fixes for init and tests

bitsandbytes/functional.py

@@ -659,7 +659,7 @@ class QuantState:
             'blocksize': self.blocksize,
             'quant_map': self.code,
             'dtype': str(self.dtype).strip('torch.'),
-            'shape': tuple(self.shape) if self.nested else None,
+            'shape': tuple(self.shape),
         }
         if self.nested:
             qs_dict.update({

tests/test_linear4bit.py

@@ -20,7 +20,7 @@ def test_linear_serialization(quant_type, compress_statistics, bias):
     device = "cuda"
     layer_shape = (300, 400)
 
-    linear = torch.nn.Linear(*layer_shape, dtype=original_dtype)  # original layer
+    linear = torch.nn.Linear(*layer_shape, dtype=original_dtype, device="cpu")  # original layer
 
     # Quantizing original layer
     linear_q = bnb.nn.Linear4bit(
@@ -30,19 +30,22 @@ def test_linear_serialization(quant_type, compress_statistics, bias):
         compute_dtype=compute_dtype,
         compress_statistics=compress_statistics,
         quant_type=quant_type,
-        device=device,
+        device="meta",  # TODO: consider both CPU, meta and CUDA creation
     )
     new_weight = bnb.nn.Params4bit(data=linear.weight, requires_grad=False)
-    linear_q.weight = new_weight.to(device)
+    linear_q.weight = new_weight
     if bias:
-        linear_q.bias.data = linear.bias.data.to(device)
+        linear_q.bias = torch.nn.Parameter(linear.bias)
+    linear_q = linear_q.to(device)
 
     # saving to state_dict:
     sd = linear_q.state_dict()
+
     # restoring from state_dict:
     bias_data2 = sd.pop("bias", None)
     weight_data2 = sd.pop("weight")
     weight2 = bnb.nn.Params4bit.from_prequantized(quantized_stats=sd, data=weight_data2)
+
     # creating new layer with same params:
     linear_q2 = bnb.nn.Linear4bit(
         linear.in_features,
@@ -51,12 +54,13 @@ def test_linear_serialization(quant_type, compress_statistics, bias):
         compute_dtype=compute_dtype,
         compress_statistics=compress_statistics,
         quant_type=quant_type,
-        device=device,                  # TODO create on meta device to save loading time
+        device="meta",
     )
     # loading weights from state_dict:
-    linear_q2.weight = weight2.to(device)
+    linear_q2.weight = weight2
     if bias:
         linear_q2.bias = torch.nn.Parameter(bias_data2)
+    linear_q2 = linear_q2.to(device)
 
     # MATCHING
     a, b = linear_q.weight, linear_q2.weight
@@ -107,6 +111,6 @@ def test_linear_serialization(quant_type, compress_statistics, bias):
             state_path_4bit
         )
         size_ratio = size_4 / size_orig
-        target_compression = 0.143 if original_dtype == torch.float32 else 0.285
+        target_compression = 0.143 if original_dtype == torch.float32 else 0.29  # these numbers get lower as weight shape increases
         ratio_error_msg = f"quantized_size {size_4:,} is larger on disk than {target_compression:.2%} of original size {size_orig:,}"
         assert size_ratio < target_compression, ratio_error_msg