[Core] Change 8-bit serialization weight format format

bitsandbytes/nn/modules.py

@@ -14,7 +14,11 @@
 from bitsandbytes.autograd._functions import get_tile_inds, undo_layout
 from bitsandbytes.functional import QuantState
 from bitsandbytes.optim import GlobalOptimManager
-from bitsandbytes.utils import OutlierTracer
+from bitsandbytes.utils import (
+    INVERSE_LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING,
+    LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING,
+    OutlierTracer,
+)
 
 T = TypeVar("T", bound="torch.nn.Module")
 
@@ -619,6 +623,16 @@ def maybe_rearrange_weight(state_dict, prefix, local_metadata, strict, missing_k
         return
     weight_format = state_dict.pop(f"{prefix}weight_format", "row")
 
+    if isinstance(weight_format, torch.Tensor):
+        weight_format = weight_format.item()
+
+    # For new weights format storage type, we explicitly check
+    # if weights_format is on the mapping
+    if isinstance(weight_format, int) and weight_format not in INVERSE_LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING:
+        raise ValueError(f"Expected supported weight format - got {weight_format}")
+    elif isinstance(weight_format, int) and weight_format in INVERSE_LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING:
+        weight_format = INVERSE_LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING[weight_format]
+
     if weight_format != "row":
         tile_indices = get_tile_inds(weight_format, weight.device)
         state_dict[f"{prefix}weight"] = undo_layout(weight, tile_indices)
@@ -711,13 +725,20 @@ def _save_to_state_dict(self, destination, prefix, keep_vars):
         if not self.state.has_fp16_weights:
             if param_from_weight is not None:
                 destination[key_name] = param_from_weight if keep_vars else param_from_weight.detach()
-                destination[format_name] = "row"
+                destination[format_name] = torch.tensor(0, dtype=torch.uint8)
             elif param_from_state is not None and not layout_reordered:
                 destination[key_name] = param_from_state if keep_vars else param_from_state.detach()
-                destination[format_name] = "row"
+                destination[format_name] = torch.tensor(0, dtype=torch.uint8)
             elif param_from_state is not None:
                 destination[key_name] = param_from_state if keep_vars else param_from_state.detach()
-                destination[format_name] = self.state.formatB
+                weights_format = self.state.formatB
+                # At this point `weights_format` is an str
+                if weights_format not in LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING:
+                    raise ValueError(f"Unrecognized weights format {weights_format}")
+
+                weights_format = LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING[weights_format]
+
+                destination[format_name] = torch.tensor(weights_format, dtype=torch.uint8)
 
     def _load_from_state_dict(
         self,

bitsandbytes/utils.py

@@ -198,3 +198,7 @@ def unpack_tensor_to_dict(tensor_data):
     unpacked_dict = json.loads(json_str)
 
     return unpacked_dict
+
+
+LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING = {"row": 0, "col32": 1, "col_turing": 2, "col_ampere": 3}
+INVERSE_LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING = {val: name for (name, val) in LINEAR_8BIT_WEIGHTS_FORMAT_MAPPING.items()}