Skip to content

P
pytensor
提交 0f82ba32 authored 作者: Brandon T. Willard's avatar Brandon T. Willard 提交者： Brandon T. Willard
浏览文件
操作

Move Numba extra_ops tests to test_extra_ops

上级 5bccb970
隐藏空白字符变更
内嵌 并排
正在显示 包含 481 行增加 和 466 行删除
tests/link/numba/test_basic.py
浏览文件 @ 0f82ba32
......@@ -32,7 +32,7 @@ from aesara.raise_op import assert_op
from aesara.scalar.basic import Composite
from aesara.scan.basic import scan
from aesara.scan.utils import until
from aesara.tensor import blas, extra_ops, nlinalg, slinalg
from aesara.tensor import blas, nlinalg, slinalg
from aesara.tensor import subtensor as at_subtensor
from aesara.tensor.elemwise import Elemwise
from aesara.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape, Unbroadcast
......@@ -1186,471 +1186,6 @@ def test_perform_type_convert():
compare_numba_and_py(out_fg, [get_test_value(i) for i in out_fg.inputs])
@pytest.mark.parametrize(
"val",
[
set_test_value(at.lscalar(), np.array(6, dtype="int64")),
],
)
def test_Bartlett(val):
g = extra_ops.bartlett(val)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"val, axis, mode",
[
(
set_test_value(
at.matrix(), np.arange(3, dtype=config.floatX).reshape((3, 1))
),
1,
"add",
),
(
set_test_value(
at.matrix(), np.arange(6, dtype=config.floatX).reshape((3, 2))
),
0,
"add",
),
(
set_test_value(
at.matrix(), np.arange(6, dtype=config.floatX).reshape((3, 2))
),
1,
"add",
),
(
set_test_value(
at.matrix(), np.arange(6, dtype=config.floatX).reshape((3, 2))
),
0,
"mul",
),
(
set_test_value(
at.matrix(), np.arange(6, dtype=config.floatX).reshape((3, 2))
),
1,
"mul",
),
],
)
def test_CumOp(val, axis, mode):
g = extra_ops.CumOp(axis=axis, mode=mode)(val)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"a, val",
[
(
set_test_value(at.lmatrix(), np.zeros((10, 2), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
)
],
)
def test_FillDiagonal(a, val):
g = extra_ops.FillDiagonal()(a, val)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"a, val, offset",
[
(
set_test_value(at.lmatrix(), np.zeros((10, 2), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
set_test_value(at.lscalar(), np.array(-1, dtype="int64")),
),
(
set_test_value(at.lmatrix(), np.zeros((10, 2), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
set_test_value(at.lscalar(), np.array(0, dtype="int64")),
),
(
set_test_value(at.lmatrix(), np.zeros((10, 3), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
),
],
)
def test_FillDiagonalOffset(a, val, offset):
g = extra_ops.FillDiagonalOffset()(a, val, offset)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"arr, shape, mode, order, exc",
[
(
tuple(set_test_value(at.lscalar(), v) for v in np.array([0])),
set_test_value(at.lvector(), np.array([2])),
"raise",
"C",
None,
),
(
tuple(set_test_value(at.lscalar(), v) for v in np.array([0, 0, 3])),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"raise",
"C",
None,
),
(
tuple(
set_test_value(at.lvector(), v)
for v in np.array([[0, 1], [2, 0], [1, 3]])
),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"raise",
"C",
None,
),
(
tuple(
set_test_value(at.lvector(), v)
for v in np.array([[0, 1], [2, 0], [1, 3]])
),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"raise",
"F",
NotImplementedError,
),
(
tuple(
set_test_value(at.lvector(), v)
for v in np.array([[0, 1, 2], [2, 0, 3], [1, 3, 5]])
),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"raise",
"C",
ValueError,
),
(
tuple(
set_test_value(at.lvector(), v)
for v in np.array([[0, 1, 2], [2, 0, 3], [1, 3, 5]])
),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"wrap",
"C",
None,
),
(
tuple(
set_test_value(at.lvector(), v)
for v in np.array([[0, 1, 2], [2, 0, 3], [1, 3, 5]])
),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"clip",
"C",
None,
),
],
)
def test_RavelMultiIndex(arr, shape, mode, order, exc):
g = extra_ops.RavelMultiIndex(mode, order)(*(arr + (shape,)))
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.raises(exc)
with cm:
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"x, repeats, axis, exc",
[
(
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
set_test_value(at.lscalar(), np.array(0, dtype="int64")),
None,
None,
),
(
set_test_value(at.lmatrix(), np.zeros((2, 2), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
None,
None,
),
(
set_test_value(at.lvector(), np.arange(2, dtype="int64")),
set_test_value(at.lvector(), np.array([1, 1], dtype="int64")),
None,
None,
),
(
set_test_value(at.lmatrix(), np.zeros((2, 2), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
0,
UserWarning,
),
],
)
def test_Repeat(x, repeats, axis, exc):
g = extra_ops.Repeat(axis)(x, repeats)
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.warns(exc)
with cm:
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"x, axis, return_index, return_inverse, return_counts, exc",
[
(
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
None,
False,
False,
False,
None,
),
(
set_test_value(at.lvector(), np.array([1, 1, 2], dtype="int64")),
None,
False,
False,
False,
None,
),
(
set_test_value(at.lmatrix(), np.array([[1, 1], [2, 2]], dtype="int64")),
None,
False,
False,
False,
None,
),
(
set_test_value(
at.lmatrix(), np.array([[1, 1], [1, 1], [2, 2]], dtype="int64")
),
0,
False,
False,
False,
UserWarning,
),
(
set_test_value(
at.lmatrix(), np.array([[1, 1], [1, 1], [2, 2]], dtype="int64")
),
0,
True,
True,
True,
UserWarning,
),
],
)
def test_Unique(x, axis, return_index, return_inverse, return_counts, exc):
g = extra_ops.Unique(return_index, return_inverse, return_counts, axis)(x)
if isinstance(g, list):
g_fg = FunctionGraph(outputs=g)
else:
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.warns(exc)
with cm:
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"arr, shape, order, exc",
[
(
set_test_value(at.lvector(), np.array([9, 15, 1], dtype="int64")),
at.as_tensor([2, 3, 4]),
"C",
None,
),
(
set_test_value(at.lvector(), np.array([1, 0], dtype="int64")),
at.as_tensor([2]),
"C",
None,
),
(
set_test_value(at.lvector(), np.array([9, 15, 1], dtype="int64")),
at.as_tensor([2, 3, 4]),
"F",
NotImplementedError,
),
],
)
def test_UnravelIndex(arr, shape, order, exc):
g = extra_ops.UnravelIndex(order)(arr, shape)
if isinstance(g, list):
g_fg = FunctionGraph(outputs=g)
else:
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.raises(exc)
with cm:
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"a, v, side, sorter, exc",
[
(
set_test_value(at.vector(), np.array([1.0, 2.0, 3.0], dtype=config.floatX)),
set_test_value(at.matrix(), rng.random((3, 2)).astype(config.floatX)),
"left",
None,
None,
),
pytest.param(
set_test_value(
at.vector(),
np.array([0.29769574, 0.71649186, 0.20475563]).astype(config.floatX),
),
set_test_value(
at.matrix(),
np.array(
[
[0.18847123, 0.39659508],
[0.56220006, 0.57428752],
[0.86720994, 0.44522637],
]
).astype(config.floatX),
),
"left",
None,
None,
marks=pytest.mark.xfail(
reason="This won't work until https://github.com/numba/numba/pull/7005 is merged"
),
),
(
set_test_value(at.vector(), np.array([1.0, 2.0, 3.0], dtype=config.floatX)),
set_test_value(at.matrix(), rng.random((3, 2)).astype(config.floatX)),
"right",
set_test_value(at.lvector(), np.array([0, 2, 1])),
UserWarning,
),
],
)
def test_Searchsorted(a, v, side, sorter, exc):
g = extra_ops.SearchsortedOp(side)(a, v, sorter)
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.warns(exc)
with cm:
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"x, shape",
[
(
set_test_value(at.vector(), rng.random(size=(2,)).astype(config.floatX)),
[set_test_value(at.lscalar(), np.array(v)) for v in [3, 2]],
),
(
set_test_value(at.vector(), rng.random(size=(2,)).astype(config.floatX)),
[at.as_tensor(3, dtype=np.int64), at.as_tensor(2, dtype=np.int64)],
),
(
set_test_value(at.vector(), rng.random(size=(2,)).astype(config.floatX)),
at.as_tensor([set_test_value(at.lscalar(), np.array(v)) for v in [3, 2]]),
),
(
set_test_value(at.vector(), rng.random(size=(2,)).astype(config.floatX)),
[at.as_tensor(3, dtype=np.int8), at.as_tensor(2, dtype=np.int64)],
),
],
)
def test_BroadcastTo(x, shape):
g = extra_ops.BroadcastTo()(x, shape)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"x, y, exc",
[
......
tests/link/numba/test_extra_ops.py 0 → 100644
浏览文件 @ 0f82ba32
import contextlib
import numpy as np
import pytest
import aesara.tensor as at
from aesara import config
from aesara.compile.sharedvalue import SharedVariable
from aesara.graph.basic import Constant
from aesara.graph.fg import FunctionGraph
from aesara.tensor import extra_ops
from tests.link.numba.test_basic import compare_numba_and_py, set_test_value
rng = np.random.default_rng(42849)
@pytest.mark.parametrize(
"val",
[
set_test_value(at.lscalar(), np.array(6, dtype="int64")),
],
)
def test_Bartlett(val):
g = extra_ops.bartlett(val)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"x, shape",
[
(
set_test_value(at.vector(), rng.random(size=(2,)).astype(config.floatX)),
[set_test_value(at.lscalar(), np.array(v)) for v in [3, 2]],
),
(
set_test_value(at.vector(), rng.random(size=(2,)).astype(config.floatX)),
[at.as_tensor(3, dtype=np.int64), at.as_tensor(2, dtype=np.int64)],
),
(
set_test_value(at.vector(), rng.random(size=(2,)).astype(config.floatX)),
at.as_tensor([set_test_value(at.lscalar(), np.array(v)) for v in [3, 2]]),
),
(
set_test_value(at.vector(), rng.random(size=(2,)).astype(config.floatX)),
[at.as_tensor(3, dtype=np.int8), at.as_tensor(2, dtype=np.int64)],
),
],
)
def test_BroadcastTo(x, shape):
g = extra_ops.BroadcastTo()(x, shape)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"val, axis, mode",
[
(
set_test_value(
at.matrix(), np.arange(3, dtype=config.floatX).reshape((3, 1))
),
1,
"add",
),
(
set_test_value(
at.matrix(), np.arange(6, dtype=config.floatX).reshape((3, 2))
),
0,
"add",
),
(
set_test_value(
at.matrix(), np.arange(6, dtype=config.floatX).reshape((3, 2))
),
1,
"add",
),
(
set_test_value(
at.matrix(), np.arange(6, dtype=config.floatX).reshape((3, 2))
),
0,
"mul",
),
(
set_test_value(
at.matrix(), np.arange(6, dtype=config.floatX).reshape((3, 2))
),
1,
"mul",
),
],
)
def test_CumOp(val, axis, mode):
g = extra_ops.CumOp(axis=axis, mode=mode)(val)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"a, val",
[
(
set_test_value(at.lmatrix(), np.zeros((10, 2), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
)
],
)
def test_FillDiagonal(a, val):
g = extra_ops.FillDiagonal()(a, val)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"a, val, offset",
[
(
set_test_value(at.lmatrix(), np.zeros((10, 2), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
set_test_value(at.lscalar(), np.array(-1, dtype="int64")),
),
(
set_test_value(at.lmatrix(), np.zeros((10, 2), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
set_test_value(at.lscalar(), np.array(0, dtype="int64")),
),
(
set_test_value(at.lmatrix(), np.zeros((10, 3), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
),
],
)
def test_FillDiagonalOffset(a, val, offset):
g = extra_ops.FillDiagonalOffset()(a, val, offset)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"arr, shape, mode, order, exc",
[
(
tuple(set_test_value(at.lscalar(), v) for v in np.array([0])),
set_test_value(at.lvector(), np.array([2])),
"raise",
"C",
None,
),
(
tuple(set_test_value(at.lscalar(), v) for v in np.array([0, 0, 3])),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"raise",
"C",
None,
),
(
tuple(
set_test_value(at.lvector(), v)
for v in np.array([[0, 1], [2, 0], [1, 3]])
),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"raise",
"C",
None,
),
(
tuple(
set_test_value(at.lvector(), v)
for v in np.array([[0, 1], [2, 0], [1, 3]])
),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"raise",
"F",
NotImplementedError,
),
(
tuple(
set_test_value(at.lvector(), v)
for v in np.array([[0, 1, 2], [2, 0, 3], [1, 3, 5]])
),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"raise",
"C",
ValueError,
),
(
tuple(
set_test_value(at.lvector(), v)
for v in np.array([[0, 1, 2], [2, 0, 3], [1, 3, 5]])
),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"wrap",
"C",
None,
),
(
tuple(
set_test_value(at.lvector(), v)
for v in np.array([[0, 1, 2], [2, 0, 3], [1, 3, 5]])
),
set_test_value(at.lvector(), np.array([2, 3, 4])),
"clip",
"C",
None,
),
],
)
def test_RavelMultiIndex(arr, shape, mode, order, exc):
g = extra_ops.RavelMultiIndex(mode, order)(*(arr + (shape,)))
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.raises(exc)
with cm:
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"x, repeats, axis, exc",
[
(
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
set_test_value(at.lscalar(), np.array(0, dtype="int64")),
None,
None,
),
(
set_test_value(at.lmatrix(), np.zeros((2, 2), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
None,
None,
),
(
set_test_value(at.lvector(), np.arange(2, dtype="int64")),
set_test_value(at.lvector(), np.array([1, 1], dtype="int64")),
None,
None,
),
(
set_test_value(at.lmatrix(), np.zeros((2, 2), dtype="int64")),
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
0,
UserWarning,
),
],
)
def test_Repeat(x, repeats, axis, exc):
g = extra_ops.Repeat(axis)(x, repeats)
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.warns(exc)
with cm:
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"x, axis, return_index, return_inverse, return_counts, exc",
[
(
set_test_value(at.lscalar(), np.array(1, dtype="int64")),
None,
False,
False,
False,
None,
),
(
set_test_value(at.lvector(), np.array([1, 1, 2], dtype="int64")),
None,
False,
False,
False,
None,
),
(
set_test_value(at.lmatrix(), np.array([[1, 1], [2, 2]], dtype="int64")),
None,
False,
False,
False,
None,
),
(
set_test_value(
at.lmatrix(), np.array([[1, 1], [1, 1], [2, 2]], dtype="int64")
),
0,
False,
False,
False,
UserWarning,
),
(
set_test_value(
at.lmatrix(), np.array([[1, 1], [1, 1], [2, 2]], dtype="int64")
),
0,
True,
True,
True,
UserWarning,
),
],
)
def test_Unique(x, axis, return_index, return_inverse, return_counts, exc):
g = extra_ops.Unique(return_index, return_inverse, return_counts, axis)(x)
if isinstance(g, list):
g_fg = FunctionGraph(outputs=g)
else:
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.warns(exc)
with cm:
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"arr, shape, order, exc",
[
(
set_test_value(at.lvector(), np.array([9, 15, 1], dtype="int64")),
at.as_tensor([2, 3, 4]),
"C",
None,
),
(
set_test_value(at.lvector(), np.array([1, 0], dtype="int64")),
at.as_tensor([2]),
"C",
None,
),
(
set_test_value(at.lvector(), np.array([9, 15, 1], dtype="int64")),
at.as_tensor([2, 3, 4]),
"F",
NotImplementedError,
),
],
)
def test_UnravelIndex(arr, shape, order, exc):
g = extra_ops.UnravelIndex(order)(arr, shape)
if isinstance(g, list):
g_fg = FunctionGraph(outputs=g)
else:
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.raises(exc)
with cm:
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
@pytest.mark.parametrize(
"a, v, side, sorter, exc",
[
(
set_test_value(at.vector(), np.array([1.0, 2.0, 3.0], dtype=config.floatX)),
set_test_value(at.matrix(), rng.random((3, 2)).astype(config.floatX)),
"left",
None,
None,
),
pytest.param(
set_test_value(
at.vector(),
np.array([0.29769574, 0.71649186, 0.20475563]).astype(config.floatX),
),
set_test_value(
at.matrix(),
np.array(
[
[0.18847123, 0.39659508],
[0.56220006, 0.57428752],
[0.86720994, 0.44522637],
]
).astype(config.floatX),
),
"left",
None,
None,
marks=pytest.mark.xfail(
reason="This won't work until https://github.com/numba/numba/pull/7005 is merged"
),
),
(
set_test_value(at.vector(), np.array([1.0, 2.0, 3.0], dtype=config.floatX)),
set_test_value(at.matrix(), rng.random((3, 2)).astype(config.floatX)),
"right",
set_test_value(at.lvector(), np.array([0, 2, 1])),
UserWarning,
),
],
)
def test_Searchsorted(a, v, side, sorter, exc):
g = extra_ops.SearchsortedOp(side)(a, v, sorter)
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.warns(exc)
with cm:
compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
Markdown 格式
0% 或
您添加了 0 人 到此讨论。请谨慎行事。
请先完成此评论的编辑！
请 注册 或者 后发表评论