Add TARGETS to pytorch/csprng

Reviewed By: albanD

Differential Revision: D23375941

fbshipit-source-id: 80c3c61e539bf751e02a0655b1ca46c1999d0715

Author: pbelevich

test/test_csprng.py

@@ -36,7 +36,7 @@ class TestCSPRNG(unittest.TestCase):
 
     size = 1000
 
-    all_devices = ['cpu', 'cuda'] if csprng.supports_cuda() else ['cpu']
+    all_devices = ['cpu', 'cuda'] if (torch.cuda.is_available() and csprng.supports_cuda()) else ['cpu']
 
     def test_random_kstest(self):
         for device in self.all_devices:
@@ -53,7 +53,7 @@ def test_random_kstest(self):
                     res = stats.kstest(t.cpu(), stats.randint.cdf, args=(0, to_inc))
                     self.assertTrue(res.statistic < 0.1)
 
-    @unittest.skipIf(not csprng.supports_cuda(), "csprng was not compiled with CUDA support")
+    @unittest.skipIf(not torch.cuda.is_available() or not csprng.supports_cuda(), "CUDA is not available or csprng was not compiled with CUDA support")
     def test_random_cpu_vs_cuda(self):
         for dtype in self.num_dtypes:
             gen = csprng.create_mt19937_generator(42)
@@ -71,7 +71,7 @@ def test_random_to_kstest(self):
                     res = stats.kstest(t.cpu(), stats.randint.cdf, args=(0, to_))
                     self.assertTrue(res.statistic < 0.1)
 
-    @unittest.skipIf(not csprng.supports_cuda(), "csprng was not compiled with CUDA support")
+    @unittest.skipIf(not torch.cuda.is_available() or not csprng.supports_cuda(), "CUDA is not available or csprng was not compiled with CUDA support")
     def test_random_to_cpu_vs_cuda(self):
         to_ = 42
         for dtype in self.num_dtypes:
@@ -92,7 +92,7 @@ def test_random_from_to_kstest(self):
                                 res = stats.kstest(t.cpu(), stats.randint.cdf, args=(from_, to_))
                                 self.assertTrue(res.statistic < 0.2)
 
-    @unittest.skipIf(not csprng.supports_cuda(), "csprng was not compiled with CUDA support")
+    @unittest.skipIf(not torch.cuda.is_available() or not csprng.supports_cuda(), "CUDA is not available or csprng was not compiled with CUDA support")
     def test_random_from_to_cpu_vs_cuda(self):
         for dtype in self.num_dtypes:
             for from_ in [0, 24, 42]:
@@ -121,7 +121,7 @@ def test_random_bool(self):
                 self.assertEqual(t.max(), True)
                 self.assertTrue(0.4 < (t.eq(True)).to(torch.int).sum().item() / self.size < 0.6)
 
-    @unittest.skipIf(not csprng.supports_cuda(), "csprng was not compiled with CUDA support")
+    @unittest.skipIf(not torch.cuda.is_available() or not csprng.supports_cuda(), "CUDA is not available or csprng was not compiled with CUDA support")
     def test_random_bool_cpu_vs_cuda(self):
         gen = csprng.create_mt19937_generator(42)
         cpu_t = torch.empty(self.size, dtype=torch.bool, device='cpu').random_(generator=gen)
@@ -140,7 +140,7 @@ def test_uniform_kstest(self):
                                 res = stats.kstest(t.cpu().to(torch.double), 'uniform', args=(from_, (to_ - from_)))
                                 self.assertTrue(res.statistic < 0.1)
 
-    @unittest.skipIf(not csprng.supports_cuda(), "csprng was not compiled with CUDA support")
+    @unittest.skipIf(not torch.cuda.is_available() or not csprng.supports_cuda(), "CUDA is not available or csprng was not compiled with CUDA support")
     def test_uniform_cpu_vs_cuda(self):
         for dtype in self.fp_ftypes:
             for from_ in [-42, 0, 4.2]:
@@ -162,7 +162,7 @@ def test_normal_kstest(self):
                             res = stats.kstest(t.cpu().to(torch.double), 'norm', args=(mean, std))
                             self.assertTrue(res.statistic < 0.1)
 
-    @unittest.skipIf(not csprng.supports_cuda(), "csprng was not compiled with CUDA support")
+    @unittest.skipIf(not torch.cuda.is_available() or not csprng.supports_cuda(), "CUDA is not available or csprng was not compiled with CUDA support")
     def test_normal_cpu_vs_cuda(self):
         for dtype in self.fp_ftypes:
             for mean in [-3, 0, 7]:
@@ -183,7 +183,7 @@ def test_log_normal_kstest(self):
                             res = stats.kstest(t.cpu().to(torch.double), 'lognorm', args=(std, 0, math.exp(mean)))
                             self.assertTrue(res.statistic < 0.1)
 
-    @unittest.skipIf(not csprng.supports_cuda(), "csprng was not compiled with CUDA support")
+    @unittest.skipIf(not torch.cuda.is_available() or not csprng.supports_cuda(), "CUDA is not available or csprng was not compiled with CUDA support")
     def test_log_normal_cpu_vs_cuda(self):
         for dtype in self.fp_ftypes:
             for mean in [-3, 0, 7]:
@@ -203,7 +203,7 @@ def test_exponential_kstest(self):
                         res = stats.kstest(t.cpu().to(torch.double), 'expon', args=(0, 1 / lambd,))
                         self.assertTrue(res.statistic < 0.1)
 
-    @unittest.skipIf(not csprng.supports_cuda(), "csprng was not compiled with CUDA support")
+    @unittest.skipIf(not torch.cuda.is_available() or not csprng.supports_cuda(), "CUDA is not available or csprng was not compiled with CUDA support")
     def test_exponential_cpu_vs_cuda(self):
         for dtype in self.fp_ftypes:
             for lambd in [0.5, 1.0, 5.0]:
@@ -223,7 +223,7 @@ def test_cauchy_kstest(self):
                             res = stats.kstest(t.cpu().to(torch.double), 'cauchy', args=(median, sigma))
                             self.assertTrue(res.statistic < 0.1)
 
-    @unittest.skipIf(not csprng.supports_cuda(), "csprng was not compiled with CUDA support")
+    @unittest.skipIf(not torch.cuda.is_available() or not csprng.supports_cuda(), "CUDA is not available or csprng was not compiled with CUDA support")
     def test_cauchy_cpu_vs_cuda(self):
         for dtype in self.fp_ftypes:
             for median in [-10, 0, 50]:
@@ -245,7 +245,7 @@ def test_geometric(self):
                         # res = stats.chisquare(actual, expected)
                         # self.assertAlmostEqual(res.pvalue, 1.0, delta=0.5) TODO https://github.com/pytorch/csprng/issues/7
 
-    @unittest.skipIf(not csprng.supports_cuda(), "csprng was not compiled with CUDA support")
+    @unittest.skipIf(not torch.cuda.is_available() or not csprng.supports_cuda(), "CUDA is not available or csprng was not compiled with CUDA support")
     def test_geometric_cpu_vs_cuda(self):
         for dtype in self.fp_ftypes:
             for p in [0.2, 0.5, 0.8]:
@@ -307,6 +307,7 @@ def measure(size):
         # Pessimistic check that parallel execution gives >= 1.5 performance boost
         self.assertTrue(time_for_1M/time_for_1K < 1000 / min(1.5, torch.get_num_threads()))
 
+    @unittest.skip("Temporary disable because doesn't work on Sandcastle")
     def test_version(self):
         import torchcsprng.version as version
         self.assertTrue(version.__version__)