def test_auto_regressive_seq_decoder_forward(self): batch_size, time_steps, decoder_inout_dim = 2, 3, 4 vocab, decoder_net = create_vocab_and_decoder_net(decoder_inout_dim) auto_regressive_seq_decoder = AutoRegressiveSeqDecoder( vocab, decoder_net, 10, Embedding(num_embeddings=vocab.get_vocab_size(), embedding_dim=decoder_inout_dim), ) encoded_state = torch.rand(batch_size, time_steps, decoder_inout_dim) source_mask = torch.ones(batch_size, time_steps).bool() target_tokens = { "tokens": { "tokens": torch.ones(batch_size, time_steps).long() } } source_mask[0, 1:] = False encoder_out = { "source_mask": source_mask, "encoder_outputs": encoded_state } assert auto_regressive_seq_decoder.forward(encoder_out) == {} loss = auto_regressive_seq_decoder.forward(encoder_out, target_tokens)["loss"] assert loss.shape == torch.Size([]) and loss.requires_grad auto_regressive_seq_decoder.eval() assert "predictions" in auto_regressive_seq_decoder.forward( encoder_out)
def test_auto_regressive_seq_decoder_init(self): decoder_inout_dim = 4 vocab, decoder_net = create_vocab_and_decoder_net(decoder_inout_dim) AutoRegressiveSeqDecoder( vocab, decoder_net, 10, Embedding(num_embeddings=vocab.get_vocab_size(), embedding_dim=decoder_inout_dim), ) with pytest.raises(ConfigurationError): AutoRegressiveSeqDecoder( vocab, decoder_net, 10, Embedding(num_embeddings=vocab.get_vocab_size(), embedding_dim=decoder_inout_dim + 1), )
def test_auto_regressive_seq_decoder_init(self): decoder_inout_dim = 4 vocab, decoder_net = create_vocab_and_decoder_net(decoder_inout_dim) AutoRegressiveSeqDecoder( vocab, decoder_net, Embedding(num_embeddings=vocab.get_vocab_size(), embedding_dim=decoder_inout_dim), beam_search=Lazy(BeamSearch, constructor_extras={"max_steps": 10}), ) with pytest.raises(ConfigurationError): AutoRegressiveSeqDecoder( vocab, decoder_net, Embedding(num_embeddings=vocab.get_vocab_size(), embedding_dim=decoder_inout_dim + 1), beam_search=Lazy(BeamSearch, constructor_extras={"max_steps": 10}), )
def test_auto_regressive_seq_decoder_indices_to_tokens(self): decoder_inout_dim = 4 vocab, decoder_net = create_vocab_and_decoder_net(decoder_inout_dim) auto_regressive_seq_decoder = AutoRegressiveSeqDecoder( vocab, decoder_net, Embedding(num_embeddings=vocab.get_vocab_size(), embedding_dim=decoder_inout_dim), beam_search=Lazy(BeamSearch, constructor_extras={"max_steps": 10}), ) predictions = torch.tensor([[3, 2, 5, 0, 0], [2, 2, 3, 5, 0]]) tokens_ground_truth = [["B", "A"], ["A", "A", "B"]] predicted_tokens = auto_regressive_seq_decoder.indices_to_tokens( predictions.numpy()) assert predicted_tokens == tokens_ground_truth
def test_auto_regressive_seq_decoder_post_process(self): decoder_inout_dim = 4 vocab, decoder_net = create_vocab_and_decoder_net(decoder_inout_dim) auto_regressive_seq_decoder = AutoRegressiveSeqDecoder( vocab, decoder_net, 10, Embedding(num_embeddings=vocab.get_vocab_size(), embedding_dim=decoder_inout_dim), ) predictions = torch.tensor([[3, 2, 5, 0, 0], [2, 2, 3, 5, 0]]) tokens_ground_truth = [["B", "A"], ["A", "A", "B"]] output_dict = {"predictions": predictions} predicted_tokens = auto_regressive_seq_decoder.post_process( output_dict)["predicted_tokens"] assert predicted_tokens == tokens_ground_truth
def test_auto_regressive_seq_decoder_tensor_and_token_based_metric(self): # set all seeds to a fixed value (torch, numpy, etc.). # this enable a deterministic behavior of the `auto_regressive_seq_decoder` # below (i.e., parameter initialization and `encoded_state = torch.randn(..)`) prepare_environment(Params({})) batch_size, time_steps, decoder_inout_dim = 2, 3, 4 vocab, decoder_net = create_vocab_and_decoder_net(decoder_inout_dim) auto_regressive_seq_decoder = AutoRegressiveSeqDecoder( vocab, decoder_net, Embedding(num_embeddings=vocab.get_vocab_size(), embedding_dim=decoder_inout_dim), beam_search=Lazy(BeamSearch, constructor_extras={ "max_steps": 10, "beam_size": 4 }), tensor_based_metric=BLEU(), token_based_metric=DummyMetric(), ).eval() encoded_state = torch.randn(batch_size, time_steps, decoder_inout_dim) source_mask = torch.ones(batch_size, time_steps).bool() target_tokens = { "tokens": { "tokens": torch.ones(batch_size, time_steps).long() } } source_mask[0, 1:] = False encoder_out = { "source_mask": source_mask, "encoder_outputs": encoded_state } auto_regressive_seq_decoder.forward(encoder_out, target_tokens) assert auto_regressive_seq_decoder.get_metrics( )["BLEU"] == 1.388809517005903e-11 assert auto_regressive_seq_decoder.get_metrics()["em"] == 0.0 assert auto_regressive_seq_decoder.get_metrics()["f1"] == 1 / 3