Merge branch 'master' into mlsum

.github/workflows/python-app.yml

@@ -32,7 +32,7 @@ jobs:
       run: |
         python -m pip install --upgrade pip
         pip install flake8 pytest pytest-cov
-        pip install -e .
+        pip install -e .[dev]
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
     - name: Lint with flake8
       run: |

docs/task_guide.md

@@ -11,7 +11,7 @@ If you haven't already, go ahead and fork the main repo, clone it, create a bran
 git clone https://github.com/<YOUR-USERNAME>/lm-evaluation-harness.git
 cd lm-evaluation-harness
 git checkout -b <task-name>
-pip install -r requirements.txt
+pip install -e ".[dev]"
 ```
 
 ## Creating Your Task File

lm_eval/base.py

@@ -121,6 +121,11 @@ class LM(abc.ABC):
 
 
 class BaseLM(LM):
+    @property
+    @abstractmethod
+    def eot_token(self):
+        pass
+
     @property
     @abstractmethod
     def eot_token_id(self):
@@ -354,8 +359,15 @@ class BaseLM(LM):
                 isinstance(max_generation_length, int) or max_generation_length is None
             )
 
+            if stopping_criteria is None:
+                until = [self.eot_token] 
+            else:
                 until = [stopping_criteria]
             primary_until = self.tok_encode(until[0])
+
+            if len(primary_until) == 0:
+                primary_until = torch.tensor([self.eot_token_id])
+
             context_enc = torch.tensor(
                 [self.tok_encode(context)[self.max_gen_toks - self.max_length :]]
             ).to(self.device)
@@ -633,14 +645,18 @@ class Task(abc.ABC):
                 # 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]
 
+            # See Webson & Pavlick (2022) https://arxiv.org/pdf/2109.01247.pdf
+            # for justification of this separator.
+            example_separator = "\n###\n"
+
             labeled_examples = (
-                "\n\n".join(
+                example_separator.join(
                     [
                         self.doc_to_text(doc) + self.doc_to_target(doc)
                         for doc in fewshotex
                     ]
                 )
-                + "\n\n"
+                + example_separator
             )
 
         example = self.doc_to_text(doc)
@@ -654,11 +670,21 @@ class PromptSourceTask(Task):
     *and* add additional custom processing, override `process_results`, `higher_is_better`, and `aggregation`.
     """
 
-    CONFIGURED_PS_METRICS = set(["Accuracy", "BLEU", "ROUGE"])
-
-    def __init__(self, data_dir=None, cache_dir=None, download_mode=None, prompt=None):
+    CONFIGURED_RANKED_CHOICE_PS_METRICS = set(["Accuracy"])
+    CONFIGURED_GENERATION_PS_METRICS = set(["BLEU", "ROUGE"])
+    SPLIT = None
+
+    def __init__(
+        self,
+        data_dir=None,
+        cache_dir=None,
+        download_mode=None,
+        prompt=None,
+        save_examples=True,
+    ):
         super().__init__(data_dir, cache_dir, download_mode)
         self.prompt = prompt
+        self.save_examples = save_examples
 
     def stopping_criteria(self) -> Optional[str]:
         """Denote where the generation should end.
@@ -752,12 +778,11 @@ class PromptSourceTask(Task):
 
             for metric in self.prompt.metadata.metrics:
                 assert (
-                    metric in self.CONFIGURED_PS_METRICS
+                    metric in self.CONFIGURED_RANKED_CHOICE_PS_METRICS
                 ), "Unexpected metric. Add it, or use a task-specific solution."
                 if metric == "Accuracy":
                     out["acc"] = pred == target
             # TODO: Add metrics here.
-            return out
         else:
             # If not, then this is a generation prompt.
             # NOTE: In the future, target will be a list of strings.
@@ -765,11 +790,11 @@ class PromptSourceTask(Task):
             out = {}
             for metric in self.prompt.metadata.metrics:
                 assert (
-                    metric in self.CONFIGURED_PS_METRICS
+                    metric in self.CONFIGURED_GENERATION_PS_METRICS
                 ), "Unexpected metric. Add it, or use a task-specific solution."
                 if metric == "BLEU":
                     out["bleu"] = (target, pred)
-                if metric == "ROUGE":
+                elif metric == "ROUGE":
                     # TODO: This computes all rouge sub-metrics. Find a generic
                     # way to handle user specified rouge sub-metrics to avoid extra
                     # compute.
@@ -778,15 +803,21 @@ class PromptSourceTask(Task):
                     rouge_scores = utils.flatten(rouge_scores)
                     # Merge all the rouge-type scores into the `out` dict.
                     out = {**out, **rouge_scores}
-                    print(out)
+
+        # TODO: Wrap process results s.t. override impl do not
+        # override the save examples.
+        if self.save_examples:
+            example = {
+                "pred": pred,
+                "target": target,
+                "answer_choices_list": answer_choices_list,
+            }
+            return out, example
         return out
 
     def higher_is_better(self):
         out = {}
         for metric in self.prompt.metadata.metrics:
-            assert (
-                metric in self.CONFIGURED_PS_METRICS
-            ), "Unexpected metric. Add it, or use a task-specific solution."
             if metric == "Accuracy":
                 out["acc"] = True
             if metric == "BLEU":
@@ -813,9 +844,6 @@ class PromptSourceTask(Task):
     def aggregation(self):
         out = {}
         for metric in self.prompt.metadata.metrics:
-            assert (
-                metric in self.CONFIGURED_PS_METRICS
-            ), "Unexpected metric. Add it, or use a task-specific solution."
             if metric == "Accuracy":
                 out["acc"] = mean
             if metric == "BLEU":
@@ -839,6 +867,125 @@ class PromptSourceTask(Task):
                 out["rougeLsum_fmeasure"] = mean
         return out
 
+    def fewshot_examples(self, k, rnd):
+        if self._training_docs is None:
+            self._training_docs = list(self.training_docs())
+        return self._get_fewshot_examples(self._training_docs, k, rnd)
+
+    def _get_fewshot_examples(self, docs, k, rnd):
+        fewshot_idx = rnd.sample(list(np.arange(len(docs))), k)
+        return [docs[idx] for idx in fewshot_idx], [int(idx) for idx in fewshot_idx]
+
+    @utils.positional_deprecated
+    def fewshot_context(
+        self, doc, num_fewshot, provide_description=None, rnd=None, description=None
+    ):
+        """Returns a fewshot context string that is made up of a prepended description
+        (if provided), the `num_fewshot` number of examples, and an appended prompt example.
+
+        :param doc: str
+            The document as returned from training_docs, validation_docs, or test_docs.
+        :param num_fewshot: int
+            The number of fewshot examples to provide in the returned context string.
+        :param provide_description: bool
+            Not implemented, and this option is deprecated and will be removed in a future version in favor of a different description providing method
+        :param rnd: random.Random
+            The pseudo-random number generator used to randomly sample examples.
+            WARNING: This is currently a required arg although it's optionalized with a default `None`.
+        :param description: str
+            The task's description that will be prepended to the fewshot examples.
+        :returns: str
+            The fewshot context.
+        """
+        assert (
+            rnd is not None
+        ), "A `random.Random` generator argument must be provided to `rnd`"
+        assert not provide_description, (
+            "The `provide_description` arg will be removed in future versions. To prepend "
+            "a custom description to the context, supply the corresponding string via the "
+            "`description` arg."
+        )
+        if provide_description is not None:
+            # nudge people to not specify it at all
+            print(
+                "WARNING: provide_description is deprecated and will be removed in a future version in favor of description_dict"
+            )
+
+        description = description + "\n\n" if description else ""
+
+        if num_fewshot == 0:
+            labeled_examples = ""
+            fewshotex, fewshotidx, fewshotsource = [], [], None
+        else:
+            # for sets with no training docs, draw from other set *but ensure no overlap with current doc*
+            if self.has_training_docs():
+                fewshotex, fewshotidx = self.fewshot_examples(k=num_fewshot, rnd=rnd)
+                fewshotsource = "train"
+            else:
+                if self._fewshot_docs is None:
+                    self._fewshot_docs = list(
+                        self.validation_docs()
+                        if self.has_validation_docs()
+                        else self.test_docs()
+                    )
+                    if self.has_validation_docs():
+                        fewshotsource = "val"
+                    elif self.test_docs():
+                        fewshotsource = "test"
+
+                fewshotex, fewshotidx = self._get_fewshot_examples(
+                    self._fewshot_docs, k=num_fewshot + 1, rnd=rnd
+                )
+                fewshotex, fewshotidx = [
+                    (shot, idx)
+                    for shot, idx in zip(fewshotex, fewshotidx)
+                    if shot != doc
+                ]
+                # get rid of the doc that's the one we're evaluating, if it's in the fewshot
+                fewshotex, fewshotidx = (
+                    fewshotex[:num_fewshot],
+                    fewshotidx[:num_fewshot],
+                )
+            # See Webson & Pavlick (2022) https://arxiv.org/pdf/2109.01247.pdf
+            # for justification of this separator.
+            example_separator = "\n###\n"
+
+            labeled_examples = (
+                example_separator.join(
+                    [
+                        self.doc_to_text(doc) + self.doc_to_target(doc)
+                        for doc in fewshotex
+                    ]
+                )
+                + example_separator
+            )
+
+        example = self.doc_to_text(doc)
+        ctx = description + labeled_examples + example
+        return (
+            ctx,
+            {
+                "fewshot_idx": fewshotidx,
+                "fewshot_source": fewshotsource,
+                "fewshot_num": num_fewshot,
+                "ctx": ctx,
+            },
+        )
+
+    def get_logging_info(self):
+        return {
+            "fixed_answer_choice_list": self.prompt.get_fixed_answer_choices_list(),
+            "dataset_path": self.DATASET_PATH,
+            "dataset_name": self.DATASET_NAME,
+            "subset": self.SPLIT,
+            "prompt_name": self.prompt.get_name(),
+            "prompt_id": self.prompt.get_id(),
+            "prompt_jinja": self.prompt.jinja,
+            "prompt_original_task": self.prompt.metadata.original_task,
+            # Placeholder for comment in post-processing.
+            "comment": "",
+        }
+
 
 class MultipleChoiceTask(Task):
     def doc_to_target(self, doc):