    def add_args(parser):
        """Add model-specific arguments to the parser."""
        # fmt: off
        parser.add_argument('--activation-fn',
                            choices=utils.get_available_activation_fns(),
                            help='activation function to use')
        parser.add_argument('--dropout', type=float, metavar='D',
                            help='dropout probability')
        parser.add_argument('--attention-dropout', type=float, metavar='D',
                            help='dropout probability for attention weights')
        parser.add_argument('--activation-dropout', '--relu-dropout', type=float, metavar='D',
                            help='dropout probability after activation in FFN.')
        parser.add_argument('--encoder-embed-path', type=str, metavar='STR',
                            help='path to pre-trained encoder embedding')
        parser.add_argument('--encoder-embed-dim', type=int, metavar='N',
                            help='encoder embedding dimension')
        parser.add_argument('--encoder-ffn-embed-dim', type=int, metavar='N',
                            help='encoder embedding dimension for FFN')
        parser.add_argument('--encoder-layers', type=int, metavar='N',
                            help='num encoder layers')
        parser.add_argument('--encoder-attention-heads', type=int, metavar='N',
                            help='num encoder attention heads')
        parser.add_argument('--encoder-normalize-before', action='store_true',
                            help='apply layernorm before each encoder block')
        parser.add_argument('--encoder-learned-pos', action='store_true',
                            help='use learned positional embeddings in the encoder')
        parser.add_argument('--decoder-embed-path', type=str, metavar='STR',
                            help='path to pre-trained decoder embedding')
        parser.add_argument('--decoder-embed-dim', type=int, metavar='N',
                            help='decoder embedding dimension')
        parser.add_argument('--decoder-ffn-embed-dim', type=int, metavar='N',
                            help='decoder embedding dimension for FFN')
        parser.add_argument('--decoder-layers', type=int, metavar='N',
                            help='num decoder layers')
        parser.add_argument('--decoder-attention-heads', type=int, metavar='N',
                            help='num decoder attention heads')
        parser.add_argument('--decoder-learned-pos', action='store_true',
                            help='use learned positional embeddings in the decoder')
        parser.add_argument('--decoder-normalize-before', action='store_true',
                            help='apply layernorm before each decoder block')
        parser.add_argument('--share-decoder-input-output-embed', action='store_true',
                            help='share decoder input and output embeddings')
        parser.add_argument('--share-all-embeddings', action='store_true',
                            help='share encoder, decoder and output embeddings'
                                 ' (requires shared dictionary and embed dim)')
        parser.add_argument('--no-token-positional-embeddings', default=False, action='store_true',
                            help='if set, disables positional embeddings (outside self attention)')
        parser.add_argument('--adaptive-softmax-cutoff', metavar='EXPR',
                            help='comma separated list of adaptive softmax cutoff points. '
                                 'Must be used with adaptive_loss criterion'),
        parser.add_argument('--adaptive-softmax-dropout', type=float, metavar='D',
                            help='sets adaptive softmax dropout for the tail projections')
        parser.add_argument('--num-embedding-chunks', type=int, metavar='N', default=1,
                            help='Number of embedding layer chunks (enables more even distribution'
                                 'of optimizer states across data parallel nodes'
                                 'when using optimizer state sharding and'
                                 'a big embedding vocabulary)')
Add model-specific arguments to the parser.
fairseq/model_parallel/models/pipeline_parallel_transformer/model.py