Make transformers warpers dynamically update their parameters

So that if you change, e.g., `top_p`, from a Lua generation modifier or
from the settings menu during generation, the rest of the generation
will use the new setting value instead of retaining the settings it had
when generation began.

aiserver.py

@@ -7,6 +7,7 @@
 
 # External packages
 import eventlet
+from transformers.generation_logits_process import RepetitionPenaltyLogitsProcessor
 eventlet.monkey_patch()
 import os
 os.environ['EVENTLET_THREADPOOL_SIZE'] = '1'
@@ -617,6 +618,18 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
 
         # Patch transformers to use our custom logit warpers
         from transformers import LogitsProcessorList, LogitsWarper, LogitsProcessor, TopKLogitsWarper, TopPLogitsWarper, TemperatureLogitsWarper
+
+        def dynamic_processor_wrap(cls, field_name, var_name):
+            old_call = cls.__call__
+            def new_call(self, *args, **kwargs):
+                setattr(self, field_name, getattr(vars, var_name))
+                return old_call(self, *args, **kwargs)
+            cls.__call__ = new_call
+        dynamic_processor_wrap(RepetitionPenaltyLogitsProcessor, "penalty", "rep_pen")
+        dynamic_processor_wrap(TopKLogitsWarper, "top_k", "top_k")
+        dynamic_processor_wrap(TopPLogitsWarper, "top_p", "top_p")
+        dynamic_processor_wrap(TemperatureLogitsWarper, "temperature", "temp")
+
         class TailFreeLogitsWarper(LogitsWarper):
 
             def __init__(self, tfs: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
@@ -628,6 +641,8 @@ if(not vars.model in ["InferKit", "Colab", "OAI", "ReadOnly", "TPUMeshTransforme
                 self.min_tokens_to_keep = min_tokens_to_keep
 
             def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
+                self.tfs = vars.tfs
+
                 if self.filter_value >= 1.0:
                     return scores
                 sorted_logits, sorted_indices = torch.sort(scores, descending=True)