fastNLP/test/modules/decoder/test_seq2seq_decoder.py

import unittest

import torch

from fastNLP.modules.encoder.seq2seq_encoder import TransformerSeq2SeqEncoder, LSTMSeq2SeqEncoder
from fastNLP.modules.decoder.seq2seq_decoder import TransformerSeq2SeqDecoder, TransformerPast, LSTMPast, \
    LSTMSeq2SeqDecoder
from fastNLP.models.seq2seq_model import TransformerSeq2SeqModel, LSTMSeq2SeqModel
from fastNLP import Vocabulary
from fastNLP.embeddings import StaticEmbedding
from fastNLP.core.utils import seq_len_to_mask


class TestTransformerSeq2SeqDecoder(unittest.TestCase):
    def test_case(self):
        vocab = Vocabulary().add_word_lst("This is a test .".split())
        vocab.add_word_lst("Another test !".split())
        embed = StaticEmbedding(vocab, embedding_dim=512)

        args = TransformerSeq2SeqModel.add_args()
        model = TransformerSeq2SeqModel.build_model(args, vocab, vocab)

        src_words_idx = torch.LongTensor([[3, 1, 2], [1, 2, 0]])
        tgt_words_idx = torch.LongTensor([[1, 2, 3, 4], [2, 3, 0, 0]])
        src_seq_len = torch.LongTensor([3, 2])

        output = model(src_words_idx, src_seq_len, tgt_words_idx)
        print(output)

        # self.assertEqual(tuple(decoder_outputs.size()), (2, 4, len(vocab)))

    def test_decode(self):
        pass  # todo


class TestLSTMDecoder(unittest.TestCase):
    def test_case(self):
        vocab = Vocabulary().add_word_lst("This is a test .".split())
        vocab.add_word_lst("Another test !".split())
        embed = StaticEmbedding(vocab, embedding_dim=512)

        encoder = BiLSTMEncoder(embed)
        decoder = LSTMDecoder(embed, bind_input_output_embed=True)

        src_words_idx = torch.LongTensor([[3, 1, 2], [1, 2, 0]])
        tgt_words_idx = torch.LongTensor([[1, 2, 3, 4], [2, 3, 0, 0]])
        src_seq_len = torch.LongTensor([3, 2])

        words, hx = encoder(src_words_idx, src_seq_len)
        encode_mask = seq_len_to_mask(src_seq_len)
        hidden = torch.cat([hx[0][-2:-1], hx[0][-1:]], dim=-1).repeat(decoder.num_layers, 1, 1)
        cell = torch.cat([hx[1][-2:-1], hx[1][-1:]], dim=-1).repeat(decoder.num_layers, 1, 1)
        past = LSTMPast(encode_outputs=words, encode_mask=encode_mask, hx=(hidden, cell))
        decoder_outputs = decoder(tgt_words_idx, past)

        print(decoder_outputs)
        print(encode_mask)

        self.assertEqual(tuple(decoder_outputs.size()), (2, 4, len(vocab)))

    def test_decode(self):
        pass  # todo