Sampling: Make validators simpler

Injecting into Pydantic fields caused issues with serialization for
documentation rendering. Rather than reinvent the wheel again,
switch to a chain of if statements for now. This may change in the
future if subclasses from the base sampler request need to be
validated as well.

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

common/sampling.py

@@ -37,8 +37,6 @@ 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(
@@ -47,21 +45,15 @@ 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),
-        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],
-        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(
@@ -88,8 +80,6 @@ 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(
@@ -134,8 +124,6 @@ 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(
@@ -160,47 +148,76 @@ class BaseSamplerRequest(BaseModel):
         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",
     )
 
+    # 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):
         """
-        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.
+        Validates sampler parameters to be within sane ranges.
         """
 
-        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}")
+        # Temperature
+        if self.temperature < 0.0:
+            raise ValueError(
+                "Temperature must be a non-negative value. " f"Got {self.temperature}"
+            )
+
+        # Smoothing factor
+        if self.smoothing_factor < 0.0:
+            raise ValueError(
+                "Smoothing factor must be a non-negative value. "
+                f"Got {self.smoothing_factor}"
+            )
+
+        # Top K
+        if self.top_k < 0:
+            raise ValueError("Top K must be a non-negative value. " f"Got {self.top_k}")
+
+        # Top P
+        if self.top_p < 0.0 or self.top_p > 1.0:
+            raise ValueError("Top P must be in [0, 1]. " f"Got {self.top_p}")
+
+        # Repetition Penalty
+        if self.repetition_penalty <= 0.0:
+            raise ValueError(
+                "Repetition penalty must be a positive value. "
+                f"Got {self.repetition_penalty}"
+            )
+
+        # Typical
+        if self.typical <= 0 and self.typical > 1:
+            raise ValueError("Typical must be in (0, 1]. " f"Got {self.typical}")
+
+        # Dynatemp values
+        if self.max_temp < 0.0:
+            raise ValueError(
+                "Max temp must be a non-negative value. ", f"Got {self.max_temp}"
+            )
+
+        if self.min_temp < 0.0:
+            raise ValueError(
+                "Min temp must be a non-negative value. ", f"Got {self.min_temp}"
+            )
+
+        if self.temp_exponent < 0.0:
+            raise ValueError(
+                "Temp exponent must be a non-negative value. ",
+                f"Got {self.temp_exponent}",
+            )
 
     def to_gen_params(self, **kwargs):
         """Converts samplers to internal generation params"""