def is bnb multi backend avaliable

src/accelerate/accelerator.py

@@ -80,6 +80,7 @@
     get_mixed_precision_context_manager,
     get_pretty_name,
     is_bf16_available,
+    is_bitsandbytes_multi_backend_available,
     is_deepspeed_available,
     is_ipex_available,
     is_lomo_available,
@@ -1443,13 +1444,7 @@ def prepare_model(self, model: torch.nn.Module, device_placement: bool = None, e
                             "you're training on. Make sure you loaded the model on the correct device using for example `device_map={'':torch.cuda.current_device()}` or `device_map={'':torch.xpu.current_device()}`"
                         )
 
-            bnb_multi_backends = False
-            try:
-                from transformers.utils import is_bitsandbytes_multi_backend_available
-                bnb_multi_backends = is_bitsandbytes_multi_backend_available()
-            except ImportError:
-                bnb_multi_backends = False
-            if ("cpu" in model_devices and not bnb_multi_backends) or "disk" in model_devices:
+            if ("cpu" in model_devices and not is_bitsandbytes_multi_backend_available()) or "disk" in model_devices:
                 raise ValueError(
                     "You can't train a model that has been loaded in 8-bit precision with CPU or disk offload. "
                     "If you want train the 8-bit model in CPU, please install bitsandbytes with multi-backend, see https://huggingface.co/docs/bitsandbytes/main/en/installation#multi-backend"

src/accelerate/utils/__init__.py

@@ -77,6 +77,7 @@
     is_8bit_bnb_available,
     is_aim_available,
     is_bf16_available,
+    is_bitsandbytes_multi_backend_available,
     is_bnb_available,
     is_boto3_available,
     is_ccl_available,

src/accelerate/utils/imports.py

@@ -190,6 +190,14 @@ def is_bnb_available():
     return _is_package_available("bitsandbytes")
 
 
+def is_bitsandbytes_multi_backend_available():
+    if not is_bnb_available():
+        return False
+    import bitsandbytes as bnb
+
+    return "multi_backend" in getattr(bnb, "features", set())
+
+
 def is_torchvision_available():
     return _is_package_available("torchvision")