Merge pull request #501 from EleutherAI/update-config

Update config

.gitignore

@@ -3,3 +3,5 @@ env
 data/
 lm_cache
 .idea
+
+*.egg-info/

.pre-commit-config.yaml

@@ -12,6 +12,7 @@ repos:
       - id: check-merge-conflict
       - id: check-symlinks
       - id: check-yaml
+        args: ['--unsafe']
       - id: destroyed-symlinks
       - id: detect-private-key
       - id: end-of-file-fixer

README.md

@@ -45,7 +45,7 @@ python main.py \
     --device cuda:0
 ```
 
-Additional arguments can be provided to the model constructor using the `--model_args` flag. Most notably, this supports the common practice of using the `revisions` feature on the Hub to store partialy trained checkpoints:
+Additional arguments can be provided to the model constructor using the `--model_args` flag. Most notably, this supports the common practice of using the `revisions` feature on the Hub to store partially trained checkpoints:
 
 ```bash
 python main.py \
@@ -64,8 +64,8 @@ To use with [PEFT](https://github.com/huggingface/peft), take the call you would
 python main.py \
     --model hf-causal \
     --model_args pretrained=EleutherAI/gpt-j-6b,peft=nomic-ai/gpt4all-j-lora \
-    --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq \ 
-    --device cuda:0 
+    --tasks openbookqa,arc_easy,winogrande,hellaswag,arc_challenge,piqa,boolq \
+    --device cuda:0
 ```
 
 Our library also supports the OpenAI API:
@@ -78,7 +78,7 @@ python main.py \
     --tasks lambada_openai,hellaswag
 ```
 
-While this functionality is only officially mantained for the official OpenAI API, it tends to also work for other hosting services that use the same API such as [goose.ai](goose.ai) with minor modification. We also have an implementation for the [TextSynth](https://textsynth.com/index.html) API, using `--model textsynth`.
+While this functionality is only officially maintained for the official OpenAI API, it tends to also work for other hosting services that use the same API such as [goose.ai](goose.ai) with minor modification. We also have an implementation for the [TextSynth](https://textsynth.com/index.html) API, using `--model textsynth`.
 
 To verify the data integrity of the tasks you're performing in addition to running the tasks themselves, you can use the `--check_integrity` flag:
 
@@ -116,7 +116,7 @@ When reporting eval harness results, please also report the version of each task
 
 ## Test Set Decontamination
 
-To address concerns about train / test contamination, we provide utilities for comparing results on a benchmark using only the data points nto found in the model trainign set. Unfortunately, outside of models trained on the Pile ans C4, its very rare that people who train models disclose the contents of the training data. However this utility can be useful to evaluate models you have trained on private data, provided you are willing to pre-compute the necessary indices. We provide computed indices for 13-gram exact match deduplication against the Pile, and plan to add additional precomputed dataset indices in the future (including C4 and min-hash LSH deduplication).
+To address concerns about train / test contamination, we provide utilities for comparing results on a benchmark using only the data points nto found in the model trainign set. Unfortunately, outside of models trained on the Pile and C4, its very rare that people who train models disclose the contents of the training data. However this utility can be useful to evaluate models you have trained on private data, provided you are willing to pre-compute the necessary indices. We provide computed indices for 13-gram exact match deduplication against the Pile, and plan to add additional precomputed dataset indices in the future (including C4 and min-hash LSH deduplication).
 
 For details on text decontamination, see the [decontamination guide](./docs/decontamination.md).
 

lm_eval/api/__init__.py

 from . import metrics
-
-METRIC_REGISTRY = {
-    "matthews_corrcoef": metrics.matthews_corrcoef,
-    "f1_score": metrics.f1_score,
-    "perplexity": metrics.perplexity,
-    "bleu": metrics.bleu,
-    "chrf": metrics.chrf,
-    "ter": metrics.ter,
-}
-
-AGGREGATION_REGISTRY = {
-    "mean": metrics.mean,
-    "median": metrics.median,
-    "perplexity": metrics.perplexity,
-}
-
-HIGHER_IS_BETTER_REGISTRY = {
-    "matthews_corrcoef": True,
-    "f1_score": True,
-    "perplexity": False,
-    "bleu": True,
-    "chrf": True,
-    "ter": False,
-
-    "acc": True,
-    "acc_norm": True,
-    "acc_mutual_info": True,
-    "word_perplexity": False,
-    "byte_perplexity": False,
-    "bits_per_byte": False,
-}
\ No newline at end of file

lm_eval/api/filter.py

@@ -3,9 +3,10 @@ from typing import List
 
 from lm_eval.api.instance import Instance
 
+
 class Filter:
     """
-    Filter classes operate on a per-task level. 
+    Filter classes operate on a per-task level.
     They take all model outputs (`instance.resps` for all `task.instances`)
     across all instances of a task, and perform operations.
     In a single run, one can configure any number of separate filters or lists of filters.
@@ -25,30 +26,33 @@ class Filter:
         [<filtered resps for instance 0>, <filtered resps for instance 1>]
         """
         return resps
-        
+
+
 @dataclass
 class FilterEnsemble:
     """
     FilterEnsemble creates a pipeline applying multiple filters.
-    Its intended usage is to stack multiple post-processing steps in order. 
-    `task.apply_filters` should use a list of FilterEnsemble classes that it stores, to apply each 
+    Its intended usage is to stack multiple post-processing steps in order.
+    `task.apply_filters` should use a list of FilterEnsemble classes that it stores, to apply each
     pipeline separately.
     """
-    name: str 
+
+    name: str
     filters: List[Filter]
 
     def apply(self, instances: List[Instance]):
 
-        resps = [inst.resps for inst in instances] # operate just on the model responses
+        resps = [
+            inst.resps for inst in instances
+        ]  # operate just on the model responses
         for f in self.filters:
             # apply filters in sequence
             out = f.apply(resps)
-            resps = out # TODO: handle the case where a filter returns multiple "buckets"
-        
+            resps = (
+                out  # TODO: handle the case where a filter returns multiple "buckets"
+            )
+
         # add the end results after filtering to filtered_requests of their respective source instances.
         # has key `self.name`: each FilterEnsemble applied in a given run should use a different name.
         for inst, resp in zip(instances, resps):
             inst.filtered_resps[self.name] = resp
-
-            
-

lm_eval/api/instance.py

 from dataclasses import dataclass, field
 from typing import Literal, Tuple
 
+
 @dataclass
 class Instance:
-    request_type: str = Literal["loglikelihood", "loglikelihood_rolling", "greedy_until"]
+    request_type: str = Literal[
+        "loglikelihood", "loglikelihood_rolling", "greedy_until"
+    ]
     doc: dict = None
     arguments: tuple = None
     idx: int = None
-    metadata: tuple = Tuple[str, int, int] # TODO: better typehints here
+    metadata: tuple = Tuple[str, int, int]  # TODO: better typehints here
     resps: list = field(default_factory=list)
     filtered_resps: dict = field(default_factory=dict)
 
@@ -19,10 +22,12 @@ class Instance:
     def __post_init__(self):
         # unpack metadata field
         self.task_name, self.doc_id, self.repeats = self.metadata
-     
+
     @property
     def args(self):
         """
         Returns (string,) where `string` is the string to calculate loglikelihood over
         """
-        return self.arguments if isinstance(self.arguments, tuple) else (self.arguments,)
+        return (
+            self.arguments if isinstance(self.arguments, tuple) else (self.arguments,)
+        )

lm_eval/api/metrics.py

@@ -10,6 +10,7 @@ import evaluate
 
 
 AGGREGATION_REGISTRY = {}
+
 METRIC_REGISTRY = {
     "acc": None,
     "acc_norm": None,
@@ -18,6 +19,21 @@ METRIC_REGISTRY = {
     "byte_perplexity": None,
 }
 
+HIGHER_IS_BETTER_REGISTRY = {
+    "matthews_corrcoef": True,
+    "f1_score": True,
+    "perplexity": False,
+    "bleu": True,
+    "chrf": True,
+    "ter": False,
+    "acc": True,
+    "acc_norm": True,
+    "acc_mutual_info": True,
+    "word_perplexity": False,
+    "byte_perplexity": False,
+    "bits_per_byte": False,
+}
+
 
 def register_metric(name):
     # TODO: do we want to enforce a certain interface to registered metrics?
@@ -28,7 +44,7 @@ def register_metric(name):
 
         METRIC_REGISTRY[name] = fn
         return fn
-    
+
     return decorate
 
 
@@ -38,12 +54,14 @@ def get_metric(name):
         return METRIC_REGISTRY[name]
     except KeyError:
         # TODO: change this print to logging?
-        print(f"Could not find registered metric '{name}' in lm-eval, \
-searching in HF Evaluate library...")
+        print(
+            f"Could not find registered metric '{name}' in lm-eval, \
+searching in HF Evaluate library..."
+        )
         try:
             metric_object = evaluate.load(name)
             return metric_object.compute
-        except:
+        except Exception:
             raise Warning(
                 "{} not found in the evaluate library!".format(name),
                 "Please check https://huggingface.co/evaluate-metric",
@@ -59,7 +77,7 @@ def register_aggregation(name):
 
         AGGREGATION_REGISTRY[name] = fn
         return fn
-    
+
     return decorate
 
 

lm_eval/api/model.py

@@ -6,14 +6,15 @@ from lm_eval import utils
 
 MODEL_REGISTRY = {}
 
+
 def register_model(*names):
     # either pass a list or a single alias.
     # function receives them as a tuple of strings
-    
+
     def decorate(cls):
-        for name in names: 
-            assert (
-                issubclass(cls, LM)
+        for name in names:
+            assert issubclass(
+                cls, LM
             ), f"Model '{name}' ({cls.__name__}) must extend LM class"
 
             assert (
@@ -22,7 +23,7 @@ def register_model(*names):
 
             MODEL_REGISTRY[name] = cls
         return cls
-    
+
     return decorate
 
 

lm_eval/api/register.py

+import os
+
+task_registry = {}
+group_registry = {}
+task2func_index = {}
+func2task_index = {}
+
+
+def register_task(name):
+    def wrapper(func):
+
+        task_registry[name] = func
+        func2task_index[func.__name__] = name
+        task2func_index[name] = func.__name__
+        return func
+
+    return wrapper
+
+
+def register_group(name):
+    def wrapper(func):
+
+        func_name = func2task_index[func.__name__]
+
+        if name in group_registry:
+            group_registry[name].append(func_name)
+        else:
+            group_registry[name] = [func_name]
+        return func
+
+    return wrapper

lm_eval/api/samplers.py

-
-
 class Sampler:
-
     def __init__(self, docs, task, fewshot_indices=None, rnd=None):
 
         self.rnd = rnd
@@ -12,15 +9,18 @@ class Sampler:
 
         self.delimiter = self.config.delimiter
 
-        self.docs = docs # HF dataset split, provided by task._fewshot_docs()
-        if fewshot_indices: # subset few-shot docs from 
+        self.docs = docs  # HF dataset split, provided by task._fewshot_docs()
+        if fewshot_indices:  # subset few-shot docs from
             self.docs = self.docs.select(fewshot_indices)
 
-
     def get_context(self, doc, num_fewshot):
 
-        # draw an extra fewshot sample if using same split as evaluting on
-        n_samples = num_fewshot + 1 if self.config.fewshot_split == self.config.test_split else num_fewshot 
+        # draw an extra fewshot sample if using same split as evaluating on
+        n_samples = (
+            num_fewshot + 1
+            if self.config.fewshot_split == self.config.test_split
+            else num_fewshot
+        )
 
         # draw `n_samples` docs from fewshot_docs
         fewshotex = self.sample(n_samples)
@@ -28,16 +28,16 @@ class Sampler:
         # get rid of the doc that's the one we're evaluating, if it's in the fewshot
         # TODO: should we just stop people from using fewshot from same split as evaluating?
         selected_docs = [x for x in fewshotex if x != doc][:num_fewshot]
-        
+
         labeled_examples = (
-                self.delimiter.join(
-                    [
-                        self.task.doc_to_text(doc) + self.task.doc_to_target(doc)
-                        for doc in selected_docs
-                    ]
-                )
-                + self.delimiter
+            self.delimiter.join(
+                [
+                    self.task.doc_to_text(doc) + self.task.doc_to_target(doc)
+                    for doc in selected_docs
+                ]
             )
+            + self.delimiter
+        )
 
         # only returns the fewshot context! Does not append the document, do this outside the object
         return labeled_examples
@@ -51,25 +51,22 @@ class Sampler:
 
 
 class BalancedSampler(Sampler):
-
     def sample(self, n):
         """
-        TODO: this should return approximately class-balanced samples from our fewshot examples. 
+        TODO: this should return approximately class-balanced samples from our fewshot examples.
         TODO: what order should they be in? maybe random?
         """
 
         pass
 
-class ManualSampler(Sampler):
 
+class ManualSampler(Sampler):
     def sample(self, n):
-        """
-
-        """
-        pass 
+        """ """
+        pass
 
 
-# TODO: how should we do design here? might be better to have a single sampler and pass more kwargs at init. 
+# TODO: how should we do design here? might be better to have a single sampler and pass more kwargs at init.
 # Depends what's easier for new user to add own functionality on top of
 
 # types of sampler:

lm_eval/api/task.py

@@ -3,6 +3,7 @@ from dataclasses import dataclass
 
 import re
 import ast
+import yaml
 import evaluate
 import random
 import itertools
@@ -11,35 +12,49 @@ import functools
 import datasets
 import numpy as np
 
-from typing import List, Union
+from typing import Union
+from collections.abc import Callable
 
-from lm_eval.api.metrics import METRIC_REGISTRY, AGGREGATION_REGISTRY
-from lm_eval.api import HIGHER_IS_BETTER_REGISTRY
-from lm_eval.api.instance import Instance
-from lm_eval.api.metrics import get_metric, get_aggregation, mean, weighted_perplexity, bits_per_byte
 from lm_eval import utils
+from lm_eval.api import samplers
+from lm_eval.api.instance import Instance
+from lm_eval.api.metrics import (
+    METRIC_REGISTRY,
+    AGGREGATION_REGISTRY,
+    HIGHER_IS_BETTER_REGISTRY,
+    get_metric,
+    get_aggregation,
+    mean,
+    weighted_perplexity,
+    bits_per_byte,
+)
+
+from lm_eval.logger import eval_logger
 from lm_eval.prompts import get_prompt
-
 from lm_eval.filters import build_filter_ensemble
-from lm_eval.api import samplers
 
 
 @dataclass
 class TaskConfig(dict):
 
+    task: str = None
+    group: str = None
     names: str = None
-    task_name: str = None # TODO: deprecate this, it'll be set in __post_init__ to be names[0]
+    reference: str = None
+    task_name: str = (
+        None  # TODO: deprecate this, it'll be set in __post_init__ to be names[0]
+    )
+    base_task: str = None
     dataset_path: str = None
     dataset_name: str = None
     training_split: str = None
     validation_split: str = None
     test_split: str = None
-    fewshot_split: str = None # TODO: assert that this not None if num_fewshot > 0. (?) assert if this is same split as one evaling (?)
-    
-    template_aliases: str = "" 
-    doc_to_text: str = ""
-    doc_to_target: str = ""
+    fewshot_split: str = None  # TODO: assert that this not None if num_fewshot > 0. (?) assert if this is same split as one evaling (?)
 
+    template_aliases: str = None
+    doc_to_text: Union[Callable, str] = None
+    doc_to_target: Union[Callable, str] = None
 
     num_fewshot: int = 0
     batch_size: int = 1
@@ -49,20 +64,26 @@ class TaskConfig(dict):
     gold_alias: str = None
     output_type: str = "greedy_until"
     delimiter: str = "\n\n"
-    filters: str = None #TODO: need to make this typehint `list`?
-    normalization: str = None # TODO: add length-normalization of various types, mutual info
+    filter_list: Union[str, list] = None
+    normalization: str = (
+        None  # TODO: add length-normalization of various types, mutual info
+    )
     should_decontaminate: bool = False
     doc_to_decontamination_query: str = None
     use_prompt: str = None
 
-    metadata: str = None # by default, not used in the code. allows for users to pass arbitrary info to tasks
+    metadata: str = None  # by default, not used in the code. allows for users to pass arbitrary info to tasks
 
     def __post_init__(self):
         # allow user-specified aliases so that users can
         # force prompt-compatibility for some prompt regardless of
         # field names in prompt
-        self.doc_to_text = self.template_aliases + self.doc_to_text
-        self.doc_to_target = self.template_aliases + self.doc_to_target
+        if self.template_aliases is not None:
+            if type(self.doc_to_text) == str:
+                self.doc_to_text = self.template_aliases + self.doc_to_text
+
+            if type(self.doc_to_target) == str:
+                self.doc_to_target = self.template_aliases + self.doc_to_target
 
         # set "task_name" metadata field based on the "primary" name set
         if self.names:
@@ -83,6 +104,7 @@ class Task(abc.ABC):
     """
 
     VERSION = None
+
     # The name of the `Task` benchmark as denoted in the HuggingFace datasets Hub
     # or a path to a custom `datasets` loading script.
     DATASET_PATH: str = None
@@ -91,6 +113,7 @@ class Task(abc.ABC):
     DATASET_NAME: str = None
 
     OUTPUT_TYPE: str = None
+
     def __init__(
         self,
         data_dir=None,
@@ -129,12 +152,15 @@ class Task(abc.ABC):
 
         if not hasattr(self, "_filters"):
             self._filters = []
-            for name, components in self._config.get("filters", [["none", ["take_first"]]]):
+            for name, components in self._config.get(
+                "filters", [["none", ["take_first"]]]
+            ):
                 filter_pipeline = build_filter_ensemble(name, components)
                 self._filters.append(filter_pipeline)
 
- 
-        self.sampler = samplers.Sampler(self.fewshot_docs(), self, rnd=random.Random()) # TODO: pass the correct docs in here
+        self.sampler = samplers.Sampler(
+            list(self.fewshot_docs()), self, rnd=random.Random()
+        )  # TODO: pass the correct docs in here
 
     def download(self, data_dir=None, cache_dir=None, download_mode=None):
         """Downloads and returns the task dataset.
@@ -215,7 +241,10 @@ class Task(abc.ABC):
         elif self.has_validation_docs():
             return self.validation_docs()
         else:
-            # TODO: should we allow this case to occur? / should raise a warning here
+            eval_logger.warning(
+                "has_training_docs and has_validation_docs are False"
+                "using test_docs but this is not recommended."
+            )
             return self.test_docs()
 
     def _process_doc(self, doc):
@@ -268,20 +297,24 @@ class Task(abc.ABC):
             ), f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!"
 
         instances = []
-        for doc_id, doc in enumerate(itertools.islice(docs, 0, limit) if limit else docs):
+        for doc_id, doc in enumerate(
+            itertools.islice(docs, 0, limit) if limit else docs
+        ):
             # sample fewshot context
             fewshot_ctx = self.fewshot_context(
                 doc, self._config.num_fewshot, rnd=random.Random()
             )
-
             # TODO: hardcoded for now: # of runs on each input to be 2. # TODO: we should override this if doing greedy gen so users don't waste time+compute
-            inst = self.construct_requests(doc=doc, ctx=fewshot_ctx, metadata=(self._config["task_name"], doc_id, 1))
+            inst = self.construct_requests(
+                doc=doc,
+                ctx=fewshot_ctx,
+                metadata=(self._config["task_name"], doc_id, self._config.repeats),
+            )
 
             if not isinstance(inst, list):
                 inst = [inst]
 
             instances.extend(inst)
-            
 
         self._instances = instances
         assert len(self._instances) != 0, "task.build_requests() did not find any docs!"
@@ -302,7 +335,7 @@ class Task(abc.ABC):
             whichever is the main split used.
         :param repeats: int
         TODO: update this docstring
-            The number of times each instance in a dataset is inferred on. Defaults to 1, 
+            The number of times each instance in a dataset is inferred on. Defaults to 1,
             can be increased for techniques like majority voting.
         """
         pass
@@ -370,7 +403,6 @@ class Task(abc.ABC):
         if num_fewshot == 0:
             labeled_examples = ""
         else:
-
             labeled_examples = self.sampler.get_context(doc, self._config.num_fewshot)
 
             # for sets with no training docs, draw from other set *but ensure no overlap with current doc*
@@ -417,13 +449,21 @@ class ConfigurableTask(Task):
     def __init__(
         self, data_dir=None, cache_dir=None, download_mode=None, config: dict = None
     ):
-        # if we are a subclass that has the CONFIG class attr set, ignore whatever is passed.
+        # Get pre-configured attributes
         self._config = self.CONFIG
-        # else, if a config was passed as kwarg: use it
-        if (self._config is None) and config:
+
+        # Use new configurations if there was no preconfiguration
+        if self._config is None:
             self._config = TaskConfig(**config)
+        # Overwrite configs
+        else:
+            if config is not None:
+                self._config.__dict__.update(config)
+
         if self._config is None:
-            raise ValueError("Must pass a config to ConfigurableTask, either in cls.CONFIG or `config` kwarg") 
+            raise ValueError(
+                "Must pass a config to ConfigurableTask, either in cls.CONFIG or `config` kwarg"
+            )
 
         if self._config.output_type is not None:
             self.OUTPUT_TYPE = self._config.output_type
@@ -441,16 +481,22 @@ class ConfigurableTask(Task):
             self._higher_is_better = {}
             for metric_config in self._config.metric_list:
 
-                metric_name = metric_config['metric']
-                aggregation = metric_config['aggregation']
-                higher_is_better = metric_config['higher_is_better']
-                kwargs = {key: metric_config[key] for key in metric_config if key not in ['metric', 'aggregation', 'higher_is_better']}
+                metric_name = metric_config["metric"]
+                aggregation = metric_config["aggregation"]
+                higher_is_better = metric_config["higher_is_better"]
+                kwargs = {
+                    key: metric_config[key]
+                    for key in metric_config
+                    if key not in ["metric", "aggregation", "higher_is_better"]
+                }
 
                 self._aggregation_list[metric_name] = AGGREGATION_REGISTRY[aggregation]
 
                 if metric_name in METRIC_REGISTRY.keys():
                     self._metric_list[metric_name] = METRIC_REGISTRY[metric_name]
-                    self._higher_is_better[metric_name] = HIGHER_IS_BETTER_REGISTRY[metric_name]
+                    self._higher_is_better[metric_name] = HIGHER_IS_BETTER_REGISTRY[
+                        metric_name
+                    ]
                 else:
                     self._higher_is_better[metric_name] = higher_is_better
                     try:
@@ -458,7 +504,7 @@ class ConfigurableTask(Task):
                         self._metric_list[metric_name] = metric_object
                         self._metric_kwargs[metric_name] = kwargs
 
-                    except Exception as ex:
+                    except Exception:
                         raise Warning(
                             "{} not found in the evaluate library!".format(metric_name),
                             "Please check https://huggingface.co/evaluate-metric",
@@ -468,13 +514,38 @@ class ConfigurableTask(Task):
         self._training_docs = None
         self._fewshot_docs = None
 
-        
         self._filters = []
-        for name, components in self._config.get("filters", [["none", ["take_first"]]]):
-            filter_pipeline = build_filter_ensemble(name, components)
+        if self._config.filter_list is not None:
+            for filter_config in self._config.filter_list:
+                for filter_pipeline in filter_config:
+                    filter_name = filter_config["name"]
+                    filter_functions = filter_config["filter"]
+                    components = []
+                    for function in filter_functions:
+                        kwargs = {
+                            key: function[key] for key in function if key != "function"
+                        }
+                        components.append([function["function"], kwargs])
+
+                    filter_pipeline = build_filter_ensemble(filter_name, components)
             self._filters.append(filter_pipeline)
-        
-        self.sampler = samplers.Sampler(list(self.fewshot_docs()), self, rnd=random.Random()) # TODO: pass the correct docs in here
+        else:
+            self._filters = [
+                build_filter_ensemble("take_first", [["take_first", None]])
+            ]
+
+        if self._config.use_prompt is not None:
+            eval_logger.info(f"loading prompt {self._config.use_prompt}")
+            self.prompt = get_prompt(
+                self._config.use_prompt, self.DATASET_PATH, self.DATASET_NAME
+            )
+        else:
+            self.prompt = None
+
+        if self.fewshot_docs() is not None:
+            self.sampler = samplers.Sampler(
+                list(self.fewshot_docs()), self, rnd=random.Random()
+            )  # TODO: pass the correct docs in here
 
     def has_training_docs(self):
         if self._config.training_split is not None:
@@ -507,12 +578,16 @@ class ConfigurableTask(Task):
             return self.dataset[self._config.test_split]
 
     def fewshot_docs(self):
-        if self._config.fewshot_split:
-            return self.dataset[self._config.fewshot_split]
-        else:
-            # TODO: warn user if fewshot split isn't explicitly set
+        if (self._config.num_fewshot > 0) and (self._config.fewshot_split is None):
+            eval_logger.warning(
+                "num_fewshot > 0 but fewshot_split is None. "
+                "using preconfigured rule."
+            )
             return super().fewshot_docs()
 
+        elif self._config.fewshot_split is not None:
+            return self.dataset[self._config.fewshot_split]
+
     def should_decontaminate(self):
         return self._config.should_decontaminate
 
@@ -532,67 +607,90 @@ class ConfigurableTask(Task):
         return doc
 
     def doc_to_text(self, doc):
-        if self._config.use_prompt is not None:
-            doc_to_text = get_prompt(self._config.use_prompt)
+
+        if self.prompt is not None:
+            doc_to_text = self.prompt
         else:
             doc_to_text = self._config.doc_to_text
-        return utils.apply_template(doc_to_text, doc)
+
+        if type(doc_to_text) == str:
+            return utils.apply_template(doc_to_text, doc)
+        elif callable(doc_to_text):
+            return doc_to_text(doc)
+        if hasattr(doc_to_text, "apply"):
+            return doc_to_text.apply(doc)[0]
+        else:
+            print(type(doc_to_text))
+            raise TypeError
 
     def doc_to_target(self, doc):
-        return utils.apply_template(self._config.doc_to_target, doc)
+
+        if self.prompt is not None:
+            doc_to_target = self.prompt
+        else:
+            doc_to_target = self._config.doc_to_target
+
+        if type(doc_to_target) == str:
+            return utils.apply_template(doc_to_target, doc)
+        elif callable(doc_to_target):
+            return doc_to_target(doc)
+        elif hasattr(doc_to_target, "apply"):
+            return doc_to_target.apply(doc)[1]
+        else:
+            raise TypeError
 
     def construct_requests(self, doc, ctx, **kwargs):
 
         if self.OUTPUT_TYPE == "loglikelihood":
-            arguments=(ctx, self.doc_to_target(doc))
+            arguments = (ctx, self.doc_to_target(doc))
         elif self.OUTPUT_TYPE == "loglikelihood_rolling":
-            arguments=(self.doc_to_target(doc),)
+            arguments = (self.doc_to_target(doc),)
         elif self.OUTPUT_TYPE == "multiple_choice":
             # we pass the user-defined answer_choices var (in aliases) and translate the result to a Python list.
             # TODO: any cleaner way to do this?
-            choices = ast.literal_eval(utils.apply_template(self._config.template_aliases + "{{answer_choices}}", doc))
+            choices = ast.literal_eval(
+                utils.apply_template(
+                    self._config.template_aliases + "{{answer_choices}}", doc
+                )
+            )
             request_list = [
                 Instance(
                     request_type="loglikelihood",
-                    doc=doc, 
+                    doc=doc,
                     arguments=(ctx, " {}".format(choice)),
                     idx=i,
                     **kwargs,
                 )
-                for i, choice in enumerate(choices) 
+                for i, choice in enumerate(choices)
             ]
             # TODO: we should raise a warning telling users this will at most ~2x runtime.
             if "acc_mutual_info" in self._metric_list.keys():
                 # if we are calculating multiple choice accuracy
                 # using mutual information instead of raw loglikelihood as metric, need unconditional lls.
 
-                # here mutual info refers to calculating 
+                # here mutual info refers to calculating
                 # log(P(choice|ctx) / P(choice)) = log(P(choice|ctx)) - log(P(choice))
                 # in other words normalizing by subtracting the unconditional logprob of each choice.
                 request_list.extend(
                     [
                         Instance(
                             request_type="loglikelihood",
-                            doc=doc, 
+                            doc=doc,
                             arguments=("", "{}".format(choice)),
                             idx=i,
                             **kwargs,
                         )
-                        for i, choice in enumerate(choices) 
+                        for i, choice in enumerate(choices)
                     ]
                 )
             return request_list
-            
+
         elif self.OUTPUT_TYPE == "greedy_until":
-            arguments=(ctx, self._config.delimiter)
+            arguments = (ctx, self._config.delimiter)
 
         return Instance(
-            request_type=self.OUTPUT_TYPE,
-            doc=doc,
-            arguments=arguments,
-            idx=0,
-            **kwargs
-            )
+            request_type=self.OUTPUT_TYPE, doc=doc, arguments=arguments, idx=0, **kwargs
+        )
 
     def process_results(self, doc, results):
 
@@ -611,11 +709,20 @@ class ConfigurableTask(Task):
                 "bits_per_byte": (loglikelihood, bytes_),
             }
         elif self.OUTPUT_TYPE == "multiple_choice":
-            lls = [res[0] for res in results] # only retain loglikelihoods, discard is_greedy
+            lls = [
+                res[0] for res in results
+            ]  # only retain loglikelihoods, discard is_greedy
             gold = int(self.doc_to_target(doc))
             # retrieve choices in List[str] form, to compute choice lengths, etc.
-            choices = ast.literal_eval(utils.apply_template(self._config.template_aliases + "{{answer_choices}}", doc))
-            if 2 * len(choices) == len(lls) and "acc_mutual_info" in self._metric_list.keys():
+            choices = ast.literal_eval(
+                utils.apply_template(
+                    self._config.template_aliases + "{{answer_choices}}", doc
+                )
+            )
+            if (
+                2 * len(choices) == len(lls)
+                and "acc_mutual_info" in self._metric_list.keys()
+            ):
                 # then we are doing mutual info.
                 # this stores the "dryrun" / unconditional answer loglikelihoods
                 lls_unconditional = lls[1::2]
@@ -636,12 +743,16 @@ class ConfigurableTask(Task):
 
             if "exact_match" in self._metric_list.keys():
                 # TODO: this gets score of 0 on arc_challenge for pythia-70m. need to test that this works properly
-                is_greedy = [res[1] for res in results] # take only the `is_greedy` results
-                is_greedy = is_greedy[gold] # take value for the gold answer
+                is_greedy = [
+                    res[1] for res in results
+                ]  # take only the `is_greedy` results
+                is_greedy = is_greedy[gold]  # take value for the gold answer
                 result_dict["exact_match"] = int(is_greedy)
 
             if "acc_mutual_info" in self._metric_list.keys():
-                lls_mutual_info = [ll_c - ll_u for ll_c, ll_u in zip(lls, lls_unconditional)]
+                lls_mutual_info = [
+                    ll_c - ll_u for ll_c, ll_u in zip(lls, lls_unconditional)
+                ]
                 acc_mutual_info = 1.0 if np.argmax(lls_mutual_info) == gold else 0.0
                 result_dict["acc_mutual_info"] = acc_mutual_info
 
@@ -654,15 +765,14 @@ class ConfigurableTask(Task):
 
             for key, result in zip(self._metric_list.keys(), results):
                 _dict = self._metric_list[key].compute(
-                    references=[gold],
-                    predictions=[result],
-                    **self._metric_kwargs[key]
+                    references=[gold], predictions=[result], **self._metric_kwargs[key]
                 )
 
                 result_dict[key] = _dict[key]
         else:
-            raise ValueError(f"Passed invalid output_type '{self.OUTPUT_TYPE}' ! Please use one of ", 
-                "'loglikelihood', 'loglikelihood_rolling', 'greedy_until'"
+            raise ValueError(
+                f"Passed invalid output_type '{self.OUTPUT_TYPE}' ! Please use one of ",
+                "'loglikelihood', 'loglikelihood_rolling', 'greedy_until'",
             )
 
         return result_dict
@@ -683,17 +793,21 @@ class MultipleChoiceTask(Task):
 
     def construct_requests(self, doc, ctx, **kwargs):
         # TODO: add mutual info here?
-        return [Instance(
+        return [
+            Instance(
                 request_type="loglikelihood",
-                doc=doc, 
+                doc=doc,
                 arguments=(ctx, " {}".format(choice)),
                 idx=i,
                 **kwargs,
             )
-            for i, choice in enumerate(doc["choices"])]
+            for i, choice in enumerate(doc["choices"])
+        ]
 
     def process_results(self, doc, results):
-        results = [res[0] for res in results] # only retain loglikelihoods, discard is_greedy TODO: do we need is_greedy anywhere? 
+        results = [
+            res[0] for res in results
+        ]  # only retain loglikelihoods, discard is_greedy TODO: do we need is_greedy anywhere?
         gold = doc["gold"]
 
         acc = 1.0 if np.argmax(results) == gold else 0.0
@@ -718,7 +832,7 @@ class MultipleChoiceTask(Task):
         }
 
 
-class PerplexityTask(Task, abc.ABC):
+class PerplexityTask(Task):
 
     OUTPUT_TYPE = "loglikelihood_rolling"
 
@@ -729,9 +843,7 @@ class PerplexityTask(Task, abc.ABC):
         assert k == 0
         return []
 
-    def fewshot_context(
-        self, doc, num_fewshot, rnd=None
-    ):
+    def fewshot_context(self, doc, num_fewshot, rnd=None):
         assert (
             num_fewshot == 0
         ), "The number of fewshot examples must be 0 for perplexity tasks."
@@ -760,7 +872,13 @@ class PerplexityTask(Task, abc.ABC):
     def construct_requests(self, doc, ctx, **kwargs):
         assert not ctx
 
-        return Instance(request_type=self.OUTPUT_TYPE, doc=doc, arguments=(self.doc_to_target(doc),), idx=0, **kwargs)
+        return Instance(
+            request_type=self.OUTPUT_TYPE,
+            doc=doc,
+            arguments=(self.doc_to_target(doc),),
+            idx=0,
+            **kwargs,
+        )
 
     def process_results(self, doc, results):
         (loglikelihood,) = results
@@ -787,118 +905,3 @@ class PerplexityTask(Task, abc.ABC):
     def count_words(cls, doc):
         """Downstream tasks with custom word boundaries should override this!"""
         return len(re.split(r"\s+", doc))
-
-
-# TODO: confirm we want this to go in this file
-
-TASK_REGISTRY = {}
-ALL_TASKS = []
-
-def register_task(*names):
-    # either pass a list or a single alias.
-    # function receives them as a tuple of strings
-
-    def decorate(cls):
-        for name in names:
-            assert (
-                issubclass(cls, Task)
-            ), f"Task '{name}' ({cls.__name__}) must extend Task class"
-
-            assert (
-                name not in TASK_REGISTRY
-            ), f"Task named '{name}' conflicts with existing task! Please register with a non-conflicting alias instead."
-
-            TASK_REGISTRY[name] = cls
-            ALL_TASKS = sorted(list(TASK_REGISTRY)) # TODO: this doesn't seem to import right.
-        return cls
-    
-    return decorate
-
-
-def register_yaml_task(yaml_path):
-    # same goal as register_task() but used to register yamls
-    import yaml
-    with open(yaml_path, "r") as f:
-        config = yaml.load(f, yaml.Loader)
-    from functools import partial
-    
-    # TODO: strip whitespace from name? 
-    # TODO: ensure num_fewshot overrides the config vals
-
-    def decorate(names, cls):
-        for name in names:
-            assert (
-                issubclass(cls, Task)
-            ), f"Task '{name}' ({cls.__name__}) must extend Task class"
-
-            assert (
-                name not in TASK_REGISTRY
-            ), f"Task named '{name}' conflicts with existing task! Please register with a non-conflicting alias instead."
-
-            TASK_REGISTRY[name] = cls
-            ALL_TASKS = sorted(list(TASK_REGISTRY)) # TODO: this doesn't seem to import properly.
-        return cls
-
-    # we create a subclass that has subclass attr CONFIG = our yaml config, and decorate with the config's specified aliases
-    names = config['names']    
-    yaml_task = decorate(
-        names, 
-        type(config['names'][0] + 'ConfigurableTask', (ConfigurableTask,), {'CONFIG': TaskConfig(**config)})
-    ) 
-
-
-##### Task registry utils and setup.
-# ALL_TASKS = sorted(list(TASK_REGISTRY))
-
-
-def get_task(task_name):
-    try:
-        return TASK_REGISTRY[task_name]
-    except KeyError:
-        print("Available tasks:")
-        pprint(TASK_REGISTRY)
-        raise KeyError(f"Missing task {task_name}")
-
-
-def get_task_name_from_object(task_object):
-    for name, class_ in TASK_REGISTRY.items():
-        if class_ is task_object:
-            return name
-
-    # TODO: scrap this
-    # this gives a mechanism for non-registered tasks to have a custom name anyways when reporting
-    return (
-        task_object.EVAL_HARNESS_NAME
-        if hasattr(task_object, "EVAL_HARNESS_NAME")
-        else type(task_object).__name__
-    )
-
-
-def get_task_name_from_config(task_config):
-    return "configurable_{dataset_path}_{dataset_name}".format(**task_config)
-
-
-def get_task_dict(task_name_list: List[Union[str, dict, Task]], num_fewshot=None): # TODO: pass num_fewshot and other cmdline overrides in a better way
-    task_name_dict = {
-        task_name: get_task(task_name)(config={"num_fewshot": num_fewshot if num_fewshot else 0, "task_name": task_name})
-        for task_name in task_name_list
-        if isinstance(task_name, str)
-    }
-    task_name_from_config_dict = {
-        get_task_name_from_config(task_config): ConfigurableTask(
-            config=task_config
-        )
-        for task_config in task_name_list
-        if isinstance(task_config, dict)
-    }
-    task_name_from_object_dict = {
-        get_task_name_from_object(task_object): task_object
-        for task_object in task_name_list
-        if isinstance(task_object, Task)
-    }
-    assert set(task_name_dict.keys()).isdisjoint(set(task_name_from_object_dict.keys()))
-    return {
-        **task_name_dict,
-        **task_name_from_config_dict,
-        **task_name_from_object_dict,
-    }
\ No newline at end of file

lm_eval/evaluator.py

-import collections
+import random
 import itertools
+import collections
+
 import numpy as np
-import random
+
+import lm_eval.api
 import lm_eval.api.metrics
-import lm_eval.models
+
 import lm_eval.tasks
-import lm_eval.api
-from lm_eval.utils import positional_deprecated, run_task_tests, make_table, get_git_commit_hash
+import lm_eval.models
+
+from lm_eval.utils import (
+    positional_deprecated,
+    run_task_tests,
+    make_table,
+    get_git_commit_hash,
+)
+
+from lm_eval.logger import eval_logger
 
 
 @positional_deprecated
@@ -65,7 +76,7 @@ def simple_evaluate(
         assert isinstance(model, lm_eval.api.model.LM)
         lm = model
 
-    task_dict = lm_eval.api.task.get_task_dict(tasks, num_fewshot=num_fewshot)
+    task_dict = lm_eval.tasks.get_task_dict(tasks, num_fewshot=num_fewshot)
 
     if check_integrity:
         run_task_tests(task_list=tasks)
@@ -73,7 +84,6 @@ def simple_evaluate(
     results = evaluate(
         lm=lm,
         task_dict=task_dict,
-        num_fewshot=num_fewshot,
         limit=limit,
         bootstrap_iters=bootstrap_iters,
         decontamination_ngrams_path=decontamination_ngrams_path,
@@ -102,7 +112,6 @@ decontaminate_suffix = "_decontaminate"
 def evaluate(
     lm,
     task_dict,
-    num_fewshot=0,
     limit=None,
     bootstrap_iters=100000,
     decontamination_ngrams_path=None,
@@ -123,20 +132,20 @@ def evaluate(
         Dictionary of results
     """
 
-    decontaminate = decontamination_ngrams_path is not None
+    # decontaminate = decontamination_ngrams_path is not None
 
     results = collections.defaultdict(dict)
     versions = collections.defaultdict(dict)
 
     requests = collections.defaultdict(list)
-    requests_origin = collections.defaultdict(list)
+    # requests_origin = collections.defaultdict(list)
 
-    docs = {}
+    # docs = {}
 
     # get lists of each type of request
     for task_name, task in task_dict.items():
         versions[task_name] = task.VERSION
-    
+
         # deterministically shuffle docs and chop off the first `limit` because sometimes docs are in some kind of order
         # task_docs = list(task_doc_func())
         # rnd = random.Random()
@@ -146,18 +155,22 @@ def evaluate(
         # for doc_id, doc in enumerate(itertools.islice(task_docs, 0, limit)):
         task.build_all_requests(limit=limit)
         # aggregate Instances by LM method requested to get output.
-        reqtype = "loglikelihood" if task.OUTPUT_TYPE == "multiple_choice" else task.OUTPUT_TYPE #TODO: this is hacky, fix in task.py
-        requests[reqtype].extend(task.instances) 
-    
+        reqtype = (
+            "loglikelihood"
+            if task.OUTPUT_TYPE == "multiple_choice"
+            else task.OUTPUT_TYPE
+        )  # TODO: this is hacky, fix in task.py
+        requests[reqtype].extend(task.instances)
+
     ### Run LM on inputs, get all outputs ###
     # execute each type of request
     for reqtype, reqs in requests.items():
-        print("Running", reqtype, "requests")
+        eval_logger.info("Running {} requests".format(reqtype))
         # create `K` copies of each request `req` based off `K = req.repeats`
         cloned_reqs = []
         for req in reqs:
             cloned_reqs.extend([req] * req.repeats)
-        
+
         # run requests through model
         resps = getattr(lm, reqtype)(cloned_reqs)
 
@@ -170,9 +183,8 @@ def evaluate(
     for task_name, task in task_dict.items():
         task.apply_filters()
 
-
     ### Collect values of metrics on all datapoints ###
-    # TODO: make metric configurable, add metric registry 
+    # TODO: make metric configurable, add metric registry
     vals = collections.defaultdict(list)
 
     # unpack results and sort back in order and return control to Task
@@ -180,21 +192,27 @@ def evaluate(
         # calculate values for each filter setup (TODO: make getting list of keys cleaner)
         # TODO: make it possible to use a different metric per key
         for key in task.instances[0].filtered_resps.keys():
-            for doc_id, doc in enumerate(itertools.islice(task.test_docs(), 0, limit) if task.has_test_docs() else task.validation_docs()):
+            for doc_id, doc in enumerate(
+                itertools.islice(task.test_docs(), 0, limit)
+                if task.has_test_docs()
+                else task.validation_docs()
+            ):
                 # subset instances to only this document id ; sort by idx
                 requests = list(filter(lambda x: x.doc_id == doc_id, task.instances))
                 requests.sort(key=lambda x: x.idx)
-                metrics = task.process_results(doc, [req.filtered_resps[key] for req in requests])
+                metrics = task.process_results(
+                    doc, [req.filtered_resps[key] for req in requests]
+                )
                 for metric, value in metrics.items():
                     vals[(task_name, key, metric)].append(value)
-    
-
 
     ### Aggregate results over all datapoints ###
     # aggregate results ; run bootstrap CIs
     for (task_name, key, metric), items in vals.items():
         task = task_dict[task_name]
-        results[task_name][metric + " - filter=" + key] = task.aggregation()[metric](items)
+        results[task_name][metric + " - filter=" + key] = task.aggregation()[metric](
+            items
+        )
 
         # hotfix: bleu, chrf, ter seem to be really expensive to bootstrap
         # so we run them less iterations. still looking for a cleaner way to do this

lm_eval/filters/__init__.py

@@ -6,10 +6,10 @@ from . import extraction
 FILTER_REGISTRY = {
     "take_first": selection.TakeFirstFilter,
     "regex": extraction.RegexFilter,
-    # TODO: implement this filter. either it should take in an arbitrary "scoring"/reward function 
-    # that takes an input and returns a scalar and then should select the max reward, 
+    # TODO: implement this filter. either it should take in an arbitrary "scoring"/reward function
+    # that takes an input and returns a scalar and then should select the max reward,
     # or should implement different filters for different ways of handling a reward model's inference.
-    #"arg_max": selection.ArgMaxFilter, 
+    # "arg_max": selection.ArgMaxFilter,
 }
 
 
@@ -17,16 +17,19 @@ def get_filter(filter_name):
     return FILTER_REGISTRY[filter_name]
 
 
-def build_filter_ensemble(name, components):
+def build_filter_ensemble(filter_name, components):
     """
     Create a filtering pipeline.
     """
+
     filters = []
-    for step in components:
+    for (function, kwargs) in components:
+        if kwargs is None:
+            f = get_filter(function)()
+        else:
             # create a filter given its name in the registry
-            f = get_filter(step)() # TODO: pass kwargs to filters properly
-
-            # add the filter as a pipeline step
-            filters.append(f)
+            f = get_filter(function)(**kwargs)  # TODO: pass kwargs to filters properly
+        # add the filter as a pipeline step
+        filters.append(f)
 
-    return FilterEnsemble(name=name, filters=filters)
+    return FilterEnsemble(name=filter_name, filters=filters)

lm_eval/filters/decontamination.py

@@ -4,7 +4,7 @@ from lm_eval.api.filter import Filter
 class DecontaminationFilter(Filter):
 
     """
-    A filter which evaluates 
+    A filter which evaluates
     """
 
     name = "track_decontamination"
@@ -12,7 +12,7 @@ class DecontaminationFilter(Filter):
     def __init__(self, path):
         """
 
-        TODO: make sure only ever run one time on the train set (should this be cached as a class var? keyed by value for "path"). 
+        TODO: make sure only ever run one time on the train set (should this be cached as a class var? keyed by value for "path").
         should further cache result on a given (task_name, doc_id)
         """
         self._decontam_results = None
@@ -21,4 +21,4 @@ class DecontaminationFilter(Filter):
         """
         Return {"no_contamination", "only_contamination"} keys for the 2 different subsets
         """
-        pass
+        pass
\ No newline at end of file

lm_eval/filters/extraction.py

@@ -4,24 +4,20 @@ from lm_eval.api.filter import Filter
 
 
 class RegexFilter(Filter):
-    """
+    """ """
 
-
-    """
-
-    def __init__(self, regex=r"#### (\-?[0-9\.\,]+)", fallback="[invalid]"):
+    def __init__(self, regex_pattern=r"#### (\-?[0-9\.\,]+)", fallback="[invalid]"):
         """
         pass a string `regex` to run `re.compile(r"regex")` on.
         `fallback` defines the output returned if no matches for the regex are located.
         """
-        self.regex_pattern = regex
-        self.regex = re.compile(regex)
-
+        self.regex_pattern = regex_pattern
+        self.regex = re.compile(regex_pattern)
         self.fallback = fallback
 
     def apply(self, resps):
         # here, we assume we have a list, in which each element is
-        # a list of model responses for some particular input/target pair. 
+        # a list of model responses for some particular input/target pair.
         # so we process each of these (same input/target response sets)
         # independently (and keep them a list.)
         def filter_set(inst):
@@ -30,7 +26,7 @@ class RegexFilter(Filter):
                 match = self.regex.search(resp)
                 if match:
                     match = match.group(1).strip()
-                    match_str.replace(",", "")
+                    match.replace(",", "")
                     # TODO: should we assume any other filtering is performed?
                 else:
                     match = self.fallback

lm_eval/filters/selection.py

 from lm_eval.api.filter import Filter
 
-class TakeFirstFilter:
 
+class TakeFirstFilter:
     def __init__(self):
         """
         Can define custom behavior here, if an individual instantiation of a Filter class should have state.
@@ -11,4 +11,4 @@ class TakeFirstFilter:
         """
         Assuming each entry of `resps` is a list of model responses, we discard all but the first response.
         """
-        return map(lambda r: r[0], resps)
+        return map(lambda r: r[0], resps)
\ No newline at end of file

lm_eval/logger.py

+import logging
+
+logging.basicConfig(
+    format="%(asctime)s,%(msecs)03d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s",
+    datefmt="%Y-%m-%d:%H:%M:%S",
+    level=logging.INFO,
+)
+eval_logger = logging.getLogger("lm-eval")

lm_eval/models/gpt2.py

@@ -6,6 +6,7 @@ from tqdm import tqdm
 import torch.nn.functional as F
 
 from lm_eval import utils
+from lm_eval.logger import eval_logger
 from lm_eval.api.model import LM, register_model
 
 
@@ -31,10 +32,10 @@ class HFLM(LM):
             if device not in ["cuda", "cpu"]:
                 device = int(device)
             self._device = torch.device(device)
-            print(f"Using device '{device}'")
+            eval_logger.info(f"Using device '{device}'")
         else:
-            print("Device not specified")
-            print(f"Cuda Available? {torch.cuda.is_available()}")
+            eval_logger.warning("Device not specified")
+            eval_logger.info(f"Cuda Available? {torch.cuda.is_available()}")
             self._device = (
                 torch.device("cuda")
                 if torch.cuda.is_available()
@@ -110,7 +111,11 @@ class HFLM(LM):
 
     def _model_generate(self, context, max_length, eos_token_id):
         return self.gpt2.generate(
-            context, max_length=max_length, pad_token_id=eos_token_id, eos_token_id=eos_token_id, do_sample=False
+            context,
+            max_length=max_length,
+            pad_token_id=eos_token_id,
+            eos_token_id=eos_token_id,
+            do_sample=False,
         )
 
     def loglikelihood(self, requests):

lm_eval/models/gpt3.py

 import os
-import numpy as np
+import time
 import transformers
-from lm_eval.api.model import LM, register_model
-from lm_eval import utils
+
+import numpy as np
+
 from tqdm import tqdm
-import time
+from lm_eval import utils
+from lm_eval.api.model import LM, register_model
 
 
 def get_result(response, ctxlen):

lm_eval/prompts/__init__.py

+from lm_eval.logger import eval_logger
+from promptsource.templates import DatasetTemplates
+
 # TODO: decide whether we want jinja2 or f-string prompts. would it be cursed to support both?
-# Prompt library. 
+# Prompt library.
 # Stores prompts in a dictionary indexed by 2 levels:
 # prompt category name, and prompt name.
 # This allows us to access prompts
 PROMPT_REGISTRY = {
     "qa-basic": {
         "question-newline-answer": "Question: {{question}}\nAnswer:",
-        "q-newline-a": "Q: {question}\nA:"
+        "q-newline-a": "Q: {{question}}\nA:",
     },
 }
 
-def get_prompt(prompt_id: str):
-    # unpack prompt name 
-    try:
-        category_name, prompt_name = prompt_id.split(":")
-    except:
-        raise ValueError(
-            f"expected only a single `:` as separator between \
-prompt category and name, but got `{prompt_id}` instead"
+
+def get_prompt(prompt_id: str, dataset_name=None, subset_name=None):
+    # unpack prompt name
+    category_name, prompt_name = prompt_id.split(":")
+    eval_logger.info(f"Loading prompt from {category_name}")
+    if category_name == "promptsource":
+        try:
+            # prompts = DatasetTemplates(dataset_name, dataset_path)
+            if subset_name is None:
+                prompts = DatasetTemplates(dataset_name=dataset_name)
+            else:
+                prompts = DatasetTemplates(
+                    dataset_name=dataset_name, subset_name=subset_name
+                )
+        except Exception:
+            raise ValueError(f"{dataset_name} and {subset_name} not found")
+        if prompt_name in prompts.all_template_names:
+            return prompts[prompt_name]
+        else:
+            raise ValueError(
+                f"{prompt_name} not in prompt list {prompts.all_template_names}"
+            )
+    else:
+        try:
+            return PROMPT_REGISTRY[category_name][prompt_name]
+        except Exception:
+            raise ValueError(
+                f"expected only a single `:` as separator between \
+                prompt category and name, but got `{prompt_id}` instead"
             )
-    return PROMPT_REGISTRY[category_name][prompt_name]
\ No newline at end of file