logging fix

config/harness/eval_llama3.yaml

@@ -2,32 +2,32 @@ eval_harness:
   task_spec:
     - task: commonsense_qa  # 5-way multiple-choice questions based on common-sense, everyday scenarios
       num_fewshot: 10
-    - task: agieval_lsat_ar  # 3-shot tests in legal domain
-      num_fewshot: 3
-    - task: arc_easy  # 10-shot, four-way MCQ questions involving grade 3-9 basic science
-      num_fewshot: 10
-    - task: arc_challenge  # a (harder) version of arc_easy
-      num_fewshot: 10
-    - task: boolq  # answer yes/no questions based on a passage
-      num_fewshot: 10
-    - task: copa  # use causal reasoning to predict the correct outcome of a given scenario
-      num_fewshot: 0
-    - task: hellaswag  # 4-way multiple choice commonsense reasoning dataset
-      num_fewshot: 0
-      task_alias: hellaswag_0shot
-    - task: hellaswag  # 4-way multiple choice commonsense reasoning dataset
-      num_fewshot: 10
-      task_alias: hellaswag_10shot
-    - task: lambada  # predict the endings of text passages
-      num_fewshot: 0
-    - task: openbookqa  # 4-way multiple choice question answering task that requires multi-step reasoning
-      num_fewshot: 0
-    - task: piqa  # answer questions based on a passage
-      num_fewshot: 10
-    - task: wsc273  # Winograd Schema Challenge
-      num_fewshot: 0
-    - task: winogrande  # Winograd challenge, extended to more domains
-      num_fewshot: 0
+#    - task: agieval_lsat_ar  # 3-shot tests in legal domain
+#      num_fewshot: 3
+#    - task: arc_easy  # 10-shot, four-way MCQ questions involving grade 3-9 basic science
+#      num_fewshot: 10
+#    - task: arc_challenge  # a (harder) version of arc_easy
+#      num_fewshot: 10
+#    - task: boolq  # answer yes/no questions based on a passage
+#      num_fewshot: 10
+#    - task: copa  # use causal reasoning to predict the correct outcome of a given scenario
+#      num_fewshot: 0
+#    - task: hellaswag  # 4-way multiple choice commonsense reasoning dataset
+#      num_fewshot: 0
+#      task_alias: hellaswag_0shot
+#    - task: hellaswag  # 4-way multiple choice commonsense reasoning dataset
+#      num_fewshot: 10
+#      task_alias: hellaswag_10shot
+#    - task: lambada  # predict the endings of text passages
+#      num_fewshot: 0
+#    - task: openbookqa  # 4-way multiple choice question answering task that requires multi-step reasoning
+#      num_fewshot: 0
+#    - task: piqa  # answer questions based on a passage
+#      num_fewshot: 10
+#    - task: wsc273  # Winograd Schema Challenge
+#      num_fewshot: 0
+#    - task: winogrande  # Winograd challenge, extended to more domains
+#      num_fewshot: 0
       # requires generation
 ##    - task: squadv2  # reading comprehension benchmark
 #      num_fewshot: 10

src/levanter/tensorstore_serialization.py

@@ -180,9 +180,10 @@ def tree_deserialize_leaves_tensorstore(
         if not allow_missing:
             raise FileNotFoundError(f"Missing paths: {missing_paths}")
         else:
-            to_log = f"Several keys were missing from the checkpoint directory {paths}:"
+            to_log = f"Several keys were missing from the checkpoint directory {checkpoint_dir}:"
+            leaf_paths = jtu.tree_leaves(leaf_key_paths, is_leaf=_is_named_or_none)
             for i in missing_indices:
-                to_log += f"\n  - {leaf_key_paths[i]}"
+                to_log += f"\n  - {leaf_paths[i]}"
             logger.warning(to_log)
 
     deser_leaves = manager.deserialize_with_paths(shardings=shardings_to_load, paths=paths_to_load)