Merge pull request #7 from Linux-cpp-lisp/device_and_dtype

Device and dtype improvements

1 files changed, 15 insertions, 40 deletions

diff --git a/tests/test_ema.py b/tests/test_ema.py
index 67a14dc..edcea4c 100644
--- a/tests/test_ema.py
+++ b/tests/test_ema.py
@@ -1,7 +1,5 @@
 import pytest
 
-import copy
-
 import torch
 
 from torch_ema import ExponentialMovingAverage
@@ -138,41 +136,18 @@ def test_explicit_params():
     assert not torch.all(model.weight == 0.0)
 
 
-@pytest.mark.parametrize("decay", [0.995])
-@pytest.mark.parametrize("use_num_updates", [True, False])
-@pytest.mark.parametrize("explicit_params", [True, False])
-def test_state_dict(decay, use_num_updates, explicit_params):
-    model = torch.nn.Linear(10, 2, bias=False)
-    with torch.no_grad():
-        model.weight.fill_(0.0)
-    ema = ExponentialMovingAverage(
-        model.parameters(),
-        decay=decay,
-        use_num_updates=False
-    )
-    state_dict = copy.deepcopy(ema.state_dict())
-
-    model2 = torch.nn.Linear(10, 2, bias=False)
-    ema2 = ExponentialMovingAverage(model2.parameters(), decay=0.0)
-    ema2.load_state_dict(state_dict)
-    assert ema2.decay == decay
-    assert torch.allclose(ema2.shadow_params[0], ema.shadow_params[0])
-
-    with torch.no_grad():
-        model2.weight.fill_(1.0)
-    if explicit_params:
-        ema2.update(model2.parameters())
-    else:
-        ema2.update()
-    assert torch.all(model2.weight == 1.0), "ema.update changed model weights"
-
-    ema.load_state_dict(ema2.state_dict())
-
-    if explicit_params:
-        ema.copy_to(model.parameters())
-    else:
-        ema.copy_to()
-    assert torch.allclose(
-        model.weight,
-        torch.full(size=(1,), fill_value=(1.0 - decay))
-    ), "average was wrong"
+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