Apply suggestions from code review

Co-authored-by: Albert Villanova del Moral <[email protected]>
Co-authored-by: Nathan Habib <[email protected]>

Author: 3 people

src/lighteval/models/model_input.py

@@ -20,7 +20,7 @@
 # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 # SOFTWARE.
 
-from dataclasses import dataclass
+from dataclasses import dataclass, asdict
 from typing import Optional
 
 
@@ -57,24 +57,7 @@ def from_dict(cls, config_dict: dict):
                 }
             }
         """
-        if "generation" not in config_dict:
-            return GenerationParameters()
-        return GenerationParameters(
-            early_stopping=config_dict["generation"].get("early_stopping", None),
-            repetition_penalty=config_dict["generation"].get("repetition_penalty", None),
-            frequency_penalty=config_dict["generation"].get("frequency_penalty", None),
-            length_penalty=config_dict["generation"].get("length_penalty", None),
-            presence_penalty=config_dict["generation"].get("presence_penalty", None),
-            max_new_tokens=config_dict["generation"].get("max_new_tokens", None),
-            min_new_tokens=config_dict["generation"].get("min_new_tokens", None),
-            seed=config_dict["generation"].get("seed", None),
-            stop_tokens=config_dict["generation"].get("stop_tokens", None),
-            temperature=config_dict["generation"].get("temperature", None),
-            top_k=config_dict["generation"].get("top_k", None),
-            min_p=config_dict["generation"].get("min_p", None),
-            top_p=config_dict["generation"].get("top_p", None),
-            truncate_prompt=config_dict["generation"].get("truncate_prompt", None),
-        )
+        return GenerationParameters(**config_dict.get("generation", {}))
 
     def to_vllm_openai_dict(self) -> dict:
         """Selects relevant generation and sampling parameters for vllm and openai models.
@@ -85,23 +68,7 @@ def to_vllm_openai_dict(self) -> dict:
         """
         # Task specific sampling params to set in model: n, best_of, use_beam_search
         # Generation specific params to set in model: logprobs, prompt_logprobs
-        args = {
-            "presence_penalty": self.presence_penalty,
-            "frequency_penalty": self.frequency_penalty,
-            "repetition_penalty": self.repetition_penalty,
-            "temperature": self.temperature,
-            "top_p": self.top_p,
-            "top_k": self.top_k,
-            "min_p": self.min_p,
-            "seed": self.seed,
-            "length_penalty": self.length_penalty,
-            "early_stopping": self.early_stopping,
-            "stop": self.stop_tokens,
-            "max_tokens": self.max_new_tokens,
-            "min_tokens": self.min_new_tokens,
-            "truncate_prompt_tokens": self.truncate_prompt,
-        }
-        return {k: v for k, v in args.items() if v is not None}
+        return {k: v for k, v in asdict(self).items() if v is not None}
 
     def to_transformers_dict(self) -> dict:
         """Selects relevant generation and sampling parameters for transformers models.
@@ -117,7 +84,7 @@ def to_transformers_dict(self) -> dict:
         args = {
             "max_new_tokens": self.max_new_tokens,
             "min_new_tokens": self.min_new_tokens,
-            "early_stopping": self.early_stopping or False,
+            "early_stopping": self.early_stopping,
             "stop_strings": self.stop_tokens,
             "temperature": self.temperature,
             "top_k": self.top_k,

src/lighteval/models/transformers/transformers_model.py

@@ -1350,9 +1350,9 @@ def _loglikelihood_single_token(
 
 class BaseModel(TransformersModel):
     def __post_init__(self):
-        super()
+        super().__post_init__()
 
-        logger.warning(
+        warnings.warn(
             "Careful, the BaseModel name is deprecated and will be removed, you should use TransformersModel instead!"
         )