add deepcopy and copy for Param4bit

.git-blame-ignore-revs

@@ -6,3 +6,6 @@ ea7c14f8ef64924f2d0ff80df3cdabf2c7299848
 
 # Remove f-prefix from strings that don't use formatting
 7727fa4c8c6c1ef2b109120aff4196a0a6bf3ed6
+
+# format tests/linear_4bit.py
+34735ba89de8235ea9da6ef409f814dcea9e2038

bitsandbytes/functional.py

@@ -706,6 +706,21 @@ def to(self, device):
             self.state2.absmax = self.state2.absmax.to(device)
             self.state2.code = self.state2.code.to(device)
 
+    def __eq__(self, other):
+        if not isinstance(other, QuantState):
+            return False
+
+        return (
+            torch.allclose(self.absmax, other.absmax, atol=1e-6) and
+            self.shape == other.shape and
+            torch.allclose(self.code, other.code, atol=1e-6) and
+            self.dtype == other.dtype and
+            self.blocksize == other.blocksize and
+            self.quant_type == other.quant_type and
+            (self.offset == other.offset if self.offset is not None and other.offset is not None else self.offset is other.offset) and
+            (self.state2 == other.state2 if self.state2 is not None and other.state2 is not None else self.state2 is other.state2)
+        )
+
 
 def quantize_blockwise(
     A: Tensor,

bitsandbytes/nn/modules.py

@@ -2,6 +2,7 @@
 #
 # This source code is licensed under the MIT license found in the
 # LICENSE file in the root directory of this source tree.
+import copy
 from typing import Any, Dict, Optional, TypeVar, Union, overload
 import warnings
 
@@ -191,7 +192,7 @@ class Params4bit(torch.nn.Parameter):
     def __new__(
             cls,
             data: Optional[torch.Tensor] = None,
-            requires_grad=True,
+            requires_grad=False,  # quantized weights should be frozen by default
             quant_state: Optional[QuantState] = None,
             blocksize: int = 64,
             compress_statistics: bool = True,
@@ -214,6 +215,37 @@ def __new__(
         self.module = module
         return self
 
+    def __getstate__(self):
+        state = self.__dict__
+        state["data"] = self.data
+        state["requires_grad"] = self.requires_grad
+        return state
+
+    def __setstate__(self, state):
+        self.requires_grad = state["requires_grad"]
+        self.blocksize = state["blocksize"]
+        self.compress_statistics = state["compress_statistics"]
+        self.quant_type = state["quant_type"]
+        self.quant_state = state["quant_state"]
+        self.data = state["data"]
+        self.quant_storage = state["quant_storage"]
+        self.bnb_quantized = state["bnb_quantized"]
+        self.module = state["module"]
+
+    def __deepcopy__(self,memo):
+        new_instance = type(self).__new__(type(self))
+        state = self.__getstate__()
+        new_instance.__setstate__(state)
+        new_instance.quant_state = copy.deepcopy(state["quant_state"])
+        new_instance.data = copy.deepcopy(state["data"])
+        return new_instance
+
+    def __copy__(self):
+        new_instance = type(self).__new__(type(self))
+        state = self.__getstate__()
+        new_instance.__setstate__(state)
+        return new_instance
+
     @classmethod
     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))
@@ -227,8 +259,13 @@ def from_prequantized(cls, data: torch.Tensor, quantized_stats: Dict[str, Any],
 
     def _quantize(self, device):
         w = self.data.contiguous().cuda(device)
-        w_4bit, quant_state = bnb.functional.quantize_4bit(w, blocksize=self.blocksize, compress_statistics=self.compress_statistics,
-                                                           quant_type=self.quant_type, quant_storage=self.quant_storage)
+        w_4bit, quant_state = bnb.functional.quantize_4bit(
+            w,
+            blocksize=self.blocksize,
+            compress_statistics=self.compress_statistics,
+            quant_type=self.quant_type,
+            quant_storage=self.quant_storage,
+        )
         self.data = w_4bit
         self.quant_state = quant_state
         if self.module is not None:

tests/test_linear4bit.py

@@ -1,4 +1,6 @@
+import copy
 import os
+import pickle
 from tempfile import TemporaryDirectory
 
 import pytest
@@ -8,13 +10,14 @@
 from tests.helpers import TRUE_FALSE
 
 storage = {
-    'uint8': torch.uint8,
-    'float16': torch.float16,
-    'bfloat16': torch.bfloat16,
-    'float32': torch.float32
+    "uint8": torch.uint8,
+    "float16": torch.float16,
+    "bfloat16": torch.bfloat16,
+    "float32": torch.float32,
 }
 
-@pytest.mark.parametrize("quant_storage", ['uint8', 'float16', 'bfloat16', 'float32'])
+
+@pytest.mark.parametrize("quant_storage", ["uint8", "float16", "bfloat16", "float32"])
 @pytest.mark.parametrize("bias", TRUE_FALSE)
 @pytest.mark.parametrize("compress_statistics", TRUE_FALSE)
 @pytest.mark.parametrize("quant_type", ["nf4", "fp4"])
@@ -24,7 +27,9 @@ def test_linear_serialization(quant_type, compress_statistics, bias, quant_stora
     device = "cuda"
     layer_shape = (300, 400)
 
-    linear = torch.nn.Linear(*layer_shape, dtype=original_dtype, device="cpu")  # original layer
+    linear = torch.nn.Linear(
+        *layer_shape, dtype=original_dtype, device="cpu"
+    )  # original layer
 
     # Quantizing original layer
     linear_q = bnb.nn.Linear4bit(
@@ -36,7 +41,9 @@ def test_linear_serialization(quant_type, compress_statistics, bias, quant_stora
         quant_type=quant_type,
         device="meta",
     )
-    new_weight = bnb.nn.Params4bit(data=linear.weight, quant_type=quant_type, requires_grad=False)
+    new_weight = bnb.nn.Params4bit(
+        data=linear.weight, quant_type=quant_type, requires_grad=False
+    )
     linear_q.weight = new_weight
     if bias:
         linear_q.bias = torch.nn.Parameter(linear.bias)
@@ -80,7 +87,12 @@ def test_linear_serialization(quant_type, compress_statistics, bias, quant_stora
         quant_storage=storage[quant_storage],
         device="meta",
     )
-    linear_qs.weight = bnb.nn.Params4bit(data=linear.weight, requires_grad=False, quant_type=quant_type, quant_storage=storage[quant_storage])
+    linear_qs.weight = bnb.nn.Params4bit(
+        data=linear.weight,
+        requires_grad=False,
+        quant_type=quant_type,
+        quant_storage=storage[quant_storage],
+    )
     if bias:
         linear_qs.bias = torch.nn.Parameter(linear.bias)
     linear_qs = linear_qs.to(device)
@@ -91,15 +103,15 @@ def test_linear_serialization(quant_type, compress_statistics, bias, quant_stora
 
     q0 = a.quant_state
     q1 = b.quant_state
-    for attr in ('code', 'dtype', 'blocksize', 'absmax'):
+    for attr in ("code", "dtype", "blocksize", "absmax"):
         c, d = getattr(q0, attr), getattr(q1, attr)
         if isinstance(c, torch.Tensor):
             assert torch.equal(c, d)
         else:
             assert c == d, f"{c} != {d}"
 
     if q0.state2 is not None:
-        for attr in ('code', 'dtype', 'blocksize', 'absmax'):
+        for attr in ("code", "dtype", "blocksize", "absmax"):
             c, d = getattr(q0.state2, attr), getattr(q1.state2, attr)
             if isinstance(c, torch.Tensor):
                 assert torch.equal(c, d)
@@ -125,7 +137,7 @@ def test_linear_serialization(quant_type, compress_statistics, bias, quant_stora
     assert torch.equal(a, c)
 
     # Test moving to CPU and back to GPU
-    linear_q2.to('cpu')
+    linear_q2.to("cpu")
     linear_q2.to(device)
     d = linear_qs(x)
     assert c.dtype == d.dtype
@@ -139,10 +151,47 @@ def test_linear_serialization(quant_type, compress_statistics, bias, quant_stora
         torch.save(linear.state_dict(), state_path)
         torch.save(linear_q.state_dict(), state_path_4bit)
 
-        size_orig, size_4 = os.path.getsize(state_path), os.path.getsize(
-            state_path_4bit
+        size_orig, size_4 = (
+            os.path.getsize(state_path),
+            os.path.getsize(state_path_4bit),
         )
         size_ratio = size_4 / size_orig
-        target_compression = 0.143 if original_dtype == torch.float32 else 0.29  # these numbers get lower as weight shape increases
+        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
+
+
+def test_copy_param():
+    tensor = torch.tensor([1.0, 2.0, 3.0, 4.0])
+    param = bnb.nn.Params4bit(data=tensor, requires_grad=False).cuda(0)
+
+    shallow_copy_param = copy.copy(param)
+    assert param.quant_state is shallow_copy_param.quant_state
+    assert param.data.data_ptr() == shallow_copy_param.data.data_ptr()
+
+
+def test_deepcopy_param():
+    tensor = torch.tensor([1.0, 2.0, 3.0, 4.0])
+    param = bnb.nn.Params4bit(data=tensor, requires_grad=False).cuda(0)
+    copy_param = copy.deepcopy(param)
+    assert param.quant_state is not copy_param.quant_state
+    assert param.data.data_ptr() != copy_param.data.data_ptr()
+
+
+def test_params4bit_real_serialization():
+    original_tensor = torch.tensor([1.0, 2.0, 3.0, 4.0], dtype=torch.float32)
+    original_param = bnb.nn.Params4bit(data=original_tensor, quant_type="fp4")
+
+    original_param.cuda(0)  # move to CUDA to trigger quantization
+
+    serialized_param = pickle.dumps(original_param)
+    deserialized_param = pickle.loads(serialized_param)
+
+    assert torch.equal(original_param.data, deserialized_param.data)
+    assert original_param.requires_grad == deserialized_param.requires_grad == False
+    assert original_param.quant_type == deserialized_param.quant_type
+    assert original_param.blocksize == deserialized_param.blocksize
+    assert original_param.compress_statistics == deserialized_param.compress_statistics
+    assert original_param.quant_state == deserialized_param.quant_state