Add .to()

2 files changed, 39 insertions, 0 deletions

diff --git a/tests/test_ema.py b/tests/test_ema.py
index aa43b14..edcea4c 100644
--- a/tests/test_ema.py
+++ b/tests/test_ema.py
@@ -134,3 +134,20 @@ def test_explicit_params():
     ema.update(model2.parameters())
     ema.copy_to()
     assert not torch.all(model.weight == 0.0)
+
+
+def test_to():
+    m = torch.nn.Linear(11, 3)
+    ema = ExponentialMovingAverage(m.parameters(), decay=0.9)
+    assert ema.shadow_params[0].dtype == torch.get_default_dtype()
+    ema.to(dtype=torch.float16)
+    assert ema.shadow_params[0].dtype == torch.float16
+    ema.store()
+    # we store whatever we get
+    assert ema.collected_params[0].dtype == torch.get_default_dtype()
+    m = m.to(torch.float16)
+    ema.store(m.parameters())
+    assert ema.collected_params[0].dtype == torch.float16
+    ema.to(dtype=torch.float64)
+    assert ema.collected_params[0].dtype == torch.float64
+    assert ema.shadow_params[0].dtype == torch.float64
diff --git a/torch_ema/ema.py b/torch_ema/ema.py
index b3487cf..2aa3004 100644
--- a/torch_ema/ema.py
+++ b/torch_ema/ema.py
@@ -165,6 +165,28 @@ class ExponentialMovingAverage:
             if param.requires_grad:
                 param.data.copy_(c_param.data)
 
+    def to(self, device=None, dtype=None) -> None:
+        r"""Move internal buffers of the ExponentialMovingAverage to `device`.
+
+        Args:
+            device: like `device` argument to `torch.Tensor.to`
+        """
+        # .to() on the tensors handles None correctly
+        self.shadow_params = [
+            p.to(device=device, dtype=dtype)
+            if p.is_floating_point()
+            else p.to(device=device)
+            for p in self.shadow_params
+        ]
+        if self.collected_params is not None:
+            self.collected_params = [
+                p.to(device=device, dtype=dtype)
+                if p.is_floating_point()
+                else p.to(device=device)
+                for p in self.collected_params
+            ]
+        return
+
     def state_dict(self) -> dict:
         r"""Returns the state of the ExponentialMovingAverage as a dict."""
         # Following PyTorch conventions, references to tensors are returned: