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Rename BartForMaskedLM -> BartForConditionalGeneration (#3114)

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* improved documentation
This commit is contained in:
Sam Shleifer
2020-03-05 17:41:18 -05:00
committed by GitHub
parent fa2aa699da
commit 857e0a0d3b
7 changed files with 75 additions and 71 deletions
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24
tests/test_modeling_bart.py
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@@ -29,7 +29,7 @@ if is_torch_available():
from transformers import (
AutoModelForSequenceClassification,
BartModel,
BartForMaskedLM,
BartForConditionalGeneration,
BartForSequenceClassification,
BartConfig,
)
@@ -97,7 +97,9 @@ def prepare_bart_inputs_dict(
@require_torch
class BARTModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (BartModel, BartForMaskedLM, BartForSequenceClassification) if is_torch_available() else ()
all_model_classes = (
(BartModel, BartForConditionalGeneration, BartForSequenceClassification) if is_torch_available() else ()
)
is_encoder_decoder = True
# TODO(SS): fix the below in a separate PR
test_pruning = False
@@ -221,8 +223,8 @@ class BartHeadTests(unittest.TestCase):
def test_lm_forward(self):
config, input_ids, batch_size = self._get_config_and_data(output_past=False)
decoder_lm_labels = ids_tensor([batch_size, input_ids.shape[1]], self.vocab_size)
lm_model = BartForMaskedLM(config)
decoder_lm_labels = ids_tensor([batch_size, input_ids.shape[1]], self.vocab_size).to(torch_device)
lm_model = BartForConditionalGeneration(config)
lm_model.to(torch_device)
loss, logits, enc_features = lm_model.forward(
input_ids=input_ids, lm_labels=decoder_lm_labels, decoder_input_ids=input_ids
@@ -243,15 +245,15 @@ class BartHeadTests(unittest.TestCase):
decoder_ffn_dim=32,
max_position_embeddings=48,
)
lm_model = BartForMaskedLM(config)
context = torch.Tensor([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]).long()
summary = torch.Tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]).long()
logits, enc_features = lm_model.forward(input_ids=context, decoder_input_ids=summary)
lm_model = BartForConditionalGeneration(config).to(torch_device)
context = torch.Tensor([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]).long().to(torch_device)
summary = torch.Tensor([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]).long().to(torch_device)
loss, logits, enc_features = lm_model.forward(input_ids=context, decoder_input_ids=summary, lm_labels=summary)
expected_shape = (*summary.shape, config.vocab_size)
self.assertEqual(logits.shape, expected_shape)
def test_generate_beam_search(self):
input_ids = torch.Tensor([[71, 82, 2], [68, 34, 2]]).long()
input_ids = torch.Tensor([[71, 82, 2], [68, 34, 2]]).long().to(torch_device)
config = BartConfig(
vocab_size=self.vocab_size,
d_model=24,
@@ -264,7 +266,7 @@ class BartHeadTests(unittest.TestCase):
max_position_embeddings=48,
output_past=True,
)
lm_model = BartForMaskedLM(config)
lm_model = BartForConditionalGeneration(config).to(torch_device)
lm_model.eval()
new_input_ids = lm_model.generate(
@@ -376,7 +378,7 @@ class BartModelIntegrationTest(unittest.TestCase):
@slow
def test_cnn_summarization_same_as_fairseq(self):
hf = BartForMaskedLM.from_pretrained("bart-large-cnn", output_past=True,).to(torch_device)
hf = BartForConditionalGeneration.from_pretrained("bart-large-cnn", output_past=True,).to(torch_device)
tok = BartTokenizer.from_pretrained("bart-large")
text = " (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian"
tokens = tok.encode(text, return_tensors="pt").to(torch_device)