[Feature] TD+NJT to(device) support

ghstack-source-id: 71812497f1efb9d20f67a7561e74d5111c4cc3f0
Pull Request resolved: #1022

tensordict/base.py

@@ -10346,6 +10346,20 @@ def _to_consolidated(self, *, device, pin_memory, num_threads, non_blocking):
         untyped_storage = storage_cast.untyped_storage()
 
         def set_(x):
+            if x.is_nested:
+                if x.layout != torch.jagged:
+                    raise RuntimeError(
+                        "to(device) with nested tensors that do not have a jagged layout is not implemented yet. "
+                        "Please raise an issue on GitHub."
+                    )
+                values = x._values
+                lengths = x._lengths
+                offsets = x._offsets
+                return torch.nested.nested_tensor_from_jagged(
+                    set_(values),
+                    offsets=set_(offsets),
+                    lengths=set_(lengths) if lengths is not None else None,
+                )
             storage_offset = x.storage_offset()
             stride = x.stride()
             return torch.empty_like(x, device=device).set_(

tensordict/utils.py

@@ -1542,9 +1542,14 @@ def assert_close(
         elif not isinstance(input1, torch.Tensor):
             continue
         if input1.is_nested:
-            input1 = input1._base
-            input2 = input2._base
-        mse = (input1.to(torch.float) - input2.to(torch.float)).pow(2).sum()
+            input1v = input1.values()
+            input2v = input2.values()
+            mse = (input1v.to(torch.float) - input2v.to(torch.float)).pow(2).sum()
+            input1o = input1.offsets()
+            input2o = input2.offsets()
+            mse = mse + (input1o.to(torch.float) - input2o.to(torch.float)).pow(2).sum()
+        else:
+            mse = (input1.to(torch.float) - input2.to(torch.float)).pow(2).sum()
         mse = mse.div(input1.numel()).sqrt().item()
 
         local_msg = f"key {key} does not match, got mse = {mse:4.4f}"

test/test_tensordict.py

@@ -7963,6 +7963,47 @@ def test_consolidate_to_device(self):
         assert td_c_device["d"] == [["a string!"] * 3]
         assert len(dataptrs) == 1
 
+    @pytest.mark.skipif(not torch.cuda.is_available(), reason="no cuda device detected")
+    def test_consolidate_to_device_njt(self):
+        td = TensorDict(
+            {
+                "a": torch.arange(3).expand(4, 3).clone(),
+                "d": "a string!",
+                "njt": torch.nested.nested_tensor_from_jagged(
+                    torch.arange(10), offsets=torch.tensor([0, 2, 5, 8, 10])
+                ),
+                "njt_lengths": torch.nested.nested_tensor_from_jagged(
+                    torch.arange(10),
+                    offsets=torch.tensor([0, 2, 5, 8, 10]),
+                    lengths=torch.tensor([2, 3, 3, 2]),
+                ),
+            },
+            device="cpu",
+            batch_size=[4],
+        )
+        device = torch.device("cuda:0")
+        td_c = td.consolidate()
+        assert td_c.device == torch.device("cpu")
+        td_c_device = td_c.to(device)
+        assert td_c_device.device == device
+        assert td_c_device.is_consolidated()
+        dataptrs = set()
+        for tensor in td_c_device.values(True, True, is_leaf=_NESTED_TENSORS_AS_LISTS):
+            assert tensor.device == device
+            if tensor.is_nested:
+                vals = tensor._values
+                dataptrs.add(vals.untyped_storage().data_ptr())
+                offsets = tensor._offsets
+                dataptrs.add(offsets.untyped_storage().data_ptr())
+                lengths = tensor._lengths
+                if lengths is not None:
+                    dataptrs.add(lengths.untyped_storage().data_ptr())
+            else:
+                dataptrs.add(tensor.untyped_storage().data_ptr())
+        assert len(dataptrs) == 1
+        assert assert_allclose_td(td_c_device.cpu(), td)
+        assert td_c_device["njt_lengths"]._lengths is not None
+
     def test_create_empty(self):
         td = LazyStackedTensorDict(stack_dim=0)
         assert td.device is None