add modeling

clap_modeling.py

@@ -0,0 +1,229 @@
+# MIT License
+
+# Copyright (c) 2024 Hustcw
+
+# Permission is hereby granted, free of charge, to any person obtaining a copy
+# of this software and associated documentation files (the "Software"), to deal
+# in the Software without restriction, including without limitation the rights
+# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+# copies of the Software, and to permit persons to whom the Software is
+# furnished to do so, subject to the following conditions:
+
+# The above copyright notice and this permission notice shall be included in all
+# copies or substantial portions of the Software.
+
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+# SOFTWARE.
+
+import torch
+import torch.utils.checkpoint
+from torch import nn
+from typing import Optional
+import torch.nn.functional as F
+
+from transformers.models.roformer.modeling_roformer import (
+    RoFormerEmbeddings,
+    RoFormerModel,
+    RoFormerEncoder,
+    RoFormerLayer,
+    RoFormerAttention,
+    RoFormerIntermediate,
+    RoFormerOutput,
+    RoFormerSelfAttention,
+    RoFormerPreTrainedModel
+)
+
+from transformers.models.mpnet.modeling_mpnet import MPNetModel
+
+from transformers import MPNetTokenizerFast, BatchEncoding
+
+class AsmTokenizer(MPNetTokenizerFast):
+
+    @property
+    def pad_token_type_id(self) -> int:
+        """
+        `int`: Id of the padding token type in the vocabulary.
+        """
+        return self.pad_token_id
+
+    def tokenize_function(self, function):
+        total_len = 0
+        tokenized_functions = {"token": [], "instr": []}
+        for key, value in function.items():
+            tokens = self.tokenize(value.replace(',', ''), max_length=20, truncation=True, add_special_tokens=False) # set max token for a instruction
+            instr_index = "INSTR" + key
+            instructions = [instr_index] * len(tokens)
+            tokenized_functions["token"].extend(tokens)
+            tokenized_functions["instr"].extend(instructions)
+            total_len += len(tokens)
+            if total_len > self.model_max_length:
+                tokenized_functions['token'] = tokenized_functions['token'][:self.model_max_length]
+                tokenized_functions['instr'] = tokenized_functions['instr'][:self.model_max_length]
+                break
+        return tokenized_functions
+
+    def encode_function(self, function):
+        tokenized_functions = self.tokenize_function(function)
+        token_ids = self.convert_tokens_to_ids(tokenized_functions["token"])
+        instr_ids = self.convert_tokens_to_ids(tokenized_functions["instr"])
+        return BatchEncoding({
+            "input_ids": token_ids,
+            "attention_mask": [1] * len(token_ids),
+            "token_type_ids": instr_ids,
+        })
+
+    @property
+    def vocab_size(self) -> int:
+        return len(self.vocab)
+
+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.jroformer = JRoFormerModel(config)
+        self.projection = nn.Linear(config.hidden_size, config.hidden_size)
+
+    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.jroformer(
+            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
+    
+class TextEncoder(MPNetModel):
+    def __init__(self, config, add_pooling_layer=True):
+        super().__init__(config, add_pooling_layer=add_pooling_layer)
+    
+    def forward(
+        self,
+        input_ids: Optional[torch.LongTensor] = None,
+        attention_mask: Optional[torch.FloatTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        head_mask: Optional[torch.FloatTensor] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        **kwargs,
+    ):
+        output = super().forward(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            position_ids=position_ids,
+            head_mask=head_mask,
+            inputs_embeds=inputs_embeds,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            **kwargs,
+        )
+        token_embeddings = output[0]
+        input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+        text_embedding = torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+        text_embedding = F.normalize(text_embedding, p=2, dim=1)
+        return text_embedding

config.json

@@ -1,8 +1,10 @@
 {
-  "_name_or_path": "./models/text-encoder",
   "architectures": [
     "TextEncoder"
   ],
+  "auto_map": {
+    "AutoModel": "clap_modeling.TextEncoder"
+  },
   "attention_probs_dropout_prob": 0.1,
   "bos_token_id": 0,
   "eos_token_id": 2,