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提交 5b935bc6 authored 作者: Brandon T. Willard's avatar Brandon T. Willard 提交者： Brandon T. Willard
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Move Numba tensor_basic tests to test_tensor_basic

上级 96b10e3f
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正在显示 包含 395 行增加 和 377 行删除
tests/link/numba/test_basic.py
浏览文件 @ 5b935bc6
...@@ -9,7 +9,6 @@ import pytest ...@@ -9,7 +9,6 @@ import pytest
import aesara.scalar as aes import aesara.scalar as aes
import aesara.scalar.math as aesm import aesara.scalar.math as aesm
import aesara.tensor as at import aesara.tensor as at
import aesara.tensor.basic as atb
import aesara.tensor.math as aem import aesara.tensor.math as aem
from aesara import config, shared from aesara import config, shared
from aesara.compile.function import function from aesara.compile.function import function
...@@ -29,7 +28,7 @@ from aesara.raise_op import assert_op ...@@ -29,7 +28,7 @@ from aesara.raise_op import assert_op
from aesara.tensor import blas from aesara.tensor import blas
from aesara.tensor import subtensor as at_subtensor from aesara.tensor import subtensor as at_subtensor
from aesara.tensor.elemwise import Elemwise from aesara.tensor.elemwise import Elemwise
from aesara.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape, Unbroadcast from aesara.tensor.shape import Reshape, Shape, Shape_i, SpecifyShape
class MyType(Type): class MyType(Type):
...@@ -501,111 +500,6 @@ def test_Shape(x, i): ...@@ -501,111 +500,6 @@ def test_Shape(x, i):
compare_numba_and_py(g_fg, []) compare_numba_and_py(g_fg, [])
@pytest.mark.parametrize(
"v, shape",
[
(0.0, (2, 3)),
(1.1, (2, 3)),
(set_test_value(at.scalar("a"), np.array(10.0, dtype=config.floatX)), (20,)),
(set_test_value(at.vector("a"), np.ones(10, dtype=config.floatX)), (20, 10)),
],
)
def test_Alloc(v, shape):
g = at.alloc(v, *shape)
g_fg = FunctionGraph(outputs=[g])
(numba_res,) = compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
assert numba_res.shape == shape
def test_AllocEmpty():
x = at.empty((2, 3), dtype="float32")
x_fg = FunctionGraph([], [x])
# We cannot compare the values in the arrays, only the shapes and dtypes
compare_numba_and_py(x_fg, [], assert_fn=compare_shape_dtype)
@pytest.mark.parametrize(
"v, offset",
[
(set_test_value(at.vector(), np.arange(10, dtype=config.floatX)), 0),
(set_test_value(at.vector(), np.arange(10, dtype=config.floatX)), 1),
(set_test_value(at.vector(), np.arange(10, dtype=config.floatX)), -1),
],
)
def test_AllocDiag(v, offset):
g = atb.AllocDiag(offset=offset)(v)
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(
"v", [set_test_value(aes.float64(), np.array(1.0, dtype="float64"))]
)
def test_TensorFromScalar(v):
g = atb.TensorFromScalar()(v)
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(
"v",
[
set_test_value(at.scalar(), np.array(1.0, dtype=config.floatX)),
],
)
def test_ScalarFromTensor(v):
g = atb.ScalarFromTensor()(v)
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))
],
)
def test_Unbroadcast():
v = set_test_value(at.row(), np.array([[1.0, 2.0]], dtype=config.floatX))
g = Unbroadcast(0)(v)
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( @pytest.mark.parametrize(
"v, shape, ndim", "v, shape, ndim",
[ [
...@@ -700,276 +594,6 @@ def test_ViewOp(v): ...@@ -700,276 +594,6 @@ def test_ViewOp(v):
) )
@pytest.mark.parametrize(
"vals, dtype",
[
(
(
set_test_value(at.scalar(), np.array(1, dtype=config.floatX)),
set_test_value(at.scalar(), np.array(2, dtype=config.floatX)),
set_test_value(at.scalar(), np.array(3, dtype=config.floatX)),
),
config.floatX,
),
(
(
set_test_value(at.dscalar(), np.array(1, dtype=np.float64)),
set_test_value(at.lscalar(), np.array(3, dtype=np.int32)),
),
"float64",
),
(
(set_test_value(at.iscalar(), np.array(1, dtype=np.int32)),),
"float64",
),
(
(set_test_value(at.scalar(dtype=bool), True),),
bool,
),
],
)
def test_MakeVector(vals, dtype):
g = atb.MakeVector(dtype)(*vals)
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(
"start, stop, step, dtype",
[
(
set_test_value(at.lscalar(), np.array(1)),
set_test_value(at.lscalar(), np.array(10)),
set_test_value(at.lscalar(), np.array(3)),
config.floatX,
),
],
)
def test_ARange(start, stop, step, dtype):
g = atb.ARange(dtype)(start, stop, step)
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(
"vals, axis",
[
(
(
set_test_value(
at.matrix(), rng.normal(size=(1, 2)).astype(config.floatX)
),
set_test_value(
at.matrix(), rng.normal(size=(1, 2)).astype(config.floatX)
),
),
0,
),
(
(
set_test_value(
at.matrix(), rng.normal(size=(2, 1)).astype(config.floatX)
),
set_test_value(
at.matrix(), rng.normal(size=(3, 1)).astype(config.floatX)
),
),
0,
),
(
(
set_test_value(
at.matrix(), rng.normal(size=(1, 2)).astype(config.floatX)
),
set_test_value(
at.matrix(), rng.normal(size=(1, 2)).astype(config.floatX)
),
),
1,
),
(
(
set_test_value(
at.matrix(), rng.normal(size=(2, 2)).astype(config.floatX)
),
set_test_value(
at.matrix(), rng.normal(size=(2, 1)).astype(config.floatX)
),
),
1,
),
],
)
def test_Join(vals, axis):
g = at.join(axis, *vals)
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))
],
)
def test_Join_view():
vals = (
set_test_value(at.matrix(), rng.normal(size=(2, 2)).astype(config.floatX)),
set_test_value(at.matrix(), rng.normal(size=(2, 2)).astype(config.floatX)),
)
g = atb.Join(view=1)(1, *vals)
g_fg = FunctionGraph(outputs=[g])
with pytest.raises(NotImplementedError):
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(
"n_splits, axis, values, sizes",
[
(
0,
0,
set_test_value(at.vector(), rng.normal(size=20).astype(config.floatX)),
set_test_value(at.vector(dtype="int64"), []),
),
(
5,
0,
set_test_value(at.vector(), rng.normal(size=5).astype(config.floatX)),
set_test_value(
at.vector(dtype="int64"), rng.multinomial(5, np.ones(5) / 5)
),
),
(
5,
0,
set_test_value(at.vector(), rng.normal(size=10).astype(config.floatX)),
set_test_value(
at.vector(dtype="int64"), rng.multinomial(10, np.ones(5) / 5)
),
),
(
5,
-1,
set_test_value(at.matrix(), rng.normal(size=(11, 7)).astype(config.floatX)),
set_test_value(
at.vector(dtype="int64"), rng.multinomial(7, np.ones(5) / 5)
),
),
(
5,
-2,
set_test_value(at.matrix(), rng.normal(size=(11, 7)).astype(config.floatX)),
set_test_value(
at.vector(dtype="int64"), rng.multinomial(11, np.ones(5) / 5)
),
),
],
)
def test_Split(n_splits, axis, values, sizes):
g = at.split(values, sizes, n_splits, axis=axis)
assert len(g) == n_splits
if n_splits == 0:
return
g_fg = FunctionGraph(outputs=[g] if n_splits == 1 else 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, offset",
[
(
set_test_value(
at.matrix(), np.arange(10 * 10, dtype=config.floatX).reshape((10, 10))
),
0,
),
(
set_test_value(at.vector(), np.arange(10, dtype=config.floatX)),
0,
),
],
)
def test_ExtractDiag(val, offset):
g = at.diag(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(
"n, m, k, dtype",
[
(set_test_value(at.lscalar(), np.array(1, dtype=np.int64)), None, 0, None),
(
set_test_value(at.lscalar(), np.array(1, dtype=np.int64)),
set_test_value(at.lscalar(), np.array(2, dtype=np.int64)),
0,
"float32",
),
(
set_test_value(at.lscalar(), np.array(1, dtype=np.int64)),
set_test_value(at.lscalar(), np.array(2, dtype=np.int64)),
1,
"int64",
),
],
)
def test_Eye(n, m, k, dtype):
g = at.eye(n, m, k, dtype=dtype)
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( @pytest.mark.parametrize(
"inputs, op, exc", "inputs, op, exc",
[ [
......
tests/link/numba/test_tensor_basic.py 0 → 100644
浏览文件 @ 5b935bc6
import numpy as np
import pytest
import aesara.scalar as aes
import aesara.tensor as at
import aesara.tensor.basic as atb
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.shape import Unbroadcast
from tests.link.numba.test_basic import (
compare_numba_and_py,
compare_shape_dtype,
set_test_value,
)
rng = np.random.default_rng(42849)
@pytest.mark.parametrize(
"v, shape",
[
(0.0, (2, 3)),
(1.1, (2, 3)),
(set_test_value(at.scalar("a"), np.array(10.0, dtype=config.floatX)), (20,)),
(set_test_value(at.vector("a"), np.ones(10, dtype=config.floatX)), (20, 10)),
],
)
def test_Alloc(v, shape):
g = at.alloc(v, *shape)
g_fg = FunctionGraph(outputs=[g])
(numba_res,) = compare_numba_and_py(
g_fg,
[
i.tag.test_value
for i in g_fg.inputs
if not isinstance(i, (SharedVariable, Constant))
],
)
assert numba_res.shape == shape
def test_AllocEmpty():
x = at.empty((2, 3), dtype="float32")
x_fg = FunctionGraph([], [x])
# We cannot compare the values in the arrays, only the shapes and dtypes
compare_numba_and_py(x_fg, [], assert_fn=compare_shape_dtype)
@pytest.mark.parametrize(
"v, offset",
[
(set_test_value(at.vector(), np.arange(10, dtype=config.floatX)), 0),
(set_test_value(at.vector(), np.arange(10, dtype=config.floatX)), 1),
(set_test_value(at.vector(), np.arange(10, dtype=config.floatX)), -1),
],
)
def test_AllocDiag(v, offset):
g = atb.AllocDiag(offset=offset)(v)
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(
"v", [set_test_value(aes.float64(), np.array(1.0, dtype="float64"))]
)
def test_TensorFromScalar(v):
g = atb.TensorFromScalar()(v)
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(
"v",
[
set_test_value(at.scalar(), np.array(1.0, dtype=config.floatX)),
],
)
def test_ScalarFromTensor(v):
g = atb.ScalarFromTensor()(v)
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))
],
)
def test_Unbroadcast():
v = set_test_value(at.row(), np.array([[1.0, 2.0]], dtype=config.floatX))
g = Unbroadcast(0)(v)
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(
"vals, dtype",
[
(
(
set_test_value(at.scalar(), np.array(1, dtype=config.floatX)),
set_test_value(at.scalar(), np.array(2, dtype=config.floatX)),
set_test_value(at.scalar(), np.array(3, dtype=config.floatX)),
),
config.floatX,
),
(
(
set_test_value(at.dscalar(), np.array(1, dtype=np.float64)),
set_test_value(at.lscalar(), np.array(3, dtype=np.int32)),
),
"float64",
),
(
(set_test_value(at.iscalar(), np.array(1, dtype=np.int32)),),
"float64",
),
(
(set_test_value(at.scalar(dtype=bool), True),),
bool,
),
],
)
def test_MakeVector(vals, dtype):
g = atb.MakeVector(dtype)(*vals)
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(
"start, stop, step, dtype",
[
(
set_test_value(at.lscalar(), np.array(1)),
set_test_value(at.lscalar(), np.array(10)),
set_test_value(at.lscalar(), np.array(3)),
config.floatX,
),
],
)
def test_ARange(start, stop, step, dtype):
g = atb.ARange(dtype)(start, stop, step)
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(
"vals, axis",
[
(
(
set_test_value(
at.matrix(), rng.normal(size=(1, 2)).astype(config.floatX)
),
set_test_value(
at.matrix(), rng.normal(size=(1, 2)).astype(config.floatX)
),
),
0,
),
(
(
set_test_value(
at.matrix(), rng.normal(size=(2, 1)).astype(config.floatX)
),
set_test_value(
at.matrix(), rng.normal(size=(3, 1)).astype(config.floatX)
),
),
0,
),
(
(
set_test_value(
at.matrix(), rng.normal(size=(1, 2)).astype(config.floatX)
),
set_test_value(
at.matrix(), rng.normal(size=(1, 2)).astype(config.floatX)
),
),
1,
),
(
(
set_test_value(
at.matrix(), rng.normal(size=(2, 2)).astype(config.floatX)
),
set_test_value(
at.matrix(), rng.normal(size=(2, 1)).astype(config.floatX)
),
),
1,
),
],
)
def test_Join(vals, axis):
g = at.join(axis, *vals)
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))
],
)
def test_Join_view():
vals = (
set_test_value(at.matrix(), rng.normal(size=(2, 2)).astype(config.floatX)),
set_test_value(at.matrix(), rng.normal(size=(2, 2)).astype(config.floatX)),
)
g = atb.Join(view=1)(1, *vals)
g_fg = FunctionGraph(outputs=[g])
with pytest.raises(NotImplementedError):
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(
"n_splits, axis, values, sizes",
[
(
0,
0,
set_test_value(at.vector(), rng.normal(size=20).astype(config.floatX)),
set_test_value(at.vector(dtype="int64"), []),
),
(
5,
0,
set_test_value(at.vector(), rng.normal(size=5).astype(config.floatX)),
set_test_value(
at.vector(dtype="int64"), rng.multinomial(5, np.ones(5) / 5)
),
),
(
5,
0,
set_test_value(at.vector(), rng.normal(size=10).astype(config.floatX)),
set_test_value(
at.vector(dtype="int64"), rng.multinomial(10, np.ones(5) / 5)
),
),
(
5,
-1,
set_test_value(at.matrix(), rng.normal(size=(11, 7)).astype(config.floatX)),
set_test_value(
at.vector(dtype="int64"), rng.multinomial(7, np.ones(5) / 5)
),
),
(
5,
-2,
set_test_value(at.matrix(), rng.normal(size=(11, 7)).astype(config.floatX)),
set_test_value(
at.vector(dtype="int64"), rng.multinomial(11, np.ones(5) / 5)
),
),
],
)
def test_Split(n_splits, axis, values, sizes):
g = at.split(values, sizes, n_splits, axis=axis)
assert len(g) == n_splits
if n_splits == 0:
return
g_fg = FunctionGraph(outputs=[g] if n_splits == 1 else 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, offset",
[
(
set_test_value(
at.matrix(), np.arange(10 * 10, dtype=config.floatX).reshape((10, 10))
),
0,
),
(
set_test_value(at.vector(), np.arange(10, dtype=config.floatX)),
0,
),
],
)
def test_ExtractDiag(val, offset):
g = at.diag(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(
"n, m, k, dtype",
[
(set_test_value(at.lscalar(), np.array(1, dtype=np.int64)), None, 0, None),
(
set_test_value(at.lscalar(), np.array(1, dtype=np.int64)),
set_test_value(at.lscalar(), np.array(2, dtype=np.int64)),
0,
"float32",
),
(
set_test_value(at.lscalar(), np.array(1, dtype=np.int64)),
set_test_value(at.lscalar(), np.array(2, dtype=np.int64)),
1,
"int64",
),
],
)
def test_Eye(n, m, k, dtype):
g = at.eye(n, m, k, dtype=dtype)
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))
],
)
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