Create custom_st.py

custom_st.py

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+from typing import List, Dict, Tuple, Union, Any, Optional
+
+import os
+import json
+import torch
+
+from torch import nn
+from transformers import AutoConfig, AutoModel, AutoTokenizer
+from transformers.utils import is_flash_attn_2_available
+
+
+INSTRUCTION_CONFIG = {
+    "nl2code": {
+        "query": "Find the most relevant code snippet given the following query:\n",
+        "passage": "Candidate code snippet:\n"
+    },
+    "qa": {
+        "query": "Find the most relevant answer given the following question:\n",
+        "passage": "Candidate answer:\n"
+    },
+    "code2code": {
+        "query": "Find an equivalent code snippet given the following code snippet:\n",
+        "passage": "Candidate code snippet:\n"
+    },
+    "code2nl": {
+        "query": "Find the most relevant comment given the following code snippet:\n",
+        "passage": "Candidate comment:\n"
+    },
+    "code2completion": {
+        "query": "Find the most relevant completion given the following start of code snippet:\n",
+        "passage": "Candidate completion:\n"
+    }
+}
+
+
+def batch(iterable, n=1):
+    items = len(iterable)
+    for ndx in range(0, items, n):
+        yield iterable[ndx : min(ndx + n, items)]
+
+
+def last_token_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0]
+    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
+    if left_padding:
+        return token_embeddings[:, -1]
+    else:
+        sequence_lengths = attention_mask.sum(dim=1) - 1
+        batch_size = token_embeddings.shape[0]
+        return token_embeddings[torch.arange(batch_size, device=token_embeddings.device), sequence_lengths].float()
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        model_name_or_path: str,
+        max_seq_length: int = None,
+        model_args: Dict[str, Any] = None,
+        tokenizer_args: Dict[str, Any] = None,
+        config_args: Dict[str, Any] = None,
+        cache_dir: str = None,
+        do_lower_case: bool = False,
+        tokenizer_name_or_path: str = None,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+        self.config_keys = ["max_seq_length", "do_lower_case"]
+        self.do_lower_case = do_lower_case
+        if model_args is None:
+            model_args = {}
+        if tokenizer_args is None:
+            tokenizer_args = {}
+        if config_args is None:
+            config_args = {}
+        
+        self.config = AutoConfig.from_pretrained(model_name_or_path, **config_args, cache_dir=cache_dir)
+
+        self.task_names = self.config.task_names
+
+        self.default_task = model_args.pop('default_task', None)
+
+        model_args["attn_implementation"] = "flash_attention_2" if is_flash_attn_2_available() else "sdpa"
+
+        self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=self.config, cache_dir=cache_dir, **model_args)
+
+        if max_seq_length is not None and "model_max_length" not in tokenizer_args:
+            tokenizer_args["model_max_length"] = max_seq_length
+        self.tokenizer = AutoTokenizer.from_pretrained(
+            tokenizer_name_or_path if tokenizer_name_or_path is not None else model_name_or_path,
+            cache_dir=cache_dir,
+            **tokenizer_args,
+        )
+
+        # No max_seq_length set. Try to infer from model
+        if max_seq_length is None:
+            if (
+                hasattr(self.auto_model, "config")
+                and hasattr(self.auto_model.config, "max_position_embeddings")
+                and hasattr(self.tokenizer, "model_max_length")
+            ):
+                max_seq_length = min(self.auto_model.config.max_position_embeddings, self.tokenizer.model_max_length)
+
+        self.max_seq_length = max_seq_length
+
+        if tokenizer_name_or_path is not None:
+            self.auto_model.config.tokenizer_class = self.tokenizer.__class__.__name__
+    
+
+    @property
+    def default_task(self):
+        return self._default_task
+
+
+    @default_task.setter
+    def default_task(self, task: Union[None, str]):
+        self._validate_task(task)
+        self._default_task = task
+        
+
+    def _validate_task(self, task: str):
+        if task and task not in self.task_names:
+            raise ValueError(
+                f"Unsupported task '{task}'. "
+                f"Supported tasks are: {', '.join(self.config.task_names)}."
+            )
+    
+
+    def forward(
+        self,
+        features: Dict[str, torch.Tensor],
+        task: Optional[str] = None
+    ) -> Dict[str, torch.Tensor]:
+        """
+        Forward pass through the model.
+        """
+        features.pop('prompt_length', None)
+        output_states = self.auto_model.forward(
+            **features,
+            output_attentions=False,
+            return_dict=True
+        )
+        output_tokens = output_states[0]
+        features.update({"token_embeddings": output_tokens, "attention_mask": features["attention_mask"]})
+        return features
+
+
+    def get_word_embedding_dimension(self) -> int:
+        return self.auto_model.config.hidden_size
+    
+
+    def tokenize(
+        self,
+        texts: Union[List[str], List[dict], List[Tuple[str, str]]],
+        padding: Union[str, bool] = True
+    ) -> Dict[str, torch.Tensor]:
+        """Tokenizes a text and maps tokens to token-ids"""
+        output = {}
+        if isinstance(texts[0], str):
+            to_tokenize = [texts]
+        elif isinstance(texts[0], dict):
+            to_tokenize = []
+            output["text_keys"] = []
+            for lookup in texts:
+                text_key, text = next(iter(lookup.items()))
+                to_tokenize.append(text)
+                output["text_keys"].append(text_key)
+            to_tokenize = [to_tokenize]
+        else:
+            batch1, batch2 = [], []
+            for text_tuple in texts:
+                batch1.append(text_tuple[0])
+                batch2.append(text_tuple[1])
+            to_tokenize = [batch1, batch2]
+
+        # strip
+        to_tokenize = [[str(s).strip() for s in col] for col in to_tokenize]
+
+        # Lowercase
+        if self.do_lower_case:
+            to_tokenize = [[s.lower() for s in col] for col in to_tokenize]
+
+        output.update(
+            self.tokenizer(
+                *to_tokenize,
+                padding=padding,
+                truncation=True,
+                return_tensors="pt",
+                max_length=self.max_seq_length,
+            )
+        )
+        return output
+    
+
+    def get_config_dict(self) -> Dict[str, Any]:
+         return {key: self.__dict__[key] for key in self.config_keys}
+
+
+    def save(self, output_path: str, safe_serialization: bool = True) -> None:
+        self.auto_model.save_pretrained(output_path, safe_serialization=safe_serialization)
+        self.tokenizer.save_pretrained(output_path)
+
+        with open(os.path.join(output_path, "sentence_transformer_config.json"), "w") as fOut:
+            json.dump(self.get_config_dict(), fOut, indent=2)
+    
+
+    @classmethod
+    def load(cls, input_path: str) -> "Transformer":
+        config_name = "sentence_transformer_config.json"
+        stransformer_config_path = os.path.join(input_path, config_name)
+        with open(stransformer_config_path) as fIn:
+            config = json.load(fIn)
+        # Don't allow configs to set trust_remote_code
+        if "model_args" in config and "trust_remote_code" in config["model_args"]:
+            config["model_args"].pop("trust_remote_code")
+        if "tokenizer_args" in config and "trust_remote_code" in config["tokenizer_args"]:
+            config["tokenizer_args"].pop("trust_remote_code")
+        if "config_args" in config and "trust_remote_code" in config["config_args"]:
+            config["config_args"].pop("trust_remote_code")
+        return cls(model_name_or_path=input_path, **config)