8000 Implement logaddexp by cloudhan · Pull Request #38384 · pytorch/pytorch · GitHub
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Implement logaddexp #38384

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Implement logaddexp #38384

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fd4cf72
add cpu logaddexp
cloudhan May 13, 2020
c4730a3
add cuda logaddexp
cloudhan May 13, 2020
8bbe55d
add logaddexp2
cloudhan May 14, 2020
84e80aa
add logaddexp derivative
cloudhan May 14, 2020
92d2a67
add logaddexp doc
cloudhan May 14, 2020
7245a52
minor fix
cloudhan May 15, 2020
0024be3
add method doc
cloudhan May 15, 2020
83f7036
add test
cloudhan May 15, 2020
9f8a876
add api coverage test
cloudhan May 15, 2020
46e0604
check memory overlap
cloudhan May 18, 2020
f3cb270
minor update for doc
cloudhan May 18, 2020
5e60285
test special case
cloudhan May 18, 2020
ec4aaac
handle special case
cloudhan May 18, 2020
65d572e
move logaddexp test to TestTorchDeviceType
cloudhan May 18, 2020
271b8ae
remove half for cuda
cloudhan May 20, 2020
204e11a
add autograd test for logaddexp
cloudhan May 20, 2020
0ed7a12
update logaddexp test
10000 cloudhan May 23, 2020
97ab0ca
update doc for logaddexp
cloudhan May 23, 2020
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update logaddexp test
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@cloudhan
cloudhan committed May 23, 2020
commit 0ed7a121797550089629f0567022f27889779fce
41 changes: 21 additions & 20 deletions test/test_torch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9604,38 +9604,39 @@ def test_lerp(self, device):
expected = start + weight * (end - start)
self.assertEqual(expected, actual)

def _test_logaddexp(self, device, dtype, base2=False):
def _test_logaddexp(self, device, dtype, base2):
if base2:
gt_func = np.logaddexp2
ref_func = np.logaddexp2
our_func = torch.logaddexp2
else:
gt_func = np.logaddexp
ref_func = np.logaddexp
our_func = torch.logaddexp

a = torch.randn(51, 2, dtype=dtype, device=device)
b = torch.randn(51, 2, dtype=dtype, device=device)
gt = torch.tensor(gt_func(a.cpu().numpy(), b.cpu().numpy()), device=device)
ours = our_func(a, b)
self.assertEqual(ours.shape, gt.shape)
self.assertTrue(torch.allclose(ours, gt))
def _test_helper(a, b):
ref = ref_func(a.cpu().numpy(), b.cpu().numpy())
v = our_func(a, b)
self.assertEqual(ref, v)

# numerical stability
# simple test
a = torch.randn(64, 2, dtype=dtype, device=device) - 0.5
b = torch.randn(64, 2, dtype=dtype, device=device) - 0.5
_test_helper(a, b)
_test_helper(a[:3], b[:3])

# large value test for numerical stability
a *= 10000
b *= 10000
a[0] = inf
b[0] = inf
a[1] = -inf
b[1] = -inf
a[2] = inf
b[2] = -inf
gt = torch.tensor(gt_func(a.cpu().numpy(), b.cpu().numpy()), device=device)
ours = our_func(a, b)
self.assertTrue(torch.allclose(ours, gt))
_test_helper(a, b)
_test_helper(a[:3], b[:3])

a = torch.tensor([float('inf'), float('-inf'), float('inf'), float("nan")], dtype=dtype, device=device)
b = torch.tensor([float('inf'), float('-inf'), float('-inf'), float("nan")], dtype=dtype, device=device)
_test_helper(a, b)

@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
@dtypes(torch.float32, torch.float64)
def test_logaddexp(self, device, dtype):
self._test_logaddexp(device, dtype)
self._test_logaddexp(device, dtype, base2=False)

@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
@dtypes(torch.float32, torch.float64)
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