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Fix usm_ndarray ctor when shape is integral numpy scalar #1467

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merged 4 commits into from
Nov 8, 2023
Merged

Fix usm_ndarray ctor when shape is integral numpy scalar #1467

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7e79083
Enable use of np.int64 to specify shape of usm_ndarray
oleksandr-pavlyk Nov 5, 2023
2bc7939
Add a test for shape being np.int64 scalar
oleksandr-pavlyk Nov 5, 2023
aadb6b4
Eliminated multiple uses of same literal constants in test_search_red…
oleksandr-pavlyk Nov 5, 2023
da59476
Changed TypeError wording per PR feedback
oleksandr-pavlyk Nov 7, 2023
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20 changes: 13 additions & 7 deletions dpctl/tensor/_usmarray.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -182,13 +182,19 @@ cdef class usm_ndarray:
cdef bint is_fp16 = False

self._reset()
if (not isinstance(shape, (list, tuple))
and not hasattr(shape, 'tolist')):
try:
<Py_ssize_t> shape
shape = [shape, ]
except Exception:
raise TypeError("Argument shape must be a list or a tuple.")
if not isinstance(shape, (list, tuple)):
if hasattr(shape, 'tolist'):
fn = getattr(shape, 'tolist')
if callable(fn):
shape = shape.tolist()
if not isinstance(shape, (list, tuple)):
try:
<Py_ssize_t> shape
shape = [shape, ]
except Exception:
raise TypeError(
"Argument shape must be a list or a tuple."
)
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Perhaps the logic of this exception and message could be improved a bit. For instance:

In [5]: x = dpt.ones(np.prod((2, 3, 4), dtype="f4"), dtype="i8")
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
File ~/repos/dpctl/dpctl/tensor/_usmarray.pyx:192, in dpctl.tensor._usmarray.usm_ndarray.__cinit__()
    191 try:
--> 192     <Py_ssize_t> shape
    193     shape = [shape, ]

TypeError: 'float' object cannot be interpreted as an integer

During handling of the above exception, another exception occurred:

TypeError                                 Traceback (most recent call last)
Cell In[5], line 1
----> 1 x = dpt.ones(np.prod((2, 3, 4), dtype="f4"), dtype="i8")

File ~/repos/dpctl/dpctl/tensor/_ctors.py:968, in ones(shape, dtype, order, device, usm_type, sycl_queue)
    966 sycl_queue = normalize_queue_device(sycl_queue=sycl_queue, device=device)
    967 dtype = _get_dtype(dtype, sycl_queue)
--> 968 res = dpt.usm_ndarray(
    969     shape,
    970     dtype=dtype,
    971     buffer=usm_type,
    972     order=order,
    973     buffer_ctor_kwargs={"queue": sycl_queue},
    974 )
    975 hev, _ = ti._full_usm_ndarray(1, res, sycl_queue)
    976 hev.wait()

File ~/repos/dpctl/dpctl/tensor/_usmarray.pyx:195, in dpctl.tensor._usmarray.usm_ndarray.__cinit__()
    193     shape = [shape, ]
    194 except Exception:
--> 195     raise TypeError(
    196         "Argument shape must be a list or a tuple."
    197     )

TypeError: Argument shape must be a list or a tuple.

It seems a bit misleading at first, because it would work for np.prod((2, 3, 4)).

nd = len(shape)
if dtype is None:
if isinstance(buffer, (dpmem._memory._Memory, usm_ndarray)):
Expand Down
1 change: 1 addition & 0 deletions dpctl/tests/test_usm_ndarray_ctor.py
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Expand Up @@ -39,6 +39,7 @@
(2, 5, 2),
(2, 2, 2, 2, 2, 2, 2, 2),
5,
np.int32(7),
],
)
@pytest.mark.parametrize("usm_type", ["shared", "host", "device"])
Expand Down
14 changes: 8 additions & 6 deletions dpctl/tests/test_usm_ndarray_reductions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -175,9 +175,11 @@ def test_search_reduction_kernels(arg_dtype):
q = get_queue_or_skip()
skip_if_dtype_not_supported(arg_dtype, q)

x = dpt.ones((24 * 1025), dtype=arg_dtype, sycl_queue=q)
x_shape = (24, 1024)
x_size = np.prod(x_shape)
x = dpt.ones(x_size, dtype=arg_dtype, sycl_queue=q)
idx = randrange(x.size)
idx_tup = np.unravel_index(idx, (24, 1025))
idx_tup = np.unravel_index(idx, x_shape)
x[idx] = 2

m = dpt.argmax(x)
Expand All @@ -194,7 +196,7 @@ def test_search_reduction_kernels(arg_dtype):
m = dpt.argmax(y)
assert m == 2 * idx

x = dpt.reshape(x, (24, 1025))
x = dpt.reshape(x, x_shape)

x[idx_tup[0], :] = 3
m = dpt.argmax(x, axis=0)
Expand All @@ -209,15 +211,15 @@ def test_search_reduction_kernels(arg_dtype):
m = dpt.argmax(x, axis=1)
assert dpt.all(m == idx)

x = dpt.ones((24 * 1025), dtype=arg_dtype, sycl_queue=q)
x = dpt.ones(x_size, dtype=arg_dtype, sycl_queue=q)
idx = randrange(x.size)
idx_tup = np.unravel_index(idx, (24, 1025))
idx_tup = np.unravel_index(idx, x_shape)
x[idx] = 0

m = dpt.argmin(x)
assert m == idx

x = dpt.reshape(x, (24, 1025))
x = dpt.reshape(x, x_shape)

x[idx_tup[0], :] = -1
m = dpt.argmin(x, axis=0)
Expand Down