[Pytorch] Fix RecursionError when dequantizing cpu-resident QuantizedTensor

[janekb04]

Description

Trying to use a QuantizedTensor in certain ways causes a RecursionError if it has been moved to the CPU. For instance,

import torch
from transformer_engine.pytorch import NVFP4Quantizer

x = torch.randn(128, 128, device="cuda")
q = NVFP4Quantizer()
xq = q(x)
xq_cpu = xq.cpu()

xq_cpu + 0

will cause:

Traceback (most recent call last):
  File "/workspace/repro.py", line 9, in <module>
    xq_cpu + 0
    ~~~~~~~^~~
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/nvfp4_tensor.py", line 806, in __torch_dispatch__
    return super().__torch_dispatch__(func, types, args, kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/quantized_tensor.py", line 804, in __torch_dispatch__
    args = tree_map(maybe_unwrap, args)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_pytree.py", line 1578, in tree_map
    return treespec.unflatten(map(func, *flat_args))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_pytree.py", line 1313, in unflatten
    leaves = list(leaves)
             ^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/quantized_tensor.py", line 772, in maybe_unwrap
    return arg.dequantize()
           ^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/nvfp4_tensor.py", line 444, in dequantize
    return _FromNVFP4Func.apply(self, dtype)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/autograd/function.py", line 625, in apply
    return super().apply(*args, **kwargs)  # type: ignore[misc]
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/storage/nvfp4_tensor_storage.py", line 58, in forward
    cuda_tensor = tensor.to(device=torch.device("cuda"))
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/nvfp4_tensor.py", line 806, in __torch_dispatch__
    return super().__torch_dispatch__(func, types, args, kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/quantized_tensor.py", line 804, in __torch_dispatch__
    args = tree_map(maybe_unwrap, args)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_pytree.py", line 1578, in tree_map
    return treespec.unflatten(map(func, *flat_args))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_pytree.py", line 1313, in unflatten
    leaves = list(leaves)
             ^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/quantized_tensor.py", line 772, in maybe_unwrap
    return arg.dequantize()
           ^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/nvfp4_tensor.py", line 445, in dequantize
    return _FromNVFP4Func.forward(None, self, dtype)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/storage/nvfp4_tensor_storage.py", line 58, in forward
    cuda_tensor = tensor.to(device=torch.device("cuda"))
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/nvfp4_tensor.py", line 806, in __torch_dispatch__
    return super().__torch_dispatch__(func, types, args, kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/quantized_tensor.py", line 804, in __torch_dispatch__
    args = tree_map(maybe_unwrap, args)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_pytree.py", line 1578, in tree_map
    return treespec.unflatten(map(func, *flat_args))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_pytree.py", line 1313, in unflatten
    leaves = list(leaves)
             ^^^^^^^^^^^^
[...]
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/quantized_tensor.py", line 772, in maybe_unwrap
    return arg.dequantize()
           ^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/nvfp4_tensor.py", line 445, in dequantize
    return _FromNVFP4Func.forward(None, self, dtype)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/storage/nvfp4_tensor_storage.py", line 58, in forward
    cuda_tensor = tensor.to(device=torch.device("cuda"))
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/nvfp4_tensor.py", line 806, in __torch_dispatch__
    return super().__torch_dispatch__(func, types, args, kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/quantized_tensor.py", line 804, in __torch_dispatch__
    args = tree_map(maybe_unwrap, args)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_pytree.py", line 1578, in tree_map
    return treespec.unflatten(map(func, *flat_args))
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_pytree.py", line 1313, in unflatten
    leaves = list(leaves)
             ^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/quantized_tensor.py", line 772, in maybe_unwrap
    return arg.dequantize()
           ^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/nvfp4_tensor.py", line 445, in dequantize
    return _FromNVFP4Func.forward(None, self, dtype)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/storage/nvfp4_tensor_storage.py", line 58, in forward
    cuda_tensor = tensor.to(device=torch.device("cuda"))
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/tensor/nvfp4_tensor.py", line 806, in __torch_dispatch__
    return super().__torch_dispatch__(func, types, args, kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/transformer_engine/pytorch/quantized_tensor.py", line 804, in __torch_dispatch__
    args = tree_map(maybe_unwrap, args)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_pytree.py", line 1576, in tree_map
    leaves, treespec = tree_flatten(tree, is_leaf=is_leaf)
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/dist-packages/torch/utils/_pytree.py", line 1485, in tree_flatten
    treespec = helper(tree, leaves)
               ^^^^^^^^^^^^^^^^^^^^
RecursionError: maximum recursion depth exceeded

I noticed this when trying to use torch.testing.assert_close on a tensor quantized with NVFP4Quantizer and a custom Pytorch implementation on the CPU.

Type of change

• Documentation change (change only to the documentation, either a fix or a new content)
• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to not work as expected)
• Infra/Build change
• Code refactoring

Changes

Please list the changes introduced in this PR:

• Change A
• Change B

Checklist:

• I have read and followed the contributing guidelines
• The functionality is complete
• I have commented my code, particularly in hard-to-understand areas
• I have made corresponding changes to the documentation
• My changes generate no new warnings
• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes