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Add torch.where overload with only condition argument #21986

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umanwizard wants to merge 2 commits into from
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Add torch.where overload with only condition argument #21986

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Add `torch.where` overload with only condition argument
Jun 19, 2019
0e34e63
Resolve merge conflicts.
Jul 2, 2019
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4 changes: 4 additions & 0 deletions aten/src/ATen/native/TensorCompare.cpp
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Expand Up @@ -102,6 +102,10 @@ Tensor where(const Tensor& condition, const Tensor& self, const Tensor& other) {
return at::_s_where(b_condition, b_self, b_other);
}

std::vector<Tensor> where(const Tensor& condition) {
return condition.nonzero_numpy();
}

Tensor _s_where_cpu(const Tensor& condition, const Tensor& self, const Tensor& other) {
Tensor ret = at::empty(self.sizes(), self.options());
AT_DISPATCH_ALL_TYPES(ret.scalar_type(), "where_cpu", [&] {
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3 changes: 3 additions & 0 deletions aten/src/ATen/native/native_functions.yaml
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Expand Up @@ -2231,6 +2231,9 @@
- func: where(Tensor condition, Tensor self, Tensor other) -> Tensor
variants: function, method

- func: where(Tensor condition) -> Tensor[]
variants: function

- func: _s_where(Tensor condition, Tensor self, Tensor other) -> Tensor
variants: function
dispatch:
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3 changes: 2 additions & 1 deletion test/test_torch.py
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Expand Up @@ -9322,8 +9322,9 @@ def gen_nontrivial_input(num_src, dtype, device):
if TEST_NUMPY:
tup1 = torch.nonzero(tensor, as_tuple=True)
tup2 = tensor.nonzero(as_tuple=True)
tup3 = torch.where(tensor)
np1 = tensor.cpu().numpy().nonzero()
for t in (tup1, tup2):
for t in (tup1, tup2, tup3):
self.assertEqual(len(t), len(np1))
for i in range(len(t)):
self.assertEqual(t[i].cpu().numpy(), np1[i])
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10 changes: 9 additions & 1 deletion torch/_torch_docs.py
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Expand Up @@ -5848,7 +5848,7 @@ def merge_dicts(*dicts):

add_docstr(torch.where,
r"""
where(condition, x, y) -> Tensor
.. function:: where(condition, x, y) -> Tensor
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@gchanan gchanan Jun 21, 2019

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isn't this either a different function or the optional x, y need to be explained in some way?

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@gchanan gchanan Jun 21, 2019

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whoops, didn't see the line below, my mistake.


Return a tensor of elements selected from either :attr:`x` or :attr:`y`, depending on :attr:`condition`.

Expand Down Expand Up @@ -5883,6 +5883,14 @@ def merge_dicts(*dicts):
tensor([[ 1.0000, 0.3139],
[ 0.3898, 1.0000],
[ 0.0478, 1.0000]])

.. function:: where(condition) -> tuple of LongTensor

``torch.where(condition)`` is identical to
``torch.nonzero(condition, as_tuple=True)``.

.. note::
See also :func:`torch.nonzero`.
""")

add_docstr(torch.logdet,
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