[WIP] Implement proper serialization of Linear8bitLt

bitsandbytes/nn/modules.py

@@ -224,6 +224,34 @@ class Linear8bitLt(nn.Linear):
 
         self.weight = Int8Params(self.weight.data, has_fp16_weights=has_fp16_weights, requires_grad=has_fp16_weights)
 
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        super()._save_to_state_dict(destination, prefix, keep_vars)
+
+        # we only need to save SCB as extra data, because CB for quantized weights is already stored in weight.data
+        weight_name = "SCB"
+
+        # case 1: .cuda was called, SCB is in self.weight
+        param_from_weight = getattr(self.weight, weight_name)
+        # case 2: self.init_8bit_state was called, SCB is in self.state
+        param_from_state = getattr(self.state, weight_name)
+
+        key_name = prefix + f"{weight_name}"
+        if param_from_weight is not None:
+            destination[key_name] = param_from_weight if keep_vars else param_from_weight.detach()
+        elif not self.state.has_fp16_weights and param_from_state is not None:
+            destination[key_name] = param_from_state if keep_vars else param_from_state.detach()
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        super()._load_from_state_dict(state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys,
+                                      error_msgs)
+        for key in unexpected_keys:
+            input_name = key[len(prefix):]
+            if input_name == "SCB":
+                input_param = state_dict[key]
+                self.weight.SCB.copy_(input_param)
+                unexpected_keys.remove(key)
+
     def init_8bit_state(self):
         self.state.CB = self.weight.CB
         self.state.SCB = self.weight.SCB

tests/test_linear8bitlt.py

-import bitsandbytes as bnb
+from copy import deepcopy
+
 import pytest
 import torch
-from bitsandbytes import functional as F
 
+import bitsandbytes as bnb
+from bitsandbytes import functional as F
 from bitsandbytes.autograd import get_inverse_transform_indices, undo_layout
 from bitsandbytes.nn.modules import Linear8bitLt
 
+
 # contributed by Alex Borzunov, see:
 # https://github.com/bigscience-workshop/petals/blob/main/tests/test_linear8bitlt.py
 
@@ -26,6 +29,7 @@ def test_layout_exact_match():
         assert restored_x.is_contiguous()
         assert torch.all(torch.eq(restored_x, x))
 
+
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
 def test_linear_no_igemmlt():
     linear = torch.nn.Linear(1024, 3072)
@@ -43,7 +47,7 @@ def test_linear_no_igemmlt():
         linear.weight.data.clone(), requires_grad=False, has_fp16_weights=False
     ).to(linear.weight.dtype)
     linear_custom.bias = linear.bias
-    linear = linear_custom.cuda()
+    linear_custom = linear_custom.cuda()
     linear = linear.half().cuda()
 
     x_ref = x.clone().cuda().requires_grad_(True)
@@ -59,3 +63,49 @@ def test_linear_no_igemmlt():
     assert not linear_custom.state.has_fp16_weights
     assert linear_custom.state.CB is not None
     assert linear_custom.state.CxB is None
+
+
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
+@pytest.mark.parametrize("has_fp16_weights", [False, True])
+def test_linear_serialization(has_fp16_weights):
+    linear = torch.nn.Linear(16, 32)
+    x = torch.randn(3, 16, dtype=torch.half)
+
+    linear_custom = Linear8bitLt(
+        linear.in_features,
+        linear.out_features,
+        linear.bias is not None,
+        has_fp16_weights=has_fp16_weights,
+        threshold=6.0,
+    )
+    linear_custom.state.force_no_igemmlt = True
+    linear_custom.weight = bnb.nn.Int8Params(
+        linear.weight.data.clone(), requires_grad=False, has_fp16_weights=has_fp16_weights
+    ).to(linear.weight.dtype)
+    linear_custom.bias = linear.bias
+    linear_custom = linear_custom.cuda()
+
+    x_first = x.clone().cuda().requires_grad_(True)
+    fx_first = linear_custom(x_first).float()
+    grad_proj = torch.randn_like(fx_first)
+    (fx_first * grad_proj).mean().backward()
+
+    state_dict = deepcopy(linear_custom.state_dict())
+
+    new_linear_custom = Linear8bitLt(
+        linear.in_features,
+        linear.out_features,
+        linear.bias is not None,
+        has_fp16_weights=has_fp16_weights,
+        threshold=6.0,
+    )
+    linear_custom.state.force_no_igemmlt = True
+    new_linear_custom = new_linear_custom.cuda()
+    new_linear_custom.load_state_dict(state_dict, strict=True)
+
+    x_second = x.clone().cuda().requires_grad_(True)
+    fx_second = new_linear_custom(x_second).float()
+    (fx_second * grad_proj).mean().backward()
+
+    assert torch.allclose(fx_first, fx_second, atol=1e-5)
+    assert torch.allclose(x_first.grad, x_second.grad, atol=1e-5)