def transformer_iwslt_de_en_dist(args):
transformer_iwslt_de_en(args)
def transformer_monotonic_iwslt_de_en(args):
transformer_iwslt_de_en(args)
base_monotonic_architecture(args)
def transformer_unidirectional_iwslt_de_en(args):
transformer_iwslt_de_en(args)
def __init__(self,
vocab: Vocabulary,
dataset_reader: DatasetReader,
source_embedder: TextFieldEmbedder,
lang2_namespace: str = "tokens",
use_bleu: bool = True) -> None:
super().__init__(vocab)
self._lang1_namespace = lang2_namespace # TODO: DO NOT HARDCODE IT
self._lang2_namespace = lang2_namespace
# TODO: do not hardcore this
self._backtranslation_src_langs = ["en", "ru"]
self._coeff_denoising = 1
self._coeff_backtranslation = 1
self._coeff_translation = 1
self._label_smoothing = 0.1
self._pad_index_lang1 = vocab.get_token_index(DEFAULT_PADDING_TOKEN,
self._lang1_namespace)
self._oov_index_lang1 = vocab.get_token_index(DEFAULT_OOV_TOKEN,
self._lang1_namespace)
self._end_index_lang1 = self.vocab.get_token_index(
END_SYMBOL, self._lang1_namespace)
self._pad_index_lang2 = vocab.get_token_index(DEFAULT_PADDING_TOKEN,
self._lang2_namespace)
self._oov_index_lang2 = vocab.get_token_index(DEFAULT_OOV_TOKEN,
self._lang2_namespace)
self._end_index_lang2 = self.vocab.get_token_index(
END_SYMBOL, self._lang2_namespace)
self._reader = dataset_reader
self._langs_list = self._reader._langs_list
self._ae_steps = self._reader._ae_steps
self._bt_steps = self._reader._bt_steps
self._para_steps = self._reader._para_steps
if use_bleu:
self._bleu = Average()
else:
self._bleu = None
args = ArgsStub()
transformer_iwslt_de_en(args)
# build encoder
if not hasattr(args, 'max_source_positions'):
args.max_source_positions = 1024
if not hasattr(args, 'max_target_positions'):
args.max_target_positions = 1024
# Dense embedding of source vocab tokens.
self._source_embedder = source_embedder
# Dense embedding of vocab words in the target space.
num_tokens_lang1 = self.vocab.get_vocab_size(self._lang1_namespace)
num_tokens_lang2 = self.vocab.get_vocab_size(self._lang2_namespace)
args.share_decoder_input_output_embed = False # TODO implement shared embeddings
lang1_dict = DictStub(num_tokens=num_tokens_lang1,
pad=self._pad_index_lang1,
unk=self._oov_index_lang1,
eos=self._end_index_lang1)
lang2_dict = DictStub(num_tokens=num_tokens_lang2,
pad=self._pad_index_lang2,
unk=self._oov_index_lang2,
eos=self._end_index_lang2)
# instantiate fairseq classes
emb_golden_tokens = FairseqEmbedding(num_tokens_lang2,
args.decoder_embed_dim,
self._pad_index_lang2)
self._encoder = TransformerEncoder(args, lang1_dict,
self._source_embedder)
self._decoder = TransformerDecoder(args, lang2_dict, emb_golden_tokens)
self._model = TransformerModel(self._encoder, self._decoder)
# TODO: do not hardcode max_len_b and beam size
self._sequence_generator_greedy = FairseqBeamSearchWrapper(
SequenceGenerator(tgt_dict=lang2_dict, beam_size=1, max_len_b=20))
self._sequence_generator_beam = FairseqBeamSearchWrapper(
SequenceGenerator(tgt_dict=lang2_dict, beam_size=7, max_len_b=20))
