Merge branch 'united' into mkultra

aiserver.py

@@ -964,7 +964,10 @@ def loadmodelsettings():
     if("nobreakmodel" in js):
         vars.nobreakmodel = js["nobreakmodel"]
     if("sampler_order" in js):
-        vars.sampler_order = js["sampler_order"]
+        sampler_order = vars.sampler_order
+        if(len(sampler_order) < 7):
+            sampler_order = [6] + sampler_order
+        vars.sampler_order = sampler_order
     if("temp" in js):
         vars.temp       = js["temp"]
     if("top_p" in js):
@@ -1095,7 +1098,10 @@ def processsettings(js):
     if("andepth" in js):
         vars.andepth    = js["andepth"]
     if("sampler_order" in js):
-        vars.sampler_order = js["sampler_order"]
+        sampler_order = vars.sampler_order
+        if(len(sampler_order) < 7):
+            sampler_order = [6] + sampler_order
+        vars.sampler_order = sampler_order
     if("temp" in js):
         vars.temp       = js["temp"]
     if("top_p" in js):
@@ -1732,8 +1738,6 @@ def patch_transformers():
     dynamic_processor_wrap(TailFreeLogitsWarper, "tfs", "tfs", cond=lambda x: x < 1.0)
     dynamic_processor_wrap(TypicalLogitsWarper, "typical", "typical", cond=lambda x: x < 1.0)
     dynamic_processor_wrap(TemperatureLogitsWarper, "temperature", "temp", cond=lambda x: x != 1.0)
-    RepetitionPenaltyLogitsProcessor.__init__ = AdvancedRepetitionPenaltyLogitsProcessor.__init__
-    RepetitionPenaltyLogitsProcessor.__call__ = AdvancedRepetitionPenaltyLogitsProcessor.__call__
 
     class LuaLogitsProcessor(LogitsProcessor):
 
@@ -1810,9 +1814,13 @@ def patch_transformers():
             self.__warper_list.append(TailFreeLogitsWarper(tfs=0.5, min_tokens_to_keep=1 + (beams > 1)))
             self.__warper_list.append(TypicalLogitsWarper(typical=0.5, min_tokens_to_keep=1 + (beams > 1)))
             self.__warper_list.append(TemperatureLogitsWarper(temperature=0.5))
+            self.__warper_list.append(AdvancedRepetitionPenaltyLogitsProcessor())
 
         def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, *args, **kwargs):
-            for k in vars.sampler_order:
+            sampler_order = 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)
             return scores
 
@@ -1945,7 +1953,7 @@ def reset_model_settings():
     vars.badwordsids = []
     vars.fp32_model  = False  # Whether or not the most recently loaded HF model was in fp32 format
     vars.modeldim    = -1     # Embedding dimension of your model (e.g. it's 4096 for GPT-J-6B and 2560 for GPT-Neo-2.7B)
-    vars.sampler_order = [0, 1, 2, 3, 4, 5]
+    vars.sampler_order = [6, 0, 1, 2, 3, 4, 5]
     vars.newlinemode = "n"
     vars.revision    = None
 
@@ -2558,8 +2566,11 @@ def load_model(use_gpu=True, gpu_layers=None, disk_layers=None, initial_load=Fal
             vars.compiling = False
         
         def tpumtjgenerate_settings_callback() -> dict:
+            sampler_order = vars.sampler_order[:]
+            if len(sampler_order) < 7:  # Add repetition penalty at beginning if it's not present
+                sampler_order = [6] + sampler_order
             return {
-                "sampler_order": vars.sampler_order,
+                "sampler_order": sampler_order,
                 "top_p": float(vars.top_p),
                 "temp": float(vars.temp),
                 "top_k": int(vars.top_k),
@@ -3666,12 +3677,16 @@ def get_message(msg):
         sendUSStatItems()
     elif(msg['cmd'] == 'samplers'):
         sampler_order = msg["data"]
+        sampler_order_min_length = 6
+        sampler_order_max_length = 7
         if(not isinstance(sampler_order, list)):
             raise ValueError(f"Sampler order must be a list, but got a {type(sampler_order)}")
-        if(len(sampler_order) != len(vars.sampler_order)):
-            raise ValueError(f"Sampler order must be a list of length {len(vars.sampler_order)}, but got a list of length {len(sampler_order)}")
+        if(not (sampler_order_min_length <= len(sampler_order) <= sampler_order_max_length)):
+            raise ValueError(f"Sampler order must be a list of length greater than or equal to {sampler_order_min_length} and less than or equal to {sampler_order_max_length}, but got a list of length {len(sampler_order)}")
         if(not all(isinstance(e, int) for e in sampler_order)):
             raise ValueError(f"Sampler order must be a list of ints, but got a list with at least one non-int element")
+        if(min(sampler_order) != 0 or max(sampler_order) != len(sampler_order) - 1 or len(set(sampler_order)) != len(sampler_order)):
+            raise ValueError(f"Sampler order list of length {len(sampler_order)} must be a permutation of the first {len(sampler_order)} nonnegative integers")
         vars.sampler_order = sampler_order
         settingschanged()
     elif(msg['cmd'] == 'list_model'):
@@ -4624,7 +4639,7 @@ def _generate(txt, minimum, maximum, found_entries):
                 gen_in, 
                 do_sample=True, 
                 max_length=int(2e9),
-                repetition_penalty=1.1,
+                repetition_penalty=1.0,
                 bad_words_ids=vars.badwordsids,
                 use_cache=True,
                 num_return_sequences=numseqs