From 587d84b1784cce30c59a12faee2a672bac49bbdd Mon Sep 17 00:00:00 2001 From: Younes Belkada <49240599+younesbelkada@users.noreply.github.com> Date: Tue, 4 Oct 2022 17:52:13 +0200 Subject: [PATCH] Add `BloomForQuestionAnswering` (#19310) * add bloom for question answering - attempt to add Bloom for question answering - adapted from `GPTJForQuestionAnswering` - Fixed `num_labels` to `2` for common tests - Added a bit of docstring - All common tests pass * Update src/transformers/models/bloom/modeling_bloom.py Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * revert changes related to `num_labels` Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> --- docs/source/en/model_doc/bloom.mdx | 5 + src/transformers/__init__.py | 2 + src/transformers/models/auto/modeling_auto.py | 1 + src/transformers/models/bloom/__init__.py | 2 + .../models/bloom/modeling_bloom.py | 93 +++++++++++++++++++ src/transformers/utils/dummy_pt_objects.py | 7 ++ tests/models/bloom/test_modeling_bloom.py | 10 ++ 7 files changed, 120 insertions(+) diff --git a/docs/source/en/model_doc/bloom.mdx b/docs/source/en/model_doc/bloom.mdx index cf415603d0..3fc48ab974 100644 --- a/docs/source/en/model_doc/bloom.mdx +++ b/docs/source/en/model_doc/bloom.mdx @@ -55,3 +55,8 @@ Several smaller versions of the models have been trained on the same dataset. BL [[autodoc]] BloomForTokenClassification - forward + +## BloomForQuestionAnswering + +[[autodoc]] BloomForQuestionAnswering + - forward diff --git a/src/transformers/__init__.py b/src/transformers/__init__.py index e5e6e6c171..b3c6cca623 100755 --- a/src/transformers/__init__.py +++ b/src/transformers/__init__.py @@ -993,6 +993,7 @@ else: "BloomPreTrainedModel", "BloomForSequenceClassification", "BloomForTokenClassification", + "BloomForQuestionAnswering", ] ) _import_structure["models.blenderbot"].extend( @@ -3857,6 +3858,7 @@ if TYPE_CHECKING: from .models.bloom import ( BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST, BloomForCausalLM, + BloomForQuestionAnswering, BloomForSequenceClassification, BloomForTokenClassification, BloomModel, diff --git a/src/transformers/models/auto/modeling_auto.py b/src/transformers/models/auto/modeling_auto.py index 8821cfb6c9..5cac7e7bda 100644 --- a/src/transformers/models/auto/modeling_auto.py +++ b/src/transformers/models/auto/modeling_auto.py @@ -572,6 +572,7 @@ MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES = OrderedDict( ("bert", "BertForQuestionAnswering"), ("big_bird", "BigBirdForQuestionAnswering"), ("bigbird_pegasus", "BigBirdPegasusForQuestionAnswering"), + ("bloom", "BloomForQuestionAnswering"), ("camembert", "CamembertForQuestionAnswering"), ("canine", "CanineForQuestionAnswering"), ("convbert", "ConvBertForQuestionAnswering"), diff --git a/src/transformers/models/bloom/__init__.py b/src/transformers/models/bloom/__init__.py index 9aea718858..ece85ac301 100644 --- a/src/transformers/models/bloom/__init__.py +++ b/src/transformers/models/bloom/__init__.py @@ -45,6 +45,7 @@ else: "BloomPreTrainedModel", "BloomForSequenceClassification", "BloomForTokenClassification", + "BloomForQuestionAnswering", ] if TYPE_CHECKING: @@ -67,6 +68,7 @@ if TYPE_CHECKING: from .modeling_bloom import ( BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST, BloomForCausalLM, + BloomForQuestionAnswering, BloomForSequenceClassification, BloomForTokenClassification, BloomModel, diff --git a/src/transformers/models/bloom/modeling_bloom.py b/src/transformers/models/bloom/modeling_bloom.py index 21eaded45b..23404d1215 100644 --- a/src/transformers/models/bloom/modeling_bloom.py +++ b/src/transformers/models/bloom/modeling_bloom.py @@ -28,6 +28,7 @@ from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_ from ...modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, CausalLMOutputWithCrossAttentions, + QuestionAnsweringModelOutput, SequenceClassifierOutputWithPast, TokenClassifierOutput, ) @@ -1167,3 +1168,95 @@ class BloomForTokenClassification(BloomPreTrainedModel): hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, ) + + +@add_start_docstrings( + """ + The BLOOM Model transformer with a span classification head on top for extractive question-answering tasks like + SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). + """, + BLOOM_START_DOCSTRING, +) +class BloomForQuestionAnswering(BloomPreTrainedModel): + _keys_to_ignore_on_load_missing = [r"h.*.self_attention.scale_mask_softmax.causal_mask", r"lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.transformer = BloomModel(config) + self.qa_outputs = nn.Linear(config.hidden_size, 2) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(BLOOM_INPUTS_DOCSTRING.format("batch_size, sequence_length")) + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + head_mask: Optional[torch.FloatTensor] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple, QuestionAnsweringModelOutput]: + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the sequence + are not taken into account for computing the loss. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.transformer( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + head_mask=head_mask, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/src/transformers/utils/dummy_pt_objects.py b/src/transformers/utils/dummy_pt_objects.py index 9f540bd283..f3583e1b61 100644 --- a/src/transformers/utils/dummy_pt_objects.py +++ b/src/transformers/utils/dummy_pt_objects.py @@ -1006,6 +1006,13 @@ class BloomForCausalLM(metaclass=DummyObject): requires_backends(self, ["torch"]) +class BloomForQuestionAnswering(metaclass=DummyObject): + _backends = ["torch"] + + def __init__(self, *args, **kwargs): + requires_backends(self, ["torch"]) + + class BloomForSequenceClassification(metaclass=DummyObject): _backends = ["torch"] diff --git a/tests/models/bloom/test_modeling_bloom.py b/tests/models/bloom/test_modeling_bloom.py index e9ae51a9f5..06cec20456 100644 --- a/tests/models/bloom/test_modeling_bloom.py +++ b/tests/models/bloom/test_modeling_bloom.py @@ -31,6 +31,7 @@ if is_torch_available(): from transformers import ( BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST, BloomForCausalLM, + BloomForQuestionAnswering, BloomForSequenceClassification, BloomForTokenClassification, BloomModel, @@ -274,6 +275,14 @@ class BloomModelTester: result = model(input_ids, attention_mask=input_mask) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) + def create_and_check_question_answering_model(self, config, input_ids, input_mask, *args): + model = BloomForQuestionAnswering(config) + model.to(torch_device) + model.eval() + + result = model(input_ids, attention_mask=input_mask) + self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels)) + def create_and_check_forward_and_backwards( self, config, input_ids, input_mask, *args, gradient_checkpointing=False ): @@ -314,6 +323,7 @@ class BloomModelTest(ModelTesterMixin, GenerationTesterMixin, unittest.TestCase) BloomForCausalLM, BloomForSequenceClassification, BloomForTokenClassification, + BloomForQuestionAnswering, ) if is_torch_available() else ()