cross platform from_pretrained

src/transformers/modeling_tf_utils.py

@@ -47,6 +47,7 @@
     SAFE_WEIGHTS_NAME,
     TF2_WEIGHTS_INDEX_NAME,
     TF2_WEIGHTS_NAME,
+    TF_WEIGHTS_NAME,
     WEIGHTS_INDEX_NAME,
     WEIGHTS_NAME,
     ModelOutput,
@@ -2392,7 +2393,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
                       save directory.
                     - The model is loaded by supplying a local directory as `pretrained_model_name_or_path` and a
                       configuration JSON file named *config.json* is found in the directory.
-            from_pt: (`bool`, *optional*, defaults to `False`):
+            from_pt (`bool`, *optional*, defaults to `False`):
                 Load the model weights from a PyTorch state_dict save file (see docstring of
                 `pretrained_model_name_or_path` argument).
             ignore_mismatched_sizes (`bool`, *optional*, defaults to `False`):
@@ -2531,7 +2532,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
         if pretrained_model_name_or_path is not None:
             pretrained_model_name_or_path = str(pretrained_model_name_or_path)
             is_local = os.path.isdir(pretrained_model_name_or_path)
-            if os.path.isdir(pretrained_model_name_or_path):
+            if is_local:
                 if from_pt and os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)):
                     # Load from a PyTorch checkpoint in priority if from_pt
                     archive_file = os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)
@@ -2559,7 +2560,9 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
                     archive_file = os.path.join(pretrained_model_name_or_path, TF2_WEIGHTS_INDEX_NAME)
                     is_sharded = True
                 # At this stage we don't have a weight file so we will raise an error.
-                elif os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME):
+                elif os.path.isfile(os.path.join(pretrained_model_name_or_path, WEIGHTS_NAME)) or os.path.isfile(
+                    os.path.join(pretrained_model_name_or_path, WEIGHTS_INDEX_NAME)
+                ):
                     raise EnvironmentError(
                         f"Error no file named {TF2_WEIGHTS_NAME} found in directory {pretrained_model_name_or_path} "
                         "but there is a file for PyTorch weights. Use `from_pt=True` to load this model from those "
@@ -2630,6 +2633,13 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
                         )
                         if resolved_archive_file is not None:
                             is_sharded = True
+                    if resolved_archive_file is None and filename == WEIGHTS_NAME:
+                        # Maybe the checkpoint is sharded, we try to grab the index name in this case.
+                        resolved_archive_file = cached_file(
+                            pretrained_model_name_or_path, WEIGHTS_INDEX_NAME, **cached_file_kwargs
+                        )
+                        if resolved_archive_file is not None:
+                            is_sharded = True
                     if resolved_archive_file is None:
                         # Otherwise, maybe there is a PyTorch or Flax model file.  We try those to give a helpful error
                         # message.
@@ -2646,8 +2656,8 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
                             )
                         else:
                             raise EnvironmentError(
-                                f"{pretrained_model_name_or_path} does not appear to have a file named"
-                                f" {TF2_WEIGHTS_NAME} or {WEIGHTS_NAME}."
+                                f"{pretrained_model_name_or_path} does not appear to have a file named {WEIGHTS_NAME},"
+                                f" {TF2_WEIGHTS_NAME} or {TF_WEIGHTS_NAME}"
                             )
 
                 except EnvironmentError:
@@ -2661,7 +2671,7 @@ def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
                         f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it"
                         " from 'https://huggingface.co/models', make sure you don't have a local directory with the"
                         f" same name. Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a"
-                        f" directory containing a file named {TF2_WEIGHTS_NAME} or {WEIGHTS_NAME}."
+                        f" directory containing a file named {WEIGHTS_NAME}, {TF2_WEIGHTS_NAME} or {TF_WEIGHTS_NAME}"
                     )
             if is_local:
                 logger.info(f"loading weights file {archive_file}")

tests/test_modeling_tf_common.py

@@ -2127,6 +2127,14 @@ def test_checkpoint_sharding_local_from_pt(self):
             for p1, p2 in zip(model.weights, ref_model.weights):
                 assert np.allclose(p1.numpy(), p2.numpy())
 
+    @is_pt_tf_cross_test
+    def test_checkpoint_sharding_hub_from_pt(self):
+        model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert-sharded", from_pt=True)
+        # the model above is the same as the model below, just a sharded pytorch version.
+        ref_model = TFBertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
+        for p1, p2 in zip(model.weights, ref_model.weights):
+            assert np.allclose(p1.numpy(), p2.numpy())
+
     def test_shard_checkpoint(self):
         # This is the model we will use, total size 340,000 bytes.
         model = tf.keras.Sequential(