Update `dtype_byte_size` to handle torch.float8_e4m3fn/float8_e5m2 types (#30488)

* Update modeling_utils/dtype_byte_size to handle float8 types

* Add a test for dtype_byte_size

* Format

* Fix bool

src/transformers/modeling_utils.py

@@ -324,7 +324,7 @@ def dtype_byte_size(dtype):
     """
     if dtype == torch.bool:
         return 1 / 8
-    bit_search = re.search(r"[^\d](\d+)$", str(dtype))
+    bit_search = re.search(r"[^\d](\d+)_?", str(dtype))
     if bit_search is None:
         raise ValueError(f"`dtype` is not a valid dtype: {dtype}.")
     bit_size = int(bit_search.groups()[0])

tests/test_modeling_utils.py

@@ -101,7 +101,12 @@ if is_torch_available():
         _prepare_4d_attention_mask,
         _prepare_4d_causal_attention_mask,
     )
-    from transformers.modeling_utils import _find_disjoint, _find_identical, shard_checkpoint
+    from transformers.modeling_utils import (
+        _find_disjoint,
+        _find_identical,
+        dtype_byte_size,
+        shard_checkpoint,
+    )
 
     # Fake pretrained models for tests
     class BaseModel(PreTrainedModel):
@@ -465,6 +470,31 @@ class ModelUtilsTest(TestCasePlus):
                         module.__class__.__name__, mistral_attention_classes[requested_attn_implementation]
                     )
 
+    def test_torch_dtype_byte_sizes(self):
+        torch_dtypes_and_bytes = [
+            (torch.double, 8),
+            (torch.float64, 8),
+            (torch.float, 4),
+            (torch.float32, 4),
+            (torch.half, 2),
+            (torch.float16, 2),
+            (torch.bfloat16, 2),
+            (torch.long, 8),
+            (torch.int64, 8),
+            (torch.int, 4),
+            (torch.int32, 4),
+            (torch.short, 2),
+            (torch.int16, 2),
+            (torch.uint8, 1),
+            (torch.int8, 1),
+            (torch.float8_e4m3fn, 1),
+            (torch.float8_e5m2, 1),
+            (torch.bool, 0.125),
+        ]
+
+        for torch_dtype, bytes_per_element in torch_dtypes_and_bytes:
+            self.assertEqual(dtype_byte_size(torch_dtype), bytes_per_element)
+
     def test_no_super_init_config_and_model(self):
         config = NoSuperInitConfig(attribute=32)
         model = NoSuperInitModel(config)