Add the eos token to exllama bad words.

The bos token was already hardcoded as a bad word id.
Store badwords in a list and iterate over them during generation.
Add the Llama eos token to the list of bad words.
Also support "single line mode", which adds newline (13) to badwords.

modeling/inference_models/exllama/class.py

@@ -95,6 +95,9 @@ class model_backend(InferenceModel):
             post_token_probs=False,
         )
 
+        # We need to wait until the tokenizer is available to fill this in.
+        self.badwordsids = []
+
     def is_valid(self, model_name, model_path, menu_path):
         gptq_model, _ = load_model_gptq_settings(model_path)
         try:
@@ -119,6 +122,7 @@ class model_backend(InferenceModel):
         self.model = self._get_model(self.get_local_model_path(), {})
         self.tokenizer = self._get_tokenizer(self.get_local_model_path())
 
+        self.badwordsids = [self.tokenizer.bos_token_id, self.tokenizer.eos_token_id]
         self.cache = ExLlamaCache(self.model)
 
         self.generator = ExLlamaGenerator(self.model, self.tokenizer.tokenizer, self.cache)
@@ -207,6 +211,10 @@ class model_backend(InferenceModel):
             return result
         object.__setattr__(self.tokenizer, '__call__', call_wrapper.__get__(self.tokenizer))
 
+        # Cache the newline token (for single line mode)
+        # Since there is only one Llama token containing newline, just encode \n
+        self.newline_tokens = self.tokenizer.encode("\n")
+
     def unload(self):
         self.model_config = None
 
@@ -275,6 +283,10 @@ class model_backend(InferenceModel):
         if seed:
             torch.manual_seed(seed)
 
+        bad_words_ids = self.badwordsids
+        if single_line:
+            bad_words_ids = list(bad_words_ids) + self.newline_tokens
+
         if not isinstance(prompt_tokens, torch.Tensor):
             gen_in = torch.tensor(prompt_tokens, dtype=torch.long)[None]
         else:
@@ -285,7 +297,8 @@ class model_backend(InferenceModel):
         trim_count = 0
         for i in range(max_new):
             logits = self.model.forward(self.generator.sequence[:, -1:], self.generator.cache)
-            logits[:, :, self.tokenizer.bos_token_id] = -10000.0
+            for bad_word_id in bad_words_ids:
+                logits[:, :, bad_word_id] = -10000.0
 
             logits = torch.unsqueeze(logits[0, -1, :], 0)