fixes to recent PR #753

bitsandbytes/functional.py

@@ -567,14 +567,14 @@ 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')
-    valid_qs_type_keys = [f"quant_state.bitsandbytes__{x}" for x in valid_quant_types]
-    valid_qs_keys = ['absmax', 'quant_map', 'nested_absmax', 'nested_quant_map', 'quant_state', 
-                     'quant_type', 'blocksize', 'dtype', 'shape', 'nested_blocksize', 'nested_dtype', 'nested_offset']
+    valid_qs_type_keys = [f"bitsandbytes__{x}" for x in valid_quant_types]
+    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
@@ -585,7 +585,7 @@ def __init__(self, absmax, shape=None, code=None, blocksize=None, quant_type=Non
         self.offset = offset
         self.state2 = state2
         self.nested = state2 is not None
-        
+
     def __get_item__(self, idx):
         """
         ensures compatibility with older quant state scheme with nested lists.
@@ -598,29 +598,32 @@ def __get_item__(self, idx):
         else:
             list_repr = [self.absmax, self.shape, self.dtype, self.blocksize, None, self.quant_type]
         return list_repr[idx]
-    
+
     @classmethod
     def from_dict(cls, qs_dict: Dict[str, Any], device: torch.device) -> 'QuantState':
         """
         unpacks components of state_dict into QuantState
         where necessary, convert into strings, torch.dtype, ints, etc.
 
         qs_dict: based on state_dict, with only relevant keys, striped of prefixes.
-         
+
         item with key `quant_state.bitsandbytes__[nf4/fp4]` may contain minor and non-tensor quant state items.        
         """
 
         # unpacking tensor with non-tensor components
-        qs_key = [k for k, v in qs_dict.items() if k in cls.valid_qs_type_keys and isinstance(v, torch.Tensor)]
+        qs_key = [k for k, v in qs_dict.items() if "quant_state" in k and isinstance(v, torch.Tensor)]
         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("Expected packed or unpacked quant_state items, found neither")
+        elif len(qs_key) != 1 or qs_key[0].split(".")[-1] not in cls.valid_qs_type_keys:
+            raise ValueError(f"There should be exactly one `quant_state` item with ending from {cls.valid_qs_type_keys}.\nDetected {qs_key}.")
+
         # unpacking minor and non-tensor quant state items if necessary
         if len(qs_key) == 1:
             qs_key = qs_key[0]
-            qs_dict |= unpack_tensor_to_dict(qs_dict.pop(qs_key))
+            qs_dict.update(unpack_tensor_to_dict(qs_dict.pop(qs_key)))
+
+        qs_dict = {k.split('.')[-1]: v for k, v in qs_dict.items()}  # strip prefixes
+        assert set(qs_dict.keys()).issubset(cls.valid_qs_keys)
 
         if 'nested_absmax' in qs_dict:
             offset = torch.tensor(float(qs_dict['nested_offset'])).to(device)
@@ -654,7 +657,7 @@ def as_dict(self, packed=False):
             'quant_type': self.quant_type,
             'absmax': self.absmax,
             'blocksize': self.blocksize,
-            'quant_map': self.code,                      
+            'quant_map': self.code,
             'dtype': str(self.dtype).strip('torch.'),
             'shape': tuple(self.shape) if self.nested else None,
         }
@@ -673,7 +676,7 @@ def as_dict(self, packed=False):
         non_tensor_dict = {k: v for k, v in qs_dict.items() if not isinstance(v, torch.Tensor)}
         qs_packed_dict["quant_state." + "bitsandbytes__" + self.quant_type] = pack_dict_to_tensor(non_tensor_dict)
         return qs_packed_dict
-                    
+
     def to(self, device):
         # make sure the quantization state is on the right device
         self.absmax = self.absmax.to(device)
@@ -682,6 +685,7 @@ def to(self, device):
             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

@@ -2,7 +2,7 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
-from typing import Optional, TypeVar, Union, overload
+from typing import Any, Dict, Optional, TypeVar, Union, overload
 
 import warnings
 import torch
@@ -139,9 +139,10 @@ def forward(self, input: Tensor) -> Tensor:
 
         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'):
+    def __new__(cls, data: Optional[torch.Tensor] = None, requires_grad=True, quant_state: QuantState = None, blocksize: int = 64, compress_statistics: bool = True, quant_type: str = 'fp4') -> "Params4bit":
         if data is None:
             data = torch.empty(0)
 
@@ -152,27 +153,16 @@ def __new__(cls, data=None, requires_grad=True, quant_state=None, blocksize=64,
         self.quant_state = quant_state
         self.data = data
         return self
-    
+
     @classmethod
-    def from_state_dict(cls, state_dict, prefix="", requires_grad=False):
-        data = state_dict.pop(prefix.rstrip('.'))
-
-        # extracting components for QuantState from state_dict
-        qs_dict = {}
-        for k, v in state_dict.items():
-            if k.replace(prefix, '').split('.')[0] in QuantState.valid_qs_keys:
-                qs_dict[k] = v        
-        state_dict = {k: v for k, v in state_dict.items() if k not in qs_dict}
-        qs_dict = {k.replace(prefix, ''): v for k, v in qs_dict.items()}
-
-        if data.device.type != "cuda":
-            raise ValueError(f"`data.device.type` must be 'cuda', detected {data.device.type}")
-
-        cls.requires_grad = requires_grad,
-        cls.quant_state = QuantState.from_dict(qs_dict=qs_dict, device=data.device)
-
-        self = torch.Tensor._make_subclass(cls, data=data.to(data.device))
-        return self, state_dict
+    def from_prequantized(cls, data: torch.Tensor, quantized_stats: Dict[str, Any], requires_grad: bool = False, device='cuda', **kwargs) -> "Params4bit":
+        self = torch.Tensor._make_subclass(cls, data.to(device))
+        self.requires_grad = requires_grad
+        self.quant_state = QuantState.from_dict(qs_dict=quantized_stats, device=device)
+        self.blocksize = self.quant_state.blocksize
+        self.compress_statistics = self.quant_state.nested
+        self.quant_type = self.quant_state.quant_type
+        return self
 
     def cuda(self, device):
         w = self.data.contiguous().half().cuda(device)
@@ -204,15 +194,16 @@ def to(self, *args, **kwargs):
                 self.quant_state.to(device)
 
             new_param = Params4bit(super().to(device=device, dtype=dtype, non_blocking=non_blocking),
-                                  requires_grad=self.requires_grad, quant_state=self.quant_state,
+                                   requires_grad=self.requires_grad, quant_state=self.quant_state,
                                    blocksize=self.blocksize, compress_statistics=self.compress_statistics,
                                    quant_type=self.quant_type)
 
             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):
+
+    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True, quant_type='fp4', device=None):
         super().__init__(input_features, output_features, bias, device)
         self.weight = Params4bit(self.weight.data, requires_grad=False, compress_statistics=compress_statistics, quant_type=quant_type)
         # self.persistent_buffers = []  # TODO consider as way to save quant state
@@ -246,18 +237,6 @@ def _save_to_state_dict(self, destination, prefix, keep_vars):
             for k, v in self.weight.quant_state.as_dict(packed=True).items():
                 destination[prefix + "weight." + k] = v if keep_vars else v.detach()
 
-    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
-                              missing_keys, unexpected_keys, error_msgs):
-        # Note: super()._load_from_state_dict() is not called here intentionally.
-        if self.bias is not None:
-            bias_data = state_dict.pop(prefix + "bias", None)
-            self.bias.data = bias_data.to(self.bias.data.device)
-
-        self.weight, state_dict = bnb.nn.Params4bit.from_state_dict(
-                        state_dict, prefix=prefix + "weight" + ".", requires_grad=False
-                    )
-        unexpected_keys.extend(state_dict.keys())
-
     def forward(self, x: torch.Tensor):
         # weights are cast automatically as Int8Params, but the bias has to be cast manually
         if self.bias is not None and self.bias.dtype != x.dtype:
@@ -280,10 +259,12 @@ def forward(self, x: torch.Tensor):
 
         return out
 
+
 class LinearFP4(Linear4bit):
-    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True,device=None):
+    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True, device=None):
         super().__init__(input_features, output_features, bias, compute_dtype, compress_statistics, 'fp4', device)
 
+
 class LinearNF4(Linear4bit):
     ''' Implements the NF4 data type.
 
@@ -295,7 +276,7 @@ class LinearNF4(Linear4bit):
         Implementation of the NF4 data type in bitsandbytes can be found in the `create_normal_map` function in
         the `functional.py` file: https://github.com/TimDettmers/bitsandbytes/blob/main/bitsandbytes/functional.py#L236.
     '''
-    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True,device=None):
+    def __init__(self, input_features, output_features, bias=True, compute_dtype=None, compress_statistics=True, device=None):
         super().__init__(input_features, output_features, bias, compute_dtype, compress_statistics, 'nf4', device)
 
 

tests/test_linear4bit.py

@@ -7,8 +7,6 @@
 import torch
 
 import bitsandbytes as bnb
-from bitsandbytes import functional as F
-from bitsandbytes.nn.modules import Linear4bit
 
 
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
@@ -41,7 +39,10 @@ def test_linear_serialization(quant_type, compress_statistics, bias):
 
     # 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,
@@ -53,15 +54,17 @@ def test_linear_serialization(quant_type, compress_statistics, bias):
         device=device,                  # TODO create on meta device to save loading time
     )
     # loading weights from state_dict:
-    linear_q2.load_state_dict(sd)    
+    linear_q2.weight = weight2.to(device)
+    if bias:
+        linear_q2.bias = torch.nn.Parameter(bias_data2)
 
     # MATCHING
     a, b = linear_q.weight, linear_q2.weight
 
     assert a.device == b.device
     assert a.dtype == b.dtype
     assert torch.equal(a, b)
-    
+
     q0 = a.quant_state
     q1 = b.quant_state
     for attr in ('code', 'dtype', 'blocksize', 'absmax'):