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| author | Tongzhou Wang <SsnL@users.noreply.github.com> | 2018-05-31 15:09:54 -0400 |
|---|---|---|
| committer | GitHub <noreply@github.com> | 2018-05-31 15:09:54 -0400 |
| commit | 85ee94b7be86867a8afde51cae4ce0baff42d93b (patch) | |
| tree | 022b7802eab8a68938a9aeaefe0d818065bf304d /test/test_distributions.py | |
| parent | bafec1637ee4562875c2c81a1e85c7f1c9e66050 (diff) | |
| download | pytorch-85ee94b7be86867a8afde51cae4ce0baff42d93b.tar.gz pytorch-85ee94b7be86867a8afde51cae4ce0baff42d93b.tar.bz2 pytorch-85ee94b7be86867a8afde51cae4ce0baff42d93b.zip | |
Add memory leak check in CUDA tests (#7270)
* Add memory leak check in CUDA tests
* Tracking multi-GPU too
* fix run_test.py not running __name__ == '__main__' content; add test for make_cuda_memory_checked_test
* add a comment
* skip if cuda
* 1. Change the wrapper to a method in common.py:TestCase
2. Refactor common constants/method that initialize CUDA context into common_cuda.py
3. Update some test files to use TEST_CUDA and TEST_MULTIGPU
* Fix MaxUnpool3d forward memory leak
* Fix MultiLabelMarginCriterion forward memory leak
* Fix MultiMarginLoss backward memory leak
* default doCUDAMemoryCheck to False
* make the wrapper skip-able
* use TEST_MULTIGPU
* add align_corners=True/False tests for Upsample; fix TEST_CUDNN
* finalize interface
* VolumetricMaxUnpooling_updateOutput
* fix test_nccl
* rename THC caching allocator methods to be clearer
* make the wrapped function a method
* address comments; revert changes to aten/src/THC/THCCachingAllocator.cpp
* fix renamed var
Diffstat (limited to 'test/test_distributions.py')
| -rw-r--r-- | test/test_distributions.py | 5 |
1 files changed, 3 insertions, 2 deletions
diff --git a/test/test_distributions.py b/test/test_distributions.py index e03b1f6fb3..fa43268d65 100644 --- a/test/test_distributions.py +++ b/test/test_distributions.py @@ -31,6 +31,7 @@ from random import shuffle import torch from common import TestCase, run_tests, set_rng_seed +from common_cuda import TEST_CUDA from torch.autograd import Variable, grad, gradcheck from torch.distributions import (Bernoulli, Beta, Binomial, Categorical, Cauchy, Chi2, Dirichlet, Distribution, @@ -63,8 +64,6 @@ try: except ImportError: TEST_NUMPY = False -TEST_CUDA = torch.cuda.is_available() - def pairwise(Dist, *params): """ @@ -578,6 +577,8 @@ def unwrap(value): class TestDistributions(TestCase): + _do_cuda_memory_leak_check = True + def _gradcheck_log_prob(self, dist_ctor, ctor_params): # performs gradient checks on log_prob distribution = dist_ctor(*ctor_params) |