import logging from typing import Optional, Tuple, Union import torch from torch import nn from torch.nn import MSELoss, CrossEntropyLoss, BCEWithLogitsLoss from transformers import GPT2Model, GPT2ForSequenceClassification from transformers.modeling_outputs import SequenceClassifierOutputWithPast logger = logging.getLogger(__name__) # Simple version # class GPT2ClassificationHeadCustomSimple(nn.Module): def __init__(self, config): super().__init__() hidden_size = config.n_embd self.dense_1 = nn.Linear(hidden_size, 2 * hidden_size) self.dense_2 = nn.Linear(2 * hidden_size, hidden_size) self.dropout = nn.Dropout(config.resid_pdrop) self.out_proj = nn.Linear(hidden_size, config.num_labels, bias=False) def forward(self, x): x = self.dense_1(x) x = torch.relu(x) x = self.dropout(x) x = self.dense_2(x) x = torch.relu(x) x = self.dropout(x) x = self.out_proj(x) return x class GPT2ForSequenceClassificationCustomSimple(GPT2ForSequenceClassification): _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head.weight"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPT2Model(config) self.score = GPT2ClassificationHeadCustomSimple(config) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() # Version with custom forward 1 # class GPT2ClassificationHeadCustom(nn.Module): def __init__(self, config): super().__init__() hidden_size = config.n_embd self.dense_1_input = nn.Linear(hidden_size, 2 * hidden_size) self.dense_1_hidden = nn.Linear(hidden_size, 2 * hidden_size) self.dense_2 = nn.Linear(4 * hidden_size, hidden_size) self.dropout = nn.Dropout(config.resid_pdrop) self.out_proj = nn.Linear(hidden_size, config.num_labels, bias=False) def forward(self, x, **kwargs): if 'hidden_states' in kwargs and kwargs['hidden_states'] is not None: # Get hidden states from second from the end hidden = kwargs['hidden_states'][-2] else: hidden = torch.zeros(x.size(), dtype=x.dtype, device=x.device) x = self.dense_1_input(x) x = torch.relu(x) x = self.dropout(x) hidden = self.dense_1_hidden(hidden) hidden = torch.relu(hidden) hidden = self.dropout(hidden) x = torch.cat((x, hidden), dim=2) x = self.dense_2(x) x = torch.relu(x) x = self.dropout(x) x = torch.relu(x) x = self.dropout(x) x = torch.relu(x) x = self.dropout(x) x = self.out_proj(x) return x class GPT2ForSequenceClassificationCustom(GPT2ForSequenceClassification): _keys_to_ignore_on_load_missing = [r"h\.\d+\.attn\.masked_bias", r"lm_head.weight"] def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels self.transformer = GPT2Model(config) self.score = GPT2ClassificationHeadCustom(config) # Model parallel self.model_parallel = False self.device_map = None # Initialize weights and apply final processing self.post_init() def forward( self, input_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, attention_mask: Optional[torch.FloatTensor] = None, token_type_ids: Optional[torch.LongTensor] = None, position_ids: Optional[torch.LongTensor] = None, head_mask: Optional[torch.FloatTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, ) -> Union[Tuple, SequenceClassifierOutputWithPast]: 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 transformer_outputs = self.transformer( input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states or self.config.use_hidden_states, return_dict=return_dict, ) hidden_states = transformer_outputs[0] logits = self.score(hidden_states, hidden_states=transformer_outputs.hidden_states) if input_ids is not None: batch_size, sequence_length = input_ids.shape[:2] else: batch_size, sequence_length = inputs_embeds.shape[:2] assert ( self.config.pad_token_id is not None or batch_size == 1 ), "Cannot handle batch sizes > 1 if no padding token is defined." if self.config.pad_token_id is None: sequence_lengths = -1 else: if input_ids is not None: sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1 else: sequence_lengths = -1 logger.warning( f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " "unexpected if using padding tokens in conjunction with `inputs_embeds.`" ) pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] 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(pooled_logits.squeeze(), labels.squeeze()) else: loss = loss_fct(pooled_logits, labels) elif self.config.problem_type == "single_label_classification": loss_fct = CrossEntropyLoss() loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) elif self.config.problem_type == "multi_label_classification": loss_fct = BCEWithLogitsLoss() loss = loss_fct(pooled_logits, labels) if not return_dict: output = (pooled_logits,) + transformer_outputs[1:] return ((loss,) + output) if loss is not None else output return SequenceClassifierOutputWithPast( loss=loss, logits=pooled_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions, )