prelim. multiple choice support

lm_eval/api/__init__.py

@@ -22,4 +22,7 @@ HIGHER_IS_BETTER_REGISTRY = {
     "bleu": True,
     "chrf": True,
     "ter": False,
+
+    "acc": True,
+    "acc_norm": True,
 }
\ No newline at end of file

lm_eval/api/instance.py

@@ -11,6 +11,7 @@ class Instance:
     resps: list = field(default_factory=list)
     filtered_resps: dict = field(default_factory=dict)
 
+    # initialized after init
     task_name: str = None
     doc_id: str = None
     repeats: str = None

lm_eval/api/metrics.py

@@ -10,7 +10,10 @@ import evaluate
 
 
 AGGREGATION_REGISTRY = {}
-METRIC_REGISTRY = {}
+METRIC_REGISTRY = {
+    "acc": None,
+    "acc_norm": None,  
+}
 
 
 def register_metric(name):
@@ -45,6 +48,7 @@ searching in HF Evaluate library...")
 
 
 def register_aggregation(name):
+    # TODO: should we enforce a specific interface to aggregation metrics?
     def decorate(fn):
         assert (
             name not in AGGREGATION_REGISTRY
@@ -155,6 +159,7 @@ def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
 
 
 @register_metric("perplexity")
+@register_aggregation("perplexity")
 def perplexity(items):
     return math.exp(-mean(items))
 

lm_eval/api/model.py

 import abc
 
+from typing import Union
+
 from lm_eval import utils
 
 MODEL_REGISTRY = {}
 
-def register_model(name):
-    # TODO: should fairseq/elk be cited for this design pattern?
+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)
             ), f"Model '{name}' ({cls.__name__}) must extend LM class"
 
             assert (
                 name not in MODEL_REGISTRY
-        ), f"Model named '{name}' conflicts with existing model!"
+            ), f"Model named '{name}' conflicts with existing model! Please register with a non-conflicting alias instead."
 
             MODEL_REGISTRY[name] = cls
         return cls

lm_eval/api/task.py

@@ -5,13 +5,15 @@ import re
 import evaluate
 import random
 import itertools
+import functools
 
 import datasets
 import numpy as np
 
 from typing import List, Union
 
-from lm_eval.api import METRIC_REGISTRY, AGGREGATION_REGISTRY, HIGHER_IS_BETTER_REGISTRY
+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
@@ -36,10 +38,11 @@ class TaskConfig(dict):
     doc_to_text: str = ""
     doc_to_target: str = ""
 
-    # aggregation: dict = None # TODO: remove, I think these 2 are obsolete w/ current metric_list impl.
-    # higher_is_better: dict = None
+
     num_fewshot: int = 0
     batch_size: int = 1
+    repeats: int = 1
+
     metric_list: str = None
     gold_alias: str = None
     output_type: str = "greedy_until"
@@ -122,7 +125,8 @@ class Task(abc.ABC):
                 filter_pipeline = build_filter_ensemble(name, components)
                 self._filters.append(filter_pipeline)
 
-        self.sampler = samplers.Sampler(self.training_docs(), self, rnd=random.Random()) # TODO: pass the correct docs in here
+ 
+        self.sampler = samplers.Sampler(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.
@@ -193,6 +197,19 @@ class Task(abc.ABC):
         """
         return []
 
+    def fewshot_docs(self):
+        """
+        :return: Iterable[obj]
+            A iterable of any object, that doc_to_text can handle
+        """
+        if self.has_training_docs():
+            return self.training_docs()
+        elif self.has_validation_docs():
+            return self.validation_docs()
+        else:
+            # TODO: should we allow this case to occur? / should raise a warning here
+            return self.test_docs()
+
     def _process_doc(self, doc):
         """
         Override this to process (detokenize, strip, replace, etc.) individual
@@ -336,33 +353,33 @@ class Task(abc.ABC):
             labeled_examples = ""
         else:
 
-            # labeled_examples = self.sampler.get_context(doc, self._config.num_fewshot)
+            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*
-            if self.has_training_docs():
-                fewshotex = self.fewshot_examples(k=num_fewshot, rnd=rnd)
-            else:
-                if self._fewshot_docs is None:
-                    self._fewshot_docs = list(
-                        self.validation_docs()
-                        if self.has_validation_docs()
-                        else self.test_docs()
-                    )
-
-                fewshotex = rnd.sample(self._fewshot_docs, num_fewshot + 1)
-
-                # get rid of the doc that's the one we're evaluating, if it's in the fewshot
-                fewshotex = [x for x in fewshotex if x != doc][:num_fewshot]
-
-            labeled_examples = (
-                "\n\n".join(
-                    [
-                        self.doc_to_text(doc) + self.doc_to_target(doc)
-                        for doc in fewshotex
-                    ]
-                )
-                + "\n\n"
-            )
+            # if self.has_training_docs():
+            #     fewshotex = self.fewshot_examples(k=num_fewshot, rnd=rnd)
+            # else:
+            #     if self._fewshot_docs is None:
+            #         self._fewshot_docs = list(
+            #             self.validation_docs()
+            #             if self.has_validation_docs()
+            #             else self.test_docs()
+            #         )
+
+            #     fewshotex = rnd.sample(self._fewshot_docs, num_fewshot + 1)
+
+            #     # get rid of the doc that's the one we're evaluating, if it's in the fewshot
+            #     fewshotex = [x for x in fewshotex if x != doc][:num_fewshot]
+
+            # labeled_examples = (
+            #     "\n\n".join(
+            #         [
+            #             self.doc_to_text(doc) + self.doc_to_target(doc)
+            #             for doc in fewshotex
+            #         ]
+            #     )
+            #     + "\n\n"
+            # )
 
         example = self.doc_to_text(doc)
         return labeled_examples + example
@@ -376,7 +393,7 @@ class Task(abc.ABC):
 class ConfigurableTask(Task):
 
     VERSION = "2.0"
-    OUTPUT_TYPE = "greedy_until"
+    OUTPUT_TYPE = None
 
     def __init__(
         self, data_dir=None, cache_dir=None, download_mode=None, config: dict = None
@@ -433,6 +450,8 @@ class ConfigurableTask(Task):
             filter_pipeline = build_filter_ensemble(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
+
     def has_training_docs(self):
         if self._config.training_split is not None:
             return True
@@ -463,6 +482,13 @@ class ConfigurableTask(Task):
         if self._config.test_split is not None:
             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
+            return super().fewshot_docs()
+
     def should_decontaminate(self):
         return self._config.should_decontaminate
 
@@ -497,6 +523,19 @@ class ConfigurableTask(Task):
             arguments=(ctx, self.doc_to_target(doc))
         elif self.OUTPUT_TYPE == "loglikelihood_rolling":
             arguments=(self.doc_to_target(doc),)
+        elif self.OUTPUT_TYPE == "multiple_choice":
+            import ast
+            return [
+                Instance(
+                    request_type="loglikelihood",
+                    doc=doc, 
+                    arguments=(ctx, " {}".format(choice)),
+                    id_=i,
+                    **kwargs,
+                )
+                for i, choice in enumerate(ast.literal_eval(utils.apply_template(self._config.template_aliases + "{{answer_choices}}", doc))) 
+                # we pass the user-defined answer_choices var (in aliases) and echo the result. TODO: any cleaner way to do this?
+            ]
         elif self.OUTPUT_TYPE == "greedy_until":
             arguments=(ctx, self._config.delimiter)
 
@@ -504,6 +543,7 @@ class ConfigurableTask(Task):
             request_type=self.OUTPUT_TYPE,
             doc=doc,
             arguments=arguments,
+            id_=0,
             **kwargs
             )
 
@@ -516,6 +556,22 @@ class ConfigurableTask(Task):
             result_dict = {"perplexity": ll, "accuracy": int(is_greedy)}
         elif self.OUTPUT_TYPE == "loglikelihood_rolling":
             pass
+        elif self.OUTPUT_TYPE == "multiple_choice":
+            lls = [res[0] for res in results] # only retain loglikelihoods, discard is_greedy 
+            gold = int(self.doc_to_target(doc)) # TODO: if `gold` here is an integer/ds label obj, map it to answer_choice
+            # TODO: remove dependence on "gold" and "choices" columns
+
+            acc = 1.0 if np.argmax(lls) == gold else 0.0
+            completion_len = np.array([float(len(i)) for i in doc["choices"]])
+            acc_norm = 1.0 if np.argmax(results / completion_len) == gold else 0.0
+
+            # TODO: set which normalization metrics should be reported, and calculate them
+            # TODO: add mutual info.
+
+            result_dict = {
+                "acc": acc,
+                "acc_norm": acc_norm,
+            } 
         elif self.OUTPUT_TYPE == "greedy_until":
 
             if self._config.gold_alias is not None:
@@ -531,6 +587,10 @@ class ConfigurableTask(Task):
                 )
 
                 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'"
+            )
 
         return result_dict
 
@@ -558,11 +618,6 @@ class MultipleChoiceTask(Task):
                 **kwargs,
             )
             for i, choice in enumerate(doc["choices"])]
-        #lls = [
-        #    rf.loglikelihood(ctx, " {}".format(choice))[0] for choice in doc["choices"]
-        # ]
-
-        # return lls
 
     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? 
@@ -668,16 +723,19 @@ class PerplexityTask(Task, abc.ABC):
 TASK_REGISTRY = {}
 ALL_TASKS = []
 
-def register_task(name):
+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!"
+            ), 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.

lm_eval/evaluator.py

@@ -145,7 +145,8 @@ 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.
-        requests[task.OUTPUT_TYPE].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

lm_eval/models/gpt2.py

@@ -9,7 +9,7 @@ from lm_eval import utils
 from lm_eval.api.model import LM, register_model
 
 
-@register_model("hf-causal")
+@register_model("hf-causal", "gpt2")
 class HFLM(LM):
     def __init__(
         self,

lm_eval/tasks/arc.yaml

 dataset_path: ai2_arc
 dataset_name: ARC-Challenge
+output_type: multiple_choice
 training_split: train
 validation_split: validation
 test_split: test
-doc_to_text: "Q: {{question}}\nA:"
-doc_to_target: "{% set answer_choices = doc['choices']['text'] %}{{answer_choices[int(doc['answerKey']) - 1]}}"
+template_aliases: "{% set answer_choices = choices['text'] %}{% set gold = choices.label.index(answerKey) %}" # set the list of possible answer choices, and set what this doc's gold answer is (set what ds column used, and what)
+doc_to_text: "Question: {{question}}\nAnswer:"
+doc_to_target: "{{gold}}"
 metric_list:
-  - metric: exact_match
+  - metric: acc
+    aggregation: mean
+    higher_is_better: true
+  - metric: acc_norm
     aggregation: mean
     higher_is_better: true
\ No newline at end of file

lm_eval/tasks/lambada.yaml

 dataset_path: EleutherAI/lambada_openai
 dataset_name: default
-output_type: "loglikelihood"
+output_type: loglikelihood
 test_split: test
-template_aliases: "{% set hypo = hypothesis %}"
+template_aliases: ""
 doc_to_text: "{{text.split(' ')[:-1]|join(' ')}}"
 doc_to_target: "{{' '+text.split(' ')[-1]}}"
 should_decontaminate: true
@@ -12,5 +12,5 @@ metric_list:
     aggregation: perplexity
     higher_is_better: true
   - metric: accuracy
-    aggregation: perplexity
+    aggregation: mean
     higher_is_better: true