Sampling: Reorder aliased params and add kobold aliases

Also add dynatemp range which is an alternative way of calculating
min and max temp.

Signed-off-by: kingbri <bdashore3@proton.me>

common/sampling.py

@@ -34,13 +34,22 @@ class BaseSamplerRequest(BaseModel):
     )
 
     stop: Optional[Union[str, List[str]]] = Field(
-        default_factory=lambda: get_default_sampler_value("stop", [])
+        default_factory=lambda: get_default_sampler_value("stop", []),
+        validation_alias=AliasChoices("stop", "stop_sequence"),
+        description="Aliases: stop_sequence",
     )
 
     banned_strings: Optional[Union[str, List[str]]] = Field(
         default_factory=lambda: get_default_sampler_value("banned_strings", [])
     )
 
+    banned_tokens: Optional[Union[List[int], str]] = Field(
+        default_factory=lambda: get_default_sampler_value("banned_tokens", []),
+        validation_alias=AliasChoices("banned_tokens", "custom_token_bans"),
+        description="Aliases: custom_token_bans",
+        examples=[[128, 330]],
+    )
+
     token_healing: Optional[bool] = Field(
         default_factory=lambda: get_default_sampler_value("token_healing", False)
     )
@@ -80,6 +89,13 @@ class BaseSamplerRequest(BaseModel):
         examples=[1.0],
     )
 
+    typical: Optional[float] = Field(
+        default_factory=lambda: get_default_sampler_value("typical", 1.0),
+        validation_alias=AliasChoices("typical", "typical_p"),
+        description="Aliases: typical_p",
+        examples=[1.0],
+    )
+
     skew: Optional[float] = Field(
         default_factory=lambda: get_default_sampler_value("skew", 0.0),
         examples=[0.0],
@@ -100,6 +116,20 @@ class BaseSamplerRequest(BaseModel):
         examples=[1.0],
     )
 
+    penalty_range: Optional[int] = Field(
+        default_factory=lambda: get_default_sampler_value("penalty_range", -1),
+        validation_alias=AliasChoices(
+            "penalty_range",
+            "repetition_range",
+            "repetition_penalty_range",
+            "rep_pen_range",
+        ),
+        description=(
+            "Aliases: repetition_range, repetition_penalty_range, "
+            "rep_pen_range"
+        ),
+    )
+
     repetition_decay: Optional[int] = Field(
         default_factory=lambda: get_default_sampler_value("repetition_decay", 0)
     )
@@ -159,28 +189,6 @@ class BaseSamplerRequest(BaseModel):
         default_factory=lambda: get_default_sampler_value("speculative_ngram"),
     )
 
-    # Aliased variables
-    typical: Optional[float] = Field(
-        default_factory=lambda: get_default_sampler_value("typical", 1.0),
-        validation_alias=AliasChoices("typical", "typical_p"),
-        description="Aliases: typical_p",
-        examples=[1.0],
-    )
-
-    penalty_range: Optional[int] = Field(
-        default_factory=lambda: get_default_sampler_value("penalty_range", -1),
-        validation_alias=AliasChoices(
-            "penalty_range",
-            "repetition_range",
-            "repetition_penalty_range",
-            "rep_pen_range",
-        ),
-        description=(
-            "Aliases: repetition_range, repetition_penalty_range, "
-            "rep_pen_range"
-        ),
-    )
-
     cfg_scale: Optional[float] = Field(
         default_factory=lambda: get_default_sampler_value("cfg_scale", 1.0),
         validation_alias=AliasChoices("cfg_scale", "guidance_scale"),
@@ -208,13 +216,6 @@ class BaseSamplerRequest(BaseModel):
         examples=[1.0],
     )
 
-    banned_tokens: Optional[Union[List[int], str]] = Field(
-        default_factory=lambda: get_default_sampler_value("banned_tokens", []),
-        validation_alias=AliasChoices("banned_tokens", "custom_token_bans"),
-        description="Aliases: custom_token_bans",
-        examples=[[128, 330]],
-    )
-
     # TODO: Return back to adaptable class-based validation But that's just too much
     # abstraction compared to simple if statements at the moment
     def validate_params(self):

endpoints/Kobold/types/generation.py

@@ -2,7 +2,7 @@ from typing import List, Optional
 
 from pydantic import BaseModel, Field
 from common import model
-from common.sampling import BaseSamplerRequest
+from common.sampling import BaseSamplerRequest, get_default_sampler_value
 from common.utils import flat_map, unwrap
 
 
@@ -10,12 +10,19 @@ class GenerateRequest(BaseSamplerRequest):
     prompt: str
     genkey: Optional[str] = None
     use_default_badwordsids: Optional[bool] = False
+    dynatemp_range: Optional[float] = Field(
+        default_factory=get_default_sampler_value("dynatemp_range")
+    )
 
     def to_gen_params(self, **kwargs):
         # Exl2 uses -1 to include all tokens in repetition penalty
         if self.penalty_range == 0:
             self.penalty_range = -1
 
+        if self.dynatemp_range:
+            self.min_temp = self.temperature - self.dynatemp_range
+            self.max_temp = self.temperature + self.dynatemp_range
+
         # Move badwordsids into banned tokens for generation
         if self.use_default_badwordsids:
             bad_words_ids = unwrap(