add unittest for dpp gather autograd compatibility

tests/unittests/bases/test_ddp.py

@@ -77,6 +77,62 @@ 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}
@@ -97,6 +153,8 @@ def _test_ddp_compositional_tensor(rank: int, worldsize: int = NUM_PROCESSES) ->
         _test_ddp_sum_cat,
         _test_ddp_gather_uneven_tensors,
         _test_ddp_gather_uneven_tensors_multidim,
+        _test_ddp_gather_autograd_same_shape,
+        _test_ddp_gather_autograd_different_shape,
         _test_ddp_compositional_tensor,
     ],
 )
@@ -105,6 +163,76 @@ def test_ddp(process):
     pytest.pool.map(process, range(NUM_PROCESSES))
 
 
+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)
+
+
+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)
+
+
+@pytest.mark.DDP()
+@pytest.mark.skipif(sys.platform == "win32", reason="DDP not available on windows")
+@pytest.mark.skipif(not USE_PYTEST_POOL, reason="DDP pool is not available.")
+@pytest.mark.skipif(not _TORCH_GREATER_EQUAL_2_1, reason="test only works on newer torch versions")
+@pytest.mark.parametrize(
+    "process",
+    [
+        _test_ddp_gather_autograd_same_shape,
+        _test_ddp_gather_autograd_different_shape,
+    ],
+)
+def test_ddp_autograd(process):
+    """Test ddp functions for autograd compatibility."""
+    pytest.pool.map(process, range(NUM_PROCESSES))
+
+
 def _test_non_contiguous_tensors(rank):
     class DummyCatMetric(Metric):
         full_state_update = True