Use huggingface_hub helper function to split state dict (huggingface#…

…31091)

* shard saving from hf hub

* index = None

* fix tests

* indent

setup.py

@@ -117,7 +117,7 @@
     "fugashi>=1.0",
     "GitPython<3.1.19",
     "hf-doc-builder>=0.3.0",
-    "huggingface-hub>=0.23.0,<1.0",
+    "huggingface-hub>=0.23.2,<1.0",
     "importlib_metadata",
     "ipadic>=1.0.0,<2.0",
     "isort>=5.5.4",

src/transformers/dependency_versions_table.py

@@ -24,7 +24,7 @@
     "fugashi": "fugashi>=1.0",
     "GitPython": "GitPython<3.1.19",
     "hf-doc-builder": "hf-doc-builder>=0.3.0",
-    "huggingface-hub": "huggingface-hub>=0.23.0,<1.0",
+    "huggingface-hub": "huggingface-hub>=0.23.2,<1.0",
     "importlib_metadata": "importlib_metadata",
     "ipadic": "ipadic>=1.0.0,<2.0",
     "isort": "isort>=5.5.4",

src/transformers/modeling_utils.py

@@ -34,6 +34,7 @@
 from zipfile import is_zipfile
 
 import torch
+from huggingface_hub import split_torch_state_dict_into_shards
 from packaging import version
 from torch import Tensor, nn
 from torch.nn import CrossEntropyLoss, Identity
@@ -362,6 +363,10 @@ def shard_checkpoint(
         weights_name (`str`, *optional*, defaults to `"pytorch_model.bin"`):
             The name of the model save file.
     """
+    logger.warning(
+        "Note that `shard_checkpoint` is deprecated and will be removed in v4.44. We recommend you using "
+        "split_torch_state_dict_into_shards from huggingface_hub library"
+    )
     max_shard_size = convert_file_size_to_int(max_shard_size)
 
     sharded_state_dicts = [{}]
@@ -2618,7 +2623,17 @@ def save_pretrained(
         else:
             weights_name = ADAPTER_SAFE_WEIGHTS_NAME if safe_serialization else ADAPTER_WEIGHTS_NAME
 
-        shards, index = shard_checkpoint(state_dict, max_shard_size=max_shard_size, weights_name=weights_name)
+        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
+        state_dict_split = split_torch_state_dict_into_shards(
+            state_dict, filename_pattern=filename_pattern, max_shard_size=max_shard_size
+        )
+        # Save index if sharded
+        index = None
+        if state_dict_split.is_sharded:
+            index = {
+                "metadata": state_dict_split.metadata,
+                "weight_map": state_dict_split.tensor_to_filename,
+            }
 
         # Clean the folder from a previous save
         for filename in os.listdir(save_directory):
@@ -2634,14 +2649,15 @@ def save_pretrained(
             if (
                 filename.startswith(weights_no_suffix)
                 and os.path.isfile(full_filename)
-                and filename not in shards.keys()
+                and filename not in state_dict_split.filename_to_tensors.keys()
                 and is_main_process
                 and reg.fullmatch(filename_no_suffix) is not None
             ):
                 os.remove(full_filename)
 
         # Save the model
-        for shard_file, shard in shards.items():
+        for shard_file, tensors in state_dict_split.filename_to_tensors.items():
+            shard = {tensor: state_dict[tensor] for tensor in tensors}
             # remake shard with onloaded parameters if necessary
             if module_map:
                 if accelerate_version < version.parse("0.31"):
@@ -2680,7 +2696,7 @@ def save_pretrained(
                 f.write(content)
             logger.info(
                 f"The model is bigger than the maximum size per checkpoint ({max_shard_size}) and is going to be "
-                f"split in {len(shards)} checkpoint shards. You can find where each parameters has been saved in the "
+                f"split in {len(state_dict_split.filename_to_tensors)} checkpoint shards. You can find where each parameters has been saved in the "
                 f"index located at {save_index_file}."
             )
 

tests/test_modeling_utils.py

@@ -669,7 +669,7 @@ def test_checkpoint_sharding_local_bin(self):
 
         with tempfile.TemporaryDirectory() as tmp_dir:
             # We use the same folder for various sizes to make sure a new save erases the old checkpoint.
-            for max_size in ["50kB", "50kiB", "100kB", "100kiB", "200kB", "200kiB"]:
+            for max_size in ["50kB", "100kB", "200kB"]:
                 model.save_pretrained(tmp_dir, max_shard_size=max_size, safe_serialization=False)
 
                 # Get each shard file and its size
@@ -686,10 +686,7 @@ def test_checkpoint_sharding_local_bin(self):
 
                 # Check a file is bigger than max_size only when it has a single weight
                 for shard_file, size in shard_to_size.items():
-                    if max_size.endswith("kiB"):
-                        max_size_int = int(max_size[:-3]) * 2**10
-                    else:
-                        max_size_int = int(max_size[:-2]) * 10**3
+                    max_size_int = int(max_size[:-2]) * 10**3
                     # Note: pickle adds some junk so the weight of the file can end up being slightly bigger than
                     # the size asked for (since we count parameters)
                     if size >= max_size_int + 50000: