Skip to content

P
pytensor
提交 3b8608ec 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 nlinalg tests to test_nlinalg

上级 0f82ba32
隐藏空白字符变更
内嵌 并排
正在显示 包含 526 行增加 和 488 行删除
tests/link/numba/test_basic.py
浏览文件 @ 3b8608ec
......@@ -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, nlinalg, slinalg
from aesara.tensor import blas
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
......@@ -1276,493 +1276,6 @@ def test_Softplus(x, exc):
)
@pytest.mark.parametrize(
"x, lower, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
True,
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
True,
None,
),
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
False,
UserWarning,
),
],
)
def test_Cholesky(x, lower, exc):
g = slinalg.Cholesky(lower)(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(
"A, x, lower, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
set_test_value(at.dvector(), rng.random(size=(3,)).astype("float64")),
"gen",
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
set_test_value(at.dvector(), rng.random(size=(3,)).astype("float64")),
"gen",
None,
),
],
)
def test_Solve(A, x, lower, exc):
g = slinalg.Solve(lower)(A, 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(
"A, x, lower, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
set_test_value(at.dvector(), rng.random(size=(3,)).astype("float64")),
"sym",
UserWarning,
),
],
)
def test_SolveTriangular(A, x, lower, exc):
g = slinalg.SolveTriangular(lower)(A, 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(
"x, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(rng.poisson(size=(3, 3)).astype("int64")),
),
None,
),
],
)
def test_Det(x, exc):
g = nlinalg.Det()(x)
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))
],
)
# We were seeing some weird results in CI where the following two almost
# sign-swapped results were being return from Numba and Python, respectively.
# The issue might be related to https://github.com/numba/numba/issues/4519.
# Regardless, I was not able to reproduce anything like it locally after
# extensive testing.
x = np.array(
[
[-0.60407637, -0.71177603, -0.35842241],
[-0.07735968, 0.50000561, -0.86256007],
[-0.7931628, 0.49332471, 0.35710434],
],
dtype=np.float64,
)
y = np.array(
[
[0.60407637, 0.71177603, -0.35842241],
[0.07735968, -0.50000561, -0.86256007],
[0.7931628, -0.49332471, 0.35710434],
],
dtype=np.float64,
)
@pytest.mark.parametrize(
"x, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(x),
),
None,
),
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(y),
),
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
None,
),
],
)
def test_Eig(x, exc):
g = nlinalg.Eig()(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(
"x, uplo, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
"L",
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
"U",
UserWarning,
),
],
)
def test_Eigh(x, uplo, exc):
g = nlinalg.Eigh(uplo)(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(
"op, x, exc, op_args",
[
(
nlinalg.MatrixInverse,
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
None,
(),
),
(
nlinalg.MatrixInverse,
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
None,
(),
),
(
nlinalg.Inv,
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
None,
(),
),
(
nlinalg.Inv,
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
None,
(),
),
(
nlinalg.MatrixPinv,
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
None,
(True,),
),
(
nlinalg.MatrixPinv,
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
None,
(False,),
),
],
)
def test_matrix_inverses(op, x, exc, op_args):
g = op(*op_args)(x)
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, mode, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
"reduced",
None,
),
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
"r",
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
"reduced",
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
"complete",
UserWarning,
),
],
)
def test_QRFull(x, mode, exc):
g = nlinalg.QRFull(mode)(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(
"x, full_matrices, compute_uv, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
True,
True,
None,
),
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
False,
True,
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
True,
True,
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
True,
False,
UserWarning,
),
],
)
def test_SVD(x, full_matrices, compute_uv, exc):
g = nlinalg.SVD(full_matrices, compute_uv)(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(
"x, y, exc",
[
......@@ -2573,6 +2086,30 @@ def test_scan_while():
compare_numba_and_py(out_fg, test_input_vals)
# We were seeing some weird results in CI where the following two almost
# sign-swapped results were being return from Numba and Python, respectively.
# The issue might be related to https://github.com/numba/numba/issues/4519.
# Regardless, I was not able to reproduce anything like it locally after
# extensive testing.
x = np.array(
[
[-0.60407637, -0.71177603, -0.35842241],
[-0.07735968, 0.50000561, -0.86256007],
[-0.7931628, 0.49332471, 0.35710434],
],
dtype=np.float64,
)
y = np.array(
[
[0.60407637, 0.71177603, -0.35842241],
[0.07735968, -0.50000561, -0.86256007],
[0.7931628, -0.49332471, 0.35710434],
],
dtype=np.float64,
)
@pytest.mark.parametrize(
"inputs, cond_fn, true_vals, false_vals",
[
......
tests/link/numba/test_nlinalg.py 0 → 100644
浏览文件 @ 3b8608ec
import contextlib
import numpy as np
import pytest
import aesara.tensor as at
from aesara.compile.sharedvalue import SharedVariable
from aesara.graph.basic import Constant
from aesara.graph.fg import FunctionGraph
from aesara.tensor import nlinalg, slinalg
from tests.link.numba.test_basic import compare_numba_and_py, set_test_value
rng = np.random.default_rng(42849)
@pytest.mark.parametrize(
"x, lower, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
True,
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
True,
None,
),
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
False,
UserWarning,
),
],
)
def test_Cholesky(x, lower, exc):
g = slinalg.Cholesky(lower)(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(
"A, x, lower, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
set_test_value(at.dvector(), rng.random(size=(3,)).astype("float64")),
"gen",
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
set_test_value(at.dvector(), rng.random(size=(3,)).astype("float64")),
"gen",
None,
),
],
)
def test_Solve(A, x, lower, exc):
g = slinalg.Solve(lower)(A, 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(
"A, x, lower, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
set_test_value(at.dvector(), rng.random(size=(3,)).astype("float64")),
"sym",
UserWarning,
),
],
)
def test_SolveTriangular(A, x, lower, exc):
g = slinalg.SolveTriangular(lower)(A, 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(
"x, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(rng.poisson(size=(3, 3)).astype("int64")),
),
None,
),
],
)
def test_Det(x, exc):
g = nlinalg.Det()(x)
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))
],
)
# We were seeing some weird results in CI where the following two almost
# sign-swapped results were being return from Numba and Python, respectively.
# The issue might be related to https://github.com/numba/numba/issues/4519.
# Regardless, I was not able to reproduce anything like it locally after
# extensive testing.
x = np.array(
[
[-0.60407637, -0.71177603, -0.35842241],
[-0.07735968, 0.50000561, -0.86256007],
[-0.7931628, 0.49332471, 0.35710434],
],
dtype=np.float64,
)
y = np.array(
[
[0.60407637, 0.71177603, -0.35842241],
[0.07735968, -0.50000561, -0.86256007],
[0.7931628, -0.49332471, 0.35710434],
],
dtype=np.float64,
)
@pytest.mark.parametrize(
"x, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(x),
),
None,
),
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(y),
),
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
None,
),
],
)
def test_Eig(x, exc):
g = nlinalg.Eig()(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(
"x, uplo, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
"L",
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
"U",
UserWarning,
),
],
)
def test_Eigh(x, uplo, exc):
g = nlinalg.Eigh(uplo)(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(
"op, x, exc, op_args",
[
(
nlinalg.MatrixInverse,
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
None,
(),
),
(
nlinalg.MatrixInverse,
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
None,
(),
),
(
nlinalg.Inv,
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
None,
(),
),
(
nlinalg.Inv,
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
None,
(),
),
(
nlinalg.MatrixPinv,
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
None,
(True,),
),
(
nlinalg.MatrixPinv,
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
None,
(False,),
),
],
)
def test_matrix_inverses(op, x, exc, op_args):
g = op(*op_args)(x)
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, mode, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
"reduced",
None,
),
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
"r",
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
"reduced",
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
"complete",
UserWarning,
),
],
)
def test_QRFull(x, mode, exc):
g = nlinalg.QRFull(mode)(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(
"x, full_matrices, compute_uv, exc",
[
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
True,
True,
None,
),
(
set_test_value(
at.dmatrix(),
(lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
),
False,
True,
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
True,
True,
None,
),
(
set_test_value(
at.lmatrix(),
(lambda x: x.T.dot(x))(
rng.integers(1, 10, size=(3, 3)).astype("int64")
),
),
True,
False,
UserWarning,
),
],
)
def test_SVD(x, full_matrices, compute_uv, exc):
g = nlinalg.SVD(full_matrices, compute_uv)(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))
],
)
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