from functools import partial from types import MethodType from typing import Callable, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn from torch import Tensor from torch.nn import CrossEntropyLoss, Module from transformers.modeling_outputs import ( BaseModelOutputWithPastAndCrossAttentions, BaseModelOutputWithPoolingAndCrossAttentions, CausalLMOutputWithCrossAttentions, MultipleChoiceModelOutput, NextSentencePredictorOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, ) from transformers.models.bert.modeling_bert import ( BertForMaskedLM, BertForMultipleChoice, BertForNextSentencePrediction, BertForPreTraining, BertForPreTrainingOutput, BertForQuestionAnswering, BertForSequenceClassification, BertForTokenClassification, BertLMHeadModel, BertModel, ) from transformers.utils import ModelOutput, logging import colossalai.shardformer.layer as col_nn from colossalai.pipeline.stage_manager import PipelineStageManager from .._utils import getattr_, setattr_ from .base_policy import ModulePolicyDescription, Policy, SubModuleReplacementDescription logger = logging.get_logger(__name__) __all__ = [ 'BertPolicy', 'BertModelPolicy', 'BertForPreTrainingPolicy', 'BertLMdHeadModelPolicy', 'BertForMaskedLMPolicy', 'BertForNextSentencePredictionPolicy', 'BertForSequenceClassificationPolicy', 'BertForTokenClassificationPolicy', 'BertForMultipleChoicePolicy', 'BertForQuestionAnsweringPolicy' ] class BertPolicy(Policy): def config_sanity_check(self): pass def preprocess(self): # reshape the embedding layer r""" Reshape the Embedding layer to make the embedding dimension divisible by world_size """ # TODO: if self.shard_config.enable_tensor_parallelism: vocab_size = self.model.config.vocab_size world_size = self.shard_config.tensor_parallel_size if vocab_size % world_size != 0: new_vocab_size = vocab_size + world_size - vocab_size % world_size self.model.resize_token_embeddings(new_vocab_size) return self.model def module_policy(self): from transformers.models.bert.modeling_bert import BertEmbeddings, BertLayer policy = {} if self.shard_config.enable_tensor_parallelism: policy[BertLayer] = ModulePolicyDescription(attribute_replacement={ "attention.self.all_head_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, "crossattention.self.all_head_size": self.model.config.hidden_size // self.shard_config.tensor_parallel_size, "attention.self.num_attention_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, "crossattention.self.num_attention_heads": self.model.config.num_attention_heads // self.shard_config.tensor_parallel_size, }, sub_module_replacement=[ SubModuleReplacementDescription( suffix="attention.self.query", target_module=col_nn.Linear1D_Col, ), SubModuleReplacementDescription( suffix="attention.self.key", target_module=col_nn.Linear1D_Col, ), SubModuleReplacementDescription( suffix="attention.self.value", target_module=col_nn.Linear1D_Col, ), SubModuleReplacementDescription( suffix="attention.self.dropout", target_module=col_nn.DropoutForParallelInput, ), SubModuleReplacementDescription( suffix="attention.output.dense", target_module=col_nn.Linear1D_Row, ), SubModuleReplacementDescription( suffix="attention.output.dropout", target_module=col_nn.DropoutForParallelInput, ), SubModuleReplacementDescription( suffix="intermediate.dense", target_module=col_nn.Linear1D_Col, ), SubModuleReplacementDescription( suffix="output.dense", target_module=col_nn.Linear1D_Row, ), SubModuleReplacementDescription( suffix="output.dropout", target_module=col_nn.DropoutForParallelInput, ) ]) policy[BertEmbeddings] = ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="word_embeddings", target_module=col_nn.VocabParallelEmbedding1D, ), SubModuleReplacementDescription( suffix="dropout", target_module=col_nn.DropoutForReplicatedInput, ) ]) # optimization configuration if self.shard_config.enable_fused_normalization: # Handle bert layer self.append_or_create_submodule_replacement(description=[ SubModuleReplacementDescription( suffix="attention.output.LayerNorm", target_module=col_nn.FusedLayerNorm, ), SubModuleReplacementDescription( suffix="output.LayerNorm", target_module=col_nn.FusedLayerNorm, ) ], policy=policy, target_key=BertLayer) # handle embedding layer self.append_or_create_submodule_replacement( description=[SubModuleReplacementDescription( suffix="LayerNorm", target_module=col_nn.FusedLayerNorm, )], policy=policy, target_key=BertEmbeddings) return policy def add_lm_head_policy(self, base_policy): from transformers.models.bert.modeling_bert import BertLMPredictionHead # optimize for tensor parallelism if self.shard_config.enable_tensor_parallelism: self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="decoder", target_module=col_nn.Linear1D_Col, kwargs={"gather_output": True}), policy=base_policy, target_key=BertLMPredictionHead) # optimize with fused normalization if self.shard_config.enable_fused_normalization: # Handle bert lm prediction head self.append_or_create_submodule_replacement(description=SubModuleReplacementDescription( suffix="transform.LayerNorm", target_module=col_nn.FusedLayerNorm, ), policy=base_policy, target_key=BertLMPredictionHead) return base_policy def postprocess(self): return self.model def set_pipeline_forward(self, model_cls: nn.Module, new_forward: Callable, policy: Dict) -> None: """If under pipeline parallel setting, replacing the original forward method of huggingface to customized forward method, and add this changing to policy.""" if self.pipeline_stage_manager: stage_manager = self.pipeline_stage_manager if self.model.__class__.__name__ == "BertModel": module = self.model else: module = self.model.bert layers_per_stage = Policy.distribute_layers(len(module.encoder.layer), stage_manager.num_stages) stage_index = Policy.get_stage_index(layers_per_stage, stage_manager.stage) method_replacement = {'forward': partial(new_forward, stage_manager=stage_manager, stage_index=stage_index)} self.append_or_create_method_replacement(description=method_replacement, policy=policy, target_key=model_cls) return # BertModel class BertModelPolicy(BertPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): policy = super().module_policy() from transformers.models.bert.modeling_bert import BertModel self.set_pipeline_forward(model_cls=BertModel, new_forward=bert_model_forward, policy=policy) return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage.""" module = self.model stage_manager = self.pipeline_stage_manager held_layers = [] layers_per_stage = self.distribute_layers(len(module.encoder.layer), stage_manager.num_stages) if stage_manager.is_first_stage(): held_layers.append(module.embeddings) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) held_layers.extend(module.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.pooler) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: """No shared params in bert model""" return [] # BertForPreTraining class BertForPreTrainingPolicy(BertPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): policy = super().module_policy() policy = self.add_lm_head_policy(policy) from transformers.models.bert.modeling_bert import BertForPreTraining self.set_pipeline_forward(model_cls=BertForPreTraining, new_forward=bert_for_pretraining_forward, policy=policy) return policy def get_held_layers(self) -> List[Module]: """Get pipeline layers for current stage""" module = self.model stage_manager = self.pipeline_stage_manager layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) held_layers = [] if stage_manager.is_first_stage(): held_layers.append(module.bert.embeddings) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.bert.pooler) held_layers.append(module.cls) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: model = self.model if self.pipeline_stage_manager: if id(model.bert.embeddings.word_embeddings.weight) == id(model.cls.predictions.decoder.weight): #tie weights return [{ 0: model.bert.embeddings.word_embeddings.weight, self.pipeline_stage_manager.num_stages - 1: model.cls.predictions.decoder.weight }] return [] def postprocess(self): if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) setattr_(self.model, v, param) return self.model # BertLMHeadModel class BertLMHeadModelPolicy(BertPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): policy = super().module_policy() policy = self.add_lm_head_policy(policy) from transformers.models.bert.modeling_bert import BertLMHeadModel self.set_pipeline_forward(model_cls=BertLMHeadModel, new_forward=bert_lm_head_model_forward, policy=policy) return policy def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ module = self.model held_layers = [] stage_manager = self.pipeline_stage_manager layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) if stage_manager.is_first_stage(): held_layers.append(module.bert.embeddings) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.bert.pooler) held_layers.append(module.cls) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: bert_model = self.model.bert if self.pipeline_stage_manager: if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight): #tie weights return [{ 0: bert_model.embeddings.word_embeddings.weight, self.pipeline_stage_manager.num_stages - 1: self.model.cls.predictions.decoder.weight }] return [] def postprocess(self): if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) setattr_(self.model, v, param) return self.model # BertForMaskedLM class BertForMaskedLMPolicy(BertPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): policy = super().module_policy() policy = self.add_lm_head_policy(policy) from transformers.models.bert.modeling_bert import BertForMaskedLM self.set_pipeline_forward(model_cls=BertForMaskedLM, new_forward=bert_for_masked_lm_forward, policy=policy) return policy def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ module = self.model held_layers = [] stage_manager = self.pipeline_stage_manager layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) if stage_manager.is_first_stage(): held_layers.append(module.bert.embeddings) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.bert.pooler) held_layers.append(module.cls) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: bert_model = self.model.bert if self.pipeline_stage_manager: if id(bert_model.embeddings.word_embeddings.weight) == id(self.model.cls.predictions.decoder.weight): #tie weights return [{ 0: bert_model.embeddings.word_embeddings.weight, self.pipeline_stage_manager.num_stages - 1: self.model.cls.predictions.decoder.weight }] return [] def postprocess(self): if self.shard_config.enable_tensor_parallelism and self.pipeline_stage_manager is None: binding_map = {"bert.embeddings.word_embeddings.weight": "cls.predictions.decoder.weight"} for k, v in binding_map.items(): param = getattr_(self.model, k) setattr_(self.model, v, param) return self.model # BertForSequenceClassification class BertForSequenceClassificationPolicy(BertPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): from transformers.models.bert.modeling_bert import BertForSequenceClassification policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: addon_module = { BertForSequenceClassification: ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", target_module=col_nn.DropoutForParallelInput, ) ]) } policy.update(addon_module) self.set_pipeline_forward(model_cls=BertForSequenceClassification, new_forward=bert_for_sequence_classification_forward, policy=policy) return policy def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ module = self.model held_layers = [] stage_manager = self.pipeline_stage_manager layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) if stage_manager.is_first_stage(): held_layers.append(module.bert.embeddings) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.bert.pooler) held_layers.append(module.dropout) held_layers.append(module.classifier) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: # no shared params for sequence classification model return [] # BertForTokenClassification class BertForTokenClassificationPolicy(BertPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): from transformers.models.bert.modeling_bert import BertForTokenClassification policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: addon_module = { BertForTokenClassification: ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", target_module=col_nn.DropoutForParallelInput, ) ]) } policy.update(addon_module) self.set_pipeline_forward(model_cls=BertForTokenClassification, new_forward=bert_for_token_classification_forward, policy=policy) return policy def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ module = self.model held_layers = [] stage_manager = self.pipeline_stage_manager layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) if stage_manager.is_first_stage(): held_layers.append(module.bert.embeddings) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.bert.pooler) held_layers.append(module.dropout) held_layers.append(module.classifier) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: # no shared params for sequence classification model return [] # BertForNextSentencePrediction class BertForNextSentencePredictionPolicy(BertPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): policy = super().module_policy() from transformers.models.bert.modeling_bert import BertForNextSentencePrediction self.set_pipeline_forward(model_cls=BertForNextSentencePrediction, new_forward=bert_for_next_sentence_prediction_forward, policy=policy) return policy def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ module = self.model held_layers = [] stage_manager = self.pipeline_stage_manager layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) if stage_manager.is_first_stage(): held_layers.append(module.bert.embeddings) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.bert.pooler) held_layers.append(module.cls) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: # no shared params for sequence classification model return [] # BertForMultipleChoice class BertForMultipleChoicePolicy(BertPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): from transformers.models.bert.modeling_bert import BertForMultipleChoice policy = super().module_policy() if self.shard_config.enable_tensor_parallelism: addon_module = { BertForMultipleChoice: ModulePolicyDescription(sub_module_replacement=[ SubModuleReplacementDescription( suffix="dropout", target_module=col_nn.DropoutForParallelInput, ) ]) } policy.update(addon_module) self.set_pipeline_forward(model_cls=BertForMultipleChoice, new_forward=bert_for_multiple_choice_forward, policy=policy) return policy def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ module = self.model held_layers = [] stage_manager = self.pipeline_stage_manager layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) if stage_manager.is_first_stage(): held_layers.append(module.bert.embeddings) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.bert.pooler) held_layers.append(module.dropout) held_layers.append(module.classifier) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: # no shared params for sequence classification model return [] class BertForQuestionAnsweringPolicy(BertPolicy): def __init__(self) -> None: super().__init__() def module_policy(self): from transformers.models.bert.modeling_bert import BertForQuestionAnswering policy = super().module_policy() self.set_pipeline_forward(model_cls=BertForQuestionAnswering, new_forward=bert_for_question_answering_forward, policy=policy) return policy def get_held_layers(self) -> List[Module]: """ get pipeline layers for current stage """ module = self.model held_layers = [] stage_manager = self.pipeline_stage_manager layers_per_stage = self.distribute_layers(len(module.bert.encoder.layer), stage_manager.num_stages) if stage_manager.is_first_stage(): held_layers.append(module.bert.embeddings) start_idx, end_idx = self.get_stage_index(layers_per_stage, stage_manager.stage) held_layers.extend(module.bert.encoder.layer[start_idx:end_idx]) if stage_manager.is_last_stage(): held_layers.append(module.bert.pooler) held_layers.append(module.qa_outputs) return held_layers def get_shared_params(self) -> List[Dict[int, Tensor]]: # no shared params for sequence classification model return [] def bert_model_forward( self: BertModel, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.FloatTensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, stage_manager: Optional[PipelineStageManager] = None, hidden_states: Optional[torch.FloatTensor] = None, # this is from the previous stage stage_index: Optional[List[int]] = None, ): # TODO: add explaination of the output here. r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): 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 (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): 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 tokens that are **masked**. past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ # debugging # preprocess: 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) return_dict = return_dict if return_dict is not None else self.config.use_return_dict 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 stage_manager.is_first_stage(): 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: 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 if token_type_ids is None: if hasattr(self.embeddings, "token_type_ids"): buffered_token_type_ids = self.embeddings.token_type_ids[:, :seq_length] buffered_token_type_ids_expanded = buffered_token_type_ids.expand(batch_size, seq_length) token_type_ids = buffered_token_type_ids_expanded else: token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device) else: input_shape = hidden_states.size()[:-1] batch_size, seq_length = input_shape device = hidden_states.device # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if use_cache: logger.warning_once('use_cache=True is not supported for pipeline models at the moment.') use_cache = False # past_key_values_length past_key_values_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 if attention_mask is None: attention_mask = torch.ones(((batch_size, seq_length + past_key_values_length)), device=device) # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length] # ourselves in which case we just need to make it broadcastable to all heads. extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(attention_mask, input_shape) attention_mask = extended_attention_mask # If a 2D or 3D attention mask is provided for the cross-attention # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length] if self.config.is_decoder and encoder_hidden_states is not None: encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size() encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length) if encoder_attention_mask is None: encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device) encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask) else: encoder_extended_attention_mask = None # Prepare head mask if needed # 1.0 in head_mask indicate we keep the head # attention_probs has shape bsz x n_heads x N x N # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) hidden_states = hidden_states if hidden_states is not None else None if stage_manager.is_first_stage(): hidden_states = self.embeddings( input_ids=input_ids, position_ids=position_ids, token_type_ids=token_type_ids, inputs_embeds=inputs_embeds, past_key_values_length=past_key_values_length, ) # inherit from bert_layer,this should be changed when we add the feature to record hidden_states all_hidden_states = () if output_hidden_states else None all_self_attentions = () if output_attentions else None all_cross_attentions = () if output_attentions and self.config.add_cross_attention else None if self.encoder.gradient_checkpointing and self.encoder.training: if use_cache: logger.warning_once( "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...") use_cache = False next_decoder_cache = () if use_cache else None start_idx, end_idx = stage_index[0], stage_index[1] # layer_outputs layer_outputs = hidden_states if hidden_states is not None else None for idx, encoder_layer in enumerate(self.encoder.layer[start_idx:end_idx], start=start_idx): if stage_manager.is_first_stage() and idx == 0: encoder_attention_mask = encoder_extended_attention_mask if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) layer_head_mask = head_mask[idx] if head_mask is not None else None past_key_value = past_key_values[idx] if past_key_values is not None else None if self.encoder.gradient_checkpointing and self.encoder.training: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs, past_key_value, output_attentions) return custom_forward layer_outputs = torch.utils.checkpoint.checkpoint( create_custom_forward(encoder_layer), hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, ) else: layer_outputs = encoder_layer( hidden_states, attention_mask, layer_head_mask, encoder_hidden_states, encoder_attention_mask, past_key_value, output_attentions, ) hidden_states = layer_outputs[0] if use_cache: next_decoder_cache += (layer_outputs[-1],) if output_attentions: all_self_attentions = all_self_attentions + (layer_outputs[1],) if self.config.add_cross_attention: all_cross_attentions = all_cross_attentions + \ (layer_outputs[2],) if output_hidden_states: all_hidden_states = all_hidden_states + (hidden_states,) # end of a stage loop sequence_output = layer_outputs[0] if layer_outputs is not None else None if stage_manager.is_last_stage(): pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + layer_outputs[1:] # return dict is not supported at this moment else: return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=next_decoder_cache, hidden_states=all_hidden_states, attentions=all_self_attentions, cross_attentions=all_cross_attentions, ) # output of non-first and non-last stages: must be a dict else: # intermediate stage always return dict return { 'hidden_states': hidden_states, } def bert_for_pretraining_forward( self: BertForPreTraining, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, next_sentence_label: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, hidden_states: Optional[torch.FloatTensor] = None, stage_manager: Optional[PipelineStageManager] = None, stage_index: Optional[List[int]] = None, ): return_dict = return_dict if return_dict is not None else self.config.use_return_dict # TODO: left the recording kv-value tensors as () or None type, this feature may be added in the future. if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if return_dict: logger.warning_once('return_dict is not supported for pipeline models at the moment') return_dict = False outputs = bert_model_forward( self.bert, input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, 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, stage_manager=stage_manager, hidden_states=hidden_states if hidden_states is not None else None, stage_index=stage_index, ) past_key_values = None all_hidden_states = None all_self_attentions = None all_cross_attentions = None if stage_manager.is_last_stage(): sequence_output, pooled_output = outputs[:2] prediction_scores, seq_relationship_score = self.cls(sequence_output, pooled_output) # the last stage for pretraining model 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 if not return_dict: output = (prediction_scores, seq_relationship_score) + outputs[2:] return ((total_loss,) + output) if total_loss is not None else output return BertForPreTrainingOutput( loss=total_loss, prediction_logits=prediction_scores, seq_relationship_logits=seq_relationship_score, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) else: hidden_states = outputs.get('hidden_states') # intermediate stage always return dict return { 'hidden_states': hidden_states, } def bert_lm_head_model_forward( self: BertLMHeadModel, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, past_key_values: Optional[List[torch.Tensor]] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, hidden_states: Optional[torch.FloatTensor] = None, stage_manager: Optional[PipelineStageManager] = None, stage_index: Optional[List[int]] = None, ): r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): 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 (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): 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 tokens that are **masked**. 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]` past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding. If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `decoder_input_ids` of shape `(batch_size, sequence_length)`. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if labels is not None: use_cache = False if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if return_dict: logger.warning_once('return_dict is not supported for pipeline models at the moment') return_dict = False outputs = bert_model_forward(self.bert, 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, past_key_values=past_key_values, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, stage_manager=stage_manager, hidden_states=hidden_states if hidden_states is not None else None, stage_index=stage_index) past_key_values = None all_hidden_states = None all_self_attentions = None all_cross_attentions = None if stage_manager.is_last_stage(): sequence_output = outputs[0] prediction_scores = self.cls(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 CausalLMOutputWithCrossAttentions( loss=lm_loss, logits=prediction_scores, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions, cross_attentions=outputs.cross_attentions, ) else: hidden_states = outputs.get('hidden_states') # intermediate stage always return dict return {'hidden_states': hidden_states} def bert_for_masked_lm_forward( self: BertForMaskedLM, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, hidden_states: Optional[torch.Tensor] = None, stage_manager: Optional[PipelineStageManager] = None, stage_index: Optional[List[int]] = None, ): r""" 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]` """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if return_dict: logger.warning_once('return_dict is not supported for pipeline models at the moment') return_dict = False outputs = bert_model_forward( self.bert, 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, hidden_states=hidden_states, stage_manager=stage_manager, stage_index=stage_index, ) if stage_manager.is_last_stage(): 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)) if not return_dict: output = (prediction_scores,) + outputs[2:] return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=prediction_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) else: hidden_states = outputs.get('hidden_states') return {'hidden_states': hidden_states} def bert_for_next_sentence_prediction_forward( self: BertForNextSentencePrediction, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, hidden_states: Optional[torch.Tensor] = None, stage_manager: Optional[PipelineStageManager] = None, stage_index: Optional[List[int]] = None, **kwargs, ): #-> Union[Tuple[torch.Tensor], NextSentencePredictorOutput]: r""" 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. Returns: Example: ```python >>> from transformers import AutoTokenizer, BertForNextSentencePrediction >>> import torch >>> tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") >>> model = BertForNextSentencePrediction.from_pretrained("bert-base-uncased") >>> 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 ``` """ if "next_sentence_label" in kwargs: warnings.warn( "The `next_sentence_label` argument is deprecated and will be removed in a future version, use" " `labels` instead.", FutureWarning, ) labels = kwargs.pop("next_sentence_label") if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if return_dict: logger.warning_once('return_dict is not supported for pipeline models at the moment') return_dict = False return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = bert_model_forward(self.bert, input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, 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, hidden_states=hidden_states, stage_manager=stage_manager, stage_index=stage_index) if stage_manager.is_last_stage(): 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)) if not return_dict: output = (seq_relationship_scores,) + outputs[2:] return ((next_sentence_loss,) + output) if next_sentence_loss is not None else output return NextSentencePredictorOutput( loss=next_sentence_loss, logits=seq_relationship_scores, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) else: hidden_states = outputs.get('hidden_states') # intermediate stage always return dict return {'hidden_states': hidden_states} def bert_for_sequence_classification_forward( self: BertForSequenceClassification, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, hidden_states: Optional[torch.Tensor] = None, stage_manager: Optional[PipelineStageManager] = None, stage_index: Optional[List[int]] = None, ): r""" 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). """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if return_dict: logger.warning_once('return_dict is not supported for pipeline models at the moment') return_dict = False return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = bert_model_forward(self.bert, input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, 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, hidden_states=hidden_states, stage_manager=stage_manager, stage_index=stage_index) if stage_manager.is_last_stage(): 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) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) else: hidden_states = outputs.get('hidden_states') return {'hidden_states': hidden_states} def bert_for_token_classification_forward( self: BertForTokenClassification, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, hidden_states: Optional[torch.Tensor] = None, stage_manager: Optional[PipelineStageManager] = None, stage_index: Optional[List[int]] = None, ): r""" 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]`. """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if return_dict: logger.warning_once('return_dict is not supported for pipeline models at the moment') return_dict = False return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = bert_model_forward( self.bert, input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, 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, hidden_states=hidden_states, stage_manager=stage_manager, stage_index=stage_index, ) if stage_manager.is_last_stage(): 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)) if not return_dict: output = (logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) else: hidden_states = outputs.get('hidden_states') return {'hidden_states': hidden_states} def bert_for_multiple_choice_forward( self: BertForMultipleChoice, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, labels: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, hidden_states: Optional[torch.Tensor] = None, stage_manager: Optional[PipelineStageManager] = None, stage_index: Optional[List[int]] = None, ): r""" 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) """ return_dict = return_dict if return_dict is not None else self.config.use_return_dict if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if return_dict: logger.warning_once('return_dict is not supported for pipeline models at the moment') return_dict = False return_dict = return_dict if return_dict is not None else self.config.use_return_dict # in our pipeline design,input ids are copied for every stage and shouldn't be none # the input_ids for multiple choice model is [batch_size, num_choices, sequence_length] if stage_manager.is_last_stage(): 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 = bert_model_forward( self.bert, input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, 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, hidden_states=hidden_states, stage_manager=stage_manager, stage_index=stage_index, ) if stage_manager.is_last_stage(): 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) if not return_dict: output = (reshaped_logits,) + outputs[2:] return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=outputs.hidden_states, attentions=outputs.attentions, ) else: hidden_states = outputs.get('hidden_states') return {'hidden_states': hidden_states} def bert_for_question_answering_forward( self: BertForQuestionAnswering, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, token_type_ids: Optional[torch.Tensor] = None, position_ids: Optional[torch.Tensor] = None, head_mask: Optional[torch.Tensor] = None, inputs_embeds: Optional[torch.Tensor] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, hidden_states: Optional[torch.Tensor] = None, stage_manager: Optional[PipelineStageManager] = None, stage_index: Optional[List[int]] = None, ): # NOTE: the arg start_position and end_position are used only for the last stage 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 if output_attentions: logger.warning_once('output_attentions=True is not supported for pipeline models at the moment.') output_attentions = False if output_hidden_states: logger.warning_once('output_hidden_states=True is not supported for pipeline models at the moment.') output_hidden_states = False if return_dict: logger.warning_once('return_dict is not supported for pipeline models at the moment') return_dict = False return_dict = return_dict if return_dict is not None else self.config.use_return_dict outputs = bert_model_forward( self.bert, input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids, 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, hidden_states=hidden_states, stage_manager=stage_manager, stage_index=stage_index, ) if stage_manager.is_last_stage(): 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, ) else: hidden_states = outputs.get('hidden_states') return {'hidden_states': hidden_states}