[Fix & Style] Refactor the grammar backend to reduce human errors and improve readability (#4030)

python/sglang/srt/constrained/base_grammar_backend.py

@@ -13,31 +13,130 @@
 # ==============================================================================
 """The baseclass of a backend for grammar-guided constrained decoding."""
 
+import logging
+from abc import ABC, abstractmethod
 from concurrent.futures import Future, ThreadPoolExecutor
 from dataclasses import dataclass
 from threading import Event, Lock
-from typing import Any, Optional, Tuple
+from typing import Dict, List, Optional, Tuple
+
+import torch
 
 from sglang.srt.server_args import ServerArgs
 
+logger = logging.getLogger(__name__)
+
+
+class BaseGrammarObject(ABC):
+    @abstractmethod
+    def try_jump_forward(self, tokenizer) -> Optional[Tuple[List[int], str]]:
+        """
+        Try to jump forward in the grammar.
+
+        Returns:
+            A jump forward helper which may be used in `jump_forward_str_state`.
+            None if the jump forward is not possible.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def jump_forward_str_state(self, helper: Tuple[List[int], str]) -> Tuple[str, int]:
+        """
+        Jump forward for the grammar.
+
+        Returns:
+            A tuple of the jump forward string and the next state of the grammar
+            (which can be used in `jump_and_retokenize` if needed).
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def jump_and_retokenize(
+        self, old_output_ids: List[int], new_output_ids: List[int], next_state: int
+    ) -> None:
+        """
+        Jump forward occurs, and update the grammar state if needed.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def allocate_vocab_mask(
+        self, vocab_size: int, batch_size: int, device
+    ) -> torch.Tensor:
+        raise NotImplementedError
+
+    @abstractmethod
+    def fill_vocab_mask(self, vocab_mask: torch.Tensor, idx: int) -> None:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def move_vocab_mask(vocab_mask: torch.Tensor, device) -> torch.Tensor:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def apply_vocab_mask(logits: torch.Tensor, vocab_mask: torch.Tensor) -> None:
+        raise NotImplementedError
+
+    @abstractmethod
+    def copy(self) -> "BaseGrammarObject":
+        raise NotImplementedError
+
 
 @dataclass
 class CacheEntry:
-    value: Any
+    value: Optional[BaseGrammarObject]
     event: Event
 
 
-class BaseGrammarObject:
-    pass
-
-
-class BaseGrammarBackend:
+class BaseGrammarBackend(ABC):
     def __init__(self):
         self.executor = ThreadPoolExecutor()
-        self.cache = {}
+        self.cache: Dict[Tuple[str, str], CacheEntry] = {}
         self.cache_lock = Lock()
 
-    def init_value(self, key: Tuple[str, str]) -> BaseGrammarObject:
+    def _not_supported(self, key_type: str, key_string: str) -> None:
+        logger.warning(f"Skip unsupported {key_type}: {key_type}={key_string}")
+
+    def dispatch_fallback(
+        self, key_type: str, key_string: str
+    ) -> Optional[BaseGrammarObject]:
+        """
+        This function should not be reached in any case.
+        """
+        raise ValueError(f"Invalid key_type: {key_type}={key_string}")
+
+    @abstractmethod
+    def dispatch_json(self, key_string: str) -> Optional[BaseGrammarObject]:
+        return self._not_supported("json", key_string)
+
+    @abstractmethod
+    def dispatch_regex(self, key_string: str) -> Optional[BaseGrammarObject]:
+        return self._not_supported("regex", key_string)
+
+    @abstractmethod
+    def dispatch_ebnf(self, key_string: str) -> Optional[BaseGrammarObject]:
+        return self._not_supported("ebnf", key_string)
+
+    @abstractmethod
+    def dispatch_structural_tag(self, key_string: str) -> Optional[BaseGrammarObject]:
+        return self._not_supported("structural_tag", key_string)
+
+    def _init_value_dispatch(self, key: Tuple[str, str]) -> Optional[BaseGrammarObject]:
+        key_type, key_string = key
+        if key_type == "json":
+            return self.dispatch_json(key_string)
+        elif key_type == "regex":
+            return self.dispatch_regex(key_string)
+        elif key_type == "ebnf":
+            return self.dispatch_ebnf(key_string)
+        elif key_type == "structural_tag":
+            return self.dispatch_structural_tag(key_string)
+        else:
+            return self.dispatch_fallback(key_type, key_string)
+
+    def _init_value(self, key: Tuple[str, str]) -> Optional[BaseGrammarObject]:
         with self.cache_lock:
             if key in self.cache:
                 cache_hit = True
@@ -50,13 +149,10 @@ class BaseGrammarBackend:
         if cache_hit:
             entry.event.wait()
         else:
-            entry.value = self.init_value_impl(key)
+            entry.value = self._init_value_dispatch(key)
             entry.event.set()
         return entry.value.copy() if entry.value else None
 
-    def init_value_impl(self, key: Tuple[str, str]) -> BaseGrammarObject:
-        raise NotImplementedError()
-
     def get_cached_value(self, key: Tuple[str, str]) -> Optional[BaseGrammarObject]:
         with self.cache_lock:
             entry = self.cache.get(key)
@@ -66,7 +162,7 @@ class BaseGrammarBackend:
             return val.copy() if val else None
 
     def get_future_value(self, key: Tuple[str, str]) -> Future:
-        return self.executor.submit(self.init_value, key)
+        return self.executor.submit(self._init_value, key)
 
     def reset(self):
         with self.cache_lock: