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[EncoderDecoder] Add encoder-decoder for roberta/ vanilla longformer (#6411)

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* add encoder-decoder for roberta

* fix headmask

* apply Sylvains suggestions

* fix typo

* Apply suggestions from code review
This commit is contained in:
Patrick von Platen
2020-08-12 18:23:30 +02:00
committed by GitHub
parent fd3de2000f
commit 0735def8e1
10 changed files with 671 additions and 357 deletions
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7
docs/source/model_doc/roberta.rst
View File

@@ -63,6 +63,13 @@ RobertaModel
:members:
RobertaForCausalLM
~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.RobertaForCausalLM
:members:
RobertaForMaskedLM
~~~~~~~~~~~~~~~~~~~~~~~~~~

1
src/transformers/__init__.py
View File

@@ -302,6 +302,7 @@ if is_torch_available():
from .tokenization_marian import MarianTokenizer
from .modeling_roberta import (
RobertaForMaskedLM,
RobertaForCausalLM,
RobertaModel,
RobertaForSequenceClassification,
RobertaForMultipleChoice,

2
src/transformers/modeling_auto.py
View File

@@ -135,6 +135,7 @@ from .modeling_reformer import (
)
from .modeling_retribert import RetriBertModel
from .modeling_roberta import (
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
@@ -250,6 +251,7 @@ MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
MODEL_FOR_CAUSAL_LM_MAPPING = OrderedDict(
[
(RobertaConfig, RobertaForCausalLM),
(BertConfig, BertLMHeadModel),
(OpenAIGPTConfig, OpenAIGPTLMHeadModel),
(GPT2Config, GPT2LMHeadModel),

39
src/transformers/modeling_bert.py
View File

@@ -683,14 +683,6 @@ BERT_INPUTS_DOCSTRING = r"""
Optionally, instead of passing :obj:`input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `input_ids` indices into associated vectors
than the model's internal embedding lookup matrix.
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask
is used in the cross-attention if the model is configured as a decoder.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
output_attentions (:obj:`bool`, `optional`, defaults to :obj:`None`):
If set to ``True``, the attentions tensors of all attention layers are returned. See ``attentions`` under returned tensors for more detail.
output_hidden_states (:obj:`bool`, `optional`, defaults to :obj:`None`):
@@ -769,6 +761,16 @@ class BertModel(BertPreTrainedModel):
output_hidden_states=None,
return_dict=None,
):
r"""
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask
is used in the cross-attention if the model is configured as a decoder.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
"""
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
output_hidden_states = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
@@ -956,7 +958,7 @@ class BertLMHeadModel(BertPreTrainedModel):
super().__init__(config)
if not config.is_decoder:
logger.info("If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`")
logger.warning("If you want to use `BertLMHeadModel` as a standalone, add `is_decoder=True.`")
self.bert = BertModel(config)
self.cls = BertOnlyMLMHead(config)
@@ -976,22 +978,27 @@ class BertLMHeadModel(BertPreTrainedModel):
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
**kwargs
):
r"""
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask
is used in the cross-attention if the model is configured as a decoder.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Labels for computing the left-to-right language modeling loss (next word prediction).
Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
in ``[0, ..., config.vocab_size]``
kwargs (:obj:`Dict[str, any]`, optional, defaults to `{}`):
Used to hide legacy arguments that have been deprecated.
Returns:
@@ -1061,8 +1068,8 @@ class BertForMaskedLM(BertPreTrainedModel):
super().__init__(config)
if config.is_decoder:
logger.info(
"If you want to use `TFBertForMaskedLM` make sure `config.is_decoder=False` for "
logger.warning(
"If you want to use `BertForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
@@ -1089,9 +1096,9 @@ class BertForMaskedLM(BertPreTrainedModel):
position_ids=None,
head_mask=None,
inputs_embeds=None,
labels=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,

12
src/transformers/modeling_encoder_decoder.py
View File

@@ -191,11 +191,9 @@ class EncoderDecoderModel(PreTrainedModel):
input_ids=None,
inputs_embeds=None,
attention_mask=None,
head_mask=None,
encoder_outputs=None,
decoder_input_ids=None,
decoder_attention_mask=None,
decoder_head_mask=None,
decoder_inputs_embeds=None,
labels=None,
**kwargs,
@@ -216,10 +214,6 @@ class EncoderDecoderModel(PreTrainedModel):
Mask to avoid performing attention on padding token indices for the encoder.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
head_mask: (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
Mask to nullify selected heads of the self-attention modules for the encoder.
Mask values selected in ``[0, 1]``:
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
encoder_outputs (:obj:`tuple(tuple(torch.FloatTensor)`, `optional`, defaults to :obj:`None`):
Tuple consists of (`last_hidden_state`, `optional`: `hidden_states`, `optional`: `attentions`)
`last_hidden_state` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`) is a sequence of hidden-states at the output of the last layer of the encoder.
@@ -231,10 +225,6 @@ class EncoderDecoderModel(PreTrainedModel):
:func:`transformers.PreTrainedTokenizer.convert_tokens_to_ids` for details.
decoder_attention_mask (:obj:`torch.BoolTensor` of shape :obj:`(batch_size, tgt_seq_len)`, `optional`, defaults to :obj:`None`):
Default behavior: generate a tensor that ignores pad tokens in decoder_input_ids. Causal mask will also be used by default.
decoder_head_mask: (:obj:`torch.FloatTensor` of shape :obj:`(num_heads,)` or :obj:`(num_layers, num_heads)`, `optional`, defaults to :obj:`None`):
Mask to nullify selected heads of the self-attention modules for the decoder.
Mask values selected in ``[0, 1]``:
``1`` indicates the head is **not masked**, ``0`` indicates the head is **masked**.
decoder_inputs_embeds (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, target_sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
Optionally, instead of passing :obj:`decoder_input_ids` you can choose to directly pass an embedded representation.
This is useful if you want more control over how to convert `decoder_input_ids` indices into associated vectors
@@ -279,7 +269,6 @@ class EncoderDecoderModel(PreTrainedModel):
input_ids=input_ids,
attention_mask=attention_mask,
inputs_embeds=inputs_embeds,
head_mask=head_mask,
return_dict=False,
**kwargs_encoder,
)
@@ -293,7 +282,6 @@ class EncoderDecoderModel(PreTrainedModel):
attention_mask=decoder_attention_mask,
encoder_hidden_states=hidden_states,
encoder_attention_mask=attention_mask,
head_mask=decoder_head_mask,
labels=labels,
return_dict=False,
**kwargs_decoder,

133
src/transformers/modeling_roberta.py
View File

@@ -24,9 +24,15 @@ import torch.nn as nn
from torch.nn import CrossEntropyLoss, MSELoss
from .configuration_roberta import RobertaConfig
from .file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_callable
from .file_utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_callable,
replace_return_docstrings,
)
from .modeling_bert import BertEmbeddings, BertLayerNorm, BertModel, BertPreTrainedModel, gelu
from .modeling_outputs import (
CausalLMOutput,
MaskedLMOutput,
MultipleChoiceModelOutput,
QuestionAnsweringModelOutput,
@@ -175,6 +181,121 @@ class RobertaModel(BertModel):
self.embeddings.word_embeddings = value
@add_start_docstrings(
"""RoBERTa Model with a `language modeling` head on top for CLM fine-tuning. """, ROBERTA_START_DOCSTRING
)
class RobertaForCausalLM(BertPreTrainedModel):
config_class = RobertaConfig
base_model_prefix = "roberta"
def __init__(self, config):
super().__init__(config)
if not config.is_decoder:
logger.warning("If you want to use `RobertaLMHeadModel` as a standalone, add `is_decoder=True.`")
self.roberta = RobertaModel(config)
self.lm_head = RobertaLMHead(config)
self.init_weights()
def get_output_embeddings(self):
return self.lm_head.decoder
@add_start_docstrings_to_callable(ROBERTA_INPUTS_DOCSTRING.format("(batch_size, sequence_length)"))
@replace_return_docstrings(output_type=CausalLMOutput, config_class=_CONFIG_FOR_DOC)
def forward(
self,
input_ids=None,
attention_mask=None,
token_type_ids=None,
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
return_dict=None,
):
r"""
encoder_hidden_states (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`, defaults to :obj:`None`):
Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention
if the model is configured as a decoder.
encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Mask to avoid performing attention on the padding token indices of the encoder input. This mask
is used in the cross-attention if the model is configured as a decoder.
Mask values selected in ``[0, 1]``:
``1`` for tokens that are NOT MASKED, ``0`` for MASKED tokens.
labels (:obj:`torch.LongTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`, defaults to :obj:`None`):
Labels for computing the left-to-right language modeling loss (next word prediction).
Indices should be in ``[-100, 0, ..., config.vocab_size]`` (see ``input_ids`` docstring)
Tokens with indices set to ``-100`` are ignored (masked), the loss is only computed for the tokens with labels
in ``[0, ..., config.vocab_size]``
Returns:
Example::
>>> from transformers import RobertaTokenizer, RobertaLMHeadModel, RobertaConfig
>>> import torch
>>> tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
>>> config = RobertaConfig.from_pretrained("roberta-base")
>>> config.is_decoder = True
>>> model = RobertaLMHeadModel.from_pretrained('roberta-base', config=config, return_dict=True)
>>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
>>> outputs = model(**inputs)
>>> prediction_logits = outputs.logits
"""
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
outputs = self.roberta(
input_ids,
attention_mask=attention_mask,
token_type_ids=token_type_ids,
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,
)
sequence_output = outputs[0]
prediction_scores = self.lm_head(sequence_output)
lm_loss = None
if labels is not None:
# we are doing next-token prediction; shift prediction scores and input ids by one
shifted_prediction_scores = prediction_scores[:, :-1, :].contiguous()
labels = labels[:, 1:].contiguous()
loss_fct = CrossEntropyLoss()
lm_loss = loss_fct(shifted_prediction_scores.view(-1, self.config.vocab_size), labels.view(-1))
if not return_dict:
output = (prediction_scores,) + outputs[2:]
return ((lm_loss,) + output) if lm_loss is not None else output
return CausalLMOutput(
loss=lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions,
)
def prepare_inputs_for_generation(self, input_ids, attention_mask=None, **model_kwargs):
input_shape = input_ids.shape
# if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly
if attention_mask is None:
attention_mask = input_ids.new_ones(input_shape)
return {"input_ids": input_ids, "attention_mask": attention_mask}
@add_start_docstrings("""RoBERTa Model with a `language modeling` head on top. """, ROBERTA_START_DOCSTRING)
class RobertaForMaskedLM(BertPreTrainedModel):
config_class = RobertaConfig
@@ -183,6 +304,12 @@ class RobertaForMaskedLM(BertPreTrainedModel):
def __init__(self, config):
super().__init__(config)
if config.is_decoder:
logger.warning(
"If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
self.roberta = RobertaModel(config)
self.lm_head = RobertaLMHead(config)
@@ -206,6 +333,8 @@ class RobertaForMaskedLM(BertPreTrainedModel):
position_ids=None,
head_mask=None,
inputs_embeds=None,
encoder_hidden_states=None,
encoder_attention_mask=None,
labels=None,
output_attentions=None,
output_hidden_states=None,
@@ -237,6 +366,8 @@ class RobertaForMaskedLM(BertPreTrainedModel):
position_ids=position_ids,
head_mask=head_mask,
inputs_embeds=inputs_embeds,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
return_dict=return_dict,

4
src/transformers/modeling_tf_bert.py
View File

@@ -862,7 +862,7 @@ class TFBertForMaskedLM(TFBertPreTrainedModel, TFMaskedLanguageModelingLoss):
super().__init__(config, *inputs, **kwargs)
if config.is_decoder:
logger.info(
logger.warning(
"If you want to use `TFBertForMaskedLM` make sure `config.is_decoder=False` for "
"bi-directional self-attention."
)
@@ -941,7 +941,7 @@ class TFBertLMHeadModel(TFBertPreTrainedModel, TFCausalLanguageModelingLoss):
super().__init__(config, *inputs, **kwargs)
if not config.is_decoder:
logger.info("If you want to use `TFBertLMHeadModel` as a standalone, add `is_decoder=True.`")
logger.warning("If you want to use `TFBertLMHeadModel` as a standalone, add `is_decoder=True.`")
self.bert = TFBertMainLayer(config, name="bert")
self.mlm = TFBertMLMHead(config, self.bert.embeddings, name="mlm___cls")

52
tests/test_modeling_bert.py
View File

@@ -152,7 +152,7 @@ class BertModelTester:
encoder_attention_mask,
)
def create_and_check_bert_model(
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BertModel(config=config)
@@ -164,7 +164,7 @@ class BertModelTester:
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_bert_model_as_decoder(
def create_and_check_model_as_decoder(
self,
config,
input_ids,
@@ -197,7 +197,7 @@ class BertModelTester:
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_bert_for_causal_lm(
def create_and_check_for_causal_lm(
self,
config,
input_ids,
@@ -215,7 +215,7 @@ class BertModelTester:
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_bert_for_masked_lm(
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BertForMaskedLM(config=config)
@@ -224,7 +224,7 @@ class BertModelTester:
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_bert_model_for_causal_lm_as_decoder(
def create_and_check_model_for_causal_lm_as_decoder(
self,
config,
input_ids,
@@ -257,7 +257,7 @@ class BertModelTester:
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_bert_for_next_sequence_prediction(
def create_and_check_for_next_sequence_prediction(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BertForNextSentencePrediction(config=config)
@@ -268,7 +268,7 @@ class BertModelTester:
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, 2))
def create_and_check_bert_for_pretraining(
def create_and_check_for_pretraining(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BertForPreTraining(config=config)
@@ -284,7 +284,7 @@ class BertModelTester:
self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
self.parent.assertEqual(result.seq_relationship_logits.shape, (self.batch_size, 2))
def create_and_check_bert_for_question_answering(
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = BertForQuestionAnswering(config=config)
@@ -300,7 +300,7 @@ class BertModelTester:
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
def create_and_check_bert_for_sequence_classification(
def create_and_check_for_sequence_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
@@ -310,7 +310,7 @@ class BertModelTester:
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
def create_and_check_bert_for_token_classification(
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
@@ -320,7 +320,7 @@ class BertModelTester:
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_bert_for_multiple_choice(
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
@@ -379,15 +379,15 @@ class BertModelTest(ModelTesterMixin, unittest.TestCase):
def test_config(self):
self.config_tester.run_common_tests()
def test_bert_model(self):
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_model(*config_and_inputs)
self.model_tester.create_and_check_model(*config_and_inputs)
def test_bert_model_as_decoder(self):
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_bert_model_as_decoder(*config_and_inputs)
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_bert_model_as_decoder_with_default_input_mask(self):
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
@@ -403,7 +403,7 @@ class BertModelTest(ModelTesterMixin, unittest.TestCase):
input_mask = None
self.model_tester.create_and_check_bert_model_as_decoder(
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
@@ -417,39 +417,39 @@ class BertModelTest(ModelTesterMixin, unittest.TestCase):
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_bert_for_causal_lm(*config_and_inputs)
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_masked_lm(*config_and_inputs)
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_causal_lm_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_bert_model_for_causal_lm_as_decoder(*config_and_inputs)
self.model_tester.create_and_check_model_for_causal_lm_as_decoder(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_multiple_choice(*config_and_inputs)
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_next_sequence_prediction(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_next_sequence_prediction(*config_and_inputs)
self.model_tester.create_and_check_for_next_sequence_prediction(*config_and_inputs)
def test_for_pretraining(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_pretraining(*config_and_inputs)
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_question_answering(*config_and_inputs)
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
def test_for_sequence_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_sequence_classification(*config_and_inputs)
self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_bert_for_token_classification(*config_and_inputs)
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
@slow
def test_model_from_pretrained(self):

632
tests/test_modeling_encoder_decoder.py
View File

@@ -24,21 +24,317 @@ from transformers.testing_utils import require_torch, slow, torch_device
# for now only run module with pytest tests/test_modeling_encoder_decoder.py::EncoderDecoderModelTest
from .test_modeling_bert import BertModelTester
from .test_modeling_common import ids_tensor
from .test_modeling_roberta import RobertaModelTester
if is_torch_available():
from transformers import BertModel, EncoderDecoderModel, EncoderDecoderConfig
from transformers.modeling_bert import BertLMHeadModel
from transformers import (
BertModel,
BertLMHeadModel,
RobertaModel,
RobertaForCausalLM,
EncoderDecoderModel,
EncoderDecoderConfig,
)
import numpy as np
import torch
@require_torch
class EncoderDecoderModelTest(unittest.TestCase):
def prepare_config_and_inputs_bert(self):
bert_model_tester = BertModelTester(self)
encoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs()
decoder_config_and_inputs = bert_model_tester.prepare_config_and_inputs_for_decoder()
class EncoderDecoderMixin:
def get_encoder_decoder_model(self, config, decoder_config):
pass
def prepare_config_and_inputs(self):
pass
def get_pretrained_model(self):
pass
def check_encoder_decoder_model_from_pretrained_configs(
self,
config,
input_ids,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
):
encoder_decoder_config = EncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
self.assertTrue(encoder_decoder_config.decoder.is_decoder)
enc_dec_model = EncoderDecoderModel(encoder_decoder_config)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
outputs_encoder_decoder = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
def check_encoder_decoder_model(
self,
config,
input_ids,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
self.assertTrue(enc_dec_model.config.decoder.is_decoder)
self.assertTrue(enc_dec_model.config.decoder.add_cross_attention)
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
encoder_outputs = (encoder_hidden_states,)
outputs_encoder_decoder = enc_dec_model(
encoder_outputs=encoder_outputs,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
def check_encoder_decoder_model_from_pretrained(
self,
config,
input_ids,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model}
enc_dec_model = EncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
def check_save_and_load(
self,
config,
input_ids,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
with torch.no_grad():
outputs = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
enc_dec_model.save_pretrained(tmpdirname)
EncoderDecoderModel.from_pretrained(tmpdirname)
after_outputs = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def check_save_and_load_encoder_decoder_model(
self,
config,
input_ids,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
with torch.no_grad():
outputs = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname:
enc_dec_model.encoder.save_pretrained(encoder_tmp_dirname)
enc_dec_model.decoder.save_pretrained(decoder_tmp_dirname)
EncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=encoder_tmp_dirname,
decoder_pretrained_model_name_or_path=decoder_tmp_dirname,
)
after_outputs = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def check_encoder_decoder_model_labels(
self,
config,
input_ids,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
labels,
**kwargs
):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
labels=labels,
)
mlm_loss = outputs_encoder_decoder[0]
# check that backprop works
mlm_loss.backward()
self.assertEqual(outputs_encoder_decoder[1].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
self.assertEqual(outputs_encoder_decoder[2].shape, (input_ids.shape + (config.hidden_size,)))
def check_encoder_decoder_model_generate(self, input_ids, config, decoder_config, **kwargs):
encoder_model, decoder_model = self.get_encoder_decoder_model(config, decoder_config)
enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
# Bert does not have a bos token id, so use pad_token_id instead
generated_output = enc_dec_model.generate(
input_ids, decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id
)
self.assertEqual(generated_output.shape, (input_ids.shape[0],) + (decoder_config.max_length,))
def test_encoder_decoder_model(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model(**input_ids_dict)
def test_encoder_decoder_model_from_pretrained_configs(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_pretrained_configs(**input_ids_dict)
def test_encoder_decoder_model_from_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_from_pretrained(**input_ids_dict)
def test_save_and_load_from_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_save_and_load(**input_ids_dict)
def test_save_and_load_from_encoder_decoder_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_save_and_load_encoder_decoder_model(**input_ids_dict)
def test_encoder_decoder_model_labels(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_labels(**input_ids_dict)
def test_encoder_decoder_model_generate(self):
input_ids_dict = self.prepare_config_and_inputs()
self.check_encoder_decoder_model_generate(**input_ids_dict)
@slow
def test_real_model_save_load_from_pretrained(self):
model_2 = self.get_pretrained_model()
model_2.to(torch_device)
input_ids = ids_tensor([13, 5], model_2.config.encoder.vocab_size)
decoder_input_ids = ids_tensor([13, 1], model_2.config.encoder.vocab_size)
attention_mask = ids_tensor([13, 5], vocab_size=2)
with torch.no_grad():
outputs = model_2(input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask,)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmp_dirname:
model_2.save_pretrained(tmp_dirname)
model_1 = EncoderDecoderModel.from_pretrained(tmp_dirname)
model_1.to(torch_device)
after_outputs = model_1(
input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask,
)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
class BertEncoderDecoderModelTest(EncoderDecoderMixin, unittest.TestCase):
def get_pretrained_model(self):
return EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-cased", "bert-base-cased")
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = BertModel(config)
decoder_model = BertLMHeadModel(decoder_config)
return encoder_model, decoder_model
def prepare_config_and_inputs(self):
model_tester = BertModelTester(self)
encoder_config_and_inputs = model_tester.prepare_config_and_inputs()
decoder_config_and_inputs = model_tester.prepare_config_and_inputs_for_decoder()
(
config,
input_ids,
@@ -77,288 +373,54 @@ class EncoderDecoderModelTest(unittest.TestCase):
"labels": decoder_token_labels,
}
def create_and_check_bert_encoder_decoder_model_from_pretrained_configs(
self,
class RoBertaEncoderDecoderModelTest(EncoderDecoderMixin, unittest.TestCase):
def get_encoder_decoder_model(self, config, decoder_config):
encoder_model = RobertaModel(config)
decoder_model = RobertaForCausalLM(decoder_config)
return encoder_model, decoder_model
def prepare_config_and_inputs(self):
model_tester = RobertaModelTester(self)
encoder_config_and_inputs = model_tester.prepare_config_and_inputs()
decoder_config_and_inputs = model_tester.prepare_config_and_inputs_for_decoder()
(
config,
input_ids,
attention_mask,
encoder_hidden_states,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = encoder_config_and_inputs
(
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
):
encoder_decoder_config = EncoderDecoderConfig.from_encoder_decoder_configs(config, decoder_config)
self.assertTrue(encoder_decoder_config.decoder.is_decoder)
enc_dec_model = EncoderDecoderModel(encoder_decoder_config)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
outputs_encoder_decoder = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
def create_and_check_bert_encoder_decoder_model(
self,
config,
input_ids,
attention_mask,
decoder_token_type_ids,
decoder_input_mask,
decoder_sequence_labels,
decoder_token_labels,
decoder_choice_labels,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
):
encoder_model = BertModel(config)
decoder_model = BertLMHeadModel(decoder_config)
enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
self.assertTrue(enc_dec_model.config.decoder.is_decoder)
self.assertTrue(enc_dec_model.config.decoder.add_cross_attention)
self.assertTrue(enc_dec_model.config.is_encoder_decoder)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
encoder_attention_mask,
) = decoder_config_and_inputs
self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
encoder_outputs = (encoder_hidden_states,)
outputs_encoder_decoder = enc_dec_model(
encoder_outputs=encoder_outputs,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
# make sure that cross attention layers are added
decoder_config.add_cross_attention = True
return {
"config": config,
"input_ids": input_ids,
"attention_mask": input_mask,
"decoder_config": decoder_config,
"decoder_input_ids": decoder_input_ids,
"decoder_token_type_ids": decoder_token_type_ids,
"decoder_attention_mask": decoder_input_mask,
"decoder_sequence_labels": decoder_sequence_labels,
"decoder_token_labels": decoder_token_labels,
"decoder_choice_labels": decoder_choice_labels,
"encoder_hidden_states": encoder_hidden_states,
"labels": decoder_token_labels,
}
self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
def create_and_check_bert_encoder_decoder_model_from_pretrained(
self,
config,
input_ids,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
):
encoder_model = BertModel(config)
decoder_model = BertLMHeadModel(decoder_config)
kwargs = {"encoder_model": encoder_model, "decoder_model": decoder_model}
enc_dec_model = EncoderDecoderModel.from_encoder_decoder_pretrained(**kwargs)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
self.assertEqual(outputs_encoder_decoder[0].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
self.assertEqual(outputs_encoder_decoder[1].shape, (input_ids.shape + (config.hidden_size,)))
def create_and_check_save_and_load(
self,
config,
input_ids,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
):
encoder_model = BertModel(config)
decoder_model = BertLMHeadModel(decoder_config)
enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
with torch.no_grad():
outputs = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmpdirname:
enc_dec_model.save_pretrained(tmpdirname)
EncoderDecoderModel.from_pretrained(tmpdirname)
after_outputs = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def create_and_check_save_and_load_encoder_decoder_model(
self,
config,
input_ids,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
**kwargs
):
encoder_model = BertModel(config)
decoder_model = BertLMHeadModel(decoder_config)
enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
enc_dec_model.eval()
with torch.no_grad():
outputs = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as encoder_tmp_dirname, tempfile.TemporaryDirectory() as decoder_tmp_dirname:
enc_dec_model.encoder.save_pretrained(encoder_tmp_dirname)
enc_dec_model.decoder.save_pretrained(decoder_tmp_dirname)
EncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=encoder_tmp_dirname,
decoder_pretrained_model_name_or_path=decoder_tmp_dirname,
)
after_outputs = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def create_and_check_bert_encoder_decoder_model_labels(
self,
config,
input_ids,
attention_mask,
encoder_hidden_states,
decoder_config,
decoder_input_ids,
decoder_attention_mask,
labels,
**kwargs
):
encoder_model = BertModel(config)
decoder_model = BertLMHeadModel(decoder_config)
enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
outputs_encoder_decoder = enc_dec_model(
input_ids=input_ids,
decoder_input_ids=decoder_input_ids,
attention_mask=attention_mask,
decoder_attention_mask=decoder_attention_mask,
labels=labels,
)
mlm_loss = outputs_encoder_decoder[0]
# check that backprop works
mlm_loss.backward()
self.assertEqual(outputs_encoder_decoder[1].shape, (decoder_input_ids.shape + (decoder_config.vocab_size,)))
self.assertEqual(outputs_encoder_decoder[2].shape, (input_ids.shape + (config.hidden_size,)))
def create_and_check_bert_encoder_decoder_model_generate(self, input_ids, config, decoder_config, **kwargs):
encoder_model = BertModel(config)
decoder_model = BertLMHeadModel(decoder_config)
enc_dec_model = EncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)
enc_dec_model.to(torch_device)
# Bert does not have a bos token id, so use pad_token_id instead
generated_output = enc_dec_model.generate(
input_ids, decoder_start_token_id=enc_dec_model.config.decoder.pad_token_id
)
self.assertEqual(generated_output.shape, (input_ids.shape[0],) + (decoder_config.max_length,))
def test_bert_encoder_decoder_model(self):
input_ids_dict = self.prepare_config_and_inputs_bert()
self.create_and_check_bert_encoder_decoder_model(**input_ids_dict)
def test_bert_encoder_decoder_model_from_pretrained_configs(self):
input_ids_dict = self.prepare_config_and_inputs_bert()
self.create_and_check_bert_encoder_decoder_model_from_pretrained_configs(**input_ids_dict)
def test_bert_encoder_decoder_model_from_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs_bert()
self.create_and_check_bert_encoder_decoder_model_from_pretrained(**input_ids_dict)
def test_save_and_load_from_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs_bert()
self.create_and_check_save_and_load(**input_ids_dict)
def test_save_and_load_from_encoder_decoder_pretrained(self):
input_ids_dict = self.prepare_config_and_inputs_bert()
self.create_and_check_save_and_load_encoder_decoder_model(**input_ids_dict)
def test_bert_encoder_decoder_model_labels(self):
input_ids_dict = self.prepare_config_and_inputs_bert()
self.create_and_check_bert_encoder_decoder_model_labels(**input_ids_dict)
def test_bert_encoder_decoder_model_generate(self):
input_ids_dict = self.prepare_config_and_inputs_bert()
self.create_and_check_bert_encoder_decoder_model_generate(**input_ids_dict)
@slow
def test_real_bert_model_from_pretrained(self):
model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
self.assertIsNotNone(model)
@slow
def test_real_bert_model_from_pretrained_add_cross_attention(self):
model = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
self.assertTrue(hasattr(model.decoder.bert.encoder.layer[0], "crossattention"))
@slow
def test_real_bert_model_save_load_from_pretrained(self):
model_2 = EncoderDecoderModel.from_encoder_decoder_pretrained("bert-base-uncased", "bert-base-uncased")
model_2.to(torch_device)
input_ids = ids_tensor([13, 5], model_2.config.encoder.vocab_size)
decoder_input_ids = ids_tensor([13, 1], model_2.config.encoder.vocab_size)
attention_mask = ids_tensor([13, 5], vocab_size=2)
with torch.no_grad():
outputs = model_2(input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask,)
out_2 = outputs[0].cpu().numpy()
out_2[np.isnan(out_2)] = 0
with tempfile.TemporaryDirectory() as tmp_dirname:
model_2.save_pretrained(tmp_dirname)
model_1 = EncoderDecoderModel.from_pretrained(tmp_dirname)
model_1.to(torch_device)
after_outputs = model_1(
input_ids=input_ids, decoder_input_ids=decoder_input_ids, attention_mask=attention_mask,
)
out_1 = after_outputs[0].cpu().numpy()
out_1[np.isnan(out_1)] = 0
max_diff = np.amax(np.abs(out_1 - out_2))
self.assertLessEqual(max_diff, 1e-5)
def get_pretrained_model(self):
return EncoderDecoderModel.from_encoder_decoder_pretrained("roberta-base", "roberta-base")

140
tests/test_modeling_roberta.py
View File

@@ -20,7 +20,7 @@ from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from .test_configuration_common import ConfigTester
from .test_modeling_common import ModelTesterMixin, ids_tensor
from .test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
if is_torch_available():
@@ -28,6 +28,7 @@ if is_torch_available():
from transformers import (
RobertaConfig,
RobertaModel,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
@@ -101,7 +102,34 @@ class RobertaModelTester:
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def create_and_check_roberta_model(
def prepare_config_and_inputs_for_decoder(self):
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
) = self.prepare_config_and_inputs()
config.is_decoder = True
encoder_hidden_states = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
encoder_attention_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def create_and_check_model(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RobertaModel(config=config)
@@ -114,7 +142,58 @@ class RobertaModelTester:
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_roberta_for_masked_lm(
def create_and_check_model_as_decoder(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
config.add_cross_attention = True
model = RobertaModel(config)
model.to(torch_device)
model.eval()
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
)
result = model(
input_ids,
attention_mask=input_mask,
token_type_ids=token_type_ids,
encoder_hidden_states=encoder_hidden_states,
)
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def create_and_check_for_causal_lm(
self,
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
):
model = RobertaForCausalLM(config=config)
model.to(torch_device)
model.eval()
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_for_masked_lm(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RobertaForMaskedLM(config=config)
@@ -123,7 +202,7 @@ class RobertaModelTester:
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
def create_and_check_roberta_for_token_classification(
def create_and_check_for_token_classification(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_labels = self.num_labels
@@ -133,7 +212,7 @@ class RobertaModelTester:
result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def create_and_check_roberta_for_multiple_choice(
def create_and_check_for_multiple_choice(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
config.num_choices = self.num_choices
@@ -151,7 +230,7 @@ class RobertaModelTester:
)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
def create_and_check_roberta_for_question_answering(
def create_and_check_for_question_answering(
self, config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
):
model = RobertaForQuestionAnswering(config=config)
@@ -187,6 +266,7 @@ class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
all_model_classes = (
(
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaModel,
RobertaForSequenceClassification,
@@ -205,25 +285,61 @@ class RobertaModelTest(ModelTesterMixin, unittest.TestCase):
def test_config(self):
self.config_tester.run_common_tests()
def test_roberta_model(self):
def test_model(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_roberta_model(*config_and_inputs)
self.model_tester.create_and_check_model(*config_and_inputs)
def test_model_as_decoder(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*config_and_inputs)
def test_model_as_decoder_with_default_input_mask(self):
# This regression test was failing with PyTorch < 1.3
(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
input_mask = None
self.model_tester.create_and_check_model_as_decoder(
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def test_for_causal_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*config_and_inputs)
def test_for_masked_lm(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_roberta_for_masked_lm(*config_and_inputs)
self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
def test_for_token_classification(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_roberta_for_token_classification(*config_and_inputs)
self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
def test_for_multiple_choice(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_roberta_for_multiple_choice(*config_and_inputs)
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
def test_for_question_answering(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_roberta_for_question_answering(*config_and_inputs)
self.model_tester.create_and_check_for_question_answering(*config_and_inputs)
@slow
def test_model_from_pretrained(self):