import math from typing import List, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from torch.utils import checkpoint from .configuration_norbert import NorbertConfig from transformers.modeling_utils import PreTrainedModel from transformers.activations import gelu_new from transformers.modeling_outputs import ( MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, BaseModelOutput ) from transformers.pytorch_utils import softmax_backward_data class Encoder(nn.Module): def __init__(self, config, activation_checkpointing=False): super().__init__() self.layers = nn.ModuleList([EncoderLayer(config) for _ in range(config.num_hidden_layers)]) for i, layer in enumerate(self.layers): layer.mlp.mlp[1].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i))) layer.mlp.mlp[-2].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i))) self.activation_checkpointing = activation_checkpointing def forward(self, hidden_states, attention_mask, relative_embedding): hidden_states, attention_probs = [hidden_states], [] for layer in self.layers: if self.activation_checkpointing: hidden_state, attention_p = checkpoint.checkpoint(layer, hidden_states[-1], attention_mask, relative_embedding) else: hidden_state, attention_p = layer(hidden_states[-1], attention_mask, relative_embedding) hidden_states.append(hidden_state) attention_probs.append(attention_p) return hidden_states, attention_probs class MaskClassifier(nn.Module): def __init__(self, config, subword_embedding): super().__init__() self.nonlinearity = nn.Sequential( nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False), nn.Linear(config.hidden_size, config.hidden_size), nn.GELU(), nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False), nn.Dropout(config.hidden_dropout_prob), nn.Linear(subword_embedding.size(1), subword_embedding.size(0)) ) self.initialize(config.hidden_size, subword_embedding) def initialize(self, hidden_size, embedding): std = math.sqrt(2.0 / (5.0 * hidden_size)) nn.init.trunc_normal_(self.nonlinearity[1].weight, mean=0.0, std=std, a=-2*std, b=2*std) self.nonlinearity[-1].weight = embedding self.nonlinearity[1].bias.data.zero_() self.nonlinearity[-1].bias.data.zero_() def forward(self, x, masked_lm_labels=None): if masked_lm_labels is not None: x = torch.index_select(x.flatten(0, 1), 0, torch.nonzero(masked_lm_labels.flatten() != -100).squeeze()) x = self.nonlinearity(x) return x class EncoderLayer(nn.Module): def __init__(self, config): super().__init__() self.attention = Attention(config) self.mlp = FeedForward(config) def forward(self, x, padding_mask, relative_embedding): attention_output, attention_probs = self.attention(x, padding_mask, relative_embedding) x = x + attention_output x = x + self.mlp(x) return x, attention_probs class GeGLU(nn.Module): def forward(self, x): x, gate = x.chunk(2, dim=-1) x = x * gelu_new(gate) return x class FeedForward(nn.Module): def __init__(self, config): super().__init__() self.mlp = nn.Sequential( nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False), nn.Linear(config.hidden_size, 2*config.intermediate_size, bias=False), GeGLU(), nn.LayerNorm(config.intermediate_size, eps=config.layer_norm_eps, elementwise_affine=False), nn.Linear(config.intermediate_size, config.hidden_size, bias=False), nn.Dropout(config.hidden_dropout_prob) ) self.initialize(config.hidden_size) def initialize(self, hidden_size): std = math.sqrt(2.0 / (5.0 * hidden_size)) nn.init.trunc_normal_(self.mlp[1].weight, mean=0.0, std=std, a=-2*std, b=2*std) nn.init.trunc_normal_(self.mlp[-2].weight, mean=0.0, std=std, a=-2*std, b=2*std) def forward(self, x): return self.mlp(x) class MaskedSoftmax(torch.autograd.Function): @staticmethod def forward(self, x, mask, dim): self.dim = dim x.masked_fill_(mask, float('-inf')) x = torch.softmax(x, self.dim) x.masked_fill_(mask, 0.0) self.save_for_backward(x) return x @staticmethod def backward(self, grad_output): output, = self.saved_tensors input_grad = softmax_backward_data(self, grad_output, output, self.dim, output) return input_grad, None, None class Attention(nn.Module): def __init__(self, config): super().__init__() self.config = config if config.hidden_size % config.num_attention_heads != 0: raise ValueError(f"The hidden size {config.hidden_size} is not a multiple of the number of attention heads {config.num_attention_heads}") self.hidden_size = config.hidden_size self.num_heads = config.num_attention_heads self.head_size = config.hidden_size // config.num_attention_heads self.in_proj_qk = nn.Linear(config.hidden_size, 2*config.hidden_size, bias=True) self.in_proj_v = nn.Linear(config.hidden_size, config.hidden_size, bias=True) self.out_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=True) self.pre_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False) self.post_layer_norm = nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=True) position_indices = torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(1) \ - torch.arange(config.max_position_embeddings, dtype=torch.long).unsqueeze(0) position_indices = self.make_log_bucket_position(position_indices, config.position_bucket_size, config.max_position_embeddings) position_indices = config.position_bucket_size - 1 + position_indices self.register_buffer("position_indices", position_indices, persistent=True) self.dropout = nn.Dropout(config.attention_probs_dropout_prob) self.scale = 1.0 / math.sqrt(3 * self.head_size) self.initialize() def make_log_bucket_position(self, relative_pos, bucket_size, max_position): sign = torch.sign(relative_pos) mid = bucket_size // 2 abs_pos = torch.where((relative_pos < mid) & (relative_pos > -mid), mid - 1, torch.abs(relative_pos).clamp(max=max_position - 1)) log_pos = torch.ceil(torch.log(abs_pos / mid) / math.log((max_position-1) / mid) * (mid - 1)).int() + mid bucket_pos = torch.where(abs_pos <= mid, relative_pos, log_pos * sign).long() return bucket_pos def initialize(self): std = math.sqrt(2.0 / (5.0 * self.hidden_size)) nn.init.trunc_normal_(self.in_proj_qk.weight, mean=0.0, std=std, a=-2*std, b=2*std) nn.init.trunc_normal_(self.in_proj_v.weight, mean=0.0, std=std, a=-2*std, b=2*std) nn.init.trunc_normal_(self.out_proj.weight, mean=0.0, std=std, a=-2*std, b=2*std) self.in_proj_qk.bias.data.zero_() self.in_proj_v.bias.data.zero_() self.out_proj.bias.data.zero_() def compute_attention_scores(self, hidden_states, relative_embedding): key_len, batch_size, _ = hidden_states.size() query_len = key_len if self.position_indices.size(0) < query_len: position_indices = torch.arange(query_len, dtype=torch.long).unsqueeze(1) \ - torch.arange(query_len, dtype=torch.long).unsqueeze(0) position_indices = self.make_log_bucket_position(position_indices, self.position_bucket_size, 512) position_indices = self.position_bucket_size - 1 + position_indices self.position_indices = position_indices.to(hidden_states.device) hidden_states = self.pre_layer_norm(hidden_states) query, key = self.in_proj_qk(hidden_states).chunk(2, dim=2) # shape: [T, B, D] value = self.in_proj_v(hidden_states) # shape: [T, B, D] query = query.reshape(query_len, batch_size * self.num_heads, self.head_size).transpose(0, 1) key = key.reshape(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1) value = value.view(key_len, batch_size * self.num_heads, self.head_size).transpose(0, 1) attention_scores = torch.bmm(query, key.transpose(1, 2) * self.scale) pos = self.in_proj_qk(self.dropout(relative_embedding)) # shape: [2T-1, 2D] query_pos, key_pos = pos.view(-1, self.num_heads, 2*self.head_size).chunk(2, dim=2) query = query.view(batch_size, self.num_heads, query_len, self.head_size) key = key.view(batch_size, self.num_heads, query_len, self.head_size) attention_c_p = torch.einsum("bhqd,khd->bhqk", query, key_pos.squeeze(1) * self.scale) attention_p_c = torch.einsum("bhkd,qhd->bhqk", key * self.scale, query_pos.squeeze(1)) position_indices = self.position_indices[:query_len, :key_len].expand(batch_size, self.num_heads, -1, -1) attention_c_p = attention_c_p.gather(3, position_indices) attention_p_c = attention_p_c.gather(2, position_indices) attention_scores = attention_scores.view(batch_size, self.num_heads, query_len, key_len) attention_scores.add_(attention_c_p) attention_scores.add_(attention_p_c) return attention_scores, value def compute_output(self, attention_probs, value): attention_probs = self.dropout(attention_probs) context = torch.bmm(attention_probs.flatten(0, 1), value) # shape: [B*H, Q, D] context = context.transpose(0, 1).reshape(context.size(1), -1, self.hidden_size) # shape: [Q, B, H*D] context = self.out_proj(context) context = self.post_layer_norm(context) context = self.dropout(context) return context def forward(self, hidden_states, attention_mask, relative_embedding): attention_scores, value = self.compute_attention_scores(hidden_states, relative_embedding) attention_probs = MaskedSoftmax.apply(attention_scores, attention_mask, -1) return self.compute_output(attention_probs, value), attention_probs.detach() class Embedding(nn.Module): def __init__(self, config): super().__init__() self.hidden_size = config.hidden_size self.word_embedding = nn.Embedding(config.vocab_size, config.hidden_size) self.word_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps, elementwise_affine=False) self.dropout = nn.Dropout(config.hidden_dropout_prob) self.relative_embedding = nn.Parameter(torch.empty(2 * config.position_bucket_size - 1, config.hidden_size)) self.relative_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) self.initialize() def initialize(self): std = math.sqrt(2.0 / (5.0 * self.hidden_size)) nn.init.trunc_normal_(self.relative_embedding, mean=0.0, std=std, a=-2*std, b=2*std) nn.init.trunc_normal_(self.word_embedding.weight, mean=0.0, std=std, a=-2*std, b=2*std) def forward(self, input_ids): word_embedding = self.dropout(self.word_layer_norm(self.word_embedding(input_ids))) relative_embeddings = self.relative_layer_norm(self.relative_embedding) return word_embedding, relative_embeddings # # HuggingFace wrappers # class NorbertPreTrainedModel(PreTrainedModel): config_class = NorbertConfig base_model_prefix = "norbert3" supports_gradient_checkpointing = True def _set_gradient_checkpointing(self, module, value=False): if isinstance(module, Encoder): module.activation_checkpointing = value def _init_weights(self, module): pass # everything is already initialized class NorbertModel(NorbertPreTrainedModel): def __init__(self, config, add_mlm_layer=False): super().__init__(config) self.config = config self.embedding = Embedding(config) self.transformer = Encoder(config, activation_checkpointing=False) self.classifier = MaskClassifier(config, self.embedding.word_embedding.weight) if add_mlm_layer else None def get_input_embeddings(self): return self.embedding.word_embedding def set_input_embeddings(self, value): self.embedding.word_embedding = value def get_contextualized_embeddings( self, input_ids: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None ) -> List[torch.Tensor]: if input_ids is not None: input_shape = input_ids.size() else: raise ValueError("You have to specify input_ids") batch_size, seq_length = input_shape device = input_ids.device if attention_mask is None: attention_mask = torch.zeros(batch_size, seq_length, dtype=torch.bool, device=device) else: attention_mask = ~attention_mask.bool() attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) static_embeddings, relative_embedding = self.embedding(input_ids.t()) contextualized_embeddings, attention_probs = self.transformer(static_embeddings, attention_mask, relative_embedding) contextualized_embeddings = [e.transpose(0, 1) for e in contextualized_embeddings] last_layer = contextualized_embeddings[-1] contextualized_embeddings = [contextualized_embeddings[0]] + [ contextualized_embeddings[i] - contextualized_embeddings[i - 1] for i in range(1, len(contextualized_embeddings)) ] return last_layer, contextualized_embeddings, attention_probs 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, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple[torch.Tensor], BaseModelOutput]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask) if not return_dict: return ( sequence_output, *([contextualized_embeddings] if output_hidden_states else []), *([attention_probs] if output_attentions else []) ) return BaseModelOutput( last_hidden_state=sequence_output, hidden_states=contextualized_embeddings if output_hidden_states else None, attentions=attention_probs if output_attentions else None ) class NorbertForMaskedLM(NorbertModel): _keys_to_ignore_on_load_unexpected = ["head"] def __init__(self, config): super().__init__(config, add_mlm_layer=True) def get_output_embeddings(self): return self.classifier.nonlinearity[-1].weight def set_output_embeddings(self, new_embeddings): self.classifier.nonlinearity[-1].weight = new_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, output_hidden_states: Optional[bool] = None, output_attentions: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor], MaskedLMOutput]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask) subword_prediction = self.classifier(sequence_output) subword_prediction[:, :, :106+1] = float("-inf") masked_lm_loss = None if labels is not None: masked_lm_loss = F.cross_entropy(subword_prediction.flatten(0, 1), labels.flatten()) if not return_dict: output = ( subword_prediction, *([contextualized_embeddings] if output_hidden_states else []), *([attention_probs] if output_attentions else []) ) return ((masked_lm_loss,) + output) if masked_lm_loss is not None else output return MaskedLMOutput( loss=masked_lm_loss, logits=subword_prediction, hidden_states=contextualized_embeddings if output_hidden_states else None, attentions=attention_probs if output_attentions else None ) class Classifier(nn.Module): def __init__(self, config, num_labels: int): super().__init__() drop_out = getattr(config, "cls_dropout", None) drop_out = config.hidden_dropout_prob if drop_out is None else drop_out self.nonlinearity = nn.Sequential( nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False), nn.Linear(config.hidden_size, config.hidden_size), nn.GELU(), nn.LayerNorm(config.hidden_size, config.layer_norm_eps, elementwise_affine=False), nn.Dropout(drop_out), nn.Linear(config.hidden_size, num_labels) ) self.initialize(config.hidden_size) def initialize(self, hidden_size): std = math.sqrt(2.0 / (5.0 * hidden_size)) nn.init.trunc_normal_(self.nonlinearity[1].weight, mean=0.0, std=std, a=-2*std, b=2*std) nn.init.trunc_normal_(self.nonlinearity[-1].weight, mean=0.0, std=std, a=-2*std, b=2*std) self.nonlinearity[1].bias.data.zero_() self.nonlinearity[-1].bias.data.zero_() def forward(self, x): x = self.nonlinearity(x) return x class NorbertForSequenceClassification(NorbertModel): _keys_to_ignore_on_load_unexpected = ["classifier"] _keys_to_ignore_on_load_missing = ["head"] def __init__(self, config): super().__init__(config, add_mlm_layer=False) self.num_labels = config.num_labels self.head = Classifier(config, self.num_labels) 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, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutput]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask) logits = self.head(sequence_output[:, 0, :]) 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() 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, *([contextualized_embeddings] if output_hidden_states else []), *([attention_probs] if output_attentions else []) ) return ((loss,) + output) if loss is not None else output return SequenceClassifierOutput( loss=loss, logits=logits, hidden_states=contextualized_embeddings if output_hidden_states else None, attentions=attention_probs if output_attentions else None ) class NorbertForTokenClassification(NorbertModel): _keys_to_ignore_on_load_unexpected = ["classifier"] _keys_to_ignore_on_load_missing = ["head"] def __init__(self, config): super().__init__(config, add_mlm_layer=False) self.num_labels = config.num_labels self.head = Classifier(config, self.num_labels) 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, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, labels: Optional[torch.LongTensor] = None, ) -> Union[Tuple[torch.Tensor], TokenClassifierOutput]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask) logits = self.head(sequence_output) loss = None if labels is not None: loss_fct = nn.CrossEntropyLoss() loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) if not return_dict: output = ( logits, *([contextualized_embeddings] if output_hidden_states else []), *([attention_probs] if output_attentions else []) ) return ((loss,) + output) if loss is not None else output return TokenClassifierOutput( loss=loss, logits=logits, hidden_states=contextualized_embeddings if output_hidden_states else None, attentions=attention_probs if output_attentions else None ) class NorbertForQuestionAnswering(NorbertModel): _keys_to_ignore_on_load_unexpected = ["classifier"] _keys_to_ignore_on_load_missing = ["head"] def __init__(self, config): super().__init__(config, add_mlm_layer=False) self.num_labels = config.num_labels self.head = Classifier(config, self.num_labels) 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, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, start_positions: Optional[torch.Tensor] = None, end_positions: Optional[torch.Tensor] = None ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask) logits = self.head(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, *([contextualized_embeddings] if output_hidden_states else []), *([attention_probs] if output_attentions else []) ) 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=contextualized_embeddings if output_hidden_states else None, attentions=attention_probs if output_attentions else None ) class NorbertForMultipleChoice(NorbertModel): _keys_to_ignore_on_load_unexpected = ["classifier"] _keys_to_ignore_on_load_missing = ["head"] def __init__(self, config): super().__init__(config, add_mlm_layer=False) self.num_labels = getattr(config, "num_labels", 2) self.head = Classifier(config, self.num_labels) 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, 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]: return_dict = return_dict if return_dict is not None else self.config.use_return_dict num_choices = input_ids.shape[1] flat_input_ids = input_ids.view(-1, input_ids.size(-1)) flat_attention_mask = attention_mask.view(-1, attention_mask.size(-1)) if attention_mask is not None else None sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(flat_input_ids, flat_attention_mask) logits = self.head(sequence_output) reshaped_logits = logits.view(-1, num_choices) loss = None if labels is not None: loss_fct = nn.CrossEntropyLoss() loss = loss_fct(reshaped_logits, labels) if not return_dict: output = ( reshaped_logits, *([contextualized_embeddings] if output_hidden_states else []), *([attention_probs] if output_attentions else []) ) return ((loss,) + output) if loss is not None else output return MultipleChoiceModelOutput( loss=loss, logits=reshaped_logits, hidden_states=contextualized_embeddings if output_hidden_states else None, attentions=attention_probs if output_attentions else None )