[Feature] support bad_words (#3055)

* support bad_words

* support online infer bad_words

* update

* add CI test

* update

* update

* update

---------

Co-authored-by: Yuanle Liu <yuanlehome@163.com>

fastdeploy/engine/sampling_params.py

@@ -20,6 +20,8 @@ import random
 from dataclasses import dataclass, fields
 from typing import Any, List, Optional, Union
 
+from fastdeploy.utils import llm_logger as logger
+
 
 @dataclass
 class SamplingParams:
@@ -97,6 +99,7 @@ class SamplingParams:
     min_tokens: int = 1
     logprobs: Optional[int] = None
     bad_words: Optional[List[str]] = None
+    _bad_words_token_ids: Optional[List[int]] = None
 
     @classmethod
     def from_dict(cls, req_dict: dict[str, Any]) -> SamplingParams:
@@ -201,11 +204,42 @@ class SamplingParams:
             raise ValueError("seed must be in [0, 922337203685477580], got " f"{self.seed}.")
 
     def update_from_tokenizer(self, tokenizer):
-        """
-        # TODO: Implement stop tokens and bad words support
-        # Currently stop tokens and bad words are not supported yet
-        """
-        pass
+        """Support bad words"""
+        if self.bad_words is None:
+            return
+        self._bad_words_token_ids = []
+        for bad_word in self.bad_words:
+            # To prohibit words both at the beginning
+            # and in the middle of text
+            # (related to add_prefix_space tokenizer parameter)
+            for add_prefix_space in [False, True]:
+                prefix = " " if add_prefix_space else ""
+                prompt = prefix + bad_word.lstrip()
+                prompt_token_ids = tokenizer.encode(text=prompt, add_special_tokens=False)["input_ids"]
+
+                if len(prompt_token_ids) != 1:
+                    logger.warning(
+                        f"Skip bad_words: {prompt}."
+                        f"Bad words should be a single token."
+                        f"Got tokens: {prompt_token_ids}."
+                    )
+                    continue
+
+                if prompt_token_ids[0] > tokenizer.vocab_size:
+                    logger.warning(
+                        f"Skip bad_words: {prompt}."
+                        f"All token id values should be satisfying:"
+                        f" 0 <= token_id < {tokenizer.vocab_size}."
+                        f"Got token: {prompt_token_ids}."
+                    )
+                    continue
+
+                if prompt_token_ids not in self._bad_words_token_ids:
+                    self._bad_words_token_ids.extend(prompt_token_ids)
+
+    @property
+    def bad_words_token_ids(self) -> Optional[List[list[int]]]:
+        return self._bad_words_token_ids
 
 
 @dataclass