Fix for async dcp checkpointing with Float8Tensors

[pstjohn]

(includes changes from #2698)

dcp.async_save fails silently with QuantizedTensor (Float8Tensor) — staged tensors contain uninitialized (NaN) data instead of actual FP8 values.

PyTorch's async save stages tensors to CPU by copying raw storage via new_empty() + deep_copy. Float8Tensor is a wrapper subclass with data_ptr()==0 (empty storage), so:

1. new_empty() falls through to default dispatch, returning a plain tensor instead of a Float8Tensor
2. The deep-copied _data/_scale_inv attributes land on the plain tensor but are ignored by DCP's write path

Changes

• quantized_tensor.py: Handle aten.new_empty.default in torch_dispatch so staging preserves the Float8Tensor subclass type
• float8_tensor_storage.py: Add a CPU fallback in dequantize() using PyTorch native FP8 dtypes, since tex.dequantize is CUDA-only and the staged tensor lives on CPU
• run_fsdp2_fused_adam.py: Remove the _dequantize_state_dict workaround — dcp.async_save now works transparently