Skip to content

P
pytensor
提交 48f5e4a9 authored 作者: Brandon T. Willard's avatar Brandon T. Willard 提交者： Brandon T. Willard
浏览文件
操作

Remove deprecated Linker.make_function

上级 9e3c69be
隐藏空白字符变更
内嵌 并排
正在显示 包含 122 行增加 和 130 行删除
aesara/link/basic.py
浏览文件 @ 48f5e4a9
......@@ -10,7 +10,7 @@ from aesara.graph.fg import FunctionGraph
from aesara.graph.type import CType
from aesara.graph.utils import MetaObject
from aesara.link.utils import gc_helper, map_storage, raise_with_op, streamline
from aesara.utils import deprecated, difference, to_return_values
from aesara.utils import difference
if TYPE_CHECKING:
......@@ -203,50 +203,6 @@ class Linker(ABC):
"""
@deprecated("Marked for deletion. Only tests use it.")
def make_function(self, unpack_single: bool = True, **kwargs) -> Callable:
"""
Returns a function that takes values corresponding to the inputs of the
fgraph used by this L{Linker} and returns values corresponding the the
outputs of that fgraph. If inplace is True, the calculations will
operate in the same storage the fgraph uses, else independent storage
will be allocated for the function.
Parameters
----------
unpack_single : bool
If `unpack_single` is True (default) and that the function has only one
output, then that output will be returned. Else, a list or tuple of
length 1 will be returned.
Examples
--------
e = x + y
fgraph = FunctionGraph([x, y], [e])
fn = MyLinker(fgraph).make_function(inplace)
print fn(1.0, 2.0) # 3.0
print e.data # 3.0 iff inplace == True (else unknown)
"""
thunk, inputs, outputs = self.make_thunk(**kwargs)
def execute(*args):
takes = len(inputs)
got = len(args)
if got != takes:
raise TypeError(
f"Function call takes exactly {takes} args ({got} given)"
)
for arg, variable in zip(args, inputs):
variable.data = arg
thunk()
if unpack_single:
return to_return_values([variable.data for variable in outputs])
else:
return [variable.data for variable in outputs]
return execute
def schedule(self, fgraph: FunctionGraph) -> List[Apply]:
"""Runs the scheduler (if set) or the toposort on the FunctionGraph.
......
aesara/link/c/basic.py
浏览文件 @ 48f5e4a9
......@@ -583,12 +583,8 @@ def struct_variable_codeblocks(fgraph, variable, policies, id, symbol_table, sub
class CLinker(Linker):
"""
Creates C code for an fgraph, compiles it and returns callables
through make_thunk and make_function that make use of the compiled
code.
r"""Generates and compiles C code for a :class:`FunctionGraph`.
no_recycling can contain a list of Variables that belong to the fgraph.
If a Variable is in no_recycling, CLinker will clear the output storage
associated to it during the computation (to avoid reusing it).
......@@ -599,9 +595,10 @@ class CLinker(Linker):
super().__init__(scheduler=schedule)
def accept(self, fgraph, no_recycling=None, profile=None):
"""
Associate linker with fgraph
r"""Associate this `Linker` with `fgraph`.
The `no_recycling` argument can contain a list of `Variable`\s that
belong to `fgraph`.
"""
if no_recycling is None:
no_recycling = []
......@@ -614,11 +611,7 @@ class CLinker(Linker):
return self
def fetch_variables(self):
"""
Fills the inputs, outputs, variables, orphans, temps and node_order
fields.
"""
"""Fills the inputs, outputs, variables, orphans, temps and node_order fields."""
fgraph = self.fgraph
self.inputs = fgraph.inputs
self.outputs = fgraph.outputs
......@@ -678,15 +671,16 @@ class CLinker(Linker):
)
def code_gen(self):
"""
Generates code for a struct that does the computation of the fgraph and
stores it in the struct_code field of the instance.
If reuse_storage is True, outputs and temporaries will be stored in
the struct so they can be reused each time a function returned by
make_function is called, which means that the output of a call will
be invalidated by the next. If reuse_storage is False, that problem
is avoided.
"""Construct and populate a C ``struct`` for the generated code.
Generates code for a ``struct`` instance that does the computation of the `FunctionGraph` and
stores it in the ``struct_code`` field of the instance.
If :attr:`CLinker.reuse_storage` is ``True``, outputs and temporaries
will be stored in the ``struct`` so they can be reused each time the
generated code is called, which means that the output of a call will be
invalidated by the next. If the value is ``False``, that problem is
avoided.
This method caches its computations.
......
doc/sandbox/interactive_debugger.rst
浏览文件 @ 48f5e4a9
......@@ -32,7 +32,7 @@ A good, simple way to do it would be to have those commands as methods of a stru
>>> a, b, c = Tensor(), Tensor(), Tensor()
>>> d = b * c
>>> e = a + d
>>> debug = DebugLinker(FunctionGraph([a, b, c], [e])).make_function()
>>> debug = make_function(DebugLinker(FunctionGraph([a, b, c], [e])))
>>> debug.set_breakpoint(d)
>>> debug.debug(10, 20, 30) # a, b, c = 10, 20, 30
Now at: Mul(b, c)
......@@ -54,5 +54,3 @@ Finished.
[630]
>>>
}}}
tests/link/c/test_basic.py
浏览文件 @ 48f5e4a9
......@@ -12,6 +12,7 @@ from aesara.graph.type import CType
from aesara.link.basic import PerformLinker
from aesara.link.c.basic import CLinker, DualLinker, OpWiseCLinker
from aesara.tensor.type import iscalar, matrix, vector
from tests.link.test_link import make_function
def as_variable(x):
......@@ -189,7 +190,7 @@ def test_clinker_straightforward():
x, y, z = inputs()
e = add(mul(add(x, y), div(x, y)), bad_sub(bad_sub(x, y), z))
lnk = CLinker().accept(FunctionGraph([x, y, z], [e]))
fn = lnk.make_function()
fn = make_function(lnk)
assert fn(2.0, 2.0, 2.0) == 2.0
......@@ -214,7 +215,7 @@ def test_clinker_literal_inlining():
z = Constant(tdouble, 4.12345678)
e = add(mul(add(x, y), div(x, y)), bad_sub(bad_sub(x, y), z))
lnk = CLinker().accept(FunctionGraph([x, y], [e]))
fn = lnk.make_function()
fn = make_function(lnk)
assert abs(fn(2.0, 2.0) + 0.12345678) < 1e-9
code = lnk.code_gen()
# print "=== Code generated ==="
......@@ -257,7 +258,7 @@ def test_clinker_single_node():
x, y, z = inputs()
node = add.make_node(x, y)
lnk = CLinker().accept(FunctionGraph(node.inputs, node.outputs))
fn = lnk.make_function()
fn = make_function(lnk)
assert fn(2.0, 7.0) == 9
......@@ -269,7 +270,7 @@ def test_clinker_dups():
x, y, z = inputs()
e = add(x, x)
lnk = CLinker().accept(FunctionGraph([x, x], [e]))
fn = lnk.make_function()
fn = make_function(lnk)
assert fn(2.0, 2.0) == 4
# note: for now the behavior of fn(2.0, 7.0) is undefined
......@@ -282,7 +283,7 @@ def test_clinker_not_used_inputs():
x, y, z = inputs()
e = add(x, y)
lnk = CLinker().accept(FunctionGraph([x, y, z], [e]))
fn = lnk.make_function()
fn = make_function(lnk)
assert fn(2.0, 1.5, 1.0) == 3.5
......@@ -294,7 +295,7 @@ def test_clinker_dups_inner():
x, y, z = inputs()
e = add(mul(y, y), add(x, z))
lnk = CLinker().accept(FunctionGraph([x, y, z], [e]))
fn = lnk.make_function()
fn = make_function(lnk)
assert fn(1.0, 2.0, 3.0) == 8.0
......@@ -303,7 +304,7 @@ def test_opwiseclinker_straightforward():
x, y, z = inputs()
e = add(mul(add(x, y), div(x, y)), bad_sub(bad_sub(x, y), z))
lnk = OpWiseCLinker().accept(FunctionGraph([x, y, z], [e]))
fn = lnk.make_function()
fn = make_function(lnk)
if config.cxx:
assert fn(2.0, 2.0, 2.0) == 2.0
else:
......@@ -316,7 +317,7 @@ def test_opwiseclinker_constant():
x = Constant(tdouble, 7.2, name="x")
e = add(mul(x, y), mul(y, z))
lnk = OpWiseCLinker().accept(FunctionGraph([y, z], [e]))
fn = lnk.make_function()
fn = make_function(lnk)
res = fn(1.5, 3.0)
assert res == 15.3
......@@ -334,7 +335,7 @@ def test_duallinker_straightforward():
x, y, z = inputs()
e = add(mul(x, y), mul(y, z)) # add and mul are correct in C and in Python
lnk = DualLinker(checker=_my_checker).accept(FunctionGraph([x, y, z], [e]))
fn = lnk.make_function()
fn = make_function(lnk)
res = fn(7.2, 1.5, 3.0)
assert res == 15.3
......@@ -348,15 +349,15 @@ def test_duallinker_mismatch():
e = bad_sub(mul(x, y), mul(y, z))
g = FunctionGraph([x, y, z], [e])
lnk = DualLinker(checker=_my_checker).accept(g)
fn = lnk.make_function()
fn = make_function(lnk)
# good
assert CLinker().accept(g).make_function()(1.0, 2.0, 3.0) == -4.0
assert make_function(CLinker().accept(g))(1.0, 2.0, 3.0) == -4.0
# good
assert OpWiseCLinker().accept(g).make_function()(1.0, 2.0, 3.0) == -4.0
assert make_function(OpWiseCLinker().accept(g))(1.0, 2.0, 3.0) == -4.0
# (purposely) wrong
assert PerformLinker().accept(g).make_function()(1.0, 2.0, 3.0) == -10.0
assert make_function(PerformLinker().accept(g))(1.0, 2.0, 3.0) == -10.0
with pytest.raises(MyExc):
# this runs OpWiseCLinker and PerformLinker in parallel and feeds
......@@ -389,7 +390,7 @@ def test_c_fail_error():
x = Constant(tdouble, 7.2, name="x")
e = add_fail(mul(x, y), mul(y, z))
lnk = OpWiseCLinker().accept(FunctionGraph([y, z], [e]))
fn = lnk.make_function()
fn = make_function(lnk)
with pytest.raises(RuntimeError):
fn(1.5, 3.0)
......
tests/link/test_link.py
浏览文件 @ 48f5e4a9
from copy import deepcopy
from typing import Callable
import numpy as np
......@@ -8,10 +9,52 @@ from aesara.graph import fg
from aesara.graph.basic import Apply, Constant, Variable, clone
from aesara.graph.op import Op
from aesara.graph.type import Type
from aesara.link.basic import Container, PerformLinker, WrapLinker
from aesara.link.basic import Container, Linker, PerformLinker, WrapLinker
from aesara.link.c.basic import OpWiseCLinker
from aesara.tensor.type import matrix, scalar
from aesara.utils import cmp
from aesara.utils import cmp, to_return_values
def make_function(linker: Linker, unpack_single: bool = True, **kwargs) -> Callable:
"""
Returns a function that takes values corresponding to the inputs of the
fgraph used by this L{Linker} and returns values corresponding the the
outputs of that fgraph. If inplace is True, the calculations will
operate in the same storage the fgraph uses, else independent storage
will be allocated for the function.
Parameters
----------
unpack_single : bool
If `unpack_single` is True (default) and that the function has only one
output, then that output will be returned. Else, a list or tuple of
length 1 will be returned.
Examples
--------
e = x + y
fgraph = FunctionGraph([x, y], [e])
fn = make_function(MyLinker(fgraph), inplace)
print fn(1.0, 2.0) # 3.0
print e.data # 3.0 iff inplace == True (else unknown)
"""
thunk, inputs, outputs = linker.make_thunk(**kwargs)
def execute(*args):
takes = len(inputs)
got = len(args)
if got != takes:
raise TypeError(f"Function call takes exactly {takes} args ({got} given)")
for arg, variable in zip(args, inputs):
variable.data = arg
thunk()
if unpack_single:
return to_return_values([variable.data for variable in outputs])
else:
return [variable.data for variable in outputs]
return execute
def as_variable(x):
......@@ -103,26 +146,26 @@ class TestPerformLinker:
def test_function(self):
x, y, z = inputs()
e = mul(add(x, y), div(x, y))
fn = perform_linker(FunctionGraph([x, y, z], [e])).make_function()
fn = make_function(perform_linker(FunctionGraph([x, y, z], [e])))
assert fn(1.0, 2.0, 3.0) == 1.5
def test_constant(self):
x, y, z = inputs()
y = Constant(tdouble, 2.0)
e = mul(add(x, y), div(x, y))
fn = perform_linker(FunctionGraph([x], [e])).make_function()
fn = make_function(perform_linker(FunctionGraph([x], [e])))
assert fn(1.0) == 1.5
def test_input_output_same(self):
x, y, z = inputs()
fn = perform_linker(FunctionGraph([x], [x])).make_function()
fn = make_function(perform_linker(FunctionGraph([x], [x])))
assert 1.0 == fn(1.0)
def test_input_dependency0(self):
x, y, z = inputs()
a, d = add(x, y), div(x, y)
e = mul(a, d)
fn = perform_linker(FunctionGraph(*clone([x, y, a], [e]))).make_function()
fn = make_function(perform_linker(FunctionGraph(*clone([x, y, a], [e]))))
assert fn(1.0, 2.0, 9.0) == 4.5
def test_skiphole(self):
......@@ -130,7 +173,7 @@ class TestPerformLinker:
a = add(x, y)
r = raise_err(a)
e = add(r, a)
fn = perform_linker(FunctionGraph(*clone([x, y, r], [e]))).make_function()
fn = make_function(perform_linker(FunctionGraph(*clone([x, y, r], [e]))))
assert fn(1.0, 2.0, 4.5) == 7.5
......
tests/scalar/test_basic.py
浏览文件 @ 48f5e4a9
......@@ -66,13 +66,14 @@ from aesara.scalar.basic import (
uint8,
)
from aesara.tensor.type import fscalar, imatrix, iscalar, matrix
from tests.link.test_link import make_function
def test_mul_add_true():
x, y, z = floats("xyz")
e = mul(add(x, y), true_div(x, y))
g = FunctionGraph([x, y], [e])
fn = DualLinker().accept(g).make_function()
fn = make_function(DualLinker().accept(g))
assert fn(1.0, 2.0) == 1.5
......@@ -121,7 +122,7 @@ class TestComposite:
c = C.make_node(x, y)
# print c.c_code(['x', 'y'], ['z'], dict(id = 0))
g = FunctionGraph([x, y], [c.out])
fn = DualLinker().accept(g).make_function()
fn = make_function(DualLinker().accept(g))
assert fn(1.0, 2.0) == 1.5
def test_flatten(self):
......@@ -144,7 +145,7 @@ class TestComposite:
assert "70.0" in c.op.c_code(c, "dummy", ["x", "y"], ["z"], dict(id=0))
# print c.c_code(['x', 'y'], ['z'], dict(id = 0))
g = FunctionGraph([x, y], [c.out])
fn = DualLinker().accept(g).make_function()
fn = make_function(DualLinker().accept(g))
assert fn(1.0, 2.0) == 36.0
def test_many_outputs(self):
......@@ -156,7 +157,7 @@ class TestComposite:
c = C.make_node(x, y, z)
# print c.c_code(['x', 'y', 'z'], ['out0', 'out1', 'out2'], dict(id = 0))
g = FunctionGraph([x, y, z], c.outputs)
fn = DualLinker().accept(g).make_function()
fn = make_function(DualLinker().accept(g))
assert fn(1.0, 2.0, 3.0) == [6.0, 7.0, 0.5]
def test_composite_printing(self):
......@@ -172,7 +173,7 @@ class TestComposite:
C = Composite([x, y, z], [e0, e1, e2, e3, e4, e5, e6, e7])
c = C.make_node(x, y, z)
g = FunctionGraph([x, y, z], c.outputs)
DualLinker().accept(g).make_function()
make_function(DualLinker().accept(g))
assert str(g) == (
"FunctionGraph(*1 -> Composite{((i0 + i1) + i2),"
......@@ -206,71 +207,71 @@ class TestComposite:
class TestLogical:
def test_gt(self):
x, y, z = floats("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [x > y])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [x > y])))
for a, b in ((3.0, 9), (3, 0.9), (3, 3)):
assert fn(a, b) == (a > b)
def test_lt(self):
x, y, z = floats("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [x < y])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [x < y])))
for a, b in ((3.0, 9), (3, 0.9), (3, 3)):
assert fn(a, b) == (a < b)
def test_le(self):
x, y, z = floats("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [x <= y])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [x <= y])))
for a, b in ((3.0, 9), (3, 0.9), (3, 3)):
assert fn(a, b) == (a <= b)
def test_ge(self):
x, y, z = floats("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [x >= y])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [x >= y])))
for a, b in ((3.0, 9), (3, 0.9), (3, 3)):
assert fn(a, b) == (a >= b)
def test_eq(self):
x, y, z = floats("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [eq(x, y)])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [eq(x, y)])))
for a, b in ((3.0, 9), (3, 0.9), (3, 3)):
assert fn(a, b) == (a == b)
def test_neq(self):
x, y, z = floats("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [neq(x, y)])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [neq(x, y)])))
for a, b in ((3.0, 9), (3, 0.9), (3, 3)):
assert fn(a, b) == (a != b)
def test_or(self):
x, y, z = ints("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [x | y])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [x | y])))
for a, b in ((0, 1), (0, 0), (1, 0), (1, 1)):
assert fn(a, b) == (a | b), (a, b)
def test_xor(self):
x, y, z = ints("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [x ^ y])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [x ^ y])))
for a, b in ((0, 1), (0, 0), (1, 0), (1, 1)):
assert fn(a, b) == (a ^ b), (a, b)
def test_and(self):
x, y, z = ints("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [and_(x, y)])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [and_(x, y)])))
for a, b in ((0, 1), (0, 0), (1, 0), (1, 1)):
assert fn(a, b) == (a & b), (a, b)
x, y, z = ints("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [x & y])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [x & y])))
for a, b in ((0, 1), (0, 0), (1, 0), (1, 1)):
assert fn(a, b) == (a & b), (a, b)
def test_not(self):
x, y, z = ints("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [invert(x)])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [invert(x)])))
for a, b in ((0, 1), (0, 0), (1, 0), (1, 1)):
assert fn(a, b) == ~a, (a,)
x, y, z = ints("xyz")
fn = DualLinker().accept(FunctionGraph([x, y], [~x])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [~x])))
for a, b in ((0, 1), (0, 0), (1, 0), (1, 1)):
assert fn(a, b) == ~a, (a,)
......
tests/scalar/test_basic_sympy.py
浏览文件 @ 48f5e4a9
import pytest
import aesara
from aesara.graph.fg import FunctionGraph
from aesara.link.c.basic import CLinker
from aesara.scalar.basic import floats
from aesara.scalar.basic_sympy import SymPyCCode
from tests.link.test_link import make_function
sympy = pytest.importorskip("sympy")
......@@ -18,8 +21,8 @@ xt, yt = floats("xy")
def test_SymPyCCode():
op = SymPyCCode([xs, ys], xs + ys)
e = op(xt, yt)
g = aesara.graph.fg.FunctionGraph([xt, yt], [e])
fn = aesara.link.c.basic.CLinker().accept(g).make_function()
g = FunctionGraph([xt, yt], [e])
fn = make_function(CLinker().accept(g))
assert fn(1.0, 2.0) == 3.0
......
tests/scalar/test_math.py
浏览文件 @ 48f5e4a9
......@@ -7,6 +7,7 @@ from aesara.compile.mode import Mode
from aesara.graph.fg import FunctionGraph
from aesara.link.c.basic import CLinker
from aesara.scalar.math import betainc, betainc_der, gammainc, gammaincc, gammal, gammau
from tests.link.test_link import make_function
def test_gammainc_python():
......@@ -21,7 +22,7 @@ def test_gammainc_nan_c():
x1 = at.dscalar()
x2 = at.dscalar()
y = gammainc(x1, x2)
test_func = CLinker().accept(FunctionGraph([x1, x2], [y])).make_function()
test_func = make_function(CLinker().accept(FunctionGraph([x1, x2], [y])))
assert np.isnan(test_func(-1, 1))
assert np.isnan(test_func(1, -1))
assert np.isnan(test_func(-1, -1))
......@@ -39,7 +40,7 @@ def test_gammaincc_nan_c():
x1 = at.dscalar()
x2 = at.dscalar()
y = gammaincc(x1, x2)
test_func = CLinker().accept(FunctionGraph([x1, x2], [y])).make_function()
test_func = make_function(CLinker().accept(FunctionGraph([x1, x2], [y])))
assert np.isnan(test_func(-1, 1))
assert np.isnan(test_func(1, -1))
assert np.isnan(test_func(-1, -1))
......@@ -49,7 +50,7 @@ def test_gammal_nan_c():
x1 = at.dscalar()
x2 = at.dscalar()
y = gammal(x1, x2)
test_func = CLinker().accept(FunctionGraph([x1, x2], [y])).make_function()
test_func = make_function(CLinker().accept(FunctionGraph([x1, x2], [y])))
assert np.isnan(test_func(-1, 1))
assert np.isnan(test_func(1, -1))
assert np.isnan(test_func(-1, -1))
......@@ -59,7 +60,7 @@ def test_gammau_nan_c():
x1 = at.dscalar()
x2 = at.dscalar()
y = gammau(x1, x2)
test_func = CLinker().accept(FunctionGraph([x1, x2], [y])).make_function()
test_func = make_function(CLinker().accept(FunctionGraph([x1, x2], [y])))
assert np.isnan(test_func(-1, 1))
assert np.isnan(test_func(1, -1))
assert np.isnan(test_func(-1, -1))
......
tests/tensor/test_elemwise.py
浏览文件 @ 48f5e4a9
......@@ -29,6 +29,7 @@ from aesara.tensor.type import (
vectors,
)
from tests import unittest_tools
from tests.link.test_link import make_function
from tests.tensor.test_math import reduce_bitwise_and
......@@ -181,7 +182,7 @@ class TestBroadcast:
x = type(aesara.config.floatX, [(entry == 1) for entry in xsh])("x")
y = type(aesara.config.floatX, [(entry == 1) for entry in ysh])("y")
e = op(aes.add)(x, y)
f = copy(linker).accept(FunctionGraph([x, y], [e])).make_function()
f = make_function(copy(linker).accept(FunctionGraph([x, y], [e])))
xv = rand_val(xsh)
yv = rand_val(ysh)
zv = xv + yv
......@@ -194,11 +195,7 @@ class TestBroadcast:
x = type(aesara.config.floatX, [(entry == 1) for entry in xsh])("x")
y = type(aesara.config.floatX, [(entry == 1) for entry in ysh])("y")
e = op(aes.add)(x, y)
f = (
copy(linker)
.accept(FunctionGraph([x, y], [e.shape]))
.make_function()
)
f = make_function(copy(linker).accept(FunctionGraph([x, y], [e.shape])))
assert tuple(f(xv, yv)) == tuple(zv.shape)
def with_linker_inplace(self, linker, op, type, rand_val):
......@@ -215,7 +212,7 @@ class TestBroadcast:
x = type(aesara.config.floatX, [(entry == 1) for entry in xsh])("x")
y = type(aesara.config.floatX, [(entry == 1) for entry in ysh])("y")
e = op(aes.Add(aes.transfer_type(0)), {0: 0})(x, y)
f = copy(linker).accept(FunctionGraph([x, y], [e])).make_function()
f = make_function(copy(linker).accept(FunctionGraph([x, y], [e])))
xv = rand_val(xsh)
yv = rand_val(ysh)
zv = xv + yv
......@@ -229,11 +226,7 @@ class TestBroadcast:
x = type(aesara.config.floatX, [(entry == 1) for entry in xsh])("x")
y = type(aesara.config.floatX, [(entry == 1) for entry in ysh])("y")
e = op(aes.Add(aes.transfer_type(0)), {0: 0})(x, y)
f = (
copy(linker)
.accept(FunctionGraph([x, y], [e.shape]))
.make_function()
)
f = make_function(copy(linker).accept(FunctionGraph([x, y], [e.shape])))
xv = rand_val(xsh)
yv = rand_val(ysh)
zv = xv + yv
......@@ -273,7 +266,7 @@ class TestBroadcast:
x = t(aesara.config.floatX, (False, False))("x")
y = t(aesara.config.floatX, (True, True))("y")
e = op(aes.Second(aes.transfer_type(0)), {0: 0})(x, y)
f = linker().accept(FunctionGraph([x, y], [e])).make_function()
f = make_function(linker().accept(FunctionGraph([x, y], [e])))
xv = rval((5, 5))
yv = rval((1, 1))
f(xv, yv)
......@@ -304,7 +297,7 @@ class TestBroadcast:
x = t(aesara.config.floatX, (False,) * 5)("x")
y = t(aesara.config.floatX, (False,) * 5)("y")
e = op(aes.add)(x, y)
f = linker().accept(FunctionGraph([x, y], [e])).make_function()
f = make_function(linker().accept(FunctionGraph([x, y], [e])))
xv = rval((2, 2, 2, 2, 2))
yv = rval((2, 2, 2, 2, 2)).transpose(4, 0, 3, 1, 2)
zv = xv + yv
......@@ -322,7 +315,7 @@ class TestBroadcast:
):
x = t(aesara.config.floatX, (False,) * 2)("x")
e = op(aes.add)(x, x)
f = linker().accept(FunctionGraph([x], [e])).make_function()
f = make_function(linker().accept(FunctionGraph([x], [e])))
xv = rval((2, 2))
zv = xv + xv
assert (f(xv) == zv).all()
......
tests/tensor/test_math.py
浏览文件 @ 48f5e4a9
......@@ -139,6 +139,7 @@ from aesara.tensor.type import (
)
from aesara.tensor.type_other import NoneConst
from tests import unittest_tools as utt
from tests.link.test_link import make_function
from tests.tensor.utils import (
_bad_build_broadcast_binary_normal,
_bad_runtime_broadcast_binary_normal,
......@@ -2387,7 +2388,7 @@ def test_divmod():
# Confirm that divmod is equivalent to the python version.
x, y = fscalars("xy")
d, r = divmod(x, y)
fn = DualLinker().accept(FunctionGraph([x, y], [d, r])).make_function()
fn = make_function(DualLinker().accept(FunctionGraph([x, y], [d, r])))
for a, b in (
(0, 1),
(1, 1),
......
tests/tensor/test_opt_uncanonicalize.py
浏览文件 @ 48f5e4a9
......@@ -21,6 +21,7 @@ from aesara.tensor.opt_uncanonicalize import (
)
from aesara.tensor.shape import reshape
from aesara.tensor.type import dtensor4, iscalar, matrix, tensor, vector
from tests.link.test_link import make_function
class TestMaxAndArgmax:
......@@ -165,7 +166,7 @@ def test_local_dimshuffle_alloc():
l = PerformLinker()
l.accept(g)
f = l.make_function()
f = make_function(l)
assert f([3, 4]).ndim == 4
......
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