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| | from __future__ import absolute_import |
| | from __future__ import division |
| | from __future__ import print_function |
| |
|
| | import torch |
| | from torch.nn import CrossEntropyLoss |
| | import math |
| |
|
| | from ...deberta import * |
| | from ...utils import * |
| |
|
| | __all__ = ['MultiChoiceModel'] |
| | class MultiChoiceModel(NNModule): |
| | def __init__(self, config, num_labels = 2, drop_out=None, **kwargs): |
| | super().__init__(config) |
| | self.num_labels = num_labels |
| | self._register_load_state_dict_pre_hook(self._pre_load_hook) |
| | self.deberta = DeBERTa(config) |
| | self.config = config |
| | pool_config = PoolConfig(self.config) |
| | output_dim = self.deberta.config.hidden_size |
| | self.pooler = ContextPooler(pool_config) |
| | output_dim = self.pooler.output_dim() |
| | drop_out = config.hidden_dropout_prob if drop_out is None else drop_out |
| | self.classifier = torch.nn.Linear(output_dim, 1) |
| | self.dropout = StableDropout(drop_out) |
| | self.apply(self.init_weights) |
| | self.deberta.apply_state() |
| |
|
| | def forward(self, input_ids, type_ids=None, input_mask=None, labels=None, position_ids=None, **kwargs): |
| | num_opts = input_ids.size(1) |
| | input_ids = input_ids.view([-1, input_ids.size(-1)]) |
| | if type_ids is not None: |
| | type_ids = type_ids.view([-1, type_ids.size(-1)]) |
| | if position_ids is not None: |
| | position_ids = position_ids.view([-1, position_ids.size(-1)]) |
| | if input_mask is not None: |
| | input_mask = input_mask.view([-1, input_mask.size(-1)]) |
| | outputs = self.deberta(input_ids, token_type_ids=type_ids, attention_mask=input_mask, |
| | position_ids=position_ids, output_all_encoded_layers=True) |
| | hidden_states = outputs['hidden_states'][-1] |
| | logits = self.classifier(self.dropout(self.pooler(hidden_states))) |
| | logits = logits.float().squeeze(-1) |
| | logits = logits.view([-1, num_opts]) |
| | loss = 0 |
| | if labels is not None: |
| | labels = labels.long() |
| | loss_fn = CrossEntropyLoss() |
| | loss = loss_fn(logits, labels) |
| |
|
| | return { |
| | 'logits' : logits, |
| | 'loss' : loss |
| | } |
| |
|
| | def _pre_load_hook(self, state_dict, prefix, local_metadata, strict, |
| | missing_keys, unexpected_keys, error_msgs): |
| | new_state = dict() |
| | bert_prefix = prefix + 'bert.' |
| | deberta_prefix = prefix + 'deberta.' |
| | for k in list(state_dict.keys()): |
| | if k.startswith(bert_prefix): |
| | nk = deberta_prefix + k[len(bert_prefix):] |
| | value = state_dict[k] |
| | del state_dict[k] |
| | state_dict[nk] = value |
| |
|
