fix averaging

config/harness/harness_nano.yaml

@@ -1,7 +1,7 @@
 eval_harness:
 #  task_spec: ["hellaswag"]
   task_spec:
-    - mmlu
+#    - mmlu
     - task: mmlu
       num_fewshot: 1
       task_alias: mmlu_1shot

src/levanter/eval_harness.py

@@ -263,8 +263,6 @@ def loglikelihood(self, requests: list[Instance]) -> list[tuple[float, bool]]:
         result_greedy = np.zeros(len(requests))
         covered_points = np.zeros(len(requests), dtype=bool)
 
-        return [ (0.0, False) ] * len(requests)
-
         total_padding = 0
         total_tokens = 0
         pbar = tqdm(total=len(requests), desc="Loglikelihood", unit="req")
@@ -294,11 +292,11 @@ def loglikelihood(self, requests: list[Instance]) -> list[tuple[float, bool]]:
             result_greedy[out_ids[valid_indices]] = out_correct[valid_indices]
             covered_points[out_ids[valid_indices]] = True
 
+            total_padding += padding_count
+            total_tokens += batch_tokens
+
             pbar.set_postfix(
-                padding=(
-                    f"{total_padding + padding_count}/{total_tokens + batch_tokens} ="
-                    f" {(total_padding + padding_count) / (total_tokens + batch_tokens):.2f}"
-                ),
+                padding=f"{total_padding}/{total_tokens} = {(total_padding) / (total_tokens):.2f}",
                 this_padding=f"{padding_count}/{batch_tokens}= {padding_count / batch_tokens:.2f}",
             )
             pbar.update(len(segments_this_batch))
@@ -455,6 +453,7 @@ def _get_task_and_rename(self, manager, our_name, task: dict | str):
 
     def _rename_tasks_for_eval_harness(self, this_task, lm_eval_task_name, our_name):
         import lm_eval.tasks as tasks
+
         # hacky, but this allows us to run multiple instances of the same task with different fewshot settings
         if isinstance(this_task, dict):
             out = {}
@@ -467,13 +466,11 @@ def _rename_tasks_for_eval_harness(self, this_task, lm_eval_task_name, our_name)
                 elif isinstance(k, str):
                     k = self._replace_name_with_our_name(k, lm_eval_task_name, our_name)
                     if isinstance(v, dict):
+                        subtask_list = self._get_child_tasks(v)
                         # ok so inexplicably, lm_eval_harness doesn't wrap the key in a ConfigurableGroup when you pass
                         # in a task dict (it seems like a mistake), so we need to do that here
                         # subtask is the name of all of the child tasks in v
-                        subtask_list = self._get_child_tasks(v)
-                        group = tasks.ConfigurableGroup(
-                            config={"group": k, "task": subtask_list}
-                        )
+                        group = tasks.ConfigurableGroup(config={"group": k, "task": subtask_list})
                         out[group] = v
                     else:
                         out[k] = v
@@ -483,7 +480,9 @@ def _rename_tasks_for_eval_harness(self, this_task, lm_eval_task_name, our_name)
             return out
 
         elif isinstance(this_task, tasks.ConfigurableTask):
-            this_task.config.task = self._replace_name_with_our_name(this_task.config.task, lm_eval_task_name, our_name)
+            this_task.config.task = self._replace_name_with_our_name(
+                this_task.config.task, lm_eval_task_name, our_name
+            )
             return this_task
         else:
             raise ValueError(f"Unknown task type: {this_task}")
@@ -503,6 +502,7 @@ def _replace_name_with_our_name(self, lm_eval_name, lm_eval_prefix, our_name_pre
 
     def _get_child_tasks(self, task_group):
         import lm_eval.tasks as tasks
+
         out = []
         for k, v in task_group.items():
             if isinstance(k, tasks.ConfigurableGroup):
@@ -642,15 +642,14 @@ def _compute_averages(outputs):
     for task_results in outputs["results"].values():
         metric_keys.update(k for k in task_results.keys() if "stderr" not in k and k != "alias")
 
-    examples_per_task = [task_samples["effective"] for task_samples in outputs["n-samples"].values()]
-
     # Compute macro and micro averages
     for metric in metric_keys:
         # Collect valid tasks for this metric
+        # We iterate over the n-samples because real tasks (as opposed to aggregates like "mmlu") have counts
         valid_tasks = [
-            (task_results.get(metric), examples_per_task[i])
-            for i, (task_name, task_results) in enumerate(outputs["results"].items())
-            if metric in task_results
+            (outputs["results"][task_name].get(metric), outputs["n-samples"][task_name]["effective"])
+            for task_name in outputs["n-samples"]
+            if outputs["results"][task_name].get(metric, None) is not None
         ]
 
         if not valid_tasks: