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Fix bincount for empty input #9757

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Fix bincount for empty input #9757

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3bca93a
Fix bincount for empty input
vishwakftw Jul 24, 2018
e615db6
Add missing device in test case
vishwakftw Jul 24, 2018
b6c9a9d
Fix build error, add doc string about behaviour for empty input
vishwakftw Jul 24, 2018
c53c2ac
Merge branch 'bincount-empty-fix' of https://github.com/vishwakftw/py…
vishwakftw Jul 24, 2018
91b67e2
Fix testing error
vishwakftw Jul 24, 2018
32e9657
CR: docs
vishwakftw Aug 7, 2018
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5 changes: 4 additions & 1 deletion aten/src/ATen/native/SummaryOps.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,10 @@ Tensor _bincount_cpu_template(
if (minlength < 0) {
AT_ERROR("minlength should be >= 0");
}
if (self.dim() != 1 || self.numel() == 0 || *self.min().data<input_t>() < 0) {
if (self.dim() == 1 && self.numel() == 0) {
return native::zeros({minlength}, kLong);
}
if (self.dim() != 1 || *self.min().data<input_t>() < 0) {
AT_ERROR("bincount only supports 1-d non-negative integral inputs.");
}

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5 changes: 4 additions & 1 deletion aten/src/ATen/native/cuda/SummaryOps.cu
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Expand Up @@ -244,7 +244,10 @@ Tensor _bincount_cuda_template(
if (minlength < 0) {
AT_ERROR("minlength should be >= 0");
}
if (self.dim() != 1 || self.numel() == 0 ||
if (self.dim() == 1 && self.numel() == 0) {
return native::zeros({minlength}, device(kCUDA).dtype(kLong));
}
if (self.dim() != 1 ||
(!std::is_same<input_t, uint8_t>::value &&
*self.min().toBackend(kCPU).data<input_t>() < 0)) {
AT_ERROR("bincount only supports 1-d non-negative integral inputs.");
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6 changes: 6 additions & 0 deletions test/test_torch.py
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Expand Up @@ -8223,6 +8223,12 @@ def _test_bincount(self, device):
with self.assertRaisesRegex(RuntimeError, 'same length'):
torch.bincount(torch.tensor([1, 0], device=device),
torch.tensor([1., 0.3, 0.5], device=device))
# 1-d input with no elements and default minlength
self.assertEqual(torch.bincount(torch.tensor([], device=device, dtype=torch.long)),
torch.zeros(0, dtype=torch.long, device=device))
# 1-d input with no elements and specified minlength
self.assertEqual(torch.bincount(torch.tensor([], device=device, dtype=torch.long), minlength=10),
torch.zeros(10, dtype=torch.long, device=device))

# test tensor method without weights
long_counts = torch.tensor(
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11 changes: 7 additions & 4 deletions torch/_torch_docs.py
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Expand Up @@ -604,19 +604,22 @@ def parse_kwargs(desc):
Count the frequency of each value in an array of non-negative ints.

The number of bins (size 1) is one larger than the largest value in
:attr:`input`. If :attr:`minlength` is specified, the number of bins is at least
:attr:`minlength`. If ``n`` is the value at position ``i``,
:attr:`input` unless :attr:`input` is empty, in which case the result is a
tensor of size 0. If :attr:`minlength` is specified, the number of bins is at least
:attr:`minlength` and if :attr:`input` is empty, then the result is tensor of size
:attr:`minlength` filled with zeros. If ``n`` is the value at position ``i``,
:math:`out[n] += weights[i]` if :attr:`weights` is specified else
:math:`out[n] += 1`.

Arguments:
input (Tensor): 1-d int tensor
weights (Tensor): optional, weight for each value in the input tensor.
Should be of same size as input tensor.
minlength (int): optional, min number of bins. Should be non-negative.
minlength (int): optional, minimum number of bins. Should be non-negative.

Shape:
output (Tensor): ``Size([max(input) + 1])``
output (Tensor): ``Size([max(input) + 1])`` if :attr:`input` is non-empty, else
``Size(0)``

Example::

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