# Copyright 2022 The HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """PyTorch ERNIE model.""" import torch import torch.nn as nn from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss from ... import initialization as init from ...cache_utils import Cache, DynamicCache, EncoderDecoderCache from ...masking_utils import create_bidirectional_mask, create_causal_mask from ...modeling_outputs import ( BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MaskedLMOutput, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from ...modeling_utils import PreTrainedModel from ...processing_utils import Unpack from ...utils import TransformersKwargs, auto_docstring, logging from ...utils.generic import can_return_tuple, check_model_inputs from ..bert.modeling_bert import ( BertCrossAttention, BertEmbeddings, BertEncoder, BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForPreTrainingOutput, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertLayer, BertLMHeadModel, BertLMPredictionHead, BertModel, BertPooler, BertSelfAttention, ) from .configuration_ernie import ErnieConfig logger = logging.get_logger(__name__) class ErnieEmbeddings(BertEmbeddings): """Construct the embeddings from word, position and token_type embeddings.""" def __init__(self, config): super().__init__(config) self.use_task_id = config.use_task_id if config.use_task_id: self.task_type_embeddings = nn.Embedding(config.task_type_vocab_size, config.hidden_size) def forward( self, input_ids: torch.LongTensor | None = None, token_type_ids: torch.LongTensor | None = None, task_type_ids: torch.LongTensor | None = None, position_ids: torch.LongTensor | None = None, inputs_embeds: torch.FloatTensor | None = None, past_key_values_length: int = 0, ) -> torch.Tensor: if input_ids is not None: input_shape = input_ids.size() else: input_shape = inputs_embeds.size()[:-1] batch_size, seq_length = input_shape if position_ids is None: position_ids = self.position_ids[:, past_key_values_length : seq_length + past_key_values_length] # Setting the token_type_ids to the registered buffer in constructor where it is all zeros, which usually occurs # when its auto-generated, registered buffer helps users when tracing the model without passing token_type_ids, solves # issue #5664 if token_type_ids is None: if hasattr(self, "token_type_ids"): # NOTE: We assume either pos ids to have bsz == 1 (broadcastable) or bsz == effective bsz (input_shape[0]) buffered_token_type_ids = self.token_type_ids.expand(position_ids.shape[0], -1) buffered_token_type_ids = torch.gather(buffered_token_type_ids, dim=1, index=position_ids) token_type_ids = buffered_token_type_ids.expand(batch_size, seq_length) else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) if inputs_embeds is None: inputs_embeds = self.word_embeddings(input_ids) token_type_embeddings = self.token_type_embeddings(token_type_ids) # .to is better than using _no_split_modules on ErnieEmbeddings as it's the first module and >1/2 the model size inputs_embeds = inputs_embeds.to(token_type_embeddings.device) embeddings = inputs_embeds + token_type_embeddings position_embeddings = self.position_embeddings(position_ids) embeddings = embeddings + position_embeddings # add `task_type_id` for ERNIE model if self.use_task_id: if task_type_ids is None: task_type_ids = torch.zeros(input_shape, dtype=torch.long, device=self.position_ids.device) task_type_embeddings = self.task_type_embeddings(task_type_ids) embeddings += task_type_embeddings embeddings = self.LayerNorm(embeddings) embeddings = self.dropout(embeddings) return embeddings class ErnieSelfAttention(BertSelfAttention): pass class ErnieCrossAttention(BertCrossAttention): pass class ErnieLayer(BertLayer): pass class ErniePooler(BertPooler): pass class ErnieLMPredictionHead(BertLMPredictionHead): pass class ErnieEncoder(BertEncoder): pass @auto_docstring class ErniePreTrainedModel(PreTrainedModel): config_class = ErnieConfig base_model_prefix = "ernie" supports_gradient_checkpointing = True _supports_flash_attn = True _supports_sdpa = True _supports_flex_attn = True _supports_attention_backend = True _can_record_outputs = { "hidden_states": ErnieLayer, "attentions": ErnieSelfAttention, "cross_attentions": ErnieCrossAttention, } @torch.no_grad() def _init_weights(self, module): """Initialize the weights""" super()._init_weights(module) if isinstance(module, ErnieLMPredictionHead): init.zeros_(module.bias) elif isinstance(module, ErnieEmbeddings): init.copy_(module.position_ids, torch.arange(module.position_ids.shape[-1]).expand((1, -1))) init.zeros_(module.token_type_ids) class ErnieModel(BertModel): _no_split_modules = ["ErnieLayer"] def __init__(self, config, add_pooling_layer=True): super().__init__(self, config) self.config = config self.gradient_checkpointing = False self.embeddings = ErnieEmbeddings(config) self.encoder = ErnieEncoder(config) self.pooler = ErniePooler(config) if add_pooling_layer else None # Initialize weights and apply final processing self.post_init() @check_model_inputs @auto_docstring def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, token_type_ids: torch.Tensor | None = None, task_type_ids: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, encoder_hidden_states: torch.Tensor | None = None, encoder_attention_mask: torch.Tensor | None = None, past_key_values: Cache | None = None, use_cache: bool | None = None, cache_position: torch.Tensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor] | BaseModelOutputWithPoolingAndCrossAttentions: r""" task_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Task type embedding is a special embedding to represent the characteristic of different tasks, such as word-aware pre-training task, structure-aware pre-training task and semantic-aware pre-training task. We assign a `task_type_id` to each task and the `task_type_id` is in the range `[0, config.task_type_vocab_size-1] """ if self.config.is_decoder: use_cache = use_cache if use_cache is not None else self.config.use_cache else: use_cache = False if self.gradient_checkpointing and self.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." ) use_cache = False if use_cache and past_key_values is None: past_key_values = ( EncoderDecoderCache(DynamicCache(config=self.config), DynamicCache(config=self.config)) if encoder_hidden_states is not None or self.config.is_encoder_decoder else DynamicCache(config=self.config) ) if input_ids is not None and inputs_embeds is not None: raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") elif input_ids is not None: self.warn_if_padding_and_no_attention_mask(input_ids, attention_mask) input_shape = input_ids.size() elif inputs_embeds is not None: input_shape = inputs_embeds.size()[:-1] else: raise ValueError("You have to specify either input_ids or inputs_embeds") batch_size, seq_length = input_shape device = input_ids.device if input_ids is not None else inputs_embeds.device past_key_values_length = past_key_values.get_seq_length() if past_key_values is not None else 0 if cache_position is None: cache_position = torch.arange(past_key_values_length, past_key_values_length + seq_length, device=device) embedding_output = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, # specific to ernie task_type_ids=task_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) attention_mask, encoder_attention_mask = self._create_attention_masks( attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, embedding_output=embedding_output, encoder_hidden_states=encoder_hidden_states, cache_position=cache_position, past_key_values=past_key_values, ) encoder_outputs = self.encoder( embedding_output, attention_mask=attention_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, cache_position=cache_position, position_ids=position_ids, **kwargs, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, ) # Copied from transformers.models.bert.modeling_bert.BertModel._create_attention_masks def _create_attention_masks( self, attention_mask, encoder_attention_mask, embedding_output, encoder_hidden_states, cache_position, past_key_values, ): if self.config.is_decoder: attention_mask = create_causal_mask( config=self.config, input_embeds=embedding_output, attention_mask=attention_mask, cache_position=cache_position, past_key_values=past_key_values, ) else: attention_mask = create_bidirectional_mask( config=self.config, input_embeds=embedding_output, attention_mask=attention_mask, ) if encoder_attention_mask is not None: encoder_attention_mask = create_bidirectional_mask( config=self.config, input_embeds=embedding_output, attention_mask=encoder_attention_mask, encoder_hidden_states=encoder_hidden_states, ) return attention_mask, encoder_attention_mask class ErnieForPreTrainingOutput(BertForPreTrainingOutput): pass class ErnieForPreTraining(BertForPreTraining): _tied_weights_keys = { "cls.predictions.decoder.bias": "cls.predictions.bias", "cls.predictions.decoder.weight": "ernie.embeddings.word_embeddings.weight", } @can_return_tuple @auto_docstring def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, token_type_ids: torch.Tensor | None = None, task_type_ids: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, labels: torch.Tensor | None = None, next_sentence_label: torch.Tensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor] | ErnieForPreTrainingOutput: r""" task_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Task type embedding is a special embedding to represent the characteristic of different tasks, such as word-aware pre-training task, structure-aware pre-training task and semantic-aware pre-training task. We assign a `task_type_id` to each task and the `task_type_id` is in the range `[0, config.task_type_vocab_size-1] labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. 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]` next_sentence_label (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring) Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Example: ```python >>> from transformers import AutoTokenizer, ErnieForPreTraining >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-1.0-base-zh") >>> model = ErnieForPreTraining.from_pretrained("nghuyong/ernie-1.0-base-zh") >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") >>> outputs = model(**inputs) >>> prediction_logits = outputs.prediction_logits >>> seq_relationship_logits = outputs.seq_relationship_logits ``` """ outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, return_dict=True, **kwargs, ) sequence_output, pooled_output = outputs[:2] prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) total_loss = None if labels is not None and next_sentence_label is not None: loss_fct = CrossEntropyLoss() masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) next_sentence_loss = loss_fct(seq_relationship_score.view(-1, 2), next_sentence_label.view(-1)) total_loss = masked_lm_loss + next_sentence_loss return ErnieForPreTrainingOutput( loss=total_loss, prediction_logits=prediction_scores, seq_relationship_logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class ErnieForCausalLM(BertLMHeadModel): @can_return_tuple @auto_docstring def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, token_type_ids: torch.Tensor | None = None, task_type_ids: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, encoder_hidden_states: torch.Tensor | None = None, encoder_attention_mask: torch.Tensor | None = None, labels: torch.Tensor | None = None, past_key_values: list[torch.Tensor] | None = None, use_cache: bool | None = None, cache_position: torch.Tensor | None = None, logits_to_keep: int | torch.Tensor = 0, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor] | CausalLMOutputWithCrossAttentions: r""" task_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Task type embedding is a special embedding to represent the characteristic of different tasks, such as word-aware pre-training task, structure-aware pre-training task and semantic-aware pre-training task. We assign a `task_type_id` to each task and the `task_type_id` is in the range `[0, config.task_type_vocab_size-1] labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): 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 n `[0, ..., config.vocab_size]` """ if labels is not None: use_cache = False outputs: BaseModelOutputWithPoolingAndCrossAttentions = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, past_key_values=past_key_values, use_cache=use_cache, cache_position=cache_position, return_dict=True, **kwargs, ) hidden_states = outputs.last_hidden_state # Only compute necessary logits, and do not upcast them to float if we are not computing the loss slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep logits = self.cls(hidden_states[:, slice_indices, :]) loss = None if labels is not None: loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size, **kwargs) return CausalLMOutputWithCrossAttentions( loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) class ErnieForMaskedLM(BertForMaskedLM): _tied_weights_keys = { "cls.predictions.decoder.bias": "cls.predictions.bias", "cls.predictions.decoder.weight": "ernie.embeddings.word_embeddings.weight", } @can_return_tuple @auto_docstring def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, token_type_ids: torch.Tensor | None = None, task_type_ids: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, encoder_hidden_states: torch.Tensor | None = None, encoder_attention_mask: torch.Tensor | None = None, labels: torch.Tensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor] | MaskedLMOutput: r""" task_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Task type embedding is a special embedding to represent the characteristic of different tasks, such as word-aware pre-training task, structure-aware pre-training task and semantic-aware pre-training task. We assign a `task_type_id` to each task and the `task_type_id` is in the range `[0, config.task_type_vocab_size-1] labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the masked language modeling loss. 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]` """ outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_attention_mask, return_dict=True, **kwargs, ) sequence_output = outputs[0] prediction_scores = self.cls(sequence_output) masked_lm_loss = None if labels is not None: loss_fct = CrossEntropyLoss() # -100 index = padding token masked_lm_loss = loss_fct(prediction_scores.view(-1, self.config.vocab_size), labels.view(-1)) return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class ErnieForNextSentencePrediction(BertForNextSentencePrediction): @can_return_tuple @auto_docstring def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, token_type_ids: torch.Tensor | None = None, task_type_ids: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, labels: torch.Tensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor] | NextSentencePredictorOutput: r""" task_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Task type embedding is a special embedding to represent the characteristic of different tasks, such as word-aware pre-training task, structure-aware pre-training task and semantic-aware pre-training task. We assign a `task_type_id` to each task and the `task_type_id` is in the range `[0, config.task_type_vocab_size-1] labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the next sequence prediction (classification) loss. Input should be a sequence pair (see `input_ids` docstring). Indices should be in `[0, 1]`: - 0 indicates sequence B is a continuation of sequence A, - 1 indicates sequence B is a random sequence. Example: ```python >>> from transformers import AutoTokenizer, ErnieForNextSentencePrediction >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("nghuyong/ernie-1.0-base-zh") >>> model = ErnieForNextSentencePrediction.from_pretrained("nghuyong/ernie-1.0-base-zh") >>> prompt = "In Italy, pizza served in formal settings, such as at a restaurant, is presented unsliced." >>> next_sentence = "The sky is blue due to the shorter wavelength of blue light." >>> encoding = tokenizer(prompt, next_sentence, return_tensors="pt") >>> outputs = model(**encoding, labels=torch.LongTensor([1])) >>> logits = outputs.logits >>> assert logits[0, 0] < logits[0, 1] # next sentence was random ``` """ outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, return_dict=True, **kwargs, ) pooled_output = outputs[1] seq_relationship_scores = self.cls(pooled_output) next_sentence_loss = None if labels is not None: loss_fct = CrossEntropyLoss() next_sentence_loss = loss_fct(seq_relationship_scores.view(-1, 2), labels.view(-1)) return NextSentencePredictorOutput( loss=next_sentence_loss, logits=seq_relationship_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class ErnieForSequenceClassification(BertForSequenceClassification): @can_return_tuple @auto_docstring def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, token_type_ids: torch.Tensor | None = None, task_type_ids: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, labels: torch.Tensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor] | SequenceClassifierOutput: r""" task_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Task type embedding is a special embedding to represent the characteristic of different tasks, such as word-aware pre-training task, structure-aware pre-training task and semantic-aware pre-training task. We assign a `task_type_id` to each task and the `task_type_id` is in the range `[0, config.task_type_vocab_size-1] labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). """ outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, return_dict=True, **kwargs, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) loss = None if labels is not None: if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" if self.config.problem_type == "regression": loss_fct = MSELoss() if self.num_labels == 1: loss = loss_fct(logits.squeeze(), labels.squeeze()) else: loss = loss_fct(logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(logits, labels) return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class ErnieForMultipleChoice(BertForMultipleChoice): @can_return_tuple @auto_docstring def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, token_type_ids: torch.Tensor | None = None, task_type_ids: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, labels: torch.Tensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor] | MultipleChoiceModelOutput: r""" input_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) token_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) task_type_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): Task type embedding is a special embedding to represent the characteristic of different tasks, such as word-aware pre-training task, structure-aware pre-training task and semantic-aware pre-training task. We assign a `task_type_id` to each task and the `task_type_id` is in the range `[0, config.task_type_vocab_size-1] position_ids (`torch.LongTensor` of shape `(batch_size, num_choices, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.max_position_embeddings - 1]`. [What are position IDs?](../glossary#position-ids) inputs_embeds (`torch.FloatTensor` of shape `(batch_size, num_choices, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `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. labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the multiple choice classification loss. Indices should be in `[0, ..., num_choices-1]` where `num_choices` is the size of the second dimension of the input tensors. (See `input_ids` above) """ num_choices = input_ids.shape[1] if input_ids is not None else inputs_embeds.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) if input_ids is not None else None attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1)) if token_type_ids is not None else None position_ids = position_ids.view(-1, position_ids.size(-1)) if position_ids is not None else None inputs_embeds = ( inputs_embeds.view(-1, inputs_embeds.size(-2), inputs_embeds.size(-1)) if inputs_embeds is not None else None ) outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, return_dict=True, **kwargs, ) pooled_output = outputs[1] pooled_output = self.dropout(pooled_output) logits = self.classifier(pooled_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class ErnieForTokenClassification(BertForTokenClassification): @can_return_tuple @auto_docstring def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, token_type_ids: torch.Tensor | None = None, task_type_ids: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, labels: torch.Tensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor] | TokenClassifierOutput: r""" task_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Task type embedding is a special embedding to represent the characteristic of different tasks, such as word-aware pre-training task, structure-aware pre-training task and semantic-aware pre-training task. We assign a `task_type_id` to each task and the `task_type_id` is in the range `[0, config.task_type_vocab_size-1] labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Labels for computing the token classification loss. Indices should be in `[0, ..., config.num_labels - 1]`. """ outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, return_dict=True, **kwargs, ) sequence_output = outputs[0] sequence_output = self.dropout(sequence_output) logits = self.classifier(sequence_output) loss = None if labels is not None: loss_fct = CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) class ErnieForQuestionAnswering(BertForQuestionAnswering): @can_return_tuple @auto_docstring def forward( self, input_ids: torch.Tensor | None = None, attention_mask: torch.Tensor | None = None, token_type_ids: torch.Tensor | None = None, task_type_ids: torch.Tensor | None = None, position_ids: torch.Tensor | None = None, inputs_embeds: torch.Tensor | None = None, start_positions: torch.Tensor | None = None, end_positions: torch.Tensor | None = None, **kwargs: Unpack[TransformersKwargs], ) -> tuple[torch.Tensor] | QuestionAnsweringModelOutput: r""" task_type_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Task type embedding is a special embedding to represent the characteristic of different tasks, such as word-aware pre-training task, structure-aware pre-training task and semantic-aware pre-training task. We assign a `task_type_id` to each task and the `task_type_id` is in the range `[0, config.task_type_vocab_size-1] """ outputs = self.ernie( input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, task_type_ids=task_type_ids, position_ids=position_ids, inputs_embeds=inputs_embeds, return_dict=True, **kwargs, ) 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 return QuestionAnsweringModelOutput( loss=total_loss, start_logits=start_logits, end_logits=end_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) __all__ = [ "ErnieForCausalLM", "ErnieForMaskedLM", "ErnieForMultipleChoice", "ErnieForNextSentencePrediction", "ErnieForPreTraining", "ErnieForQuestionAnswering", "ErnieForSequenceClassification", "ErnieForTokenClassification", "ErnieModel", "ErniePreTrainedModel", ]