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Problem
Params4bit.__deepcopy__copies its tensor and quantization state without sharing the caller's deepcopy memo, while leavingmodulepointing to the original layer. A copied Linear4bit therefore retains the source module in memory. Quantizing a copy made before quantization writes its new QuantState into the original module instead of its own. Copying an already quantized module also duplicates the QuantState independently for the module and its weight.Change
Register the new parameter in the memo before recursively copying its state with that same memo. This preserves the module/weight cycle and shared QuantState references inside the copy, while isolating it from the original object graph.
Reproducer
On unchanged main, quantizing the copy assigns the original module's QuantState and the copy keeps that original module alive.
Validation
Base
a616886cacf86701514f0cb1d6be760f967481c0; patch921db3aba1095030e4a608984bca0b1b31d731ad.git diff --checkpass.BNB_TEST_DEVICE=cpu OMP_NUM_THREADS=2 python -m pytest tests/test_linear4bit.py -k copy -q BNB_TEST_DEVICE=cpu OMP_NUM_THREADS=2 python -m pytest tests/test_linear4bit.py tests/test_modules.py -k 'not torch_compile and not fsdp_state_dict' -qPython 3.12.14 / PyTorch 2.15.0+cpu, with the current CPU native library built from source. GPU, distributed execution and the complete repository suite were not run.
Checked the module history, current PR catalog and all-state related issues/PRs. #1060 introduces the current standalone-parameter copy implementation; its tests do not cover owning modules, shared references, or source-module retention.