Support model.to int8 weight only quantized model

Summary:
registering fields as buffers so they get picked up in `model.to`

Test Plan:
python test/quantization/test_quant_api.py -k test_int8_wo_quant_save_load
Reviewers:

Subscribers:

Tasks:

Tags:

test/quantization/test_quant_api.py

@@ -8,6 +8,7 @@
 # This test takes a long time to run
 import unittest
 import torch
+import os
 from torch._export import capture_pre_autograd_graph
 from torch.ao.quantization.quantize_pt2e import (
     prepare_pt2e,
@@ -18,9 +19,10 @@
     get_symmetric_quantization_config,
 )
 
-from torchao.quantization.quant_api import _replace_with_custom_fn_if_matches_filter
-from torchao.quantization.quant_api import apply_dynamic_quant
 from torchao.quantization.quant_api import (
+    _replace_with_custom_fn_if_matches_filter,
+    apply_dynamic_quant,
+    apply_weight_only_int8_quant,
     Quantizer,
     TwoStepQuantizer,
 )
@@ -137,6 +139,26 @@ def test_dynamic_quant_gpu_unified_api_eager_mode_impl(self):
         compiled = m(*example_inputs)
         torch.testing.assert_close(quantized, compiled, atol=0, rtol=0)
 
+    @unittest.skipIf(not torch.cuda.is_available(), "Need CUDA available")
+    def test_int8_wo_quant_save_load(self):
+        m = M().eval().cpu()
+        apply_weight_only_int8_quant(m)
+        example_inputs = m.example_inputs()
+        ref = m(*example_inputs)
+        _TMP_FN = "_test.pt"
+        torch.save(m.state_dict(), _TMP_FN)
+
+        state_dict = torch.load(_TMP_FN)
+        os.remove(_TMP_FN)
+        m2 = M().eval()
+        apply_weight_only_int8_quant(m2)
+        m2.load_state_dict(state_dict)
+        m2 = m2.to(device="cuda")
+        example_inputs = map(lambda x: x.cuda(), example_inputs)
+        res = m2(*example_inputs)
+
+        torch.testing.assert_close(ref, res.cpu())
+
     @unittest.skipIf(not TORCH_VERSION_AFTER_2_4, "skipping when torch verion is 2.4 or lower")
     def test_8da4w_quantizer(self):
         from torchao.quantization.quant_api import Int8DynActInt4WeightQuantizer

torchao/quantization/weight_only.py

@@ -22,9 +22,8 @@ def __init__(self, *args, **kwargs):
         scales = kwargs.pop("scales")
         super().__init__(*args, **kwargs)
 
-        self.w_int8 = w_int8
-
-        self.scales = scales
+        self.register_buffer("w_int8", w_int8)
+        self.register_buffer("scales", scales)
 
     def forward(self, x, *args, **kwargs):
         """