merged main

lm_eval/models/optimum_lm.py

@@ -28,7 +28,7 @@ class OptimumLM(HFLM):
 
         super().__init__(
             device=self.openvino_device,
-            backend=kwargs.get("backend", "causal"),
+            backend=kwargs.pop("backend", "causal"),
             **kwargs,
         )
 

lm_eval/models/utils.py

@@ -6,6 +6,7 @@ from functools import wraps
 from typing import (
     Any,
     Callable,
+    Dict,
     Iterable,
     Iterator,
     List,
@@ -357,65 +358,164 @@ class Collator:
     A class for reordering and batching elements of an array.
 
     This class allows for sorting an array based on a provided sorting function, grouping elements based on a grouping function, and generating batches from the sorted and grouped data.
+
+    Objects of this class have the group_by attribute which determines the method for grouping
+    the data while batching it. Three options include "gen_kwargs", "contexts", or None:
+        If group_by == "gen_kwargs" then requests will be grouped by gen_kwargs
+        If group_by == "contexts" then requests will be grouped by context + cont[:-1]
+        If None then requests will just be reordered by length descending.
     """
 
     def __init__(
         self,
         arr: List,
-        sort_fn: Callable,
+        sort_fn: Callable = lambda x: x,
         group_fn: Callable = lambda x: x[1],
-        grouping: bool = False,
+        group_by: Union[Literal["gen_kwargs", "contexts"], None] = None,
     ) -> None:
-        self.grouping = grouping
-        self.fn = sort_fn
-        self.group_fn = lambda x: group_fn(x[1])  # first index are enumerated indices
-        self.reorder_indices: List = []
-        self.size = len(arr)
-        self.arr_with_indices: Iterable[Any] = tuple(enumerate(arr))  # [indices, (arr)]
-        if self.grouping is True:
-            self.group_by_index()
+        self._group_by = group_by
+        # 0 indices are enumerated indices. Apply functions to original arr.
+        self._sort_fn = lambda x: sort_fn(x[1])
+        self._group_fn = lambda x: group_fn(x[1])
+        self._reorder_indices: List = []
+        self._size = len(arr)
+        self._arr_with_indices: Union[Dict, Tuple[Tuple[int, Any], ...]] = tuple(
+            enumerate(arr)
+        )  # [indices, (arr)]
+        if self._group_by == "contexts":
+            self._group_by_context()
+        elif self._group_by == "gen_kwargs":
+            self._group_by_index()
+
+    def _group_by_index(self) -> None:
+        """Group the elements of a list based on their indices."""
+        self._arr_with_indices = self.group(
+            self._arr_with_indices, fn=self._group_fn, group_by="gen_kwargs"
+        )
 
-    def group_by_index(self) -> None:
-        self.arr_with_indices = self.group(
-            self.arr_with_indices, fn=self.group_fn, values=False
+    def _group_by_context(self) -> None:
+        """Group the array with indices by context."""
+        self._arr_with_indices = self.group(
+            self._arr_with_indices, fn=self._group_fn, group_by="contexts"
         )
 
     def get_batched(self, n: int = 1, batch_fn: Optional[Callable] = None) -> Iterator:
         """
-        Generates and yields batches from the reordered array.
+        Generates and yields batches from the reordered array. The method of grouping and batching
+        depends on the parameter `group_by`.
+        If `group_by` is set to "gen_kwargs", it will batch the
+        re-ordered values with same gen_kwargs for each batch.
+        If `group_by` is "contexts", it caches the requests by context before batching.
+        If `group_by` is neither "gen_kwargs" nor "contexts", it yields the reordered array
 
         Parameters:
         - n (int): The size of each batch. Defaults to 1.
-        - batch_fn (Optional[Callable[[int, Iterable], int]]): A function to determine the size of each batch. Defaults to None.
+        - batch_fn ([Callable[[int, Iterable], int]] | None): A function to determine the size of
+          each batch. Optional, defaults to None.
+
+        Returns:
+        Iterator: An iterator over batches of reordered elements grouped as per the `group_by`
+                  attribute.
 
         Yields:
-        Iterator: An iterator over batches of reordered elements.
+        List of batched elements according to the `group_by` attribute.
         """
-        if self.grouping:
+        if self._group_by == "gen_kwargs":
             for (
                 key,
                 values,
-            ) in self.arr_with_indices.items():  # type: ignore
+            ) in self._arr_with_indices.items():  # type: ignore
                 values = self._reorder(values)
                 batch = self.get_chunks(values, n=n, fn=batch_fn)
                 yield from batch
+        elif self._group_by == "contexts":
+            # Get one sample from each key
+            values = self._reorder(
+                [value[0] for value in self._arr_with_indices.values()]
+            )
+            batch = self.get_chunks(values, n=n, fn=batch_fn)
+            yield from batch
         else:
-            values = self._reorder(self.arr_with_indices)  # type: ignore
+            values = self._reorder(self._arr_with_indices)  # type: ignore
             batch = self.get_chunks(values, n=n, fn=batch_fn)
             yield from batch
 
-    def _reorder(self, arr: Union[List, Tuple[Tuple[int, Any], ...]]) -> List:
+    def get_cache(
+        self,
+        req_str: Tuple[str, str] = None,
+        cxt_toks: List[int] = None,
+        cont_toks: List[int] = None,
+        logits: torch.Tensor = None,
+    ) -> Iterator[Tuple[Tuple[str, str], List[int], torch.Tensor]]:
+        """
+        Retrieves cached single-token continuations and their associated arguments, updating indices as necessary.
+
+        The behavior of this function varies depending on how the `group_by` attribute is set:
+
+        - When `group_by` is "contexts":
+            The function identifies single-token continuations by checking for keys that equate to
+            [context+continuation][-1] and logs the indices for re-ordering.
+            In this mode, this function can work in two scenarios:
+
+            1. Cache Hit - Single Match:
+                If a single matching context-continuation pair is found in the cache,
+                the function yields the original arguments.
+
+            2. Cache Hit - Multiple Matches:
+                If multiple matching context-continuation pairs are found in the cache,
+                the function expands the logits batch dimension to match the number of cache hits.
+                It updates the original requests and continuation tokens.
+
+        - When `group_by` is not set to "contexts":
+            This method yields the original arguments, logits and continuation tokens,
+            without checking for one-token continuations.
+
+        Parameters:
+        - req_str (tuple[str, str]): Original strings used for CachingLM.
+        - cxt_toks (list[int]): Full context tokens used for lookup.
+        - cont_toks (list[int]): Continuation tokens for which logits were generated.
+        - logits (torch.Tensor [1, seq_length, vocab_size]): Logits generated by the model given context and continuation keys.
+
+        Yields:
+        - Iterator:
+            - req_str (tuple[str, str]): strings used for CachingLM.
+            - cont_toks (list[int]) : continuation tokens.
+            - logits (torch.Tensor [1, seq_length, vocab_size]): The original logits (repeated cache hit times)
+        """
+        if self._group_by == "contexts":
+            cache_hit: List[
+                Tuple[int, Tuple[Tuple[str, str], List[int], List[int]]]
+            ] = self._arr_with_indices.pop(tuple(cxt_toks + cont_toks[:-1]))
+            if (cache_size := len(cache_hit)) == 1:
+                self._reorder_indices.extend(x[0] for x in cache_hit)
+                yield req_str, cont_toks, logits
+            else:
+                # If we have matching requests then expand the batch dimension (no-op) and
+                # yield each along with its corresponding args.
+                multilogits = logits.expand(cache_size, -1, -1).chunk(cache_size)
+                indices, req_str, cont_toks = zip(
+                    *[(x[0], x[1][0], x[-1][-1]) for x in cache_hit]
+                )
+                self._reorder_indices.extend(indices)
+                for c_key, cont_tok, logit in zip(req_str, cont_toks, multilogits):
+                    yield c_key, cont_tok, logit
+        else:
+            yield req_str, cont_toks, logits
+
+    def _reorder(self, arr: Union[List, Tuple[Tuple[int, Any], ...]]) -> Iterator:
         """
         Reorders the elements in the array based on the sorting function.
 
         Parameters:
-        - arr (Union[List, Tuple[Tuple[int, Any], ...]]): The array or iterable to be reordered.
+        - arr (list | tuple[tuple[int, Any], ...]]): The array or iterable to be reordered.
 
         Yields:
-        List: Yields reordered elements one by one.
+            Iterator
         """
-        arr = sorted(arr, key=lambda x: self.fn(x[1]))
-        self.reorder_indices.extend([x[0] for x in arr])
+        arr = sorted(arr, key=self._sort_fn)
+        if not self._group_by == "contexts":
+            # If grouped by contexts then indices will be set in get_cache()
+            self._reorder_indices.extend([x[0] for x in arr])
         yield from [x[1] for x in arr]
 
     def get_original(self, newarr: List) -> List:
@@ -423,15 +523,15 @@ class Collator:
         Restores the original order of elements from the reordered list.
 
         Parameters:
-        - newarr (List): The reordered array.
+        - newarr (list): The reordered array.
 
         Returns:
-        List: The array with elements restored to their original order.
+        list: The array with elements restored to their original order.
         """
-        res = [None] * self.size
-        cov = [False] * self.size
+        res = [None] * self._size
+        cov = [False] * self._size
 
-        for ind, v in zip(self.reorder_indices, newarr):
+        for ind, v in zip(self._reorder_indices, newarr):
             res[ind] = v
             cov[ind] = True
 
@@ -440,23 +540,36 @@ class Collator:
         return res
 
     def __len__(self):
-        return self.size
+        return self._size
 
     @staticmethod
-    def group(arr: Iterable, fn: Callable, values: bool = False) -> Iterable:
+    def group(
+        arr: Iterable,
+        fn: Callable,
+        group_by: Literal["gen_kwargs", "contexts"] = "gen_kwargs",
+    ) -> dict:
         """
         Groups elements of an iterable based on a provided function.
 
+
+        The `group_by` parameter determines the method of grouping.
+        If `group_by` is "contexts", the elements are grouped by [context + cont][:-1].
+        If `group_by` is "gen_kwargs", the elements are grouped based on the gen_kwargs dict.
+
         Parameters:
         - arr (Iterable): The iterable to be grouped.
         - fn (Callable): The function to determine the grouping.
         - values (bool): If True, returns the values of the group. Defaults to False.
 
         Returns:
-        Iterable: An iterable of grouped elements.
+        Iterator: An iterable of grouped elements.
         """
         res = collections.defaultdict(list)
         for ob in arr:
+            # where ob == [context + cont]
+            if group_by == "contexts":
+                res[tuple(fn(ob))].append(ob)
+            else:
                 try:
                     hashable_dict = tuple(
                         (
@@ -468,11 +581,9 @@ class Collator:
                         for key, value in sorted(fn(ob).items())
                     )
                     res[hashable_dict].append(ob)
-            except TypeError:
-                res[fn(ob)].append(ob)
-        if not values:
+                except (TypeError, AttributeError):
+                    res[tuple(fn(ob))].append(ob)
         return res
-        return res.values()
 
     @staticmethod
     def get_chunks(_iter, n: int = 0, fn=None):

lm_eval/models/vllm_causallms.py

@@ -5,7 +5,7 @@ from typing import List, Literal, Optional, Tuple, Union
 from tqdm import tqdm
 
 from lm_eval.api.instance import Instance
-from lm_eval.api.model import LM
+from lm_eval.api.model import TemplateLM
 from lm_eval.api.registry import register_model
 from lm_eval.models.utils import Collator, divide
 from lm_eval.utils import (
@@ -35,7 +35,7 @@ def run_inference_one_model(
 
 
 @register_model("vllm")
-class VLLM(LM):
+class VLLM(TemplateLM):
     _DEFAULT_MAX_LENGTH = 2048
 
     def __init__(
@@ -47,6 +47,7 @@ class VLLM(LM):
         tokenizer: Optional[str] = None,
         tokenizer_mode: Literal["auto", "slow"] = "auto",
         tokenizer_revision: Optional[str] = None,
+        add_bos_token: Optional[bool] = False,
         tensor_parallel_size: int = 1,
         quantization: Optional[str] = None,
         max_gen_toks: int = 256,
@@ -114,6 +115,7 @@ class VLLM(LM):
             trust_remote_code=trust_remote_code,
             tokenizer_revision=tokenizer_revision,
         )
+        self.add_bos_token = add_bos_token
 
         self._max_gen_toks = max_gen_toks
 
@@ -147,10 +149,12 @@ class VLLM(LM):
         self,
         string: str,
         left_truncate_len=None,
-        add_special_tokens=False,
+        add_special_tokens=None,
         truncation=False,
     ):
         """ """
+        if not add_special_tokens:
+            add_special_tokens = False or self.add_bos_token
         encoding = self.tokenizer.encode(
             string, add_special_tokens=add_special_tokens, truncation=truncation
         )
@@ -194,37 +198,6 @@ class VLLM(LM):
         )
         return outputs
 
-    def _encode_pair(
-        self, context: str, continuation: str
-    ) -> Tuple[List[int], List[int]]:
-        n_spaces = len(context) - len(context.rstrip())
-        if n_spaces > 0:
-            continuation = context[-n_spaces:] + continuation
-            context = context[:-n_spaces]
-
-        whole_enc = self.tok_encode(context + continuation, add_special_tokens=False)
-        context_enc = self.tok_encode(context, add_special_tokens=False)
-
-        context_enc_len = len(context_enc)
-        continuation_enc = whole_enc[context_enc_len:]
-        return context_enc, continuation_enc
-
-    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
-        new_reqs = []
-        for context, continuation in [req.args for req in requests]:
-            if context == "":
-                # end of text as context
-                context_enc, continuation_enc = (
-                    [self.eot_token_id],
-                    self.tok_encode(continuation),
-                )
-            else:
-                context_enc, continuation_enc = self._encode_pair(context, continuation)
-
-            new_reqs.append(((context, continuation), context_enc, continuation_enc))
-
-        return self._loglikelihood_tokens(new_reqs)
-
     def loglikelihood_rolling(self, requests: List[Instance]) -> List[float]:
         loglikelihoods = []
 
@@ -276,12 +249,16 @@ class VLLM(LM):
         # we group requests by their generation_kwargs,
         # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling
         # in the same batch.
-        re_ords = Collator(requests, _collate_gen, grouping=True)
+        re_ords = Collator(requests, _collate_gen, group_by="gen_kwargs")
         chunks = re_ords.get_batched(
             n=int(self.batch_size) if self.batch_size != "auto" else 0, batch_fn=None
         )
 
-        pbar = tqdm(total=len(requests), disable=(self.rank != 0))
+        pbar = tqdm(
+            total=len(requests),
+            disable=(self.rank != 0),
+            desc="Running generate_until requests",
+        )
         # for each different set of kwargs, we execute all requests, by batch.
         for chunk in chunks:
             context_and_encoding, all_gen_kwargs = zip(*chunk)
@@ -356,7 +333,11 @@ class VLLM(LM):
             n=int(self.batch_size) if self.batch_size != "auto" else 0, batch_fn=None
         )
 
-        pbar = tqdm(total=len(requests), disable=disable_tqdm)
+        pbar = tqdm(
+            total=len(requests),
+            disable=disable_tqdm,
+            desc="Running loglikelihood requests",
+        )
         for chunk in chunks:
             inputs = []
             ctxlens = []

lm_eval/prompts/__init__.py

-import os
 import ast
-
+import os
 from typing import Dict
+
 from lm_eval import utils
 from lm_eval.utils import eval_logger
 
+
 # Prompt library.
 # Stores prompts in a dictionary indexed by 2 levels:
 # prompt category name, and prompt name.

lm_eval/tasks/__init__.py

-import os
 import abc
 import collections
-
+import logging
+import os
 from functools import partial
-from typing import List, Union, Dict
+from typing import Dict, List, Union
 
 from lm_eval import utils
-from lm_eval.api.task import Task, ConfigurableTask
-
-import logging
+from lm_eval.api.task import ConfigurableTask, Task
 
 
 class TaskManager:
@@ -16,20 +14,14 @@ class TaskManager:
     and an optional directory if provided.
 
     """
-    def __init__(
-        self,
-        verbosity="INFO",
-        include_path=None
-        ) -> None:
 
+    def __init__(self, verbosity="INFO", include_path=None) -> None:
         self.verbosity = verbosity
         self.include_path = include_path
         self.logger = utils.eval_logger
         self.logger.setLevel(getattr(logging, f"{verbosity}"))
 
-        self._task_index = self.initialize_tasks(
-            include_path=include_path
-            )
+        self._task_index = self.initialize_tasks(include_path=include_path)
         self._all_tasks = sorted(list(self._task_index.keys()))
 
         self.task_group_map = collections.defaultdict(list)
@@ -65,27 +57,29 @@ class TaskManager:
         return self._task_index
 
     def match_tasks(self, task_list):
-        return utils.pattern_match(
-            task_list, self.all_tasks
-        )
+        return utils.pattern_match(task_list, self.all_tasks)
 
     def _name_is_registered(self, name):
         if name in self.all_tasks:
             return True
         return False
 
-    def _name_is_task(self, name):
+    def _name_is_task(self, name) -> bool:
         if self._name_is_registered(name) and ("task" in self.task_index[name]["type"]):
             return True
         return False
 
     def _name_is_group(self, name):
-        if self._name_is_registered(name) and (self.task_index[name]["type"] == "group"):
+        if self._name_is_registered(name) and (
+            self.task_index[name]["type"] == "group"
+        ):
             return True
         return False
 
     def _name_is_python_task(self, name):
-        if self._name_is_registered(name) and (self.task_index[name]["type"] == "python_task"):
+        if self._name_is_registered(name) and (
+            self.task_index[name]["type"] == "python_task"
+        ):
             return True
         return False
 
@@ -117,7 +111,7 @@ class TaskManager:
             return utils.load_yaml_config(yaml_path, mode="full")
 
     def _get_tasklist(self, name):
-        assert self._name_is_task(name) == False
+        assert self._name_is_task(name) is False
         return self.task_index[name]["task"]
 
     def _process_alias(self, config, group=None):
@@ -174,7 +168,9 @@ class TaskManager:
                     group_config = self._get_config(name_or_config)
                     if set(group_config.keys()) > set(["task", "group"]):
                         update_config = {
-                            k:v for k,v in group_config.items() if k not in ["task", "group"]
+                            k: v
+                            for k, v in group_config.items()
+                            if k not in ["task", "group"]
                         }
                     yaml_path = self._get_yaml_path(group_name)
 
@@ -183,9 +179,8 @@ class TaskManager:
                         update_config.pop("group_alias")
 
         if isinstance(name_or_config, dict):
-
             if update_config is not None:
-                name_or_config={
+                name_or_config = {
                     **name_or_config,
                     **update_config,
                 }
@@ -196,7 +191,9 @@ class TaskManager:
                 # if self._name_is_task(name) is False:
                 if self._name_is_group(name):
                     group_name = name
-                    update_config = {k:v for k,v in name_or_config.items() if k != "task"}
+                    update_config = {
+                        k: v for k, v in name_or_config.items() if k != "task"
+                    }
                     subtask_list = self._get_tasklist(name)
                     if subtask_list == -1:
                         subtask_list = self._get_config(name)["task"]
@@ -207,36 +204,53 @@ class TaskManager:
                         # Check if this is a duplicate.
                         if parent_name is not None:
                             name_or_config["group"] = parent_name
-                            num_duplicate = len(list(filter(lambda x: x.startswith(name), self.task_group_map[parent_name])))
+                            num_duplicate = len(
+                                list(
+                                    filter(
+                                        lambda x: x.startswith(name),
+                                        self.task_group_map[parent_name],
+                                    )
+                                )
+                            )
                             if num_duplicate > 0:
                                 name = f"{name}-{num_duplicate}"
                             self.task_group_map[parent_name].append(name)
 
-                        task_config={
+                        task_config = {
                             **base_task_config,
                             **name_or_config,
                         }
                     else:
                         task_config = name_or_config
-                    return load_task(task_config, task=name, group=parent_name, yaml_path=yaml_path)
+                    return load_task(
+                        task_config, task=name, group=parent_name, yaml_path=yaml_path
+                    )
             else:
                 group_name = name_or_config["group"]
                 subtask_list = name_or_config["task"]
-                # update_config = {k:v for k,v in name_or_config.items() if k != "task"}
                 if set(name_or_config.keys()) > set(["task", "group"]):
                     update_config = {
-                        k:v for k,v in name_or_config.items() if k not in ["task", "group"]
+                        k: v
+                        for k, v in name_or_config.items()
+                        if k not in ["task", "group"]
                     }
 
         all_subtasks = {}
-        if (parent_name is not None):
+        if parent_name is not None:
             all_subtasks = {group_name: (parent_name, None)}
 
-        fn = partial(self._load_individual_task_or_group, parent_name=group_name, update_config=update_config, yaml_path=yaml_path)
-        all_subtasks = {**all_subtasks, **dict(collections.ChainMap(*map(fn, subtask_list)))}
+        fn = partial(
+            self._load_individual_task_or_group,
+            parent_name=group_name,
+            update_config=update_config,
+            yaml_path=yaml_path,
+        )
+        all_subtasks = {
+            **all_subtasks,
+            **dict(collections.ChainMap(*map(fn, subtask_list))),
+        }
         return all_subtasks
 
-
     def load_task_or_group(self, task_list: Union[str, list] = None) -> dict:
         """Loads a dictionary of task objects from a list
 
@@ -250,12 +264,7 @@ class TaskManager:
             task_list = [task_list]
 
         all_loaded_tasks = dict(
-            collections.ChainMap(
-                *map(
-                    self._load_individual_task_or_group,
-                    task_list
-                )
-            )
+            collections.ChainMap(*map(self._load_individual_task_or_group, task_list))
         )
         return all_loaded_tasks
 
@@ -343,6 +352,7 @@ class TaskManager:
 
         return tasks_and_groups
 
+
 def include_path(task_dir):
     logger = utils.eval_logger
     logger.setLevel(getattr(logging, "INFO"))
@@ -352,6 +362,7 @@ def include_path(task_dir):
     )
     return 0
 
+
 def initialize_tasks(verbosity="INFO"):
     logger = utils.eval_logger
     logger.setLevel(getattr(logging, f"{verbosity}"))
@@ -362,6 +373,7 @@ def initialize_tasks(verbosity="INFO"):
     )
     return 0
 
+
 def get_task_name_from_config(task_config: Dict[str, str]) -> str:
     if "task" in task_config:
         return task_config["task"]
@@ -370,6 +382,7 @@ def get_task_name_from_config(task_config: Dict[str, str]) -> str:
     else:
         return "{dataset_path}".format(**task_config)
 
+
 def get_task_name_from_object(task_object):
     if hasattr(task_object, "config"):
         return task_object._config["task"]
@@ -382,7 +395,10 @@ def get_task_name_from_object(task_object):
         else type(task_object).__name__
     )
 
-def get_task_dict(task_name_list: List[Union[str, Dict, Task]], task_manager: TaskManager = None):
+
+def get_task_dict(
+    task_name_list: List[Union[str, Dict, Task]], task_manager: TaskManager = None
+):
     """Creates a dictionary of task objects from either a name of task, config, or prepared Task object.
 
     :param task_name_list: List[Union[str, Dict, Task]]
@@ -409,7 +425,9 @@ def get_task_dict(task_name_list: List[Union[str, Dict, Task]], task_manager: Ta
         if task_manager is None:
             task_manager = TaskManager()
 
-        task_name_from_string_dict = task_manager.load_task_or_group(string_task_name_list)
+        task_name_from_string_dict = task_manager.load_task_or_group(
+            string_task_name_list
+        )
 
     for task_element in others_task_name_list:
         if isinstance(task_element, dict):
@@ -427,6 +445,7 @@ def get_task_dict(task_name_list: List[Union[str, Dict, Task]], task_manager: Ta
     assert set(task_name_from_string_dict.keys()).isdisjoint(
         set(task_name_from_object_dict.keys())
     )
+
     return {
         **task_name_from_string_dict,
         **task_name_from_config_dict,

lm_eval/tasks/ammlu/README.md

+# ArabicMMLU
+
+### Paper
+
+ArabicMMLU: Measuring massive multitask language understanding in Arabic
+This dataset has been translated from the original MMLU with the help of GPT-4.
+
+The original data [MMLU](https://arxiv.org/pdf/2009.03300v3.pdf)
+
+The translation has been done with AceGPT researchers [AceGPT](https://arxiv.org/abs/2309.12053)
+
+ArabicMMLU is a comprehensive evaluation benchmark specifically designed to evaluate the knowledge and reasoning abilities of LLMs within the context of Arabic language and culture.
+ArabicMMLU covers a wide range of subjects, comprising 57 topics that span from elementary to advanced professional levels.
+
+Homepage: [AceGPT Homepage](https://github.com/FreedomIntelligence/AceGPT/tree/main/eval/benchmark_eval/benchmarks/MMLUArabic)
+
+### Citation
+
+
+### Groups and Tasks
+
+#### Groups
+
+- `ammlu`: All 57 subjects of the ArabicMMLU dataset, evaluated following the methodology in MMLU's original implementation.
+
+#### Tasks
+
+
+The following tasks evaluate subjects in the ArabicMMLU dataset using loglikelihood-based multiple-choice scoring:
+- `ammlu_{subject_english}`
+
+### Checklist
+
+* [x] Is the task an existing benchmark in the literature?
+  * [x] Have you referenced the original paper that introduced the task?
+  * [x] If yes, does the original paper provide a reference implementation?
+    * [x] Yes, original implementation contributed by author of the benchmark
+
+If other tasks on this dataset are already supported:
+* [x] Is the "Main" variant of this task clearly denoted?
+* [x] Have you provided a short sentence in a README on what each new variant adds / evaluates?
+* [x] Have you noted which, if any, published evaluation setups are matched by this variant?

lm_eval/tasks/ammlu/_default_template_yaml

+group: ammlu
+dataset_path: Hennara/ammlu
+test_split: test
+fewshot_split: dev
+fewshot_config:
+  sampler: first_n
+output_type: multiple_choice
+doc_to_text: "{{Question.strip()}}\nA. {{A}}\nB. {{B}}\nC. {{C}}\nD. {{D}}\nالجواب："
+doc_to_choice: ["A", "B", "C", "D"]
+doc_to_target: "{{['A', 'B', 'C', 'D'].index(Answer)}}"
+metric_list:
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
+    aggregation: mean
+    higher_is_better: true
+metadata:
+  version: 0.0

lm_eval/tasks/ammlu/_generate_configs.py

+"""
+Take in a YAML, and output all other splits with this YAML
+"""
+import argparse
+import os
+
+import yaml
+from tqdm import tqdm
+
+
+SUBJECTS = {
+    "abstract_algebra": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "anatomy": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "astronomy": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "business_ethics": "علوم أخرى",
+    "clinical_knowledge": "علوم أخرى",
+    "college_biology": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "college_chemistry": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "college_computer_science": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "college_mathematics": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "college_medicine": "علوم أخرى",
+    "college_physics": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "computer_security": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "conceptual_physics": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "econometrics": "العلوم الإجتماعية",
+    "electrical_engineering": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "elementary_mathematics": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "formal_logic": "العلوم الانسانية",
+    "global_facts": "علوم أخرى",
+    "high_school_biology": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "high_school_chemistry": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "high_school_computer_science": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "high_school_european_history": "العلوم الانسانية",
+    "high_school_geography": "العلوم الإجتماعية",
+    "high_school_government_and_politics": "العلوم الإجتماعية",
+    "high_school_macroeconomics": "العلوم الإجتماعية",
+    "high_school_mathematics": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "high_school_microeconomics": "العلوم الإجتماعية",
+    "high_school_physics": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "high_school_psychology": "العلوم الإجتماعية",
+    "high_school_statistics": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "high_school_us_history": "العلوم الانسانية",
+    "high_school_world_history": "العلوم الانسانية",
+    "human_aging": "علوم أخرى",
+    "human_sexuality": "العلوم الإجتماعية",
+    "international_law": "العلوم الانسانية",
+    "jurisprudence": "العلوم الانسانية",
+    "logical_fallacies": "العلوم الانسانية",
+    "machine_learning": "ألعلوم وتقنية المعلومات و الرياضيات",
+    "management": "علوم أخرى",
+    "marketing": "علوم أخرى",
+    "medical_genetics": "علوم أخرى",
+    "miscellaneous": "علوم أخرى",
+    "moral_disputes": "العلوم الانسانية",
+    "moral_scenarios": "العلوم الانسانية",
+    "nutrition": "علوم أخرى",
+    "philosophy": "العلوم الانسانية",
+    "prehistory": "العلوم الانسانية",
+    "professional_accounting": "علوم أخرى",
+    "professional_law": "العلوم الانسانية",
+    "professional_medicine": "علوم أخرى",
+    "professional_psychology": "العلوم الإجتماعية",
+    "public_relations": "العلوم الإجتماعية",
+    "security_studies": "العلوم الإجتماعية",
+    "sociology": "العلوم الإجتماعية",
+    "us_foreign_policy": "العلوم الإجتماعية",
+    "virology": "علوم أخرى",
+    "world_religions": "العلوم الانسانية",
+}
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--base_yaml_path", required=True)
+    parser.add_argument("--save_prefix_path", default="ammlu")
+    parser.add_argument("--cot_prompt_path", default=None)
+    parser.add_argument("--task_prefix", default="")
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    args = parse_args()
+
+    # get filename of base_yaml so we can `"include": ` it in our other YAMLs.
+    base_yaml_name = os.path.split(args.base_yaml_path)[-1]
+    with open(args.base_yaml_path, encoding="utf-8") as f:
+        base_yaml = yaml.full_load(f)
+
+    if args.cot_prompt_path is not None:
+        import json
+
+        with open(args.cot_prompt_path, encoding="utf-8") as f:
+            cot_file = json.load(f)
+
+    for subject_eng, category in tqdm(SUBJECTS.items()):
+        if args.cot_prompt_path is not None:
+            description = cot_file[subject_eng]
+        else:
+            description = f"فم بعملية التقييم في مجال {category} \n\n"
+
+        yaml_dict = {
+            "include": base_yaml_name,
+            "task": f"ammlu_{args.task_prefix}_{subject_eng}"
+            if args.task_prefix != ""
+            else f"ammlu_{subject_eng}",
+            "dataset_name": subject_eng,
+            "description": description,
+        }
+
+        file_save_path = args.save_prefix_path + f"_{subject_eng}.yaml"
+        print(f"Saving yaml for subset {subject_eng} to {file_save_path}")
+        with open(file_save_path, "w", encoding="utf-8") as yaml_file:
+            yaml.dump(
+                yaml_dict,
+                yaml_file,
+                width=float("inf"),
+                allow_unicode=True,
+                default_style='"',
+            )

lm_eval/tasks/ammlu/ammlu_abstract_algebra.yaml

+"dataset_name": "abstract_algebra"
+"description": "فم بعملية التقييم في مجال ألعلوم وتقنية المعلومات و الرياضيات \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_abstract_algebra"

lm_eval/tasks/ammlu/ammlu_anatomy.yaml

+"dataset_name": "anatomy"
+"description": "فم بعملية التقييم في مجال ألعلوم وتقنية المعلومات و الرياضيات \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_anatomy"

lm_eval/tasks/ammlu/ammlu_astronomy.yaml

+"dataset_name": "astronomy"
+"description": "فم بعملية التقييم في مجال ألعلوم وتقنية المعلومات و الرياضيات \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_astronomy"

lm_eval/tasks/ammlu/ammlu_business_ethics.yaml

+"dataset_name": "business_ethics"
+"description": "فم بعملية التقييم في مجال علوم أخرى \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_business_ethics"

lm_eval/tasks/ammlu/ammlu_clinical_knowledge.yaml

+"dataset_name": "clinical_knowledge"
+"description": "فم بعملية التقييم في مجال علوم أخرى \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_clinical_knowledge"

lm_eval/tasks/ammlu/ammlu_college_biology.yaml

+"dataset_name": "college_biology"
+"description": "فم بعملية التقييم في مجال ألعلوم وتقنية المعلومات و الرياضيات \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_college_biology"

lm_eval/tasks/ammlu/ammlu_college_chemistry.yaml

+"dataset_name": "college_chemistry"
+"description": "فم بعملية التقييم في مجال ألعلوم وتقنية المعلومات و الرياضيات \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_college_chemistry"

lm_eval/tasks/ammlu/ammlu_college_computer_science.yaml

+"dataset_name": "college_computer_science"
+"description": "فم بعملية التقييم في مجال ألعلوم وتقنية المعلومات و الرياضيات \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_college_computer_science"

lm_eval/tasks/ammlu/ammlu_college_mathematics.yaml

+"dataset_name": "college_mathematics"
+"description": "فم بعملية التقييم في مجال ألعلوم وتقنية المعلومات و الرياضيات \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_college_mathematics"

lm_eval/tasks/ammlu/ammlu_college_medicine.yaml

+"dataset_name": "college_medicine"
+"description": "فم بعملية التقييم في مجال علوم أخرى \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_college_medicine"

lm_eval/tasks/ammlu/ammlu_college_physics.yaml

+"dataset_name": "college_physics"
+"description": "فم بعملية التقييم في مجال ألعلوم وتقنية المعلومات و الرياضيات \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_college_physics"

lm_eval/tasks/ammlu/ammlu_computer_security.yaml

+"dataset_name": "computer_security"
+"description": "فم بعملية التقييم في مجال ألعلوم وتقنية المعلومات و الرياضيات \n\n"
+"include": "_default_template_yaml"
+"task": "ammlu_computer_security"