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Fix half tensor printing plus speedup large tensor printing #14418

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Fix half tensor printing plus speedup large tensor printing #14418

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11 changes: 11 additions & 0 deletions test/test_torch.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8299,6 +8299,10 @@ def test_print(self):
obj = t(100, 100).fill_(1)
obj.__repr__()
str(obj)
# test half tensor
obj = torch.rand(100, 100, device='cpu').half()
obj.__repr__()
str(obj)
for t in torch._storage_classes:
if t.is_cuda and not torch.cuda.is_available():
continue
Expand Down Expand Up @@ -8353,6 +8357,13 @@ def test_print(self):
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([0., 0., 0., ..., 0., 0., 0.])''')

# test internal summary function
x = torch.rand(1, 20, 5, 30)
summary = torch._tensor_str.get_summarized_data(x)
self.assertEqual(summary.shape, (1, 6, 5, 6))
first_and_last = [0, 1, 2, -3, -2, -1]
self.assertEqual(summary, x[:, first_and_last][..., first_and_last])

# test device
if torch.cuda.is_available():
x = torch.tensor([123], device='cuda:0')
Expand Down
10 changes: 6 additions & 4 deletions torch/_tensor_str.py
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Original file line number Diff line number Diff line change
Expand Up @@ -190,6 +190,8 @@ def _tensor_str(self, indent):
return '[]'

summarize = self.numel() > PRINT_OPTS.threshold
if self.dtype is torch.float16:
self = self.float()
formatter = _Formatter(get_summarized_data(self) if summarize else self)
return _tensor_str_with_formatter(self, indent, formatter, summarize)

Expand Down Expand Up @@ -220,12 +222,12 @@ def get_summarized_data(self):
else:
return self
if self.size(0) > 2 * PRINT_OPTS.edgeitems:
start = [get_summarized_data(self[i]).reshape(-1) for i in range(0, PRINT_OPTS.edgeitems)]
end = ([get_summarized_data(self[i]).reshape(-1)
start = [self[i] for i in range(0, PRINT_OPTS.edgeitems)]
end = ([self[i]
for i in range(len(self) - PRINT_OPTS.edgeitems, len(self))])
return torch.cat((start + end))
return torch.stack([get_summarized_data(x) for x in (start + end)])
else:
return self
return torch.stack([get_summarized_data(x) for x in self])


def _str(self):
Expand Down