Match the number of outputs of backward with forward for AllGatherGrad

pytorch_lightning/utilities/distributed.py

@@ -172,7 +172,7 @@ def backward(ctx, *grad_output):
 
         torch.distributed.all_reduce(grad_output, op=torch.distributed.ReduceOp.SUM, async_op=False, group=ctx.group)
 
-        return grad_output[torch.distributed.get_rank()]
+        return grad_output[torch.distributed.get_rank()], None
 
 
 def all_gather_ddp_if_available(

tests/utilities/test_all_gather_grad.py

@@ -95,3 +95,26 @@ def training_epoch_end(self, outputs) -> None:
 
     trainer.fit(model)
     assert model.training_epoch_end_called
+
+
+@RunIf(min_gpus=2, skip_windows=True, special=True)
+def test_all_gather_sync_grads(tmpdir):
+
+    class TestModel(BoringModel):
+
+        training_step_called = False
+
+        def training_step(self, batch, batch_idx):
+            self.training_step_called = True
+            tensor = torch.rand(2, 2, requires_grad=True, device=self.device)
+            gathered_tensor = self.all_gather(tensor, sync_grads=True)
+            assert gathered_tensor.shape == torch.Size([2, 2, 2])
+
+            loss = gathered_tensor.sum()
+
+            return loss
+
+    model = TestModel()
+    trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True, gpus=2)
+    trainer.fit(model)
+    assert model.training_step_called