remove redundant functions

tests/unittests/bases/test_ddp.py

@@ -77,62 +77,6 @@ def _test_ddp_gather_uneven_tensors_multidim(rank: int, worldsize: int = NUM_PRO
         assert (val == torch.ones_like(val)).all()
 
 
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_2_1, reason="test only works on newer torch versions")
-def _test_ddp_gather_autograd_same_shape(rank: int, worldsize: int = NUM_PROCESSES) -> None:
-    """Test that ddp gather preserves local rank's autograd graph for same-shaped tensors across ranks.
-
-    This function tests that ``torchmetrics.utilities.distributed.gather_all_tensors`` works as intended in
-    preserving the local rank's autograd graph upon the gather. The function compares derivative values obtained
-    with the local rank results from the ``gather_all_tensors`` output and the original local rank tensor.
-    This test only considers tensors of the same shape across different ranks.
-
-    Note that this test only works for torch>=2.0.
-
-    """
-    tensor = torch.ones(50, requires_grad=True)
-    result = gather_all_tensors(tensor)
-    assert len(result) == worldsize
-    scalar1 = 0
-    scalar2 = 0
-    for idx in range(worldsize):
-        if idx == rank:
-            scalar1 = scalar1 + torch.sum(tensor * torch.ones_like(tensor))
-        else:
-            scalar1 = scalar1 + torch.sum(result[idx] * torch.ones_like(result[idx]))
-        scalar2 = scalar2 + torch.sum(result[idx] * torch.ones_like(result[idx]))
-    gradient1 = torch.autograd.grad(scalar1, [tensor], retain_graph=True)[0]
-    gradient2 = torch.autograd.grad(scalar2, [tensor])[0]
-    assert torch.allclose(gradient1, gradient2)
-
-
-@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_2_1, reason="test only works on newer torch versions")
-def _test_ddp_gather_autograd_different_shape(rank: int, worldsize: int = NUM_PROCESSES) -> None:
-    """Test that ddp gather preserves local rank's autograd graph for differently-shaped tensors across ranks.
-
-    This function tests that ``torchmetrics.utilities.distributed.gather_all_tensors`` works as intended in
-    preserving the local rank's autograd graph upon the gather. The function compares derivative values obtained
-    with the local rank results from the ``gather_all_tensors`` output and the original local rank tensor.
-    This test considers tensors of different shapes across different ranks.
-
-    Note that this test only works for torch>=2.0.
-
-    """
-    tensor = torch.ones(rank + 1, 2 - rank, requires_grad=True)
-    result = gather_all_tensors(tensor)
-    assert len(result) == worldsize
-    scalar1 = 0
-    scalar2 = 0
-    for idx in range(worldsize):
-        if idx == rank:
-            scalar1 = scalar1 + torch.sum(tensor * torch.ones_like(tensor))
-        else:
-            scalar1 = scalar1 + torch.sum(result[idx] * torch.ones_like(result[idx]))
-        scalar2 = scalar2 + torch.sum(result[idx] * torch.ones_like(result[idx]))
-    gradient1 = torch.autograd.grad(scalar1, [tensor], retain_graph=True)[0]
-    gradient2 = torch.autograd.grad(scalar2, [tensor])[0]
-    assert torch.allclose(gradient1, gradient2)
-
-
 def _test_ddp_compositional_tensor(rank: int, worldsize: int = NUM_PROCESSES) -> None:
     dummy = DummyMetricSum()
     dummy._reductions = {"x": torch.sum}