Merge branch 'main' into fix-empty-input-to-base-model

community_tasks/arabic_evals.py

@@ -72,7 +72,6 @@ def mmlu_arabic(line, task_name: str = None):
         choices=LETTER_INDICES_AR[:4],
         gold_index=gold_ix,
         instruction=instruction,
-        target_for_fewshot_sorting=LETTER_INDICES_AR[gold_ix],
     )
 
 
@@ -181,7 +180,6 @@ def arabic_exams(line, task_name: str = None):
         choices=LETTER_INDICES_AR[:4],
         gold_index=answer_index,
         instruction=instruction,
-        target_for_fewshot_sorting=choices[answer_index],
     )
 
 
@@ -231,7 +229,6 @@ def alghafa_prompt(line, task_name: str = None):
         choices=choices,
         gold_index=answer_index,
         instruction=instruction,
-        target_for_fewshot_sorting=choices[answer_index],
     )
 
 
@@ -371,7 +368,6 @@ def __init__(
 def boolq_prompt_arabic(line, task_name: str = None):
     question = line["question"]
     passage = line["passage"]
-    answer = "نعم" if line["answer"] else "لا"
     instruction = "بناء على المقطع التالي، أجب عن السؤال ب نعم أو لا"
     query = f"""{instruction}
     المقطع :
@@ -387,7 +383,6 @@ def boolq_prompt_arabic(line, task_name: str = None):
         choices=["نعم", "لا"],
         gold_index=0 if line["answer"] else 1,
         instruction=instruction,
-        target_for_fewshot_sorting=answer,
     )
 
 
@@ -423,7 +418,6 @@ def copa_prompt_arabic(line, task_name: str = None):
         choices=choices,
         gold_index=answer,
         instruction="",
-        target_for_fewshot_sorting=choices[answer],
     )
 
 
@@ -468,7 +462,6 @@ def hellaswag_prompt_arabic(line, task_name: str = None):
         choices=endings,
         gold_index=answer_index,
         instruction=instruction,
-        target_for_fewshot_sorting=endings[answer_index],
     )
 
 
@@ -506,7 +499,6 @@ def toxigen_prompt_arabic(line, task_name: str = None):
         choices=["لا", "نعم"],
         gold_index=label,
         instruction=instruction,
-        target_for_fewshot_sorting="نعم" if label == 1 else "لا",
     )
 
 
@@ -558,7 +550,6 @@ def sciq_prompt_arabic(line, task_name: str = None):
         choices=choices,
         gold_index=answer_index,
         instruction=instruction,
-        target_for_fewshot_sorting=choices[answer_index],
     )
 
 

community_tasks/serbian_eval.py

@@ -200,8 +200,6 @@ def serbian_eval_prompt(line: dict, task_name: Optional[str] = None) -> Doc:
             - choices (list of str): The list of available answer choices.
             - gold_index (int): The index of the correct answer.
             - instruction (str): The instruction shown to the user in Serbian.
-            - target_for_fewshot_sorting (Union[str, list of str]): The correct answer, either as a
-              string (for regular tasks) or a list of strings (for MMLU tasks).
     """
 
     question = line["query"]
@@ -226,16 +224,12 @@ def serbian_eval_prompt(line: dict, task_name: Optional[str] = None) -> Doc:
 
     query += "\n\nKrajnji odgovor:"
 
-    # Finalize target_for_fewshot_sorting as we handle mmlu task group as string
-    target_for_fewshot_sorting = [choices[gold_index]] if task_name and "mmlu" in task_name else choices[gold_index]
-
     return Doc(
         task_name=task_name,
         query=query,
         choices=choices,
         gold_index=gold_index,
         instruction=instruction,
-        target_for_fewshot_sorting=target_for_fewshot_sorting,
     )
 
 

examples/model_configs/endpoint_model.yaml

@@ -10,8 +10,8 @@ model:
     accelerator: "gpu"
     region: "eu-west-1"
     vendor: "aws"
-    instance_size: "medium"
-    instance_type: "g5.2xlarge"
+    instance_size: "x1"
+    instance_type: "nvidia-a10g"
     framework: "pytorch"
     endpoint_type: "protected"
     namespace: null # The namespace under which to launch the endopint. Defaults to the current user's namespace

examples/nanotron/custom_evaluation_tasks.py

@@ -333,7 +333,6 @@ def mmlu_harness(line, task_name: str = None):
         task_name=task_name,
         query=prompt,
         choices=[" A", " B", " C", " D"],
-        target_for_fewshot_sorting=[" A", " B", " C", " D"][gold_ix],
         gold_index=gold_ix,
         instruction=f"The following are multiple choice questions (with answers) about {topic.replace('_', ' ')}.\n\n",
     )

examples/nanotron/custom_task.py

@@ -36,7 +36,7 @@ def mmlu_signs(line, topic):
     return {
         "query": prompt,
         "choices": [" +", " *", " =", " #"] if is_few_shots else ["+", "*", "=", "#"],
-        "target_for_fewshot_sorting": [" +", " *", " =", " #"][gold_ix],
+        "fewshot_sorting_class": [" +", " *", " =", " #"][gold_ix],
         "gold_index": gold_ix,
         "instruction": f"The following are multiple choice questions (with answers) about  {topic.replace('_', ' ')}.\n\n",
     }
@@ -58,7 +58,7 @@ def mmlu_numbers(line, topic):
     return {
         "query": prompt,
         "choices": [" 1", " 2", " 3", " 4"] if is_few_shots else ["1", "2", "3", "4"],
-        "target_for_fewshot_sorting": [" 1", " 2", " 3", " 4"][gold_ix],
+        "fewshot_sorting_class": [" 1", " 2", " 3", " 4"][gold_ix],
         "gold_index": gold_ix,
         "instruction": f"The following are multiple choice questions (with answers) about  {topic.replace('_', ' ')}.\n\n",
     }

src/lighteval/models/vllm_model.py

@@ -98,7 +98,8 @@ def tokenizer(self):
 
     def cleanup(self):
         destroy_model_parallel()
-        del self.model.llm_engine.model_executor.driver_worker
+        if self.model is not None:
+            del self.model.llm_engine.model_executor.driver_worker
         self.model = None
         gc.collect()
         ray.shutdown()

src/lighteval/tasks/default_prompts.py

@@ -176,7 +176,6 @@ def bbh_harness(line, task_name: str = None):
         query=query,
         choices=choices,
         gold_index=correct_index,
-        target_for_fewshot_sorting=choices,
         instruction=line.get("task_prefix", None),
     )
 
@@ -196,7 +195,6 @@ def bbh_lighteval(line, task_name: str = None):
         query=query,
         choices=LETTER_INDICES[: len(line["choices"])],
         gold_index=line["target_idx"],
-        target_for_fewshot_sorting=LETTER_INDICES[: len(line["choices"])],
         instruction=line.get("task_prefix", None),
     )
 
@@ -207,7 +205,6 @@ def bbh(line, instruction, choices, task_name: str = None):
         query=f"{instruction}Q: {line['input']}\nA:",
         choices=choices,
         gold_index=choices.index(line["target"]),
-        target_for_fewshot_sorting=[f" {c}" for c in choices],
         instruction=instruction,
     )
 
@@ -799,7 +796,6 @@ def hellaswag_generative(line, task_name: str = None):
         choices=[" " + i for i in LETTER_INDICES[: len(line["endings"])]],
         gold_index=gold_ix,  # -1 for test,
         instruction="The following are multiple choice questions (with answers) about common sense.\n\n",
-        target_for_fewshot_sorting=line["endings"][gold_ix] if gold_ix > -1 else "",
     )
 
 
@@ -1352,7 +1348,6 @@ def mmlu(line, topic, task_name: str = None):
         choices=[" A", " B", " C", " D"] if is_few_shots else ["A", "B", "C", "D"],
         gold_index=gold_ix,
         instruction=f"The following are multiple choice questions (with answers) about  {topic.replace('_', ' ')}.\n\n",
-        target_for_fewshot_sorting=[" A", " B", " C", " D"][gold_ix],
     )
 
 
@@ -1373,7 +1368,6 @@ def custom_mmlu_thom(line, task_name: str = None):
         choices=[" A", " B", " C", " D"] if is_few_shots else ["A", "B", "C", "D"],
         gold_index=gold_ix,
         instruction=f"The following are multiple choice questions (with answers) about  {topic.replace('_', ' ')}.\n\n",
-        target_for_fewshot_sorting=[" A", " B", " C", " D"][gold_ix],
     )
 
 
@@ -1613,15 +1607,13 @@ def mmlu_harness(line, task_name: str = None):
     query += "Answer:"
 
     gold_ix = LETTER_INDICES.index(line["answer"]) if isinstance(line["answer"], str) else line["answer"]
-    "__few_shots" in line and line["__few_shots"] is True  # We are adding few shots
 
     return Doc(
         task_name=task_name,
         query=query,
         choices=[" A", " B", " C", " D"],
         gold_index=gold_ix,
         instruction=f"The following are multiple choice questions (with answers) about {topic.replace('_', ' ')}.\n\n",
-        target_for_fewshot_sorting=[" A", " B", " C", " D"][gold_ix],
     )
 
 
@@ -1638,8 +1630,8 @@ def mmlu_helm(line, task_name: str = None):
         query=query,
         choices=[" A", " B", " C", " D"],
         gold_index=gold_ix,
+        fewshot_sorting_class=line["choices"][gold_ix],
         instruction=f"The following are multiple choice questions (with answers) about {subject.replace('_', ' ')}.\n\n",
-        target_for_fewshot_sorting=line["choices"][gold_ix],  # specific to HELM evals
     )
 
 
@@ -1816,7 +1808,6 @@ def openbookqa_helm(line, task_name: str = None):
         choices=["A", "B", "C", "D", "E"],
         gold_index=gold_ix,
         instruction="The following are multiple choice questions (with answers) about common sense.\n",
-        target_for_fewshot_sorting=line["choices"]["text"][gold_ix],  # specific to HELM evals
     )
 
 
@@ -1837,14 +1828,13 @@ def piqa_helm(line, task_name: str = None):
     query += "Answer: "
 
     gold_ix = int(line["label"])
-
+    is_few_shots = line.get("__few_shots", False)
     return Doc(
         task_name=task_name,
         query=query,
-        choices=["A", "B"],
+        choices=["A", "B"] if not is_few_shots else [line["sol1"], line["sol2"]],
         gold_index=gold_ix,
         instruction="The following are multiple choice questions (with answers) about common sense.\n",
-        target_for_fewshot_sorting=[line["sol1"], line["sol2"]][gold_ix],
     )
 
 
@@ -1877,13 +1867,11 @@ def pubmed_qa_helm(line, task_name: str = None):
     )
     query += f"\n\nQuestion: {line['question']}\nAnswer: "
     gold_ix = ["yes", "no", "maybe"].index(line["final_decision"])
-
     return Doc(
         task_name=task_name,
         query=query,
         choices=["A", "B", "C"],
         gold_index=gold_ix,
-        target_for_fewshot_sorting=["yes", "no", "maybe"][gold_ix],
     )
 
 
@@ -2263,13 +2251,11 @@ def truthful_qa_helm(line, task_name: str = None):
     query = f"Question: {line['question']}\n"
     query += "".join([f"{key}. {choice}\n" for key, choice in zip(LETTER_INDICES, line["choices"])])
     query += "Answer:"
-
     return Doc(
         task_name=task_name,
         query=query,
         choices=LETTER_INDICES[: len(line["choices"])],
         gold_index=line["gold_index"],
-        target_for_fewshot_sorting=line["choices"][line["gold_index"]],
     )
 
 

src/lighteval/tasks/lighteval_task.py

@@ -340,21 +340,6 @@ def eval_docs(self) -> list[Doc]:
                 self._docs = self.remove_duplicate_docs(self._docs)
         return self._docs
 
-    def doc_to_target(self, formatted_doc: Doc, few_shot: bool = False) -> str:
-        """
-        Returns the target of the given document.
-
-        Args:
-            formatted_doc (Doc): Formatted document.
-            few_shot (bool, optional): Whether the document is used for few
-                shot examples. Defaults to False.
-
-        Returns:
-            str: Target of the document, which is the correct answer for a document.
-        """
-        # likely we mostly need one example not all
-        return as_list(formatted_doc.get_golds(few_shot=few_shot))[0]
-
     def construct_requests(
         self, formatted_doc: Doc, context: str, document_id_seed: str, current_task_name: str
     ) -> Dict[RequestType, List[Request]]:

src/lighteval/tasks/prompt_manager.py

@@ -65,20 +65,33 @@ def doc_to_text(doc: Doc, return_instructions: bool = False) -> Union[str, Tuple
         )
 
     @staticmethod
-    def doc_to_target(formatted_doc: Doc, few_shot: bool = False) -> str:
+    def doc_to_target(formatted_doc: Doc) -> str:
         """
         Returns the target of the given document.
 
         Args:
             formatted_doc (Doc): Formatted document.
-            few_shot (bool, optional): Whether the document is used for few
-                shot examples. Defaults to False.
 
         Returns:
             str: Target of the document, which is the correct answer for a document.
         """
-        # likely we mostly need one example not all
-        return as_list(formatted_doc.get_golds(few_shot=few_shot))[0]
+        return as_list(formatted_doc.get_golds())[0]
+
+    @staticmethod
+    def doc_to_fewshot_sorting_class(formatted_doc: Doc) -> str:
+        """
+        In some cases, when selecting few-shot samples, we want to use specific document classes
+        which need to be specified separately from the target.
+        For example, a document where the gold is a json might want to use only one of the keys of
+        the json to define sorting classes in few shot samples. Else we take the gold.
+
+        Args:
+            formatted_doc (Doc): Formatted document.
+
+        Returns:
+            str: Class of the
+        """
+        return formatted_doc.fewshot_sorting_class or PromptManager.doc_to_target(formatted_doc)
 
     def add_context_to_doc(
         self,
@@ -255,9 +268,7 @@ def get_examples(
 class FewShotSelectionMethod:
     sorting: str  # sorting method for the overall few shot pool (balanced, random, sequential)
     with_sampling: bool  # samples item randomly from the few shot pool
-    fewshotpool_unique: (
-        bool
-    )  # set to true if you are CERTAIN there is no intersection between the few shot pool and your evaluation set
+    fewshotpool_unique: bool  # set to true if you are CERTAIN there is no intersection between the few shot pool and your evaluation set
 
 
 class FewShotSelection(Enum):
@@ -356,16 +367,16 @@ def _init_fewshot_sampling_balanced(
     ):
         fewshotpool = self.task.fewshot_docs()
 
-        # rnd = random.Random(variance_seed)
         random.seed(variance_seed)
 
-        # Build up balanced selection based on labels
-        # Sort by counts of labels
+        # Build up balanced selection based on fewshot_sorting_class
+        # (or the gold target, if the class is undefined)
         label_to_instances = defaultdict(list)
         for instance in fewshotpool:
-            target = PromptManager.doc_to_target(instance, few_shot=True)
+            target = PromptManager.doc_to_fewshot_sorting_class(instance)
             label_to_instances[target].append(instance)
 
+        # Sort by counts of class labels
         counts_to_labels = defaultdict(list)
         for label, instances in sorted(label_to_instances.items()):
             counts_to_labels[len(instances)].append(label)

src/lighteval/tasks/requests.py

@@ -178,7 +178,7 @@ class Doc:
 
     # For few-shot
     instruction: Optional[str] = ""
-    target_for_fewshot_sorting: Optional[str] = None  # will probably have to be removed in the future
+    fewshot_sorting_class: Optional[str] = None  # class to use to select balanced few-shot samples
 
     # Filled when parsing and adding the few-shot context
     ctx: Optional[str] = ""
@@ -194,18 +194,12 @@ def __post_init__(self):
         if self.instruction is None:
             self.instruction = ""
 
-    def get_golds(self, few_shot: bool = False):
+    def get_golds(self):
         """Return gold targets extracted from the target dict"""
         gold_indices = as_list(self.gold_index)
-        if few_shot and self.target_for_fewshot_sorting is not None:
-            choices = self.target_for_fewshot_sorting
-            if isinstance(choices, str):  # correct choice is already selected
-                return choices
-        else:
-            choices = self.choices
         golds = []
         for gold_ix in gold_indices:
-            golds.extend(as_list(choices[gold_ix]))
+            golds.extend(as_list(self.choices[gold_ix]))
         return golds
 
     def __repr__(self):