Save function fix

optimum/neuron/trainers.py

@@ -48,6 +48,7 @@
 from .utils import (
     DynamicPatch,
     ModelPatcher,
+    Patcher,
     is_torch_xla_available,
     patch_within_function,
 )
@@ -62,6 +63,7 @@
     prepare_environment_for_neuron,
     set_neuron_cc_optlevel_for_model,
     skip_first_batches,
+    torch_xla_safe_save_file,
 )
 
 
@@ -402,20 +404,27 @@ def _save_xla(self, output_dir: Optional[str] = None):
             xm.mark_step()
             parallelizer.save_model_checkpoint(self.model, output_dir, as_sharded=True, optimizer=self.optimizer)
         else:
+            safe_save_function_patcher = Patcher(
+                [("transformers.modeling_utils.safe_save_file", torch_xla_safe_save_file)]
+            )
             if not isinstance(self.model, PreTrainedModel):
                 if isinstance(unwrap_model(self.model), PreTrainedModel):
-                    unwrap_model(self.model).save_pretrained(
-                        output_dir,
-                        is_main_process=self.args.should_save,
-                        state_dict=self.model.state_dict(),
-                        save_function=xm.save,
-                    )
+                    with safe_save_function_patcher:
+                        unwrap_model(self.model).save_pretrained(
+                            output_dir,
+                            is_main_process=self.args.should_save,
+                            state_dict=self.model.state_dict(),
+                            save_function=xm.save,
+                        )
                 else:
                     logger.info("Trainer.model is not a `PreTrainedModel`, only saving its state dict.")
                     state_dict = self.model.state_dict()
                     xm.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME))
             else:
-                self.model.save_pretrained(output_dir, is_main_process=self.args.should_save, save_function=xm.save)
+                with safe_save_function_patcher:
+                    self.model.save_pretrained(
+                        output_dir, is_main_process=self.args.should_save, save_function=xm.save
+                    )
 
         if self.tokenizer is not None and self.args.should_save:
             self.tokenizer.save_pretrained(output_dir)

optimum/neuron/utils/cache_utils.py

@@ -133,14 +133,22 @@ def create_custom_cache_repo(repo_id: str = CACHE_REPO_NAME, private: bool = Tru
 
 
 def is_private_repo(repo_id: str) -> bool:
+    """Tells whether `repo_id` is private."""
     if _DISABLE_IS_PRIVATE_REPO_CHECK:
         return False
-    HfApi().list_repo_files(repo_id=repo_id, token=HfFolder.get_token())
-    private = False
     try:
-        HfApi().list_repo_files(repo_id=repo_id, token=False)
+        HfApi().model_info(repo_id=repo_id, token=HfFolder.get_token())
+        private_to_user = False
     except RepositoryNotFoundError:
+        private_to_user = True
+    if private_to_user:
         private = True
+    else:
+        try:
+            HfApi().model_info(repo_id=repo_id, token=False)
+            private = False
+        except RepositoryNotFoundError:
+            private = True
     return private
 
 
@@ -921,10 +929,20 @@ def push_to_cache_on_hub(
     overwrite_existing: bool = False,
     local_path_to_path_in_repo: Optional[Union[Literal["default"], Callable[[Path], Path]]] = None,
     fail_when_could_not_push: bool = False,
-) -> CachedModelOnTheHub:
+) -> Optional[CachedModelOnTheHub]:
     if cache_repo_id is None:
         cache_repo_id = get_hf_hub_cache_repos()[0]
 
+    if not has_write_access_to_repo(cache_repo_id):
+        error_message = (
+            f"Could not push the cached model to {cache_repo_id} because you do not have write access to this repo."
+        )
+        if fail_when_could_not_push:
+            raise ValueError(error_message)
+        if is_main_worker():
+            logger.warning(error_message)
+        return
+
     try:
         create_registry_file_if_does_not_exist(cache_repo_id)
         _REGISTRY_FILE_EXISTS[cache_repo_id] = True
@@ -933,10 +951,15 @@ def push_to_cache_on_hub(
 
     is_cache_repo_private = is_private_repo(cache_repo_id)
     if neuron_hash.is_private and not is_cache_repo_private:
-        raise ValueError(
-            f"Cannot push the cached model to {cache_repo_id} because this repo is not private but the original model is "
-            "coming from private repo."
+        error_message = (
+            f"Could not push the cached model to {cache_repo_id} because this repo is not private but the original "
+            "model is coming from private repo."
         )
+        if fail_when_could_not_push:
+            raise ValueError(error_message)
+        if is_main_worker():
+            logger.warning(error_message)
+        return
 
     if local_path_to_path_in_repo == "default":
         local_path_to_path_in_repo = functools.partial(default_local_path_to_path_in_repo, neuron_hash=neuron_hash)
@@ -970,10 +993,7 @@ def push_to_cache_on_hub(
                 f"{local_cache_dir_or_file}"
             )
 
-    could_not_push_message = (
-        "Could not push the cached model to the repo {cache_repo_id}, most likely due to not having the write permission "
-        "for this repo. Exact error:\n{error}."
-    )
+    could_not_push_message = "Could not push the cached model to the repo {cache_repo_id}. Exact error:\n{error}."
     success = True
     if local_cache_dir_or_file.is_dir():
         try:

optimum/neuron/utils/training_utils.py

@@ -50,7 +50,7 @@
 from ...utils.logging import set_verbosity as set_verbosity_optimum
 from ..generation import NeuronGenerationMixin
 from . import is_torch_xla_available
-from .require_utils import requires_torch_xla
+from .require_utils import requires_neuronx_distributed, requires_safetensors, requires_torch_xla
 
 
 if TYPE_CHECKING:
@@ -315,6 +315,28 @@ def skip_first_batches(dataloader, num_batches=0):
     return dataloader
 
 
+@requires_neuronx_distributed
+@requires_safetensors
+def torch_xla_safe_save_file(
+    tensors: Dict[str, torch.Tensor],
+    filename: Union[str, os.PathLike],
+    metadata: Optional[Dict[str, str]] = None,
+    master_only: bool = True,
+    global_master: bool = False,
+):
+    """
+    Torch XLA compatible implementation of `safetensors.torch.save_file`.
+    """
+    from neuronx_distributed.parallel_layers.utils import move_all_tensor_to_cpu
+    from safetensors.torch import save_file
+    from torch_xla.core.xla_model import is_master_ordinal
+
+    should_write_data = not master_only or is_master_ordinal(local=not global_master)
+    cpu_data = move_all_tensor_to_cpu(tensors, convert=should_write_data)
+    if should_write_data:
+        save_file(cpu_data, filename, metadata=metadata)
+
+
 def get_model_param_count(model, trainable_only=False):
     """Wrapper around `transformers.trainer_pt_utils.get_model_param_count` to handle tensor parallelism."""
     # TODO: make it work for TP

tests/test_cache_utils.py

@@ -278,7 +278,6 @@ def remove_repo():
             if orig_token:
                 HfFolder.save_token(orig_token)
 
-    @is_staging_test
     def test_has_write_access_to_repo(self):
         orig_token = HfFolder.get_token()
         wrong_token = "random_string"
@@ -292,7 +291,6 @@ def test_has_write_access_to_repo(self):
         self.assertTrue(has_write_access_to_repo(self.CUSTOM_CACHE_REPO))
         self.assertTrue(has_write_access_to_repo(self.CUSTOM_PRIVATE_CACHE_REPO))
 
-    @is_staging_test
     def test_list_in_registry(self):
         def _test_list_in_registry(use_private_cache_repo: bool):
             if use_private_cache_repo:
@@ -466,7 +464,6 @@ def test_neuron_hash_is_private(self):
 
         bert_model = BertModel.from_pretrained("hf-internal-testing/tiny-random-bert")
         neuron_hash = NeuronHash(bert_model, input_shapes, data_type, neuron_compiler_version=DUMMY_COMPILER_VERSION)
-
         self.assertFalse(neuron_hash.is_private)
 
         with TemporaryDirectory() as tmpdirname:
@@ -494,8 +491,10 @@ def test_push_to_hub_fails_with_private_model_and_public_repo(self):
 
             # The model being loaded locally is assumed to be private, push to hub should prevent from pushing to a
             # public repo.
-            with self.assertRaisesRegex(ValueError, "Cannot push the cached model"):
-                push_to_cache_on_hub(neuron_hash, cached_files[0], self.CUSTOM_CACHE_REPO)
+            with self.assertRaisesRegex(ValueError, "Could not push the cached model"):
+                push_to_cache_on_hub(
+                    neuron_hash, cached_files[0], self.CUSTOM_CACHE_REPO, fail_when_could_not_push=True
+                )
 
             # It should work when using a private repo.
             cached_model_on_the_hub = push_to_cache_on_hub(
@@ -547,7 +546,6 @@ def test_push_to_hub_overwrite_existing(self):
             # With a directory
             with self.assertLogs("optimum", level="INFO") as cm:
                 push_to_cache_on_hub(neuron_hash, cache_dir, self.CUSTOM_PRIVATE_CACHE_REPO)
-                print(cm.output)
                 self.assertIn("Did not push the cached model located at", cm.output[0])
 
             with self.assertLogs("optimum", level="WARNING") as cm:
@@ -636,7 +634,7 @@ def test_push_to_hub_without_writing_rights(self):
             set_custom_cache_repo_name_in_hf_home(f"{TRANSFORMERS_USER}/{repo_name}")
             with self.assertLogs("optimum", "WARNING") as cm:
                 push_to_cache_on_hub(neuron_hash, get_neuron_cache_path())
-                self.assertTrue(any("Could not push the cached model to the repo" in output for output in cm.output))
+                self.assertTrue(any("Could not push the cached model to" in output for output in cm.output))
 
             self.set_hf_hub_token(TRANSFORMERS_TOKEN)
             delete_repo(repo_name, repo_type="model")

tests/utils.py

@@ -256,6 +256,7 @@ def push_tiny_pretrained_model_cache_to_hub(
             push_to_cache_on_hub(
                 neuron_hash,
                 tmp_cache_dir,
+                fail_when_could_not_push=True,
             )
             if cache_dir is not None:
                 for file_or_dir in tmp_cache_dir.iterdir():