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