task."""
    # The source should be less than *args.max_positions* and the "target"
    # has max length 1.
    return (self.args.max_positions, 1)
```

--------------------------------

### Computing Loss with Fairseq Criterion

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/criterions.rst

This snippet demonstrates the basic usage of a fairseq criterion object to compute the loss value. It takes a model and a batch of data as input and returns the calculated loss.

```Python
loss = criterion(model, batch)
```

--------------------------------

### Reordering and Updating Incremental State in Python

Source: https://github.com/layer6ai-labs/t-fixup/blob/master/docs/tutorial_simple_lstm.rst

This Python snippet demonstrates how to retrieve, reorder, and update an incremental state within a model. It fetches the previous state, reorders its components (likely hidden and cell states in an RNN/LSTM context) based on a `new_order`, and then saves the reordered state back. This is typically used during beam search or other generation processes where the order of hypotheses changes.

```Python
prev_state = utils.get_incremental_state(
    self, incremental_state, 'prev_state',
)

# Reorder batches according to *new_order*.
reordered_state = (
    prev_state[0].index_select(1, new_order),  # hidden
    prev_state[1].index_select(1, new_order),  # cell
)

# Update the cached state.
utils.set_incremental_state(
    self, incremental_state, 'prev_state', reordered_state,
)
```

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