cleanup 0

bitsandbytes/functional.py

@@ -567,6 +567,7 @@ def estimate_quantiles(A: Tensor, out: Tensor = None, offset: float = 1 / 512, n
 
     return out
 
+
 class QuantState:
     """container for quantization state components to work with Params4bit and similar clases"""
     valid_quant_types = ('fp4', 'nf4')
@@ -574,7 +575,6 @@ class QuantState:
     valid_qs_keys = ['absmax', 'quant_map', 'nested_absmax', 'nested_quant_map', 'quant_state', 
                      'quant_type', 'blocksize', 'dtype', 'shape', 'nested_blocksize', 'nested_dtype', 'nested_offset']
 
-    
     def __init__(self, absmax, shape=None, code=None, blocksize=None, quant_type=None, dtype=None, offset=None, state2=None):
         self.absmax = absmax
         self.shape = shape
@@ -615,7 +615,7 @@ class QuantState:
         if not len(qs_key) and 'quant_type' not in qs_dict:
             raise ValueError("Expected packed or unpacked quant_state items, found neither") 
         elif len(qs_key) != 1:
-            raise ValueError(f"There should be exaclly one quant_state item with key from {self.valid_qs_type_keys}. Detected {len(qs_ley)} such items")
+            raise ValueError(f"There should be exaclly one quant_state item with key from {cls.valid_qs_type_keys}. Detected {len(qs_key)} such items")
 
         # unpacking minor and non-tensor quant state items if necessary
         if len(qs_key) == 1:
@@ -682,6 +682,7 @@ class QuantState:
             self.state2.absmax = self.state2.absmax.to(device)
             self.state2.code = self.state2.code.to(device)
 
+
 def quantize_blockwise(A: Tensor, code: Tensor = None, absmax: Tensor = None, out: Tensor = None, blocksize=4096, nested=False) -> Tensor:
     """
     Quantize tensor A in blocks of size 4096 values.

bitsandbytes/nn/modules.py

@@ -139,6 +139,7 @@ class Embedding(torch.nn.Embedding):
 
         return emb
 
+
 class Params4bit(torch.nn.Parameter):
 
     def __new__(cls, data=None, requires_grad=True, quant_state=None, blocksize=64, compress_statistics=True, quant_type='fp4'):
@@ -170,9 +171,9 @@ class Params4bit(torch.nn.Parameter):
 
         cls.requires_grad = requires_grad
         cls.quant_state = QuantState.from_dict(qs_dict=qs_dict, device=data.device)
-        cls.blocksize = cls.quant_state.blocksize
-        cls.compress_statistics = cls.quant_state.nested
-        cls.quant_type = cls.quant_state.quant_type
+        cls.blocksize = cls.quant_state.blocksize             # this attribute can be deprecated - it duplicates same one in quant_state
+        cls.compress_statistics = cls.quant_state.nested      # this attribute can be deprecated - it duplicates quant_state.nested
+        cls.quant_type = cls.quant_state.quant_type           # this attribute can be deprecated - it duplicates same one in quant_state
 
         self = torch.Tensor._make_subclass(cls, data=data.to(data.device))
         return self, state_dict
@@ -213,6 +214,7 @@ class Params4bit(torch.nn.Parameter):
 
             return new_param
 
+
 class Linear4bit(nn.Linear):
 
     def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True, quant_type='fp4',device=None):