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add ndarrays.flags.f_contiguous #62

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Merged
merged 2 commits into from
Feb 23, 2023
Merged

add ndarrays.flags.f_contiguous #62

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3b28331
ENH: implement flags.f_contiguous
ev-br Feb 22, 2023
d42dc66
ENH: writeable and owndata flags
ev-br Feb 22, 2023
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17 changes: 16 additions & 1 deletion torch_np/_ndarray.py
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Original file line number Diff line number Diff line change
Expand Up @@ -112,7 +112,22 @@ def base(self):
@property
def flags(self):
# Note contiguous in torch is assumed C-style
return Flags({"C_CONTIGUOUS": self._tensor.is_contiguous()})

# check if F contiguous
from itertools import accumulate

f_strides = tuple(accumulate(list(self._tensor.shape), func=lambda x, y: x * y))
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this is of course just torch.cumprod(self._tensor.shape, 0); my instinct from numpy usage is to avoid array operations for small tuples etc.

f_strides = (1,) + f_strides[:-1]
is_f_contiguous = f_strides == self._tensor.stride()

return Flags(
{
"C_CONTIGUOUS": self._tensor.is_contiguous(),
"F_CONTIGUOUS": is_f_contiguous,
"OWNDATA": self._tensor._base is None,
"WRITEABLE": True, # pytorch does not have readonly tensors
}
)

@property
def T(self):
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1 change: 0 additions & 1 deletion torch_np/tests/numpy_tests/core/test_indexing.py
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Expand Up @@ -377,7 +377,6 @@ def test_subclass_writeable(self):
assert_(d[...].flags.writeable)
assert_(d[0].flags.writeable)

@pytest.mark.xfail(reason="can't determine f-style contiguous in torch")
def test_memory_order(self):
# This is not necessary to preserve. Memory layouts for
# more complex indices are not as simple.
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2 changes: 1 addition & 1 deletion torch_np/tests/numpy_tests/core/test_numeric.py
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Expand Up @@ -2194,7 +2194,7 @@ def check_function(self, func, fill_value=None):
**fill_kwarg)

assert_equal(arr.dtype, dtype)
# assert_(getattr(arr.flags, self.orders[order])) # XXX: no ndarray.flags
assert_(getattr(arr.flags, self.orders[order]))

if fill_value is not None:
val = fill_value
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2 changes: 1 addition & 1 deletion torch_np/tests/numpy_tests/lib/test_function_base.py
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Expand Up @@ -272,7 +272,7 @@ def test_basic(self):
assert_equal(a[0, 0], 1)
assert_equal(a_copy[0, 0], 10)

@pytest.mark.xfail(reason="ndarray.flags not implemented")
@pytest.mark.xfail(reason="order='F' not implemented")
def test_order(self):
# It turns out that people rely on np.copy() preserving order by
# default; changing this broke scikit-learn:
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