[Bug fix] Fake quantized model save after HF accelerate hooks are added

modelopt/torch/quantization/nn/modules/tensor_quantizer.py

@@ -156,6 +156,10 @@ class TensorQuantizer(nn.Module):
         "_padding",
         # Extra flags added by huggingface
         "_is_hf_initialized",
+        # Extra flags added by accelerate
+        "_hf_hook",
+        "_old_forward",
+        "forward",
         # Extra flags added by deepspeed
         "ds_external_parameters",
         "all_parameters",

tests/unit/torch/quantization/plugins/test_accelerate.py

@@ -13,12 +13,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import pickle
+
 import pytest
 import torch
 import torch.nn as nn
 
 import modelopt.torch.quantization as mtq
-from modelopt.torch.quantization.nn import QuantLinearConvBase
+from modelopt.torch.quantization.nn import QuantLinearConvBase, TensorQuantizer
 
 try:
     from accelerate.hooks import ModelHook, add_hook_to_module
@@ -51,3 +53,30 @@ def test_linear_with_accelerate_monkey_patched_forward():
 
     assert module_test.input_quantizer.amax is not None
     assert module_test.weight_quantizer.amax is not None
+
+
+def test_tensor_quantizer_modelopt_state_with_accelerate_hook():
+    """Verify accelerate hook attributes are excluded from modelopt state.
+
+    When accelerate's add_hook_to_module patches a TensorQuantizer, it adds
+    _hf_hook, _old_forward, and an instance-level forward (a functools.partial
+    wrapping a local function). These must be excluded from the modelopt state
+    dict, otherwise torch.save / pickle will fail with:
+        AttributeError: Can't get local object 'add_hook_to_module.<locals>.new_forward'
+    """
+    tq = TensorQuantizer()
+    add_hook_to_module(tq, ModelHook())
+
+    # The hook should have injected these instance attributes
+    assert hasattr(tq, "_hf_hook")
+    assert hasattr(tq, "_old_forward")
+    assert "forward" in tq.__dict__
+
+    # None of the accelerate attributes should appear in the modelopt state
+    state = tq.get_modelopt_state()
+    accelerate_attrs = {"_hf_hook", "_old_forward", "forward"}
+    leaked = accelerate_attrs & state.keys()
+    assert not leaked, f"Accelerate attributes leaked into modelopt state: {leaked}"
+
+    # The state dict must be picklable (torch.save uses pickle internally)
+    pickle.dumps(state)