Model: Reformat and clean up

modeling/inference_model.py

@@ -360,9 +360,8 @@ class InferenceModel:
                     # amount (controlled by halt), or Dynamic WI has not told us to
                     # stop temporarily to insert WI, we can assume that we are done
                     # generating. We shall break.
-                    if (
+                    if self.gen_state.get("halt") or not self.gen_state.get(
-                        self.gen_state.get("halt")
+                        "regeneration_required"
-                        or not self.gen_state.get("regeneration_required")
                     ):
                         break
 

modeling/inference_models/api.py

@@ -40,7 +40,7 @@ class APIInferenceModel(InferenceModel):
         gen_settings: GenerationSettings,
         single_line: bool = False,
         batch_count: int = 1,
-        **kwargs
+        **kwargs,
     ):
         decoded_prompt = utils.decodenewlines(self.tokenizer.decode(prompt_tokens))
 

modeling/inference_models/basic_api.py

@@ -36,7 +36,7 @@ class BasicAPIInferenceModel(InferenceModel):
         gen_settings: GenerationSettings,
         single_line: bool = False,
         batch_count: int = 1,
-        **kwargs
+        **kwargs,
     ):
         decoded_prompt = utils.decodenewlines(self.tokenizer.decode(prompt_tokens))
 

modeling/inference_models/hf_torch.py

@@ -53,6 +53,7 @@ except ModuleNotFoundError as e:
 # scores for each token.
 LOG_SAMPLER_NO_EFFECT = False
 
+
 class HFTorchInferenceModel(HFInferenceModel):
     def __init__(
         self,
@@ -249,9 +250,7 @@ class HFTorchInferenceModel(HFInferenceModel):
                 ret = []
 
                 for alt_phrase in [phrase, f" {phrase}"]:
-                    ret.append(
+                    ret.append(model_self.tokenizer.encode(alt_phrase))
-                        model_self.tokenizer.encode(alt_phrase)
-                    )
 
                 return ret
 
@@ -433,13 +432,6 @@ class HFTorchInferenceModel(HFInferenceModel):
                 *args,
                 **kwargs,
             ):
-                # sampler_order = utils.koboldai_vars.sampler_order[:]
-                # if (
-                #     len(sampler_order) < 7
-                # ):  # Add repetition penalty at beginning if it's not present
-                #     sampler_order = [6] + sampler_order
-                # for k in sampler_order:
-                #     scores = self.__warper_list[k](input_ids, scores, *args, **kwargs)
                 scores = model_self._apply_warpers(scores=scores, input_ids=input_ids)
                 visualize_probabilities(model_self, scores)
                 return scores
@@ -469,7 +461,7 @@ class HFTorchInferenceModel(HFInferenceModel):
         gen_settings: GenerationSettings,
         single_line: bool = False,
         batch_count: int = 1,
-        **kwargs
+        **kwargs,
     ) -> GenerationResult:
         if not isinstance(prompt_tokens, torch.Tensor):
             gen_in = torch.tensor(prompt_tokens, dtype=torch.long)[None]

modeling/inference_models/horde.py

@@ -42,7 +42,7 @@ class HordeInferenceModel(InferenceModel):
         gen_settings: GenerationSettings,
         single_line: bool = False,
         batch_count: int = 1,
-        **kwargs
+        **kwargs,
     ) -> GenerationResult:
         decoded_prompt = utils.decodenewlines(self.tokenizer.decode(prompt_tokens))
 

modeling/inference_models/openai.py

@@ -18,6 +18,7 @@ class OpenAIAPIError(Exception):
 
 class OpenAIAPIInferenceModel(InferenceModel):
     """InferenceModel for interfacing with OpenAI's generation API."""
+
     def _load(self, save_model: bool, initial_load: bool) -> None:
         self.tokenizer = self._get_tokenizer("gpt2")
 
@@ -28,7 +29,7 @@ class OpenAIAPIInferenceModel(InferenceModel):
         gen_settings: GenerationSettings,
         single_line: bool = False,
         batch_count: int = 1,
-        **kwargs
+        **kwargs,
     ) -> GenerationResult:
         # Taken mainly from oairequest()
 

modeling/stoppers.py

@@ -131,7 +131,7 @@ class Stoppers:
         # a newline.
         if "newline_in_sequence" not in model.gen_state:
             model.gen_state["newline_in_sequence"] = [False] * len(input_ids)
-        
+
         for sequence_idx, batch_sequence in enumerate(input_ids):
             if model.tokenizer.decode(batch_sequence[-1]) == "\n":
                 model.gen_state["newline_in_sequence"][sequence_idx] = True