import torch import torch.nn as nn import numpy as np from torch.autograd import Function from transformers import PreTrainedModel from transformers.models.bert.modeling_bert import ( BertEmbeddings, BertEncoder, BertPooler ) from typing import Union, Tuple, Optional, List from transformers.modeling_outputs import ( SequenceClassifierOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, BaseModelOutputWithPoolingAndCrossAttentions ) from transformers.modeling_attn_mask_utils import ( _prepare_4d_attention_mask_for_sdpa, _prepare_4d_causal_attention_mask_for_sdpa, ) from transformers.utils import ModelOutput from .configuration_pure_bert import PureBertConfig class CovarianceFunction(Function): @staticmethod def forward(ctx, inputs): x = inputs b, c, h, w = x.data.shape m = h * w x = x.view(b, c, m) I_hat = (-1.0 / m / m) * torch.ones(m, m, device=x.device) + ( 1.0 / m ) * torch.eye(m, m, device=x.device) I_hat = I_hat.view(1, m, m).repeat(b, 1, 1).type(x.dtype) y = x @ I_hat @ x.transpose(-1, -2) ctx.save_for_backward(inputs, I_hat) return y @staticmethod def backward(ctx, grad_output): inputs, I_hat = ctx.saved_tensors x = inputs b, c, h, w = x.data.shape m = h * w x = x.view(b, c, m) grad_input = grad_output + grad_output.transpose(1, 2) grad_input = grad_input @ x @ I_hat grad_input = grad_input.reshape(b, c, h, w) return grad_input class Covariance(nn.Module): def __init__(self): super(Covariance, self).__init__() def _covariance(self, x): return CovarianceFunction.apply(x) def forward(self, x): # x should be [batch_size, seq_len, embed_dim] if x.dim() == 2: x = x.transpose(-1, -2) C = self._covariance(x[None, :, :, None]) C = C.squeeze(dim=0) return C class PFSA(torch.nn.Module): """ https://openreview.net/pdf?id=isodM5jTA7h """ def __init__(self, input_dim, alpha=1): super(PFSA, self).__init__() self.input_dim = input_dim self.alpha = alpha def forward_one_sample(self, x): x = x.transpose(1, 2)[..., None] k = torch.mean(x, dim=[-1, -2], keepdim=True) kd = torch.sqrt((k - k.mean(dim=1, keepdim=True)).pow(2).sum(dim=1, keepdim=True)) # [B, 1, 1, 1] qd = torch.sqrt((x - x.mean(dim=1, keepdim=True)).pow(2).sum(dim=1, keepdim=True)) # [B, 1, T, 1] C_qk = (((x - x.mean(dim=1, keepdim=True)) * (k - k.mean(dim=1, keepdim=True))).sum(dim=1, keepdim=True)) / (qd * kd) A = (1 - torch.sigmoid(C_qk)) ** self.alpha out = x * A out = out.squeeze(dim=-1).transpose(1, 2) return out def forward(self, input_values, attention_mask=None): """ x: [B, T, F] """ out = [] b, t, f = input_values.shape for x, mask in zip(input_values, attention_mask): x = x.view(1, t, f) # x_in = x[:, :sum(mask), :] x_in = x[:, :int(mask.sum().item()), :] x_out = self.forward_one_sample(x_in) x_expanded = torch.zeros_like(x, device=x.device) x_expanded[:, :x_out.shape[-2], :x_out.shape[-1]] = x_out out.append(x_expanded) out = torch.vstack(out) out = out.view(b, t, f) return out class PURE(torch.nn.Module): def __init__( self, in_dim, svd_rank=16, num_pc_to_remove=1, center=False, num_iters=2, alpha=1, disable_pcr=False, disable_pfsa=False, disable_covariance=True, *args, **kwargs ): super().__init__() self.in_dim = in_dim self.svd_rank = svd_rank self.num_pc_to_remove = num_pc_to_remove self.center = center self.num_iters = num_iters self.do_pcr = not disable_pcr self.do_pfsa = not disable_pfsa self.do_covariance = not disable_covariance self.attention = PFSA(in_dim, alpha=alpha) def _compute_pc(self, X, attention_mask): """ x: (B, T, F) """ pcs = [] bs, seqlen, dim = X.shape for x, mask in zip(X, attention_mask): rank = int(mask.sum().item()) x = x[:rank, :] if self.do_covariance: x = Covariance()(x) q = self.svd_rank else: q = min(self.svd_rank, rank) _, _, V = torch.pca_lowrank(x, q=q, center=self.center, niter=self.num_iters) # _, _, Vh = torch.linalg.svd(x_, full_matrices=False) # V = Vh.mH pc = V.transpose(0, 1)[:self.num_pc_to_remove, :] # pc: [K, F] pcs.append(pc) # pcs = torch.vstack(pcs) # pcs = pcs.view(bs, self.num_pc_to_remove, dim) return pcs def _remove_pc(self, X, pcs): """ [B, T, F], [B, ..., F] """ b, t, f = X.shape out = [] for i, (x, pc) in enumerate(zip(X, pcs)): # v = [] # for j, t in enumerate(x): # t_ = t # for c_ in c: # t_ = t_.view(f, 1) - c_.view(f, 1) @ c_.view(1, f) @ t.view(f, 1) # v.append(t_.transpose(-1, -2)) # v = torch.vstack(v) v = x - x @ pc.transpose(0, 1) @ pc out.append(v[None, ...]) out = torch.vstack(out) return out def forward(self, input_values, attention_mask=None, *args, **kwargs): """ PCR -> Attention x: (B, T, F) """ x = input_values if self.do_pcr: pc = self._compute_pc(x, attention_mask) # pc: [B, K, F] xx = self._remove_pc(x, pc) # xx = xt - xt @ pc.transpose(1, 2) @ pc # [B, T, F] * [B, F, K] * [B, K, F] = [B, T, F] else: xx = x if self.do_pfsa: xx = self.attention(xx, attention_mask) return xx class StatisticsPooling(torch.nn.Module): def __init__(self, return_mean=True, return_std=True): super().__init__() # Small value for GaussNoise self.eps = 1e-5 self.return_mean = return_mean self.return_std = return_std if not (self.return_mean or self.return_std): raise ValueError( "both of statistics are equal to False \n" "consider enabling mean and/or std statistic pooling" ) def forward(self, input_values, attention_mask=None): """Calculates mean and std for a batch (input tensor). Arguments --------- x : torch.Tensor It represents a tensor for a mini-batch. """ x = input_values if attention_mask is None: if self.return_mean: mean = x.mean(dim=1) if self.return_std: std = x.std(dim=1) else: mean = [] std = [] for snt_id in range(x.shape[0]): # Avoiding padded time steps lengths = torch.sum(attention_mask, dim=1) relative_lengths = lengths / torch.max(lengths) actual_size = torch.round(relative_lengths[snt_id] * x.shape[1]).int() # actual_size = int(torch.round(lengths[snt_id] * x.shape[1])) # computing statistics if self.return_mean: mean.append( torch.mean(x[snt_id, 0:actual_size, ...], dim=0) ) if self.return_std: std.append(torch.std(x[snt_id, 0:actual_size, ...], dim=0)) if self.return_mean: mean = torch.stack(mean) if self.return_std: std = torch.stack(std) if self.return_mean: gnoise = self._get_gauss_noise(mean.size(), device=mean.device) gnoise = gnoise mean += gnoise if self.return_std: std = std + self.eps # Append mean and std of the batch if self.return_mean and self.return_std: pooled_stats = torch.cat((mean, std), dim=1) pooled_stats = pooled_stats.unsqueeze(1) elif self.return_mean: pooled_stats = mean.unsqueeze(1) elif self.return_std: pooled_stats = std.unsqueeze(1) return pooled_stats def _get_gauss_noise(self, shape_of_tensor, device="cpu"): """Returns a tensor of epsilon Gaussian noise. Arguments --------- shape_of_tensor : tensor It represents the size of tensor for generating Gaussian noise. """ gnoise = torch.randn(shape_of_tensor, device=device) gnoise -= torch.min(gnoise) gnoise /= torch.max(gnoise) gnoise = self.eps * ((1 - 9) * gnoise + 9) return gnoise class PureBertPreTrainedModel(PreTrainedModel): """ An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. """ config_class = PureBertConfig base_model_prefix = "bert" supports_gradient_checkpointing = True _supports_sdpa = True def _init_weights(self, module): """Initialize the weights""" if isinstance(module, nn.Linear): # Slightly different from the TF version which uses truncated_normal for initialization # cf https://github.com/pytorch/pytorch/pull/5617 module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) if module.padding_idx is not None: module.weight.data[module.padding_idx].zero_() elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) class PureBertModel(PureBertPreTrainedModel): """ The model can behave as an encoder (with only self-attention) as well as a decoder, in which case a layer of cross-attention is added between the self-attention layers, following the architecture described in [Attention is all you need](https://arxiv.org/abs/1706.03762) by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser and Illia Polosukhin. To behave as an decoder the model needs to be initialized with the `is_decoder` argument of the configuration set to `True`. To be used in a Seq2Seq model, the model needs to initialized with both `is_decoder` argument and `add_cross_attention` set to `True`; an `encoder_hidden_states` is then expected as an input to the forward pass. """ _no_split_modules = ["BertEmbeddings", "BertLayer"] def __init__(self, config, add_pooling_layer=True): super().__init__(config) self.config = config self.embeddings = BertEmbeddings(config) self.encoder = BertEncoder(config) self.pooler = BertPooler(config) if add_pooling_layer else None self.attn_implementation = config._attn_implementation self.position_embedding_type = config.position_embedding_type # Initialize weights and apply final processing self.post_init() def get_input_embeddings(self): return self.embeddings.word_embeddings def set_input_embeddings(self, value): self.embeddings.word_embeddings = value def _prune_heads(self, heads_to_prune): """ Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel """ for layer, heads in heads_to_prune.items(): self.encoder.layer[layer].attention.prune_heads(heads) def forward( self, 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, ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPoolingAndCrossAttentions]: 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)` or `(batch_size, sequence_length, target_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`). """ 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 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_length = past_key_values[0][0].shape[2] if past_key_values is not None else 0 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) embedding_output = 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, ) if attention_mask is None: attention_mask = torch.ones((batch_size, seq_length + past_key_values_length), device=device) use_sdpa_attention_masks = ( self.attn_implementation == "sdpa" and self.position_embedding_type == "absolute" and head_mask is None and not output_attentions ) # Expand the attention mask if use_sdpa_attention_masks and attention_mask.dim() == 2: # Expand the attention mask for SDPA. # [bsz, seq_len] -> [bsz, 1, seq_len, seq_len] if self.config.is_decoder: extended_attention_mask = _prepare_4d_causal_attention_mask_for_sdpa( attention_mask, input_shape, embedding_output, past_key_values_length, ) else: extended_attention_mask = _prepare_4d_attention_mask_for_sdpa( attention_mask, embedding_output.dtype, tgt_len=seq_length ) else: # 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 = self.get_extended_attention_mask(attention_mask, input_shape) # 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) if use_sdpa_attention_masks and encoder_attention_mask.dim() == 2: # Expand the attention mask for SDPA. # [bsz, seq_len] -> [bsz, 1, seq_len, seq_len] encoder_extended_attention_mask = _prepare_4d_attention_mask_for_sdpa( encoder_attention_mask, embedding_output.dtype, tgt_len=seq_length ) else: 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) encoder_outputs = self.encoder( embedding_output, attention_mask=extended_attention_mask, head_mask=head_mask, encoder_hidden_states=encoder_hidden_states, encoder_attention_mask=encoder_extended_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, ) sequence_output = encoder_outputs[0] pooled_output = self.pooler(sequence_output) if self.pooler is not None else None if not return_dict: return (sequence_output, pooled_output) + encoder_outputs[1:] return BaseModelOutputWithPoolingAndCrossAttentions( last_hidden_state=sequence_output, pooler_output=pooled_output, past_key_values=encoder_outputs.past_key_values, hidden_states=encoder_outputs.hidden_states, attentions=encoder_outputs.attentions, cross_attentions=encoder_outputs.cross_attentions, ) class PureBertForSequenceClassification(PureBertPreTrainedModel): def __init__( self, config, label_smoothing=0.0, ): super().__init__(config) self.label_smoothing = label_smoothing self.num_labels = config.num_labels self.config = config self.bert = PureBertModel(config, add_pooling_layer=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.pure = PURE( in_dim=config.hidden_size, svd_rank=config.svd_rank, num_pc_to_remove=config.num_pc_to_remove, center=config.center, num_iters=config.num_iters, alpha=config.alpha, disable_pcr=config.disable_pcr, disable_pfsa=config.disable_pfsa, disable_covariance=config.disable_covariance ) self.mean = StatisticsPooling(return_mean=True, return_std=False) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def forward_pure_embeddings( self, 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, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: 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 outputs = 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, ) token_embeddings = outputs.last_hidden_state token_embeddings = self.pure(token_embeddings, attention_mask) return ModelOutput( last_hidden_state=token_embeddings, ) def forward( self, 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, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: 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 outputs = 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, ) token_embeddings = outputs.last_hidden_state token_embeddings = self.pure(token_embeddings, attention_mask) pooled_output = self.mean(token_embeddings).squeeze(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 = nn.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 = nn.CrossEntropyLoss(label_smoothing=self.label_smoothing) loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = nn.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, ) class PureBertForMultipleChoice(PureBertPreTrainedModel): def __init__( self, config, label_smoothing=0.0, ): super().__init__(config) self.label_smoothing = label_smoothing self.bert = PureBertModel(config) self.pure = PURE( in_dim=config.hidden_size, svd_rank=config.svd_rank, num_pc_to_remove=config.num_pc_to_remove, center=config.center, num_iters=config.num_iters, alpha=config.alpha, disable_pcr=config.disable_pcr, disable_pfsa=config.disable_pfsa, disable_covariance=config.disable_covariance ) self.mean = StatisticsPooling(return_mean=True, return_std=False) classifier_dropout = ( config.classifier_dropout if config.classifier_dropout is not None else config.hidden_dropout_prob ) self.dropout = nn.Dropout(classifier_dropout) self.classifier = nn.Linear(config.hidden_size, 1) # Initialize weights and apply final processing self.post_init() def forward( self, 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, ) -> Union[Tuple[torch.Tensor], MultipleChoiceModelOutput]: 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 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.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, ) token_embeddings = outputs.last_hidden_state token_embeddings = self.pure(token_embeddings, attention_mask) pooled_output = self.mean(token_embeddings).squeeze(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 = nn.CrossEntropyLoss(label_smoothing=self.label_smoothing) 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, ) class PureBertForQuestionAnswering(PureBertPreTrainedModel): def __init__( self, config, label_smoothing=0.0, ): super().__init__(config) self.num_labels = config.num_labels self.label_smoothing = label_smoothing self.bert = PureBertModel(config, add_pooling_layer=False) self.pure = PURE( in_dim=config.hidden_size, svd_rank=config.svd_rank, num_pc_to_remove=config.num_pc_to_remove, center=config.center, num_iters=config.num_iters, alpha=config.alpha, disable_pcr=config.disable_pcr, disable_pfsa=config.disable_pfsa, disable_covariance=config.disable_covariance ) self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) # Initialize weights and apply final processing self.post_init() def forward( self, 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, ) -> Union[Tuple[torch.Tensor], 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.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, ) token_embeddings = outputs.last_hidden_state sequence_output = self.pure(token_embeddings, attention_mask) 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 = nn.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, )