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check_sparse_tensor_invariants — PyTorch 2.7 documentation

class torch.sparse.check_sparse_tensor_invariants(enable=True)[source][source]¶

A tool to control checking sparse tensor invariants.

The following options exists to manage sparsr tensor invariants checking in sparse tensor construction:

1. Using a context manager:

   with torch.sparse.check_sparse_tensor_invariants():
       run_my_model()
   

2. Using a procedural approach:

   prev_checks_enabled = torch.sparse.check_sparse_tensor_invariants.is_enabled()
   torch.sparse.check_sparse_tensor_invariants.enable()
   
   run_my_model()
   
   if not prev_checks_enabled:
       torch.sparse.check_sparse_tensor_invariants.disable()
   

3. Using function decoration:

   @torch.sparse.check_sparse_tensor_invariants()
   def run_my_model():
       ...
   
   run_my_model()
   

4. Using check_invariants keyword argument in sparse tensor constructor call. For example:

   >>> torch.sparse_csr_tensor([0, 1, 3], [0, 1], [1, 2], check_invariants=True)
   Traceback (most recent call last):
     File "<stdin>", line 1, in <module>
   RuntimeError: `crow_indices[..., -1] == nnz` is not satisfied.
   

static disable()[source][source]¶

Disable sparse tensor invariants checking in sparse tensor constructors.

See torch.sparse.check_sparse_tensor_invariants.enable() for more information.

static enable()[source][source]¶

Enable sparse tensor invariants checking in sparse tensor constructors.

Note

The sparse tensor invariants check flag is effective to all sparse tensor constructors, both in Python and ATen.

The flag can be locally overridden by the check_invariants optional argument of the sparse tensor constructor functions.

static is_enabled()[source][source]¶

Return True if the sparse tensor invariants checking is enabled.

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