update

optrans_modeling.py

@@ -0,0 +1,138 @@
+import numpy as np
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from typing import Optional, Tuple
+import torch.nn.functional as F
+from transformers import BatchEncoding
+from transformers import MPNetTokenizerFast
+
+
+from transformers.models.roformer.modeling_roformer import (
+    RoFormerEmbeddings,
+    RoFormerModel,
+    RoFormerEncoder,
+    RoFormerLayer,
+    RoFormerAttention,
+    RoFormerIntermediate,
+    RoFormerOutput,
+    RoFormerSelfAttention,
+    RoFormerPreTrainedModel
+)
+
+from transformers.models.mpnet.modeling_mpnet import MPNetModel
+
+
+class JRoFormerEmbeddings(RoFormerEmbeddings):
+    """Construct the embeddings from word and token_type embeddings."""
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.word_embeddings = nn.Embedding(
+            config.vocab_size, config.embedding_size, padding_idx=config.pad_token_id
+        )
+        self.token_type_embeddings = self.word_embeddings
+
+
+class JRoFormerSelfAttention(RoFormerSelfAttention):
+    def __init__(self, config):
+        super().__init__(config)
+        self.query = nn.Linear(
+            config.hidden_size, self.all_head_size, bias=config.use_bias
+        )
+        self.key = nn.Linear(
+            config.hidden_size, self.all_head_size, bias=config.use_bias
+        )
+        self.value = nn.Linear(
+            config.hidden_size, self.all_head_size, bias=config.use_bias
+        )
+
+
+class JRoFormerAttention(RoFormerAttention):
+    def __init__(self, config):
+        super().__init__(config)
+        self.self = JRoFormerSelfAttention(config)
+
+
+class JRoFormerLayer(RoFormerLayer):
+    def __init__(self, config):
+        super().__init__(config)
+        self.attention = JRoFormerAttention(config)
+        self.is_decoder = config.is_decoder
+        self.add_cross_attention = config.add_cross_attention
+        if self.add_cross_attention:
+            if not self.is_decoder:
+                raise ValueError(
+                    f"{self} should be used as a decoder model if cross attention is added"
+                )
+            self.crossattention = RoFormerAttention(config)
+        self.intermediate = RoFormerIntermediate(config)
+        self.output = RoFormerOutput(config)
+
+
+class JRoFormerEncoder(RoFormerEncoder):
+    def __init__(self, config):
+        super().__init__(config)
+        self.layer = nn.ModuleList(
+            [JRoFormerLayer(config) for _ in range(config.num_hidden_layers)]
+        )
+
+
+class JRoFormerModel(RoFormerModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        self.embeddings = JRoFormerEmbeddings(config)
+
+        if config.embedding_size != config.hidden_size:
+            self.embeddings_project = nn.Linear(
+                config.embedding_size, config.hidden_size
+            )
+
+        self.encoder = JRoFormerEncoder(config)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+class AsmEncoder(RoFormerPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        self.roformer = JRoFormerModel(config)
+        self.projection = nn.Linear(config.hidden_size, config.bla_dim)
+
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        token_type_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        encoder_hidden_states: Optional[torch.FloatTensor] = None,
+        encoder_attention_mask: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ):
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.roformer(
+            input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_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=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        token_embeddings = outputs[0]
+        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).to(token_embeddings.dtype)
+        asm_embedding = torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+        asm_embedding = self.projection(asm_embedding)
+        asm_embedding = F.normalize(asm_embedding, p=2, dim=1)
+
+        return asm_embedding