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Diffstat (limited to 'ldm/modules/encoders/xlmr.py')
| -rw-r--r-- | ldm/modules/encoders/xlmr.py | 137 |
1 files changed, 0 insertions, 137 deletions
diff --git a/ldm/modules/encoders/xlmr.py b/ldm/modules/encoders/xlmr.py deleted file mode 100644 index beab3fdf..00000000 --- a/ldm/modules/encoders/xlmr.py +++ /dev/null @@ -1,137 +0,0 @@ -from transformers import BertPreTrainedModel,BertModel,BertConfig -import torch.nn as nn -import torch -from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig -from transformers import XLMRobertaModel,XLMRobertaTokenizer -from typing import Optional - -class BertSeriesConfig(BertConfig): - def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initializer_range=0.02, layer_norm_eps=1e-12, pad_token_id=0, position_embedding_type="absolute", use_cache=True, classifier_dropout=None,project_dim=512, pooler_fn="average",learn_encoder=False,model_type='bert',**kwargs): - - super().__init__(vocab_size, hidden_size, num_hidden_layers, num_attention_heads, intermediate_size, hidden_act, hidden_dropout_prob, attention_probs_dropout_prob, max_position_embeddings, type_vocab_size, initializer_range, layer_norm_eps, pad_token_id, position_embedding_type, use_cache, classifier_dropout, **kwargs) - self.project_dim = project_dim - self.pooler_fn = pooler_fn - self.learn_encoder = learn_encoder - -class RobertaSeriesConfig(XLMRobertaConfig): - def __init__(self, pad_token_id=1, bos_token_id=0, eos_token_id=2,project_dim=512,pooler_fn='cls',learn_encoder=False, **kwargs): - super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) - self.project_dim = project_dim - self.pooler_fn = pooler_fn - self.learn_encoder = learn_encoder - - -class BertSeriesModelWithTransformation(BertPreTrainedModel): - - _keys_to_ignore_on_load_unexpected = [r"pooler"] - _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] - config_class = BertSeriesConfig - - def __init__(self, config=None, **kargs): - # modify initialization for autoloading - if config is None: - config = XLMRobertaConfig() - config.attention_probs_dropout_prob= 0.1 - config.bos_token_id=0 - config.eos_token_id=2 - config.hidden_act='gelu' - config.hidden_dropout_prob=0.1 - config.hidden_size=1024 - config.initializer_range=0.02 - config.intermediate_size=4096 - config.layer_norm_eps=1e-05 - config.max_position_embeddings=514 - - config.num_attention_heads=16 - config.num_hidden_layers=24 - config.output_past=True - config.pad_token_id=1 - config.position_embedding_type= "absolute" - - config.type_vocab_size= 1 - config.use_cache=True - config.vocab_size= 250002 - config.project_dim = 768 - config.learn_encoder = False - super().__init__(config) - self.roberta = XLMRobertaModel(config) - self.transformation = nn.Linear(config.hidden_size,config.project_dim) - self.pre_LN=nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.tokenizer = XLMRobertaTokenizer.from_pretrained('xlm-roberta-large') - self.pooler = lambda x: x[:,0] - self.post_init() - - def encode(self,c): - device = next(self.parameters()).device - text = self.tokenizer(c, - truncation=True, - max_length=77, - return_length=False, - return_overflowing_tokens=False, - padding="max_length", - return_tensors="pt") - text["input_ids"] = torch.tensor(text["input_ids"]).to(device) - text["attention_mask"] = torch.tensor( - text['attention_mask']).to(device) - features = self(**text) - return features['projection_state'] - - 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, - head_mask: Optional[torch.Tensor] = None, - inputs_embeds: Optional[torch.Tensor] = None, - encoder_hidden_states: Optional[torch.Tensor] = None, - encoder_attention_mask: Optional[torch.Tensor] = None, - output_attentions: Optional[bool] = None, - return_dict: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - ) : - r""" - """ - - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - - outputs = self.roberta( - input_ids=input_ids, - attention_mask=attention_mask, - token_type_ids=token_type_ids, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - encoder_hidden_states=encoder_hidden_states, - encoder_attention_mask=encoder_attention_mask, - output_attentions=output_attentions, - output_hidden_states=True, - return_dict=return_dict, - ) - - # last module outputs - sequence_output = outputs[0] - - - # project every module - sequence_output_ln = self.pre_LN(sequence_output) - - # pooler - pooler_output = self.pooler(sequence_output_ln) - pooler_output = self.transformation(pooler_output) - projection_state = self.transformation(outputs.last_hidden_state) - - return { - 'pooler_output':pooler_output, - 'last_hidden_state':outputs.last_hidden_state, - 'hidden_states':outputs.hidden_states, - 'attentions':outputs.attentions, - 'projection_state':projection_state, - 'sequence_out': sequence_output - } - - -class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation): - base_model_prefix = 'roberta' - config_class= RobertaSeriesConfig
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