Further reduce the number of alls to head for cached objects

src/transformers/utils/hub.py

@@ -120,6 +120,9 @@ def is_offline_mode():
 HUGGINGFACE_CO_PREFIX = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/{model_id}/resolve/{revision}/{filename}"
 HUGGINGFACE_CO_EXAMPLES_TELEMETRY = HUGGINGFACE_CO_RESOLVE_ENDPOINT + "/api/telemetry/examples"
 
+# Return value when trying to load a file from cache but the file does not exist in the distant repo.
+_CACHED_NO_EXIST = object()
+
 
 def get_cached_models(cache_dir: Union[str, Path] = None) -> List[Tuple]:
     """
@@ -222,6 +225,22 @@ def extract_commit_hash(resolved_file: Optional[str], commit_hash: Optional[str]
 def try_to_load_from_cache(cache_dir, repo_id, filename, revision=None, commit_hash=None):
     """
     Explores the cache to return the latest cached file for a given revision.
+
+    Args:
+        cache_dir (`str` or `os.PathLike`): The folder where the cached files lie.
+        repo_id (`str`): The ID of the repo on huggingface.co.
+        filename (`str`): The filename to look for inside `repo_id`.
+        revision (`str`, *optional*):
+            The specific model version to use. Will default to `"main"` if it's not provided and no `commit_hash` is
+            provided either.
+        commit_hash (`str`, *optional*): The (full) commit hash to look for inside the cache.
+
+    Returns:
+        `Optional[str]` or `_CACHED_NO_EXIST`:
+            Will return `None` if the file was not cached. Otherwise:
+            - The exact path to the cached file if it's found in the cache
+            - A special value `_CACHED_NO_EXIST` if the file does not exist at the given commit hash and this fact was
+              cached.
     """
     if commit_hash is not None and revision is not None:
         raise ValueError("`commit_hash` and `revision` are mutually exclusive, pick one only.")
@@ -244,6 +263,9 @@ def try_to_load_from_cache(cache_dir, repo_id, filename, revision=None, commit_h
             with open(os.path.join(model_cache, "refs", revision)) as f:
                 commit_hash = f.read()
 
+    if os.path.isfile(os.path.join(model_cache, ".no_exist", commit_hash, filename)):
+        return _CACHED_NO_EXIST
+
     cached_shas = os.listdir(os.path.join(model_cache, "snapshots"))
     if commit_hash not in cached_shas:
         # No cache for this revision and we won't try to return a random revision
@@ -338,7 +360,10 @@ def cached_file(
         resolved_file = os.path.join(os.path.join(path_or_repo_id, subfolder), filename)
         if not os.path.isfile(resolved_file):
             if _raise_exceptions_for_missing_entries:
-                raise EnvironmentError(f"Could not locate {full_filename} inside {path_or_repo_id}.")
+                raise EnvironmentError(
+                    f"{path_or_repo_id} does not appear to have a file named {full_filename}. Checkout "
+                    f"'https://huggingface.co/{path_or_repo_id}/{revision}' for available files."
+                )
             else:
                 return None
         return resolved_file
@@ -352,7 +377,12 @@ def cached_file(
         # If the file is cached under that commit hash, we return it directly.
         resolved_file = try_to_load_from_cache(cache_dir, path_or_repo_id, full_filename, commit_hash=_commit_hash)
         if resolved_file is not None:
-            return resolved_file
+            if resolved_file is not _CACHED_NO_EXIST:
+                return resolved_file
+            elif not _raise_exceptions_for_missing_entries:
+                return None
+            else:
+                raise EnvironmentError(f"Could not locate {full_filename} inside {path_or_repo_id}.")
 
     user_agent = http_user_agent(user_agent)
     try:

tests/models/auto/test_modeling_auto.py

@@ -370,6 +370,5 @@ def test_cached_model_has_minimum_calls_to_head(self):
         with RequestCounter() as counter:
             _ = AutoModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded")
             self.assertEqual(counter.get_request_count, 0)
-            # There is no pytorch_model.bin so we still get one call for this one.
-            self.assertEqual(counter.head_request_count, 2)
+            self.assertEqual(counter.head_request_count, 1)
             self.assertEqual(counter.other_request_count, 0)

tests/models/auto/test_modeling_tf_auto.py

@@ -303,6 +303,5 @@ def test_cached_model_has_minimum_calls_to_head(self):
         with RequestCounter() as counter:
             _ = TFAutoModel.from_pretrained("ArthurZ/tiny-random-bert-sharded")
             self.assertEqual(counter.get_request_count, 0)
-            # There is no pytorch_model.bin so we still get one call for this one.
-            self.assertEqual(counter.head_request_count, 2)
+            self.assertEqual(counter.head_request_count, 1)
             self.assertEqual(counter.other_request_count, 0)

tests/models/auto/test_tokenization_auto.py

@@ -349,6 +349,5 @@ def test_cached_tokenizer_has_minimum_calls_to_head(self):
         with RequestCounter() as counter:
             _ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert")
             self.assertEqual(counter.get_request_count, 0)
-            # We still have one extra call because the model does not have a added_tokens.json file
-            self.assertEqual(counter.head_request_count, 2)
+            self.assertEqual(counter.head_request_count, 1)
             self.assertEqual(counter.other_request_count, 0)

tests/pipelines/test_pipelines_common.py

@@ -884,8 +884,7 @@ def test_cached_pipeline_has_minimum_calls_to_head(self):
         with RequestCounter() as counter:
             _ = pipeline("text-classification", model="hf-internal-testing/tiny-random-bert")
             self.assertEqual(counter.get_request_count, 0)
-            # We still have one extra call because the model does not have a added_tokens.json file
-            self.assertEqual(counter.head_request_count, 2)
+            self.assertEqual(counter.head_request_count, 1)
             self.assertEqual(counter.other_request_count, 0)