Fix `align_module_device`, ensure only cpu tensors for `get_state_dict_offloaded_model`

[kylesayrs]

Background

• Previous PR #3204 introduce an unintended behavior change where a module being aligned would also attempt to align parameters belonging to its submodules. This is a problem for functions like get_state_dict_offloaded_model which calls align_module_device on non-leaf modules.

tests/test_modeling_utils.py:808
    state_dict = get_state_dict_offloaded_model(model)
src/accelerate/utils/modeling.py:1532: in get_state_dict_offloaded_model                                                                                                             
    with align_module_device(module, "cpu"): 
/usr/lib/python3.10/contextlib.py:135: in __enter__                                                                                                                                  
    return next(self.gen)  
src/accelerate/utils/modeling.py:1929: in align_module_device                                                                                                                        
    set_module_tensor_to_device(module, name, execution_device)
ValueError: weight is on the meta device, we need a `value` to put in on cpu.

Purpose

• Fix align_module_device bug where the function attempts to align meta tensors belonging to submodule parameters
• Fix get_state_dict_offloaded_model(model) behavior to match model.state_dict()
• Introduce tests for get_state_dict_offloaded_model

Changes

• align_module_device now only aligns parameters directly attached to the parent
• Move all tensors in module state dict to cpu before returning, including both parameters and buffers
• Add usage tests for get_state_dict_offloaded_model

Testing

• Added tests fail without changes, but pass with changes
• Use below script to test end-to-end

test_e2e.py

```python3 from transformers import AutoModelForCausalLM from accelerate import cpu_offload from accelerate.utils.modeling import get_state_dict_offloaded_model model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.2-1B-Instruct") cpu_offload(model) state_dict = get_state_dict_offloaded_model(model) ```

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