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Copy pathtest_usm_ndarray_manipulation.py
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1582 lines (1237 loc) · 40 KB
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# Data Parallel Control (dpctl)
#
# Copyright 2020-2025 Intel Corporation
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import itertools
import numpy as np
import pytest
from numpy.testing import assert_, assert_array_equal, assert_raises_regex
import dpctl
import dpctl.tensor as dpt
from dpctl.tensor._numpy_helper import AxisError
from dpctl.tests.helper import get_queue_or_skip
from dpctl.utils import ExecutionPlacementError
def test_permute_dims_incorrect_type():
X_list = list([[1, 2, 3], [4, 5, 6]])
X_tuple = tuple(X_list)
Xnp = np.array(X_list)
pytest.raises(TypeError, dpt.permute_dims, X_list, (1, 0))
pytest.raises(TypeError, dpt.permute_dims, X_tuple, (1, 0))
pytest.raises(TypeError, dpt.permute_dims, Xnp, (1, 0))
def test_permute_dims_empty_array():
q = get_queue_or_skip()
Xnp = np.empty((10, 0))
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.permute_dims(X, (1, 0))
Ynp = np.transpose(Xnp, (1, 0))
assert_array_equal(Ynp, dpt.asnumpy(Y))
def test_permute_dims_0d_1d():
q = get_queue_or_skip()
Xnp_0d = np.array(1, dtype="int64")
X_0d = dpt.asarray(Xnp_0d, sycl_queue=q)
Y_0d = dpt.permute_dims(X_0d, ())
assert_array_equal(dpt.asnumpy(Y_0d), dpt.asnumpy(X_0d))
Xnp_1d = np.random.randint(0, 2, size=6, dtype="int64")
X_1d = dpt.asarray(Xnp_1d, sycl_queue=q)
Y_1d = dpt.permute_dims(X_1d, (0))
assert_array_equal(dpt.asnumpy(Y_1d), dpt.asnumpy(X_1d))
pytest.raises(ValueError, dpt.permute_dims, X_1d, ())
pytest.raises(AxisError, dpt.permute_dims, X_1d, (1))
pytest.raises(ValueError, dpt.permute_dims, X_1d, (1, 0))
pytest.raises(
ValueError, dpt.permute_dims, dpt.reshape(X_1d, (2, 3)), (1, 1)
)
@pytest.mark.parametrize("shapes", [(2, 2), (1, 4), (3, 3, 3), (4, 1, 3)])
def test_permute_dims_2d_3d(shapes):
q = get_queue_or_skip()
Xnp_size = np.prod(shapes)
Xnp = np.random.randint(0, 2, size=Xnp_size, dtype="int64").reshape(shapes)
X = dpt.asarray(Xnp, sycl_queue=q)
X_ndim = X.ndim
if X_ndim == 2:
Y = dpt.permute_dims(X, (1, 0))
Ynp = np.transpose(Xnp, (1, 0))
elif X_ndim == 3:
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.permute_dims(X, (2, 0, 1))
Ynp = np.transpose(Xnp, (2, 0, 1))
assert_array_equal(Ynp, dpt.asnumpy(Y))
def test_expand_dims_incorrect_type():
X_list = [1, 2, 3, 4, 5]
with pytest.raises(TypeError):
dpt.permute_dims(X_list, axis=1)
def test_expand_dims_0d():
q = get_queue_or_skip()
Xnp = np.array(1, dtype="int64")
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.expand_dims(X, axis=0)
Ynp = np.expand_dims(Xnp, axis=0)
assert_array_equal(Ynp, dpt.asnumpy(Y))
Y = dpt.expand_dims(X, axis=-1)
Ynp = np.expand_dims(Xnp, axis=-1)
assert_array_equal(Ynp, dpt.asnumpy(Y))
pytest.raises(AxisError, dpt.expand_dims, X, axis=1)
pytest.raises(AxisError, dpt.expand_dims, X, axis=-2)
@pytest.mark.parametrize("shapes", [(3,), (3, 3), (3, 3, 3)])
def test_expand_dims_1d_3d(shapes):
q = get_queue_or_skip()
Xnp_size = np.prod(shapes)
Xnp = np.random.randint(0, 2, size=Xnp_size, dtype="int64").reshape(shapes)
X = dpt.asarray(Xnp, sycl_queue=q)
shape_len = len(shapes)
for axis in range(-shape_len - 1, shape_len):
Y = dpt.expand_dims(X, axis=axis)
Ynp = np.expand_dims(Xnp, axis=axis)
assert_array_equal(Ynp, dpt.asnumpy(Y))
pytest.raises(AxisError, dpt.expand_dims, X, axis=shape_len + 1)
pytest.raises(AxisError, dpt.expand_dims, X, axis=-shape_len - 2)
@pytest.mark.parametrize(
"axes", [(0, 1, 2), (0, -1, -2), (0, 3, 5), (0, -3, -5)]
)
def test_expand_dims_tuple(axes):
q = get_queue_or_skip()
Xnp = np.empty((3, 3, 3), dtype="u1")
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.expand_dims(X, axis=axes)
Ynp = np.expand_dims(Xnp, axis=axes)
assert_array_equal(Ynp, dpt.asnumpy(Y))
def test_expand_dims_incorrect_tuple():
try:
X = dpt.empty((3, 3, 3), dtype="i4")
except dpctl.SyclDeviceCreationError:
pytest.skip("No SYCL devices available")
with pytest.raises(AxisError):
dpt.expand_dims(X, axis=(0, -6))
with pytest.raises(AxisError):
dpt.expand_dims(X, axis=(0, 5))
with pytest.raises(ValueError):
dpt.expand_dims(X, axis=(1, 1))
def test_squeeze_incorrect_type():
X_list = [1, 2, 3, 4, 5]
with pytest.raises(TypeError):
dpt.permute_dims(X_list, 1)
def test_squeeze_0d():
q = get_queue_or_skip()
Xnp = np.array(1)
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.squeeze(X)
Ynp = Xnp.squeeze()
assert_array_equal(Ynp, dpt.asnumpy(Y))
Y = dpt.squeeze(X, 0)
Ynp = Xnp.squeeze(0)
assert_array_equal(Ynp, dpt.asnumpy(Y))
Y = dpt.squeeze(X, (0))
Ynp = Xnp.squeeze((0))
assert_array_equal(Ynp, dpt.asnumpy(Y))
Y = dpt.squeeze(X, -1)
Ynp = Xnp.squeeze(-1)
assert_array_equal(Ynp, dpt.asnumpy(Y))
pytest.raises(AxisError, dpt.squeeze, X, 1)
pytest.raises(AxisError, dpt.squeeze, X, -2)
pytest.raises(AxisError, dpt.squeeze, X, (1))
pytest.raises(AxisError, dpt.squeeze, X, (-2))
pytest.raises(ValueError, dpt.squeeze, X, (0, 0))
@pytest.mark.parametrize(
"shapes",
[
(0),
(1),
(1, 2),
(2, 1),
(1, 1),
(2, 2),
(1, 0),
(0, 1),
(1, 2, 1),
(2, 1, 2),
(2, 2, 2),
(1, 1, 1),
(1, 0, 1),
(0, 1, 0),
],
)
def test_squeeze_without_axes(shapes):
q = get_queue_or_skip()
Xnp = np.empty(shapes, dtype="u1")
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.squeeze(X)
Ynp = Xnp.squeeze()
assert_array_equal(Ynp, dpt.asnumpy(Y))
@pytest.mark.parametrize("axes", [0, 2, (0), (2), (0, 2)])
def test_squeeze_axes_arg(axes):
q = get_queue_or_skip()
Xnp = np.array([[[1], [2], [3]]], dtype="u1")
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.squeeze(X, axes)
Ynp = Xnp.squeeze(axes)
assert_array_equal(Ynp, dpt.asnumpy(Y))
@pytest.mark.parametrize("axes", [1, -2, (1), (-2), (0, 0), (1, 1)])
def test_squeeze_axes_arg_error(axes):
q = get_queue_or_skip()
Xnp = np.array([[[1], [2], [3]]], dtype="u1")
X = dpt.asarray(Xnp, sycl_queue=q)
pytest.raises(ValueError, dpt.squeeze, X, axes)
@pytest.mark.parametrize(
"data",
[
[np.array(0, dtype="u1"), (0,)],
[np.array(0, dtype="u1"), (1,)],
[np.array(0, dtype="u1"), (3,)],
[np.ones(1, dtype="u1"), (1,)],
[np.ones(1, dtype="u1"), (2,)],
[np.ones(1, dtype="u1"), (1, 2, 3)],
[np.arange(3, dtype="u1"), (3,)],
[np.arange(3, dtype="u1"), (1, 3)],
[np.arange(3, dtype="u1"), (2, 3)],
[np.ones(0, dtype="u1"), 0],
[np.ones(1, dtype="u1"), 1],
[np.ones(1, dtype="u1"), 2],
[np.ones(1, dtype="u1"), (0,)],
[np.ones((1, 2), dtype="u1"), (0, 2)],
[np.ones((2, 1), dtype="u1"), (2, 0)],
],
)
def test_broadcast_to_succeeds(data):
q = get_queue_or_skip()
Xnp, target_shape = data
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.broadcast_to(X, target_shape)
Ynp = np.broadcast_to(Xnp, target_shape)
assert_array_equal(dpt.asnumpy(Y), Ynp)
@pytest.mark.parametrize(
"data",
[
[(0,), ()],
[(1,), ()],
[(3,), ()],
[(3,), (1,)],
[(3,), (2,)],
[(3,), (4,)],
[(1, 2), (2, 1)],
[(1, 1), (1,)],
[(1,), -1],
[(1,), (-1,)],
[(1, 2), (-1, 2)],
],
)
def test_broadcast_to_raises(data):
q = get_queue_or_skip()
orig_shape, target_shape = data
Xnp = np.zeros(orig_shape, dtype="i1")
X = dpt.asarray(Xnp, sycl_queue=q)
pytest.raises(ValueError, dpt.broadcast_to, X, target_shape)
def assert_broadcast_correct(input_shapes):
q = get_queue_or_skip()
np_arrays = [np.zeros(s, dtype="i1") for s in input_shapes]
out_np_arrays = np.broadcast_arrays(*np_arrays)
usm_arrays = [dpt.asarray(Xnp, sycl_queue=q) for Xnp in np_arrays]
out_usm_arrays = dpt.broadcast_arrays(*usm_arrays)
for Xnp, X in zip(out_np_arrays, out_usm_arrays):
assert_array_equal(
Xnp, dpt.asnumpy(X), err_msg=f"Failed for {input_shapes})"
)
def assert_broadcast_arrays_raise(input_shapes):
q = get_queue_or_skip()
usm_arrays = [dpt.asarray(np.zeros(s), sycl_queue=q) for s in input_shapes]
pytest.raises(ValueError, dpt.broadcast_arrays, *usm_arrays)
def test_broadcast_arrays_same():
q = get_queue_or_skip()
Xnp = np.arange(10)
Ynp = np.arange(10)
res_Xnp, res_Ynp = np.broadcast_arrays(Xnp, Ynp)
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.asarray(Ynp, sycl_queue=q)
res_X, res_Y = dpt.broadcast_arrays(X, Y)
assert_array_equal(res_Xnp, dpt.asnumpy(res_X))
assert_array_equal(res_Ynp, dpt.asnumpy(res_Y))
def test_broadcast_arrays_one_off():
q = get_queue_or_skip()
Xnp = np.array([[1, 2, 3]])
Ynp = np.array([[1], [2], [3]])
res_Xnp, res_Ynp = np.broadcast_arrays(Xnp, Ynp)
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.asarray(Ynp, sycl_queue=q)
res_X, res_Y = dpt.broadcast_arrays(X, Y)
assert_array_equal(res_Xnp, dpt.asnumpy(res_X))
assert_array_equal(res_Ynp, dpt.asnumpy(res_Y))
@pytest.mark.parametrize(
"shapes",
[
(),
(1,),
(3,),
(0, 1),
(0, 3),
(1, 0),
(3, 0),
(1, 3),
(3, 1),
(3, 3),
],
)
def test_broadcast_arrays_same_shapes(shapes):
for shape in shapes:
single_input_shapes = [shape]
assert_broadcast_correct(single_input_shapes)
double_input_shapes = [shape, shape]
assert_broadcast_correct(double_input_shapes)
triple_input_shapes = [shape, shape, shape]
assert_broadcast_correct(triple_input_shapes)
@pytest.mark.parametrize(
"shapes",
[
[[(1,), (3,)]],
[[(1, 3), (3, 3)]],
[[(3, 1), (3, 3)]],
[[(1, 3), (3, 1)]],
[[(1, 1), (3, 3)]],
[[(1, 1), (1, 3)]],
[[(1, 1), (3, 1)]],
[[(1, 0), (0, 0)]],
[[(0, 1), (0, 0)]],
[[(1, 0), (0, 1)]],
[[(1, 1), (0, 0)]],
[[(1, 1), (1, 0)]],
[[(1, 1), (0, 1)]],
],
)
def test_broadcast_arrays_same_len_shapes(shapes):
# Check that two different input shapes of the same length, but some have
# ones, broadcast to the correct shape.
for input_shapes in shapes:
assert_broadcast_correct(input_shapes)
assert_broadcast_correct(input_shapes[::-1])
@pytest.mark.parametrize(
"shapes",
[
[[(), (3,)]],
[[(3,), (3, 3)]],
[[(3,), (3, 1)]],
[[(1,), (3, 3)]],
[[(), (3, 3)]],
[[(1, 1), (3,)]],
[[(1,), (3, 1)]],
[[(1,), (1, 3)]],
[[(), (1, 3)]],
[[(), (3, 1)]],
[[(), (0,)]],
[[(0,), (0, 0)]],
[[(0,), (0, 1)]],
[[(1,), (0, 0)]],
[[(), (0, 0)]],
[[(1, 1), (0,)]],
[[(1,), (0, 1)]],
[[(1,), (1, 0)]],
[[(), (1, 0)]],
[[(), (0, 1)]],
],
)
def test_broadcast_arrays_different_len_shapes(shapes):
# Check that two different input shapes (of different lengths) broadcast
# to the correct shape.
for input_shapes in shapes:
assert_broadcast_correct(input_shapes)
assert_broadcast_correct(input_shapes[::-1])
@pytest.mark.parametrize(
"shapes",
[
[[(3,), (4,)]],
[[(2, 3), (2,)]],
[[(3,), (3,), (4,)]],
[[(1, 3, 4), (2, 3, 3)]],
],
)
def test_incompatible_shapes_raise_valueerror(shapes):
for input_shapes in shapes:
assert_broadcast_arrays_raise(input_shapes)
assert_broadcast_arrays_raise(input_shapes[::-1])
def test_broadcast_arrays_no_args():
with pytest.raises(ValueError):
dpt.broadcast_arrays()
def test_flip_axis_incorrect():
q = get_queue_or_skip()
X_np = np.ones((4, 4))
X = dpt.asarray(X_np, sycl_queue=q)
pytest.raises(AxisError, dpt.flip, dpt.asarray(np.ones(4)), axis=1)
pytest.raises(AxisError, dpt.flip, X, axis=2)
pytest.raises(AxisError, dpt.flip, X, axis=-3)
pytest.raises(AxisError, dpt.flip, X, axis=(0, 3))
def test_flip_0d():
q = get_queue_or_skip()
Xnp = np.array(1, dtype="int64")
X = dpt.asarray(Xnp, sycl_queue=q)
Ynp = np.flip(Xnp)
Y = dpt.flip(X)
assert_array_equal(Ynp, dpt.asnumpy(Y))
pytest.raises(AxisError, dpt.flip, X, axis=0)
pytest.raises(AxisError, dpt.flip, X, axis=1)
pytest.raises(AxisError, dpt.flip, X, axis=-1)
def test_flip_1d():
q = get_queue_or_skip()
Xnp = np.arange(6)
X = dpt.asarray(Xnp, sycl_queue=q)
for ax in range(-X.ndim, X.ndim):
Ynp = np.flip(Xnp, axis=ax)
Y = dpt.flip(X, axis=ax)
assert_array_equal(Ynp, dpt.asnumpy(Y))
Ynp = np.flip(Xnp, axis=0)
Y = dpt.flip(X, axis=0)
assert_array_equal(Ynp, dpt.asnumpy(Y))
@pytest.mark.parametrize(
"shapes",
[
(3, 2),
(2, 3),
(2, 2),
(3, 3),
(3, 2, 3),
(2, 3, 2),
(2, 2, 2),
(3, 3, 3),
],
)
def test_flip_2d_3d(shapes):
q = get_queue_or_skip()
Xnp_size = np.prod(shapes)
Xnp = np.arange(Xnp_size).reshape(shapes)
X = dpt.asarray(Xnp, sycl_queue=q)
for ax in range(-X.ndim, X.ndim):
Y = dpt.flip(X, axis=ax)
Ynp = np.flip(Xnp, axis=ax)
assert_array_equal(Ynp, dpt.asnumpy(Y))
@pytest.mark.parametrize(
"shapes",
[
(1,),
(3,),
(2, 3),
(3, 2),
(2, 2),
(1, 2, 3),
(2, 1, 3),
(2, 3, 1),
(3, 2, 1),
(3, 3, 3),
],
)
def test_flip_default_axes(shapes):
q = get_queue_or_skip()
Xnp_size = np.prod(shapes)
Xnp = np.arange(Xnp_size).reshape(shapes)
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.flip(X)
Ynp = np.flip(Xnp)
assert_array_equal(Ynp, dpt.asnumpy(Y))
@pytest.mark.parametrize(
"shapes",
[
(0),
(1),
(1, 1),
(1, 0),
(0, 1),
(1, 1, 1),
(1, 0, 1),
(0, 1, 0),
],
)
def test_flip_empty_0_size_dim(shapes):
q = get_queue_or_skip()
X = dpt.empty(shapes, sycl_queue=q)
dpt.flip(X)
@pytest.mark.parametrize(
"data",
[
[(2, 3), (0, 1)],
[(2, 3), (1, 0)],
[(2, 3), ()],
[(2, 1, 3), (0, 2)],
[(3, 1, 2), (2, 0)],
[(3, 3, 3), (2,)],
[(1, 2, 3), [0, -2]],
[(3, 1, 2), [-1, 0]],
[(3, 3, 3), [-2, -1]],
],
)
def test_flip_multiple_axes(data):
q = get_queue_or_skip()
shape, axes = data
Xnp_size = np.prod(shape)
Xnp = np.arange(Xnp_size).reshape(shape)
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.flip(X, axis=axes)
Ynp = np.flip(Xnp, axis=axes)
assert_array_equal(Ynp, dpt.asnumpy(Y))
def test_roll_scalar():
q = get_queue_or_skip()
Xnp = np.ones([], dtype="f4")
X = dpt.asarray(Xnp, sycl_queue=q)
Y = dpt.roll(X, 1)
Ynp = np.roll(Xnp, 1)
assert_array_equal(Ynp, dpt.asnumpy(Y))
with pytest.raises(AxisError):
dpt.roll(X, 1, axis=0)
with pytest.raises(AxisError):
dpt.roll(X, 1, axis=1)
@pytest.mark.parametrize(
"data",
[
[2, None],
[-2, None],
[2, 0],
[-2, 0],
[2, ()],
[11, 0],
],
)
def test_roll_1d(data):
q = get_queue_or_skip()
Xnp = np.arange(10)
X = dpt.asarray(Xnp, sycl_queue=q)
sh, ax = data
Y = dpt.roll(X, sh, axis=ax)
Ynp = np.roll(Xnp, sh, axis=ax)
assert_array_equal(Ynp, dpt.asnumpy(Y))
Y = dpt.roll(X, sh, axis=ax)
Ynp = np.roll(Xnp, sh, axis=ax)
assert_array_equal(Ynp, dpt.asnumpy(Y))
@pytest.mark.parametrize(
"data",
[
[1, None],
[1, 0],
[1, 1],
[1, ()],
# Roll multiple axes at once
[1, (0, 1)],
[(1, 0), (0, 1)],
[(-1, 0), (1, 0)],
[(0, 1), (0, 1)],
[(0, -1), (0, 1)],
[(1, 1), (0, 1)],
[(-1, -1), (0, 1)],
# Roll the same axis multiple times.
[1, (0, 0)],
[1, (1, 1)],
# Roll more than one turn in either direction.
[6, 1],
[-4, 1],
],
)
def test_roll_2d(data):
q = get_queue_or_skip()
Xnp = np.arange(10).reshape(2, 5)
X = dpt.asarray(Xnp, sycl_queue=q)
sh, ax = data
Y = dpt.roll(X, sh, axis=ax)
Ynp = np.roll(Xnp, sh, axis=ax)
assert_array_equal(Ynp, dpt.asnumpy(Y))
def test_roll_out_bounds_shifts():
"See gh-1857"
get_queue_or_skip()
x = dpt.arange(4)
y = dpt.roll(x, np.uint64(2**63 + 2))
expected = dpt.roll(x, 2)
assert dpt.all(y == expected)
x_empty = x[1:1]
y = dpt.roll(x_empty, 11)
assert y.size == 0
x_2d = dpt.reshape(x, (2, 2))
y = dpt.roll(x_2d, np.uint64(2**63 + 1), axis=1)
expected = dpt.roll(x_2d, 1, axis=1)
assert dpt.all(y == expected)
x_2d_empty = x_2d[:, 1:1]
y = dpt.roll(x_2d_empty, 3, axis=1)
expected = dpt.empty_like(x_2d_empty)
assert dpt.all(y == expected)
def test_roll_validation():
get_queue_or_skip()
X = dict()
with pytest.raises(TypeError):
dpt.roll(X)
X = dpt.empty((1, 2, 3))
shift = ((2, 3, 1), (1, 2, 3))
with pytest.raises(ValueError):
dpt.roll(X, shift=shift, axis=(0, 1, 2))
def test_concat_incorrect_type():
Xnp = np.ones((2, 2))
with pytest.raises(TypeError):
dpt.concat()
with pytest.raises(TypeError):
dpt.concat([])
with pytest.raises(TypeError):
dpt.concat(Xnp)
with pytest.raises(TypeError):
dpt.concat([Xnp, Xnp])
def test_concat_incorrect_queue():
q1 = get_queue_or_skip()
q2 = get_queue_or_skip()
X = dpt.ones((2, 2), sycl_queue=q1)
Y = dpt.ones((2, 2), sycl_queue=q2)
pytest.raises(ValueError, dpt.concat, [X, Y])
def test_concat_different_dtype():
q = get_queue_or_skip()
X = dpt.ones((2, 2), dtype=np.int64, sycl_queue=q)
Y = dpt.ones((3, 2), dtype=np.uint32, sycl_queue=q)
XY = dpt.concat([X, Y])
assert XY.dtype is X.dtype
assert XY.shape == (5, 2)
assert XY.sycl_queue == q
X1 = dpt.arange(10, dtype="i2", sycl_queue=q)
Y1 = dpt.arange(5, dtype="i4", sycl_queue=q)
XY1 = dpt.concat([X1[::2], Y1[::-1]], axis=None)
assert XY1.shape == (10,)
assert XY1.sycl_queue == q
assert XY1.dtype == Y1.dtype
def test_concat_incorrect_ndim():
q = get_queue_or_skip()
X = dpt.ones((2, 2), sycl_queue=q)
Y = dpt.ones((2, 2, 2), sycl_queue=q)
pytest.raises(ValueError, dpt.concat, [X, Y])
@pytest.mark.parametrize(
"data",
[
[(2, 2), (3, 3), 0],
[(2, 2), (3, 3), 1],
[(3, 2), (3, 3), 0],
[(2, 3), (3, 3), 1],
],
)
def test_concat_incorrect_shape(data):
q = get_queue_or_skip()
Xshape, Yshape, axis = data
X = dpt.ones(Xshape, sycl_queue=q)
Y = dpt.ones(Yshape, sycl_queue=q)
pytest.raises(ValueError, dpt.concat, [X, Y], axis=axis)
@pytest.mark.parametrize(
"data",
[
[(6,), 0],
[(2, 3), 1],
[(3, 2), -1],
[(1, 6), 0],
[(2, 1, 3), 2],
],
)
def test_concat_1array(data):
q = get_queue_or_skip()
Xshape, axis = data
Xnp = np.arange(6).reshape(Xshape)
X = dpt.asarray(Xnp, sycl_queue=q)
Ynp = np.concatenate([Xnp], axis=axis)
Y = dpt.concat([X], axis=axis)
assert_array_equal(Ynp, dpt.asnumpy(Y))
Ynp = np.concatenate((Xnp,), axis=axis)
Y = dpt.concat((X,), axis=axis)
assert_array_equal(Ynp, dpt.asnumpy(Y))
@pytest.mark.parametrize(
"data",
[
[(1,), (1,), 0],
[(0, 2), (0, 2), 1],
[(0, 2), (2, 2), 0],
[(2, 1), (2, 2), -1],
[(2, 2, 2), (2, 1, 2), 1],
[(3, 3, 3), (2, 2), None],
],
)
def test_concat_2arrays(data):
q = get_queue_or_skip()
Xshape, Yshape, axis = data
Xnp = np.ones(Xshape)
X = dpt.asarray(Xnp, sycl_queue=q)
Ynp = np.zeros(Yshape)
Y = dpt.asarray(Ynp, sycl_queue=q)
Znp = np.concatenate([Xnp, Ynp], axis=axis)
Z = dpt.concat([X, Y], axis=axis)
assert_array_equal(Znp, dpt.asnumpy(Z))
@pytest.mark.parametrize(
"data",
[
[(1,), (1,), (1,), 0],
[(0, 2), (2, 2), (1, 2), 0],
[(2, 1, 2), (2, 2, 2), (2, 4, 2), 1],
],
)
def test_concat_3arrays(data):
q = get_queue_or_skip()
Xshape, Yshape, Zshape, axis = data
Xnp = np.ones(Xshape)
X = dpt.asarray(Xnp, sycl_queue=q)
Ynp = np.zeros(Yshape)
Y = dpt.asarray(Ynp, sycl_queue=q)
Znp = np.full(Zshape, 2.0)
Z = dpt.asarray(Znp, sycl_queue=q)
Rnp = np.concatenate([Xnp, Ynp, Znp], axis=axis)
R = dpt.concat([X, Y, Z], axis=axis)
assert_array_equal(Rnp, dpt.asnumpy(R))
def test_concat_axis_none_strides():
q = get_queue_or_skip()
Xnp = np.arange(0, 18).reshape((6, 3))
X = dpt.asarray(Xnp, sycl_queue=q)
Ynp = np.arange(20, 36).reshape((4, 2, 2))
Y = dpt.asarray(Ynp, sycl_queue=q)
Znp = np.concatenate([Xnp[::2], Ynp[::2]], axis=None)
Z = dpt.concat([X[::2], Y[::2]], axis=None)
assert_array_equal(Znp, dpt.asnumpy(Z))
def test_stack_incorrect_shape():
q = get_queue_or_skip()
X = dpt.ones((1,), sycl_queue=q)
Y = dpt.ones((2,), sycl_queue=q)
with pytest.raises(ValueError):
dpt.stack([X, Y], axis=0)
@pytest.mark.parametrize(
"data",
[
[(6,), 0],
[(2, 3), 1],
[(3, 2), -1],
[(1, 6), 2],
[(2, 1, 3), 2],
],
)
def test_stack_1array(data):
q = get_queue_or_skip()
shape, axis = data
Xnp = np.arange(6).reshape(shape)
X = dpt.asarray(Xnp, sycl_queue=q)
Ynp = np.stack([Xnp], axis=axis)
Y = dpt.stack([X], axis=axis)
assert_array_equal(Ynp, dpt.asnumpy(Y))
Ynp = np.stack((Xnp,), axis=axis)
Y = dpt.stack((X,), axis=axis)
assert_array_equal(Ynp, dpt.asnumpy(Y))
@pytest.mark.parametrize(
"data",
[
[(1,), 0],
[(0, 2), 0],
[(2, 0), 0],
[(2, 3), 0],
[(2, 3), 1],
[(2, 3), 2],
[(2, 3), -1],
[(2, 3), -2],
[(2, 2, 2), 1],
],
)
def test_stack_2arrays(data):
q = get_queue_or_skip()
shape, axis = data
Xnp = np.ones(shape)
X = dpt.asarray(Xnp, sycl_queue=q)
Ynp = np.zeros(shape)
Y = dpt.asarray(Ynp, sycl_queue=q)
Znp = np.stack([Xnp, Ynp], axis=axis)
Z = dpt.stack([X, Y], axis=axis)
assert_array_equal(Znp, dpt.asnumpy(Z))
@pytest.mark.parametrize(
"data",
[
[(1,), 0],
[(0, 2), 0],
[(2, 1, 2), 1],
],
)
def test_stack_3arrays(data):
q = get_queue_or_skip()
shape, axis = data
Xnp = np.ones(shape)
X = dpt.asarray(Xnp, sycl_queue=q)
Ynp = np.zeros(shape)
Y = dpt.asarray(Ynp, sycl_queue=q)
Znp = np.full(shape, 2.0)
Z = dpt.asarray(Znp, sycl_queue=q)
Rnp = np.stack([Xnp, Ynp, Znp], axis=axis)
R = dpt.stack([X, Y, Z], axis=axis)
assert_array_equal(Rnp, dpt.asnumpy(R))
def test_can_cast():
q = get_queue_or_skip()
# incorrect input
X = dpt.ones((2, 2), dtype=dpt.int16, sycl_queue=q)
pytest.raises(TypeError, dpt.can_cast, X, 1)
pytest.raises(TypeError, dpt.can_cast, X, X)
X_np = np.ones((2, 2), dtype=np.int16)
assert dpt.can_cast(X, "float32") == np.can_cast(X_np, "float32")
assert dpt.can_cast(X, dpt.int32) == np.can_cast(X_np, np.int32)
assert dpt.can_cast(X, dpt.int64) == np.can_cast(X_np, np.int64)
def test_result_type():
q = get_queue_or_skip()
usm_ar = dpt.ones((2), dtype=dpt.int16, sycl_queue=q)
np_ar = dpt.asnumpy(usm_ar)
X = [usm_ar, dpt.int32, "int64", usm_ar]
X_np = [np_ar, np.int32, "int64", np_ar]
assert dpt.result_type(*X) == np.result_type(*X_np)
X = [usm_ar, dpt.int32, "int64", True]
X_np = [np_ar, np.int32, "int64", True]
assert dpt.result_type(*X) == np.result_type(*X_np)
X = [usm_ar, dpt.int32, "int64", 2]
X_np = [np_ar, np.int32, "int64", 2]