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ENH: torch dtype promotions #298

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ENH: torch dtype promotions #298

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110 changes: 57 additions & 53 deletions array_api_compat/torch/_aliases.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,9 +21,9 @@
try:
# torch >=2.3
_int_dtypes |= {torch.uint16, torch.uint32, torch.uint64}
_HAS_LARGE_UINT = True
except AttributeError:
pass

_HAS_LARGE_UINT = False

_array_api_dtypes = {
torch.bool,
Expand All @@ -34,54 +34,55 @@
torch.complex128,
}

_promotion_table = {
# bool
(torch.bool, torch.bool): torch.bool,
_promotion_table = {
# ints
(torch.int8, torch.int8): torch.int8,
(torch.int8, torch.int16): torch.int16,
(torch.int8, torch.int32): torch.int32,
(torch.int8, torch.int64): torch.int64,
(torch.int16, torch.int8): torch.int16,
(torch.int16, torch.int16): torch.int16,
(torch.int16, torch.int32): torch.int32,
(torch.int16, torch.int64): torch.int64,
(torch.int32, torch.int8): torch.int32,
(torch.int32, torch.int16): torch.int32,
(torch.int32, torch.int32): torch.int32,
(torch.int32, torch.int64): torch.int64,
(torch.int64, torch.int8): torch.int64,
(torch.int64, torch.int16): torch.int64,
(torch.int64, torch.int32): torch.int64,
(torch.int64, torch.int64): torch.int64,
# uints
(torch.uint8, torch.uint8): torch.uint8,
# ints and uints (mixed sign)
(torch.int8, torch.uint8): torch.int16,
(torch.int16, torch.uint8): torch.int16,
(torch.int32, torch.uint8): torch.int32,
(torch.int64, torch.uint8): torch.int64,
(torch.uint8, torch.int8): torch.int16,
(torch.uint8, torch.int16): torch.int16,
(torch.uint8, torch.int32): torch.int32,
(torch.uint8, torch.int64): torch.int64,
# floats
(torch.float32, torch.float32): torch.float32,
(torch.float32, torch.float64): torch.float64,
(torch.float64, torch.float32): torch.float64,
(torch.float64, torch.float64): torch.float64,
# complexes
(torch.complex64, torch.complex64): torch.complex64,
(torch.complex64, torch.complex128): torch.complex128,
(torch.complex128, torch.complex64): torch.complex128,
(torch.complex128, torch.complex128): torch.complex128,
# Mixed float and complex
(torch.float32, torch.complex64): torch.complex64,
(torch.float32, torch.complex128): torch.complex128,
(torch.float64, torch.complex64): torch.complex128,
(torch.float64, torch.complex128): torch.complex128,
}

if _HAS_LARGE_UINT: # torch >=2.3
_promotion_table.update(
{
# uints
(torch.uint8, torch.uint16): torch.uint16,
(torch.uint8, torch.uint32): torch.uint32,
(torch.uint8, torch.uint64): torch.uint64,
(torch.uint16, torch.uint32): torch.uint32,
(torch.uint16, torch.uint64): torch.uint64,
(torch.uint32, torch.uint64): torch.uint64,
# ints and uints (mixed sign)
(torch.uint16, torch.int8): torch.int32,
(torch.uint16, torch.int16): torch.int32,
(torch.uint16, torch.int32): torch.int32,
(torch.uint16, torch.int64): torch.int64,
(torch.uint32, torch.int8): torch.int64,
(torch.uint32, torch.int16): torch.int64,
(torch.uint32, torch.int32): torch.int64,
(torch.uint32, torch.int64): torch.int64,
}
)

_promotion_table.update({(b, a): c for (a, b), c in _promotion_table.items()})
_promotion_table.update({(a, a): a for a in _array_api_dtypes})


def _two_arg(f):
@_wraps(f)
Expand Down Expand Up @@ -275,6 +276,31 @@ def _reduce_multiple_axes(f, x, axis, keepdims=False, **kwargs):
out = torch.unsqueeze(out, a)
return out


def _sum_prod_no_axis(x: Array, dtype: DType | None) -> Array:
"""
Implements `sum(..., axis=())` and `prod(..., axis=())`.

Works around https://github.com/pytorch/pytorch/issues/29137
"""
if dtype is not None:
return x.clone() if dtype == x.dtype else x.to(dtype)

if x.dtype in (torch.int8, torch.int16, torch.int32):
return x.to(torch.int64)

if _HAS_LARGE_UINT and x.dtype in (torch.uint8, torch.uint16, torch.uint32):
return x.to(torch.uint64)

if x.dtype == torch.uint8:
# We can't upcast uint8 according to the spec because there is no
# torch.uint64, so at least upcast to int64 which is what prod does
# when axis=None.
return x.to(torch.int64)

return x.clone()


def prod(x: Array,
/,
*,
Expand All @@ -283,20 +309,9 @@ def prod(x: Array,
keepdims: bool = False,
**kwargs) -> Array:
x = torch.asarray(x)
ndim = x.ndim

# https://github.com/pytorch/pytorch/issues/29137. Separate from the logic
# below because it still needs to upcast.
if axis == ():
if dtype is None:
# We can't upcast uint8 according to the spec because there is no
# torch.uint64, so at least upcast to int64 which is what sum does
# when axis=None.
if x.dtype in [torch.int8, torch.int16, torch.int32, torch.uint8]:
return x.to(torch.int64)
return x.clone()
return x.to(dtype)

return _sum_prod_no_axis(x, dtype)
# torch.prod doesn't support multiple axes
# (https://github.com/pytorch/pytorch/issues/56586).
if isinstance(axis, tuple):
Expand All @@ -305,7 +320,7 @@ def prod(x: Array,
# torch doesn't support keepdims with axis=None
# (https://github.com/pytorch/pytorch/issues/71209)
res = torch.prod(x, dtype=dtype, **kwargs)
res = _axis_none_keepdims(res, ndim, keepdims)
res = _axis_none_keepdims(res, x.ndim, keepdims)
return res

return torch.prod(x, axis, dtype=dtype, keepdims=keepdims, **kwargs)
Expand All @@ -319,25 +334,14 @@ def sum(x: Array,
keepdims: bool = False,
**kwargs) -> Array:
x = torch.asarray(x)
ndim = x.ndim

# https://github.com/pytorch/pytorch/issues/29137.
# Make sure it upcasts.
if axis == ():
if dtype is None:
# We can't upcast uint8 according to the spec because there is no
# torch.uint64, so at least upcast to int64 which is what sum does
# when axis=None.
if x.dtype in [torch.int8, torch.int16, torch.int32, torch.uint8]:
return x.to(torch.int64)
return x.clone()
return x.to(dtype)

return _sum_prod_no_axis(x, dtype)
if axis is None:
# torch doesn't support keepdims with axis=None
# (https://github.com/pytorch/pytorch/issues/71209)
res = torch.sum(x, dtype=dtype, **kwargs)
res = _axis_none_keepdims(res, ndim, keepdims)
res = _axis_none_keepdims(res, x.ndim, keepdims)
return res

return torch.sum(x, axis, dtype=dtype, keepdims=keepdims, **kwargs)
Expand Down
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