False alarm about leak in TestNN.test_variable_sequence_cuda (#17242)

Summary:
`TestNN.test_variable_sequence_cuda` sometimes brakes due to CUDA leak.
The cause appears to be too small tolerance breaking float16 sub-test of the test above.
When it breaks it calls abort disrupting correct tear down of the test
and false alarming about the leak.

~~Also, removed annoying **Upsample** module warning.
IMHO this warning is wrong because the module **Upsample** is not deprecated. Seems like it's been mixed
with `nn.functional.upsample` function which is indeed deprecated in favor of `nn.functional.interpolate`, see `torch/nn/functional.py:2387` for details (this replacement is also performed in `test_nn.py`).~~
Pull Request resolved: https://github.com/pytorch/pytorch/pull/17242

Differential Revision: D14141686

Pulled By: soumith

fbshipit-source-id: faa8f87440d94bdc6ab0ff00be6dad82353115c4

diff --git a/test/test_nn.py b/test/test_nn.py
index 0d4ff86..3dff195 100644 (file)
--- a/test/test_nn.py
+++ b/test/test_nn.py
@@ -4595,9 +4595,10 @@ class TestNN(NNTestCase):
             unpacked, unpacked_len = rnn_utils.pad_packed_sequence(packed_out)
 
             # Check forward
-            self.assertEqual(packed_hidden, seq_hidden)
-            self.assertEqual(unpacked, seq_out)
-            self.assertEqual(unpacked_len, lengths)
+            prec = dtype2prec[dtype]
+            self.assertEqual(packed_hidden, seq_hidden, prec)
+            self.assertEqual(unpacked, seq_out, prec)
+            self.assertEqual(unpacked_len, lengths, prec)
 
             # Check backward
             seq_out.sum().backward()
@@ -6404,7 +6405,7 @@ class TestNN(NNTestCase):
         self.assertEqual(torch.ones(1, 1, 4), out_t.data)
 
         input = torch.randn(1, 1, 2, requires_grad=True)
-        gradcheck(lambda x: F.upsample(x, 4, mode='nearest'), [input])
+        gradcheck(lambda x: F.interpolate(x, 4, mode='nearest'), [input])
 
     def test_upsamplingLinear1d(self):
         for align_corners in [True, False]:
@@ -6419,7 +6420,7 @@ class TestNN(NNTestCase):
                 self.assertEqual(torch.ones(1, 1, out_size), out_t.data)
 
                 input = torch.randn(1, 1, 2, requires_grad=True)
-                gradcheck(lambda x: F.upsample(x, out_size, **kwargs), (input,))
+                gradcheck(lambda x: F.interpolate(x, out_size, **kwargs), (input,))
 
     def test_upsamplingLinear1d_spatial_invariance(self):
         m = nn.Upsample(scale_factor=3, mode='linear', align_corners=False)
@@ -6437,10 +6438,10 @@ class TestNN(NNTestCase):
 
         input = torch.randn(1, 1, 2, 2, requires_grad=True)
         self.assertEqual(
-            F.upsample(input, 4, mode='nearest'),
-            F.upsample(input, scale_factor=2, mode='nearest'))
-        gradcheck(lambda x: F.upsample(x, 4, mode='nearest'), [input])
-        gradgradcheck(lambda x: F.upsample(x, 4, mode='nearest'), [input])
+            F.interpolate(input, 4, mode='nearest'),
+            F.interpolate(input, scale_factor=2, mode='nearest'))
+        gradcheck(lambda x: F.interpolate(x, 4, mode='nearest'), [input])
+        gradgradcheck(lambda x: F.interpolate(x, 4, mode='nearest'), [input])
 
     def test_upsamplingBilinear2d(self):
         for align_corners in [True, False]:
@@ -6455,7 +6456,7 @@ class TestNN(NNTestCase):
                 self.assertEqual(torch.ones(1, 1, out_size, out_size), out_t.data)
 
                 input = torch.randn(1, 1, 2, 2, requires_grad=True)
-                gradcheck(lambda x: F.upsample(x, out_size, **kwargs), [input])
+                gradcheck(lambda x: F.interpolate(x, out_size, **kwargs), [input])
 
     def test_upsamplingBicubic2d(self):
         # test output against known input
@@ -6497,7 +6498,7 @@ class TestNN(NNTestCase):
         self.assertEqual(torch.ones(1, 1, 4, 4, 4), out_t.data)
 
         input = torch.randn(1, 1, 2, 2, 2, requires_grad=True)
-        gradcheck(lambda x: F.upsample(x, 4, mode='nearest'), [input])
+        gradcheck(lambda x: F.interpolate(x, 4, mode='nearest'), [input])
 
     def test_upsamplingTrilinear3d(self):
         for align_corners in [True, False]:
@@ -6513,10 +6514,10 @@ class TestNN(NNTestCase):
 
                 input = torch.randn(1, 1, 2, 2, 2, requires_grad=True)
                 self.assertEqual(
-                    F.upsample(input, (out_size, out_size, out_size), **kwargs),
-                    F.upsample(input, scale_factor=scale_factor, **kwargs))
-                gradcheck(lambda x: F.upsample(x, out_size, **kwargs), [input])
-                gradgradcheck(lambda x: F.upsample(x, out_size, **kwargs), [input])
+                    F.interpolate(input, (out_size, out_size, out_size), **kwargs),
+                    F.interpolate(input, scale_factor=scale_factor, **kwargs))
+                gradcheck(lambda x: F.interpolate(x, out_size, **kwargs), [input])
+                gradgradcheck(lambda x: F.interpolate(x, out_size, **kwargs), [input])
 
     def test_upsamplingTrilinear3d_spatial_invariance(self):
         m = nn.Upsample(scale_factor=3, mode='trilinear', align_corners=False)