Updates for pydantic serialization / deserialization to rebase on cur…

…rent main, address review comments, and finalize last pieces as well as test fixes

.pre-commit-config.yaml

@@ -28,6 +28,8 @@ repos:
         datasets,
         loguru,
         numpy,
+        pydantic,
+        pyyaml,
         openai,
         requests,
         transformers,
@@ -38,4 +40,5 @@ repos:
         # types
         types-click,
         types-requests,
+        types-PyYAML,
       ]

pyproject.toml

@@ -30,6 +30,8 @@ dependencies = [
     "loguru",
     "numpy",
     "openai",
+    "pydantic>=2.0.0",
+    "pyyaml>=6.0.0",
     "requests",
     "transformers",
 ]
@@ -48,7 +50,12 @@ dev = [
     "pytest-mock~=3.14.0",
     "ruff~=0.5.2",
     "tox~=4.16.0",
-    "types-requests~=2.32.0"
+    "types-requests~=2.32.0",
+
+    # type-checking
+    "types-click",
+    "types-requests",
+    "types-PyYAML",
 ]
 
 

src/guidellm/backend/base.py

@@ -91,16 +91,18 @@ def submit(self, request: TextGenerationRequest) -> TextGenerationResult:
 
         logger.info(f"Submitting request with prompt: {request.prompt}")
 
-        result = TextGenerationResult(TextGenerationRequest(prompt=request.prompt))
+        result = TextGenerationResult(
+            request=TextGenerationRequest(prompt=request.prompt)
+        )
         result.start(request.prompt)
 
         for response in self.make_request(request):  # GenerativeResponse
             if response.type_ == "token_iter" and response.add_token:
                 result.output_token(response.add_token)
             elif response.type_ == "final":
                 result.end(
-                    response.prompt_token_count,
-                    response.output_token_count,
+                    prompt_token_count=response.prompt_token_count,
+                    output_token_count=response.output_token_count,
                 )
 
         logger.info(f"Request completed with output: {result.output}")

src/guidellm/core/distribution.py

@@ -1,7 +1,8 @@
-from typing import List, Optional, Union
+from typing import List, Sequence
 
 import numpy as np
 from loguru import logger
+from pydantic import Field
 
 from guidellm.core.serializable import Serializable
 
@@ -14,24 +15,12 @@ class Distribution(Serializable):
     statistical analyses.
     """
 
-    def __init__(self, **data):
-        super().__init__(**data)
-        logger.debug(f"Initialized Distribution with data: {self.data}")
+    data: Sequence[float] = Field(
+        default_factory=list, description="The data points of the distribution."
+    )
 
-    def __str__(self) -> str:
-        """
-        Return a string representation of the Distribution.
-        """
-        return (
-            f"Distribution(mean={self.mean:.2f}, median={self.median:.2f}, "
-            f"min={self.min}, max={self.max}, count={len(self.data)})"
-        )
-
-    def __repr__(self) -> str:
-        """
-        Return an unambiguous string representation of the Distribution for debugging.
-        """
-        return f"Distribution(data={self.data})"
+    def __str__(self):
+        return f"Distribution({self.describe()})"
 
     @property
     def mean(self) -> float:
@@ -99,7 +88,7 @@ def percentile(self, percentile: float) -> float:
             logger.warning("No data points available to calculate percentile.")
             return 0.0
 
-        percentile_value = np.percentile(self._data, percentile).item()
+        percentile_value = np.percentile(self.data, percentile).item()
         logger.debug(f"Calculated {percentile}th percentile: {percentile_value}")
         return percentile_value
 
@@ -180,15 +169,15 @@ def describe(self) -> dict:
         logger.debug(f"Generated description: {description}")
         return description
 
-    def add_data(self, new_data: Union[List[int], List[float]]):
+    def add_data(self, new_data: Sequence[float]):
         """
         Add new data points to the distribution.
         :param new_data: A list of new numerical data points to add.
         """
-        self.data.extend(new_data)
+        self.data = list(self.data) + list(new_data)
         logger.debug(f"Added new data: {new_data}")
 
-    def remove_data(self, remove_data: Union[List[int], List[float]]):
+    def remove_data(self, remove_data: Sequence[float]):
         """
         Remove specified data points from the distribution.
         :param remove_data: A list of numerical data points to remove.

src/guidellm/core/request.py

@@ -1,5 +1,7 @@
 import uuid
-from typing import Dict, Optional, Any
+from typing import Any, Dict, Optional
+
+from pydantic import Field
 
 from guidellm.core.serializable import Serializable
 
@@ -9,24 +11,18 @@ class TextGenerationRequest(Serializable):
     A class to represent a text generation request for generative AI workloads.
     """
 
-    id: str
-    prompt: str
-    prompt_token_count: Optional[int]
-    generated_token_count: Optional[int]
-    params: Dict[str, Any]
-
-    def __init__(
-        self,
-        prompt: str,
-        prompt_token_count: Optional[int] = None,
-        generated_token_count: Optional[int] = None,
-        params: Optional[Dict[str, Any]] = None,
-        id: Optional[str] = None,
-    ):
-        super().__init__(
-            id=str(uuid.uuid4()) if id is None else id,
-            prompt=prompt,
-            prompt_token_count=prompt_token_count,
-            generated_token_count=generated_token_count,
-            params=params or {},
-        )
+    id: str = Field(
+        default_factory=lambda: str(uuid.uuid4()),
+        description="The unique identifier for the request.",
+    )
+    prompt: str = Field(description="The input prompt for the text generation.")
+    prompt_token_count: Optional[int] = Field(
+        default=None, description="The number of tokens in the input prompt."
+    )
+    generate_token_count: Optional[int] = Field(
+        default=None, description="The number of tokens to generate."
+    )
+    params: Dict[str, Any] = Field(
+        default_factory=dict,
+        description="The parameters for the text generation request.",
+    )