Sampling: Add universal validation system

Rather than maintaining yet another function to validate sampler
ranges/values, embed them in fields which allows for less
maintainence in the future.

Also add validation for existing samplers that can corrupt
the sampling stack if set improperly.

Signed-off-by: kingbri <[email protected]>

common/sampling.py

@@ -37,6 +37,8 @@ class BaseSamplerRequest(BaseModel):
     temperature: Optional[float] = Field(
         default_factory=lambda: get_default_sampler_value("temperature", 1.0),
         examples=[1.0],
+        sample_validator=lambda value: value >= 0.0,
+        validation_error="Temperature must be a non-negative value",
     )
 
     temperature_last: Optional[bool] = Field(
@@ -45,14 +47,21 @@ class BaseSamplerRequest(BaseModel):
 
     smoothing_factor: Optional[float] = Field(
         default_factory=lambda: get_default_sampler_value("smoothing_factor", 0.0),
+        sample_validator=lambda value: value >= 0.0,
+        validation_error="Smoothing factor must be a non-negative value",
     )
 
     top_k: Optional[int] = Field(
-        default_factory=lambda: get_default_sampler_value("top_k", 0)
+        default_factory=lambda: get_default_sampler_value("top_k", 0),
+        sample_validator=lambda value: value >= 0,
+        validation_error="Top K must be a non-negative value",
     )
 
     top_p: Optional[float] = Field(
-        default_factory=lambda: get_default_sampler_value("top_p", 1.0), examples=[1.0]
+        default_factory=lambda: get_default_sampler_value("top_p", 1.0),
+        examples=[1.0],
+        sample_validator=lambda value: value >= 0.0 and value <= 1.0,
+        validation_error="Top P must be in [0, 1]",
     )
 
     top_a: Optional[float] = Field(
@@ -64,7 +73,8 @@ class BaseSamplerRequest(BaseModel):
     )
 
     tfs: Optional[float] = Field(
-        default_factory=lambda: get_default_sampler_value("tfs", 1.0)
+        default_factory=lambda: get_default_sampler_value("tfs", 1.0),
+        examples=[1.0],
     )
 
     frequency_penalty: Optional[float] = Field(
@@ -78,6 +88,8 @@ class BaseSamplerRequest(BaseModel):
     repetition_penalty: Optional[float] = Field(
         default_factory=lambda: get_default_sampler_value("repetition_penalty", 1.0),
         examples=[1.0],
+        sample_validator=lambda value: value > 0.0,
+        validation_error="Repetition penalty must be a positive value",
     )
 
     repetition_decay: Optional[int] = Field(
@@ -122,6 +134,8 @@ class BaseSamplerRequest(BaseModel):
         validation_alias=AliasChoices("typical", "typical_p"),
         description="Aliases: typical_p",
         examples=[1.0],
+        sample_validator=lambda value: value > 0.0 and value <= 1.0,
+        validation_error="Typical must be in (0, 1]",
     )
 
     penalty_range: Optional[int] = Field(
@@ -145,26 +159,57 @@ class BaseSamplerRequest(BaseModel):
         default_factory=lambda: get_default_sampler_value("max_temp", 1.0),
         validation_alias=AliasChoices("max_temp", "dynatemp_high"),
         description="Aliases: dynatemp_high",
+        examples=[1.0],
+        sample_validator=lambda value: value >= 0.0,
+        validation_error="Max temperature must be a non-negative value",
     )
 
     min_temp: Optional[float] = Field(
         default_factory=lambda: get_default_sampler_value("min_temp", 1.0),
         validation_alias=AliasChoices("min_temp", "dynatemp_low"),
         description="Aliases: dynatemp_low",
+        examples=[1.0],
+        sample_validator=lambda value: value >= 0.0,
+        validation_error="Min temperature must be a non-negative value",
     )
 
     temp_exponent: Optional[float] = Field(
         default_factory=lambda: get_default_sampler_value("temp_exponent", 1.0),
         validation_alias=AliasChoices("temp_exponent", "dynatemp_exponent"),
         examples=[1.0],
+        sample_validator=lambda value: value >= 0.0,
+        validation_error="Temperature exponent must be a non-negative value",
     )
 
+    def validate_params(self):
+        """
+        Validates if the class field satisfies a condition if present.
+
+        Validators are present in the extras section of a Pydantic field
+        to make it easy for adding more samplers if needed.
+        """
+
+        for field_name, field_info in self.model_fields.items():
+            extra_field_info = unwrap(field_info.json_schema_extra, {})
+            if not extra_field_info:
+                continue
+
+            sample_validator = extra_field_info.get("sample_validator")
+            validation_error = unwrap(extra_field_info.get("validation_error"), "")
+
+            if sample_validator:
+                value = getattr(self, field_name)
+                if not sample_validator(value):
+                    raise ValueError(f"{validation_error}. Got {value}")
+
     def to_gen_params(self, **kwargs):
         """Converts samplers to internal generation params"""
 
         # Add forced overrides if present
         apply_forced_sampler_overrides(self)
 
+        self.validate_params()
+
         # Convert stop to an array of strings
         if isinstance(self.stop, str):
             self.stop = [self.stop]