Add pin_memory support

CHANGELOG.md

@@ -8,6 +8,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 ### Added
 
 - Added support for inferring `stype.categorical` from boolean columns in `utils.infer_series_stype` ([#421](https://github.com/pyg-team/pytorch-frame/pull/421))
+- Added `pin_memory()` to `TensorFrame`, `MultiEmbeddingTensor`, and `MultiNestedTensor` ([#437](https://github.com/pyg-team/pytorch-frame/pull/437))
 
 ### Changed
 

test/data/test_multi_embedding_tensor.py

@@ -476,3 +476,17 @@ def test_cat(device):
     # case: list of non-MultiEmbeddingTensor should raise error
     with pytest.raises(AssertionError):
         MultiEmbeddingTensor.cat([object()], dim=0)
+
+
+def test_pin_memory():
+    met, _ = get_fake_multi_embedding_tensor(
+        num_rows=2,
+        num_cols=3,
+    )
+    assert not met.is_pinned()
+    assert not met.values.is_pinned()
+    assert not met.offset.is_pinned()
+    met = met.pin_memory()
+    assert met.is_pinned()
+    assert met.values.is_pinned()
+    assert met.offset.is_pinned()

test/data/test_multi_nested_tensor.py

@@ -87,7 +87,7 @@ def test_fillna_col():
 
 
 @withCUDA
-def test_multi_nested_tensor_basics(device):
+def test_basics(device):
     num_rows = 8
     num_cols = 10
     max_value = 100
@@ -317,7 +317,7 @@ def test_multi_nested_tensor_basics(device):
                                           cloned_multi_nested_tensor)
 
 
-def test_multi_nested_tensor_different_num_rows():
+def test_different_num_rows():
     tensor_mat = [
         [torch.tensor([1, 2, 3]),
          torch.tensor([4, 5])],
@@ -331,3 +331,19 @@ def test_multi_nested_tensor_different_num_rows():
             match="The length of each row must be the same",
     ):
         MultiNestedTensor.from_tensor_mat(tensor_mat)
+
+
+def test_pin_memory():
+    num_rows = 10
+    num_cols = 3
+    tensor = MultiNestedTensor.from_tensor_mat(
+        [[torch.randn(random.randint(0, 10)) for _ in range(num_cols)]
+         for _ in range(num_rows)])
+
+    assert not tensor.is_pinned()
+    assert not tensor.values.is_pinned()
+    assert not tensor.offset.is_pinned()
+    tensor = tensor.pin_memory()
+    assert tensor.is_pinned()
+    assert tensor.values.is_pinned()
+    assert tensor.offset.is_pinned()

test/data/test_tensor_frame.py

@@ -230,3 +230,18 @@ def test_custom_tf_get_col_feat():
     assert torch.equal(feat, feat_dict['numerical'][:, 0:1])
     feat = tf.get_col_feat('num_2')
     assert torch.equal(feat, feat_dict['numerical'][:, 1:2])
+
+
+def test_pin_memory(get_fake_tensor_frame):
+    def assert_is_pinned(tf: TensorFrame, expected: bool) -> bool:
+        for value in tf.feat_dict.values():
+            if isinstance(value, dict):
+                for v in value.values():
+                    assert v.is_pinned() is expected
+            else:
+                assert value.is_pinned() is expected
+
+    tf = get_fake_tensor_frame(10)
+    assert_is_pinned(tf, expected=False)
+    tf = tf.pin_memory()
+    assert_is_pinned(tf, expected=True)

torch_frame/data/multi_tensor.py

@@ -93,6 +93,12 @@ def cpu(self, *args, **kwargs):
     def cuda(self, *args, **kwargs):
         return self._apply(lambda x: x.cuda(*args, **kwargs))
 
+    def pin_memory(self, *args, **kwargs):
+        return self._apply(lambda x: x.pin_memory(*args, **kwargs))
+
+    def is_pinned(self) -> bool:
+        return self.values.is_pinned()
+
     # Helper Functions ########################################################
 
     def _apply(self, fn: Callable[[Tensor], Tensor]) -> _MultiTensor:

torch_frame/data/tensor_frame.py

@@ -340,6 +340,17 @@ def fn(x):
 
         return self._apply(fn)
 
+    def pin_memory(self, *args, **kwargs):
+        def fn(x):
+            if isinstance(x, dict):
+                for key in x:
+                    x[key] = x[key].pin_memory(*args, **kwargs)
+            else:
+                x = x.pin_memory(*args, **kwargs)
+            return x
+
+        return self._apply(fn)
+
     # Helper Functions ########################################################
 
     def _apply(self, fn: Callable[[TensorData], TensorData]) -> TensorFrame: