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_ltgbert import LtgbertConfig from transformers.modeling_utils import PreTrainedModel from transformers.activations import gelu_new from transformers.modeling_outputs import ( MaskedLMOutput, MultipleChoiceModelOutput, QuestionAnsweringModelOutput, SequenceClassifierOutput, TokenClassifierOutput, BaseModelOutput, CausalLMOutput ) from transformers.pytorch_utils import softmax_backward_data class InPlaceSetSlice(torch.autograd.Function): @staticmethod def forward(ctx, full_tensor, last_slice, x_idx, x_val): full_tensor[x_idx] = x_val ctx.x_idx = x_idx ret = torch.Tensor().to(full_tensor.device) ret.set_(full_tensor[:x_idx + 1]) return ret @staticmethod def backward(ctx, grad_out): if ctx.x_idx == 0: return None, None, None, grad_out[ctx.x_idx] else: return None, grad_out[:ctx.x_idx], None, grad_out[ctx.x_idx] def apply_inplace_set(x_acc, x_idx, x_val): full_tensor, last_slice = x_acc new_slice = InPlaceSetSlice.apply(full_tensor, last_slice, x_idx, x_val) return full_tensor, new_slice class DWAModules(torch.nn.Module): def __init__(self, hidden_size, n_blocks): super().__init__() self.n_blocks = n_blocks self.alphas = nn.ParameterList([nn.Parameter(torch.zeros(i + 2)) for i in range(n_blocks)]) self.accumulator = None self._init_weights() def _init_weights(self): for module in self.alphas: module.data.zero_() module.data[-1] = 1.0 def init_accumulator(self, x): self.accumulator = (torch.zeros((self.n_blocks + 1, *x.shape), device=x.device, dtype=x.dtype), None) self.accumulator = apply_inplace_set(self.accumulator, 0, x) def forward(self, x, block_idx): assert self.accumulator is not None, "`init_accumulator(x)` needs to be called first" self.accumulator = apply_inplace_set( self.accumulator, block_idx + 1, x ) x = torch.tensordot(self.alphas[block_idx], self.accumulator[1], dims=1) return x class Encoder(nn.Module): def __init__(self, config): super().__init__() self.attention_layers = nn.ModuleList([Attention(config) for _ in range(config.num_hidden_layers)]) self.mlp_layers = nn.ModuleList([FeedForward(config) for _ in range(config.num_hidden_layers)]) self.dwa_modules = DWAModules(config.hidden_size, config.num_hidden_layers * 2) for i, layer in enumerate(self.mlp_layers): layer.mlp[1].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i))) layer.mlp[-2].weight.data *= math.sqrt(1.0 / (2.0 * (1 + i))) def forward(self, x, attention_mask, relative_embedding): hidden_states, attention_probs = [x], [] self.dwa_modules.init_accumulator(x) for i, (attention_layer, mlp_layer) in enumerate(zip(self.attention_layers, self.mlp_layers)): attention_output, attention_p = attention_layer(x, attention_mask, relative_embedding) x = x + attention_output x = self.dwa_modules(x, block_idx=i * 2) x = x + mlp_layer(x) x = self.dwa_modules(x, block_idx=i * 2 + 1) hidden_states.append(x) 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)) ) 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) ) 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_vg = nn.Linear(config.hidden_size, 2*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=False) 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) 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 forward(self, hidden_states, attention_mask, 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.config.position_bucket_size, 512) position_indices = self.config.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, gate = self.in_proj_vg(hidden_states).chunk(2, dim=2) # shape: [T, B, D] gate = F.gelu(gate) 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) query_pos, key_pos = self.in_proj_qk(self.dropout(relative_embedding)).chunk(2, dim=-1) # shape: [2T-1, D] query_pos = query_pos.view(-1, self.num_heads, self.head_size) # shape: [2T-1, H, D] key_pos = key_pos.view(-1, self.num_heads, self.head_size) # shape: [2T-1, H, D] 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) attention_probs = MaskedSoftmax.apply(attention_scores, attention_mask, -1) 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 = context * gate context = self.post_layer_norm(context) context = self.out_proj(context) context = self.dropout(context) return context, 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) 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 LtgbertPreTrainedModel(PreTrainedModel): config_class = LtgbertConfig supports_gradient_checkpointing = False def _set_gradient_checkpointing(self, module, value=False): raise NotImplementedError("Gradient checkpointing is not supported by this model") def _init_weights(self, module): std = math.sqrt(2.0 / (5.0 * self.hidden_size)) if isinstance(module, nn.Linear): nn.init.trunc_normal_(module.weight.data, mean=0.0, std=std, a=-2*std, b=2*std) if module.bias is not None: module.bias.data.zero_() elif isinstance(module, nn.Embedding): nn.init.trunc_normal_(module.weight.data, mean=0.0, std=std, a=-2*std, b=2*std) elif isinstance(module, nn.LayerNorm): module.bias.data.zero_() module.weight.data.fill_(1.0) class LtgbertModel(LtgbertPreTrainedModel): def __init__(self, config, add_mlm_layer=False, **kwargs): super().__init__(config, **kwargs) self.config = config self.hidden_size = config.hidden_size self.embedding = Embedding(config) self.transformer = Encoder(config) 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() if self.config.is_decoder: attention_mask = attention_mask.unsqueeze(1).unsqueeze(2) | torch.triu(torch.ones(seq_length, seq_length, dtype=torch.bool, device=device), 1).unsqueeze(0).unsqueeze(0) else: 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, **kwargs ) -> 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 LtgbertForMaskedLM(LtgbertModel): _keys_to_ignore_on_load_unexpected = ["head"] def __init__(self, config, **kwargs): super().__init__(config, add_mlm_layer=True, **kwargs) 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, **kwargs ) -> 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[:, :, :16+1] = float("-inf") masked_lm_loss = None if labels is not None: labels_flatten = labels[:, 1:].flatten() subword_prediction_flatten = subword_prediction[:, :-1].flatten(0, 1) masked_lm_loss = F.cross_entropy(subword_prediction_flatten, 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__() self.temperature = config.temperature 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) ) def forward(self, x): x = self.nonlinearity(x) / self.temperature return x class LtgbertForCausalLM(LtgbertModel): _keys_to_ignore_on_load_unexpected = ["head"] def __init__(self, config, **kwargs): config.is_decoder = True super().__init__(config, add_mlm_layer=True, **kwargs) 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 get_input_embeddings(self): return self.embedding.word_embedding def set_input_embeddings(self, value): self.embedding.word_embedding = value def set_decoder(self, decoder): self.transformer = decoder def get_decoder(self): return self.transformer def can_generate(self): return True def forward( self, input_ids: torch.LongTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, token_type_ids: Optional[torch.Tensor] = None, past_key_values = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, cache_position: Optional[torch.LongTensor] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None ) -> Union[Tuple, CausalLMOutput]: assert inputs_embeds is None, "inputs_embeds is not supported for now" assert past_key_values is None, "past_key_values is not supported for now" assert not use_cache, "use_cache is not supported for now" # assert cache_position is None, "cache_position is not supported for now" sequence_output, contextualized_embeddings, attention_probs = self.get_contextualized_embeddings(input_ids, attention_mask) subword_prediction = self.classifier(sequence_output) # subword_prediction[:, :, :16+1] = float("-inf") masked_lm_loss = None if labels is not None: labels_flatten = labels[:, 1:].flatten() subword_prediction_flatten = subword_prediction[:, :-1].flatten(0, 1) masked_lm_loss = F.cross_entropy(subword_prediction_flatten, 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 CausalLMOutput( 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 ) def prepare_inputs_for_generation( self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, position_ids=None, use_cache=True, num_logits_to_keep=None, **kwargs, ): # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens # Exception 1: when passing input_embeds, input_ids may be missing entries # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here if past_key_values is not None: if inputs_embeds is not None: # Exception 1 input_ids = input_ids[:, -cache_position.shape[0] :] elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2) input_ids = input_ids[:, cache_position] if attention_mask is not None and position_ids is None: # create position_ids on the fly for batch generation position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: position_ids = position_ids[:, -input_ids.shape[1] :] # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture. position_ids = position_ids.clone(memory_format=torch.contiguous_format) # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and cache_position[0] == 0: model_inputs = {"inputs_embeds": inputs_embeds} else: model_inputs = {"input_ids": input_ids.contiguous()} # `contiguous()` needed for compilation use cases if num_logits_to_keep is not None: model_inputs["num_logits_to_keep"] = num_logits_to_keep model_inputs.update( { "position_ids": position_ids, "cache_position": cache_position, "past_key_values": past_key_values, "use_cache": use_cache, "attention_mask": attention_mask, } ) return model_inputs class LtgbertForSequenceClassification(LtgbertModel): _keys_to_ignore_on_load_unexpected = ["classifier"] _keys_to_ignore_on_load_missing = ["head"] def __init__(self, config, **kwargs): super().__init__(config, add_mlm_layer=False, **kwargs) 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, **kwargs ) -> 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 LtgbertForTokenClassification(LtgbertModel): _keys_to_ignore_on_load_unexpected = ["classifier"] _keys_to_ignore_on_load_missing = ["head"] def __init__(self, config, **kwargs): super().__init__(config, add_mlm_layer=False, **kwargs) 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, **kwargs ) -> 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 LtgbertForQuestionAnswering(LtgbertModel): _keys_to_ignore_on_load_unexpected = ["classifier"] _keys_to_ignore_on_load_missing = ["head"] def __init__(self, config, **kwargs): super().__init__(config, add_mlm_layer=False, **kwargs) 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, **kwargs ) -> 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 LtgbertForMultipleChoice(LtgbertModel): _keys_to_ignore_on_load_unexpected = ["classifier"] _keys_to_ignore_on_load_missing = ["head"] def __init__(self, config, **kwargs): super().__init__(config, add_mlm_layer=False, **kwargs) 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, **kwargs ) -> 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 )