Data loading: support for arbitrary prefix of data files

adaptor/objectives/objective_base.py

@@ -1,6 +1,7 @@
 import abc
 import itertools
 import logging
+import os.path
 from functools import partial
 from typing import List, Union, Optional, Iterable, Tuple, Dict, Sequence, Any, Iterator
 
@@ -21,7 +22,7 @@ class Objective(abc.ABC):
     """
 
     compatible_head: Head
-    given_id: Optional[str]
+    given_id: Optional[str] = ""
     epoch: int
     num_steps: int
     last_input: Optional[Union[BatchEncoding, Dict[str, torch.Tensor]]]
@@ -135,26 +136,30 @@ def __init__(self,
             self.dataset_length["eval"] = self._compute_data_source_length(val_texts_or_path)
 
     def _check_supported_data_source_format(self, path: str) -> None:
+        if not os.path.exists(path):
+            raise FileNotFoundError("Objective %s: Given path '%s' does not exist" % (self, path))
+
         # when the passed data source is a file, we check that it is in a supported format:
         # we support .txt and .tar.gz files
         supported_file_formats = ['.txt', '.gz']
 
         if not any(path.endswith(suffix) for suffix in supported_file_formats):
-            raise ValueError("Objective %s's given {val_}texts_or_path `%s` is not a List "
-                             "and does not end with one of supported suffixes: ['.txt', '.tar.gz']."
-                             "If you want to use a file data source, please pass it in a supported format."
-                             % (self, path))
+            logger.warning("Objective %s's given {val_}texts_or_path `%s` is not a List "
+                            "and does not end with one of supported suffixes: ['.txt', '.gz']."
+                            "We'll assume that the file is a line-separated plaintext file." % (self, path))
 
     def _compute_data_source_length(self, texts_or_path: Union[str, List[str]]) -> int:
         if isinstance(texts_or_path, str):
-            if texts_or_path.endswith('.txt'):
-                with open(self.texts_path, "rb") as f:
-                    return sum(1 for _ in f)  # more efficient line count
+
             if texts_or_path.endswith('.gz'):
                 import io
                 import gzip
                 with io.TextIOWrapper(io.BufferedReader(gzip.open(texts_or_path))) as f:
                     return sum(1 for _ in f)  # more efficient line count
+            else:
+                with open(self.texts_path, "rb") as f:
+                    return sum(1 for _ in f)  # more efficient line count
+
         elif isinstance(texts_or_path, list):
             return len(texts_or_path)
         else:
@@ -169,6 +174,9 @@ def per_objective_log(self, split: str) -> Dict[str, float]:
         :return: Dict of the format {split + objective_name + evaluator_name: evaluator_value}
         """
         out_logs = {}
+        if split == "eval" and self.val_texts is None and self.val_texts_path is None:
+            logger.warning("Skipping evaluation for %s" % self)
+            return out_logs
         # aggregate per-progress_bar-steps, or per-evaluation-steps, keep the results of unprocessed evaluations
         loss_history = self.loss_history[split][-self.max_samples_per_log[split]:]
         mean_loss = sum(loss_history) / len(loss_history) if len(loss_history) else float("inf")

adaptor/utils.py

@@ -48,16 +48,17 @@ def iter_text_file_per_line(path: str) -> Iterable[str]:
         At this point, `path` is checked to be of a supported format.
         :param path: file path
         """
-        if path.endswith(".txt"):
-            with open(path) as f:
-                for line in f:
-                    yield line.strip()
-        elif path.endswith(".gz"):
+        if path.endswith(".gz"):
             import gzip
             import io
             with io.TextIOWrapper(io.BufferedReader(gzip.open(path))) as file:
                 for line in file:
                     yield line.strip()
+        else:
+            # assumes plain, newline-separated text file
+            with open(path) as f:
+                for line in f:
+                    yield line.strip()
 
 
 class TransformerAdaptationDataset(AdaptationDataset):