Merge pull request #2 from 920232796/master

fix bugs

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
\ No newline at end of file