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from typing import Optional, Tuple, Union |
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import torch |
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from torch.nn import BCEWithLogitsLoss |
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from transformers import PreTrainedModel, PreTrainedTokenizer |
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from transformers.modeling_outputs import BaseModelOutputWithPast, SequenceClassifierOutputWithPast |
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class MeasurementPredictorMixin(PreTrainedModel): |
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def __init__(self, config): |
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super().__init__(config) |
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self.sensor_token = config.sensor_token |
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self.sensor_token_id = config.sensor_token_id |
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self.n_sensors = config.n_sensors |
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self.sensor_probes = torch.nn.ModuleList([ |
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torch.nn.Linear(config.emb_dim, 1) for _ in range(config.n_sensors) |
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]) |
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self.use_aggregated = config.use_aggregated |
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if config.use_aggregated: |
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self.aggregate_probe = torch.nn.Linear(config.emb_dim, 1) |
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self.sensors_weight = config.sensors_weight |
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self.aggregate_weight = config.aggregate_weight |
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def check_tokenizer(self, tokenizer: PreTrainedTokenizer): |
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sensor_token_id = tokenizer.convert_tokens_to_ids(tokenizer.tokenize(self.sensor_token))[0] |
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assert sensor_token_id == self.sensor_token_id |
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def set_sensor_token(self, sensor_token: str, tokenizer: PreTrainedTokenizer): |
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sensor_token_id = tokenizer.tokenize(sensor_token)[0] |
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self.sensor_token = sensor_token |
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self.sensor_token_id = sensor_token_id |
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def forward( |
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self, |
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input_ids: Optional[torch.LongTensor] = None, |
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past_key_values: Optional[Tuple[Tuple[torch.Tensor]]] = None, |
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attention_mask: Optional[torch.FloatTensor] = None, |
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position_ids: Optional[torch.LongTensor] = None, |
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head_mask: Optional[torch.FloatTensor] = None, |
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inputs_embeds: Optional[torch.FloatTensor] = None, |
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labels: Optional[torch.LongTensor] = None, |
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use_cache: Optional[bool] = None, |
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output_attentions: Optional[bool] = None, |
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output_hidden_states: Optional[bool] = None, |
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return_dict: Optional[bool] = None, |
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) -> Union[Tuple, SequenceClassifierOutputWithPast]: |
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r""" |
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labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): |
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Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set |
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`labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` |
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are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` |
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""" |
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return_dict = return_dict if return_dict is not None else self.config.use_return_dict |
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base_model_output: BaseModelOutputWithPast = self.base_model( |
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input_ids, |
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past_key_values=past_key_values, |
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attention_mask=attention_mask, |
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position_ids=position_ids, |
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head_mask=head_mask, |
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inputs_embeds=inputs_embeds, |
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use_cache=use_cache, |
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output_attentions=output_attentions, |
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output_hidden_states=output_hidden_states, |
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return_dict=return_dict, |
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) |
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tensor_token_mask = torch.where(input_ids == self.sensor_token_id)[1] |
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sensor_embs = base_model_output.last_hidden_state[:, tensor_token_mask, :] |
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sensor_logits = torch.concat([self.sensor_probes[i](sensor_embs[:, i, :]) |
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for i in range(self.n_sensors)], dim=-1) |
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logits = sensor_logits |
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if self.use_aggregated: |
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last_emb = base_model_output.last_hidden_state[:, -1, :] |
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aggregate_logits = self.aggregate_probe(last_emb) |
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logits = torch.concat([logits, aggregate_logits], dim=-1) |
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loss = None |
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if labels is not None: |
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loss_fct = BCEWithLogitsLoss() |
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sensor_loss = loss_fct(sensor_logits, labels[:, :self.n_sensors]) * self.sensors_weight |
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loss = sensor_loss |
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if self.use_aggregated: |
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aggregate_loss = loss_fct(aggregate_logits, labels[:, -1:]) * self.aggregate_weight |
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loss += aggregate_loss |
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if not return_dict: |
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output = (logits, ) + base_model_output[1:] |
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return ((loss,) + output) if loss is not None else output |
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return SequenceClassifierOutputWithPast( |
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loss=loss, |
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logits=logits, |
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past_key_values=base_model_output.past_key_values, |
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hidden_states=base_model_output.hidden_states, |
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attentions=base_model_output.attentions, |
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) |
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