Option to run LabelModel on GPU #1466
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Codecov Report
@@ Coverage Diff @@
## master #1466 +/- ##
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+ Coverage 97.58% 97.59% +<.01%
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Files 55 55
Lines 2032 2034 +2
Branches 334 334
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+ Hits 1983 1985 +2
Misses 22 22
Partials 27 27
|
bhancock8
approved these changes
Sep 20, 2019
| @@ -334,7 +336,7 @@ def get_conditional_probs(self) -> np.ndarray: | |||
| np.ndarray | |||
| An [m, k + 1, k] np.ndarray conditional probabilities table. | |||
| """ | |||
| return self._get_conditional_probs(self.mu.detach().numpy()) | |||
| return self._get_conditional_probs(self.mu.clone().cpu().detach().numpy()) | |||
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Why is clone() necessary here but not the other places where you're adding .cpu().detach()?
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good catch, probably added during debugging. removed
| @@ -750,7 +752,7 @@ def _count_accurate_lfs(self, mu: np.ndarray) -> int: | |||
| int | |||
| Number of LFs better than random | |||
| """ | |||
| P = self.P.numpy() | |||
| P = self.P.clone().cpu().detach().numpy() | |||
| @@ -95,7 +95,9 @@ def test_generate_O(self): | |||
| [1 / 4, 0, 0, 1 / 4, 0, 1 / 4], | |||
| ] | |||
| ) | |||
| np.testing.assert_array_almost_equal(label_model.O.numpy(), true_O) | |||
| np.testing.assert_array_almost_equal( | |||
| label_model.O.cpu().detach().numpy(), true_O | |||
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Feels very strange to see `label_model.O', but I know that's not part of this PR's scope.
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Description of proposed changes
Fixed some parameter definitions to allow LabelModel to run on GPU.
Related issue(s)
Fixes #1430
Test plan
device='cuda'for LabelModel tests.Checklist
Need help on these? Just ask!
tox -e complexand/ortox -e sparkif appropriate.