diff --git a/coverage/coverage-.xml b/coverage/coverage-.xml
new file mode 100644
index 0000000000..5192121c8d
--- /dev/null
+++ b/coverage/coverage-.xml
@@ -0,0 +1,48682 @@
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+</coverage>
diff --git a/pytensor/compile/sharedvalue.py b/pytensor/compile/sharedvalue.py
index 8c6f0726a4..80f93b79bd 100644
--- a/pytensor/compile/sharedvalue.py
+++ b/pytensor/compile/sharedvalue.py
@@ -1,6 +1,7 @@
"""Provide a simple user friendly API to PyTensor-managed memory."""
import copy
+import warnings
from contextlib import contextmanager
from functools import singledispatch
from typing import TYPE_CHECKING
@@ -134,6 +135,10 @@ def set_value(self, new_value, borrow=False):
self.container.value = copy.deepcopy(new_value)
def get_test_value(self):
+ warnings.warn(
+ "test_value machinery is deprecated and will stop working in the future.",
+ FutureWarning,
+ )
return self.get_value(borrow=True, return_internal_type=True)
def clone(self, **kwargs):
diff --git a/pytensor/configdefaults.py b/pytensor/configdefaults.py
index a81fd63905..105a647591 100644
--- a/pytensor/configdefaults.py
+++ b/pytensor/configdefaults.py
@@ -7,6 +7,7 @@
import socket
import sys
import textwrap
+import warnings
from pathlib import Path
import numpy as np
@@ -1282,6 +1283,12 @@ def add_caching_dir_configvars():
else:
gcc_version_str = "GCC_NOT_FOUND"
+if config.compute_test_value != "off":
+ warnings.warn(
+ "test_value machinery is deprecated and will stop working in the future.",
+ FutureWarning,
+ )
+
# TODO: The caching dir resolution is a procedural mess of helper functions, local variables
# and config definitions. And the result is also not particularly pretty..
add_caching_dir_configvars()
diff --git a/pytensor/graph/basic.py b/pytensor/graph/basic.py
index 6b4ca7570d..ee001df796 100644
--- a/pytensor/graph/basic.py
+++ b/pytensor/graph/basic.py
@@ -790,6 +790,10 @@ def __init__(self, type: _TypeType, data: Any, name: str | None = None):
add_tag_trace(self)
def get_test_value(self):
+ warnings.warn(
+ "test_value machinery is deprecated and will stop working in the future.",
+ FutureWarning,
+ )
return self.data
def signature(self):
diff --git a/pytensor/graph/op.py b/pytensor/graph/op.py
index 684add6308..024dd299f1 100644
--- a/pytensor/graph/op.py
+++ b/pytensor/graph/op.py
@@ -717,6 +717,11 @@ def get_test_values(*args: Variable) -> Any | list[Any]:
if config.compute_test_value == "off":
return []
+ warnings.warn(
+ "test_value machinery is deprecated and will stop working in the future.",
+ FutureWarning,
+ )
+
rval = []
for i, arg in enumerate(args):
diff --git a/pytensor/graph/utils.py b/pytensor/graph/utils.py
index d797504ae6..69efa9d3b0 100644
--- a/pytensor/graph/utils.py
+++ b/pytensor/graph/utils.py
@@ -1,6 +1,7 @@
import linecache
import sys
import traceback
+import warnings
from abc import ABCMeta
from collections.abc import Sequence
from io import StringIO
@@ -282,9 +283,19 @@ def info(self):
# These two methods have been added to help Mypy
def __getattribute__(self, name):
+ if name == "test_value":
+ warnings.warn(
+ "test_value machinery is deprecated and will stop working in the future.",
+ FutureWarning,
+ )
return super().__getattribute__(name)
def __setattr__(self, name: str, value: Any) -> None:
+ if name == "test_value":
+ warnings.warn(
+ "test_value machinery is deprecated and will stop working in the future.",
+ FutureWarning,
+ )
self.__dict__[name] = value
@@ -299,6 +310,11 @@ def __init__(self, attr, attr_filter):
def __setattr__(self, attr, obj):
if getattr(self, "attr", None) == attr:
+ if attr == "test_value":
+ warnings.warn(
+ "test_value machinery is deprecated and will stop working in the future.",
+ FutureWarning,
+ )
obj = self.attr_filter(obj)
return object.__setattr__(self, attr, obj)
diff --git a/pytensor/scalar/basic.py b/pytensor/scalar/basic.py
index ca58006d24..f4f9bf169c 100644
--- a/pytensor/scalar/basic.py
+++ b/pytensor/scalar/basic.py
@@ -1231,7 +1231,7 @@ def supports_c_code(self, inputs, outputs):
tmp_s_input.append(tmp)
mapping[ii] = tmp_s_input[-1]
- with config.change_flags(compute_test_value="ignore"):
+ with config.change_flags(compute_test_value="off"):
s_op = self(*tmp_s_input, return_list=True)
# if the scalar_op don't have a c implementation,
diff --git a/tests/compile/test_builders.py b/tests/compile/test_builders.py
index d99b13edfc..265a2f65b9 100644
--- a/tests/compile/test_builders.py
+++ b/tests/compile/test_builders.py
@@ -523,11 +523,12 @@ def test_infer_shape(self):
@config.change_flags(compute_test_value="raise")
def test_compute_test_value(self):
- x = scalar("x")
- x.tag.test_value = np.array(1.0, dtype=config.floatX)
- op = OpFromGraph([x], [x**3])
- y = scalar("y")
- y.tag.test_value = np.array(1.0, dtype=config.floatX)
+ with pytest.warns(FutureWarning):
+ x = scalar("x")
+ x.tag.test_value = np.array(1.0, dtype=config.floatX)
+ op = OpFromGraph([x], [x**3])
+ y = scalar("y")
+ y.tag.test_value = np.array(1.0, dtype=config.floatX)
f = op(y)
grad_f = grad(f, y)
assert grad_f.tag.test_value is not None
diff --git a/tests/compile/test_ops.py b/tests/compile/test_ops.py
index 461c7793ad..d9819ef621 100644
--- a/tests/compile/test_ops.py
+++ b/tests/compile/test_ops.py
@@ -33,9 +33,7 @@ def cumprod(x):
def test_2arg(self):
x = dmatrix("x")
- x.tag.test_value = np.zeros((2, 2))
y = dvector("y")
- y.tag.test_value = [0, 0, 0, 0]
@as_op([dmatrix, dvector], dvector)
def cumprod_plus(x, y):
@@ -49,9 +47,7 @@ def cumprod_plus(x, y):
def test_infer_shape(self):
x = dmatrix("x")
- x.tag.test_value = np.zeros((2, 2))
y = dvector("y")
- y.tag.test_value = [0, 0, 0, 0]
def infer_shape(fgraph, node, shapes):
x, y = shapes
diff --git a/tests/graph/test_compute_test_value.py b/tests/graph/test_compute_test_value.py
index ea59ff68f8..0a2c2bf830 100644
--- a/tests/graph/test_compute_test_value.py
+++ b/tests/graph/test_compute_test_value.py
@@ -67,7 +67,8 @@ def perform(self, node, inputs, outputs):
test_input = SomeType()()
orig_object = object()
- test_input.tag.test_value = orig_object
+ with pytest.warns(FutureWarning):
+ test_input.tag.test_value = orig_object
res = InplaceOp(False)(test_input)
assert res.tag.test_value is orig_object
@@ -76,10 +77,11 @@ def perform(self, node, inputs, outputs):
assert res.tag.test_value is not orig_object
def test_variable_only(self):
- x = matrix("x")
- x.tag.test_value = np.random.random((3, 4)).astype(config.floatX)
- y = matrix("y")
- y.tag.test_value = np.random.random((4, 5)).astype(config.floatX)
+ with pytest.warns(FutureWarning):
+ x = matrix("x")
+ x.tag.test_value = np.random.random((3, 4)).astype(config.floatX)
+ y = matrix("y")
+ y.tag.test_value = np.random.random((4, 5)).astype(config.floatX)
# should work
z = dot(x, y)
@@ -88,14 +90,16 @@ def test_variable_only(self):
assert _allclose(f(x.tag.test_value, y.tag.test_value), z.tag.test_value)
# this test should fail
- y.tag.test_value = np.random.random((6, 5)).astype(config.floatX)
+ with pytest.warns(FutureWarning):
+ y.tag.test_value = np.random.random((6, 5)).astype(config.floatX)
with pytest.raises(ValueError):
dot(x, y)
def test_compute_flag(self):
x = matrix("x")
y = matrix("y")
- y.tag.test_value = np.random.random((4, 5)).astype(config.floatX)
+ with pytest.warns(FutureWarning):
+ y.tag.test_value = np.random.random((4, 5)).astype(config.floatX)
# should skip computation of test value
with config.change_flags(compute_test_value="off"):
@@ -111,10 +115,11 @@ def test_compute_flag(self):
dot(x, y)
def test_string_var(self):
- x = matrix("x")
- x.tag.test_value = np.random.random((3, 4)).astype(config.floatX)
- y = matrix("y")
- y.tag.test_value = np.random.random((4, 5)).astype(config.floatX)
+ with pytest.warns(FutureWarning):
+ x = matrix("x")
+ x.tag.test_value = np.random.random((3, 4)).astype(config.floatX)
+ y = matrix("y")
+ y.tag.test_value = np.random.random((4, 5)).astype(config.floatX)
z = pytensor.shared(np.random.random((5, 6)).astype(config.floatX))
@@ -134,7 +139,8 @@ def f(x, y, z):
def test_shared(self):
x = matrix("x")
- x.tag.test_value = np.random.random((3, 4)).astype(config.floatX)
+ with pytest.warns(FutureWarning):
+ x.tag.test_value = np.random.random((3, 4)).astype(config.floatX)
y = pytensor.shared(np.random.random((4, 6)).astype(config.floatX), "y")
# should work
@@ -190,21 +196,21 @@ def test_constant(self):
def test_incorrect_type(self):
x = vector("x")
with pytest.raises(TypeError):
- # Incorrect shape for test value
- x.tag.test_value = np.empty((2, 2))
+ with pytest.warns(FutureWarning):
+ # Incorrect shape for test value
+ x.tag.test_value = np.empty((2, 2))
x = fmatrix("x")
with pytest.raises(TypeError):
- # Incorrect dtype (float64) for test value
- x.tag.test_value = np.random.random((3, 4))
+ with pytest.warns(FutureWarning):
+ # Incorrect dtype (float64) for test value
+ x.tag.test_value = np.random.random((3, 4))
def test_overided_function(self):
# We need to test those as they mess with Exception
# And we don't want the exception to be changed.
x = matrix()
- x.tag.test_value = np.zeros((2, 3), dtype=config.floatX)
y = matrix()
- y.tag.test_value = np.zeros((2, 2), dtype=config.floatX)
with pytest.raises(ValueError):
x.__mul__(y)
@@ -212,8 +218,9 @@ def test_scan(self):
# Test the compute_test_value mechanism Scan.
k = iscalar("k")
A = vector("A")
- k.tag.test_value = 3
- A.tag.test_value = np.random.random((5,)).astype(config.floatX)
+ with pytest.warns(FutureWarning):
+ k.tag.test_value = 3
+ A.tag.test_value = np.random.random((5,)).astype(config.floatX)
def fx(prior_result, A):
return prior_result * A
@@ -233,8 +240,9 @@ def test_scan_err1(self):
# This test should fail when building fx for the first time
k = iscalar("k")
A = matrix("A")
- k.tag.test_value = 3
- A.tag.test_value = np.random.random((5, 3)).astype(config.floatX)
+ with pytest.warns(FutureWarning):
+ k.tag.test_value = 3
+ A.tag.test_value = np.random.random((5, 3)).astype(config.floatX)
def fx(prior_result, A):
return dot(prior_result, A)
@@ -253,8 +261,9 @@ def test_scan_err2(self):
# but when calling the scan's perform()
k = iscalar("k")
A = matrix("A")
- k.tag.test_value = 3
- A.tag.test_value = np.random.random((5, 3)).astype(config.floatX)
+ with pytest.warns(FutureWarning):
+ k.tag.test_value = 3
+ A.tag.test_value = np.random.random((5, 3)).astype(config.floatX)
def fx(prior_result, A):
return dot(prior_result, A)
@@ -288,7 +297,8 @@ def perform(self, node, inputs, outputs):
output[0] = input + 1
i = ps.int32("i")
- i.tag.test_value = 3
+ with pytest.warns(FutureWarning):
+ i.tag.test_value = 3
o = IncOnePython()(i)
@@ -304,7 +314,8 @@ def perform(self, node, inputs, outputs):
)
def test_no_perform(self):
i = ps.int32("i")
- i.tag.test_value = 3
+ with pytest.warns(FutureWarning):
+ i.tag.test_value = 3
# Class IncOneC is defined outside of the TestComputeTestValue
# so it can be pickled and unpickled
diff --git a/tests/graph/test_destroyhandler.py b/tests/graph/test_destroyhandler.py
index 16a654da26..9549503e25 100644
--- a/tests/graph/test_destroyhandler.py
+++ b/tests/graph/test_destroyhandler.py
@@ -2,7 +2,6 @@
import pytest
-from pytensor.configdefaults import config
from pytensor.graph.basic import Apply, Constant, Variable, clone
from pytensor.graph.destroyhandler import DestroyHandler
from pytensor.graph.features import ReplaceValidate
@@ -408,7 +407,6 @@ def test_value_repl():
assert g.consistent()
-@config.change_flags(compute_test_value="off")
def test_value_repl_2():
x, y, z = inputs()
sy = sigmoid(y)
diff --git a/tests/graph/test_fg.py b/tests/graph/test_fg.py
index f2550d348e..63ad67eb57 100644
--- a/tests/graph/test_fg.py
+++ b/tests/graph/test_fg.py
@@ -241,18 +241,19 @@ def test_change_input(self):
@config.change_flags(compute_test_value="raise")
def test_replace_test_value(self):
- var1 = MyVariable("var1")
- var1.tag.test_value = 1
- var2 = MyVariable("var2")
- var2.tag.test_value = 2
- var3 = op1(var2, var1)
- var4 = op2(var3, var2)
- var4.tag.test_value = np.array([1, 2])
- var5 = op3(var4, var2, var2)
- fg = FunctionGraph([var1, var2], [var3, var5], clone=False)
-
- var6 = op3()
- var6.tag.test_value = np.array(0)
+ with pytest.warns(FutureWarning):
+ var1 = MyVariable("var1")
+ var1.tag.test_value = 1
+ var2 = MyVariable("var2")
+ var2.tag.test_value = 2
+ var3 = op1(var2, var1)
+ var4 = op2(var3, var2)
+ var4.tag.test_value = np.array([1, 2])
+ var5 = op3(var4, var2, var2)
+ fg = FunctionGraph([var1, var2], [var3, var5], clone=False)
+
+ var6 = op3()
+ var6.tag.test_value = np.array(0)
assert var6.tag.test_value.shape != var4.tag.test_value.shape
diff --git a/tests/graph/test_op.py b/tests/graph/test_op.py
index 5ec545015b..0c92017349 100644
--- a/tests/graph/test_op.py
+++ b/tests/graph/test_op.py
@@ -131,26 +131,30 @@ def perform(self, node, inputs, outputs):
def test_test_value_python_objects():
- for x in ([0, 1, 2], 0, 0.5, 1):
- assert np.all(op.get_test_value(x) == x)
+ with pytest.warns(FutureWarning):
+ for x in ([0, 1, 2], 0, 0.5, 1):
+ assert np.all(op.get_test_value(x) == x)
def test_test_value_ndarray():
x = np.zeros((5, 5))
- v = op.get_test_value(x)
+ with pytest.warns(FutureWarning):
+ v = op.get_test_value(x)
assert np.all(v == x)
def test_test_value_constant():
x = pt.as_tensor_variable(np.zeros((5, 5)))
- v = op.get_test_value(x)
+ with pytest.warns(FutureWarning):
+ v = op.get_test_value(x)
assert np.all(v == np.zeros((5, 5)))
def test_test_value_shared():
x = shared(np.zeros((5, 5)))
- v = op.get_test_value(x)
+ with pytest.warns(FutureWarning):
+ v = op.get_test_value(x)
assert np.all(v == np.zeros((5, 5)))
@@ -158,12 +162,12 @@ def test_test_value_shared():
@config.change_flags(compute_test_value="raise")
def test_test_value_op():
x = log(np.ones((5, 5)))
- v = op.get_test_value(x)
+ with pytest.warns(FutureWarning):
+ v = op.get_test_value(x)
assert np.allclose(v, np.zeros((5, 5)))
-@config.change_flags(compute_test_value="off")
def test_get_test_values_no_debugger():
"""Tests that `get_test_values` returns `[]` when debugger is off."""
@@ -176,7 +180,8 @@ def test_get_test_values_ignore():
"""Tests that `get_test_values` returns `[]` when debugger is set to "ignore" and some values are missing."""
x = vector()
- assert op.get_test_values(x) == []
+ with pytest.warns(FutureWarning):
+ assert op.get_test_values(x) == []
def test_get_test_values_success():
@@ -184,19 +189,20 @@ def test_get_test_values_success():
for mode in ["ignore", "warn", "raise"]:
with config.change_flags(compute_test_value=mode):
- x = vector()
- x.tag.test_value = np.zeros((4,), dtype=config.floatX)
- y = np.zeros((5, 5))
+ with pytest.warns(FutureWarning):
+ x = vector()
+ x.tag.test_value = np.zeros((4,), dtype=config.floatX)
+ y = np.zeros((5, 5))
- iters = 0
+ iters = 0
- for x_val, y_val in op.get_test_values(x, y):
- assert x_val.shape == (4,)
- assert y_val.shape == (5, 5)
+ for x_val, y_val in op.get_test_values(x, y):
+ assert x_val.shape == (4,)
+ assert y_val.shape == (5, 5)
- iters += 1
+ iters += 1
- assert iters == 1
+ assert iters == 1
@config.change_flags(compute_test_value="raise")
@@ -204,8 +210,9 @@ def test_get_test_values_exc():
"""Tests that `get_test_values` raises an exception when debugger is set to raise and a value is missing."""
with pytest.raises(TestValueError):
- x = vector()
- assert op.get_test_values(x) == []
+ with pytest.warns(FutureWarning):
+ x = vector()
+ assert op.get_test_values(x) == []
def test_op_invalid_input_types():
diff --git a/tests/link/jax/test_basic.py b/tests/link/jax/test_basic.py
index 5e783984e0..7c0cfbe5dc 100644
--- a/tests/link/jax/test_basic.py
+++ b/tests/link/jax/test_basic.py
@@ -12,7 +12,7 @@
from pytensor.graph import RewriteDatabaseQuery
from pytensor.graph.basic import Apply
from pytensor.graph.fg import FunctionGraph
-from pytensor.graph.op import Op, get_test_value
+from pytensor.graph.op import Op
from pytensor.ifelse import ifelse
from pytensor.link.jax import JAXLinker
from pytensor.raise_op import assert_op
@@ -192,16 +192,14 @@ def test_jax_ifelse():
compare_jax_and_py(x_fg, [])
a = dscalar("a")
- a.tag.test_value = np.array(0.2, dtype=config.floatX)
x = ifelse(a < 0.5, true_vals, false_vals)
x_fg = FunctionGraph([a], [x]) # I.e. False
- compare_jax_and_py(x_fg, [get_test_value(i) for i in x_fg.inputs])
+ compare_jax_and_py(x_fg, [np.array(0.2, dtype=config.floatX)])
def test_jax_checkandraise():
p = scalar()
- p.tag.test_value = 0
res = assert_op(p, p < 1.0)
@@ -210,8 +208,8 @@ def test_jax_checkandraise():
def set_test_value(x, v):
- x.tag.test_value = v
- return x
+ test_values_dict = {x: v}
+ return test_values_dict
def test_OpFromGraph():
diff --git a/tests/link/jax/test_elemwise.py b/tests/link/jax/test_elemwise.py
index 856d8c4881..de0b618639 100644
--- a/tests/link/jax/test_elemwise.py
+++ b/tests/link/jax/test_elemwise.py
@@ -7,7 +7,6 @@
from pytensor.compile import get_mode
from pytensor.configdefaults import config
from pytensor.graph.fg import FunctionGraph
-from pytensor.graph.op import get_test_value
from pytensor.tensor import elemwise as pt_elemwise
from pytensor.tensor.math import all as pt_all
from pytensor.tensor.math import prod
@@ -46,7 +45,6 @@ def test_jax_Dimshuffle():
def test_jax_CAReduce():
a_pt = vector("a")
- a_pt.tag.test_value = np.r_[1, 2, 3].astype(config.floatX)
x = pt_sum(a_pt, axis=None)
x_fg = FunctionGraph([a_pt], [x])
@@ -54,7 +52,6 @@ def test_jax_CAReduce():
compare_jax_and_py(x_fg, [np.r_[1, 2, 3].astype(config.floatX)])
a_pt = matrix("a")
- a_pt.tag.test_value = np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)
x = pt_sum(a_pt, axis=0)
x_fg = FunctionGraph([a_pt], [x])
@@ -67,7 +64,6 @@ def test_jax_CAReduce():
compare_jax_and_py(x_fg, [np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)])
a_pt = matrix("a")
- a_pt.tag.test_value = np.c_[[1, 2, 3], [1, 2, 3]].astype(config.floatX)
x = prod(a_pt, axis=0)
x_fg = FunctionGraph([a_pt], [x])
@@ -83,30 +79,30 @@ def test_jax_CAReduce():
@pytest.mark.parametrize("axis", [None, 0, 1])
def test_softmax(axis):
x = matrix("x")
- x.tag.test_value = np.arange(6, dtype=config.floatX).reshape(2, 3)
out = softmax(x, axis=axis)
fgraph = FunctionGraph([x], [out])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, [np.arange(6, dtype=config.floatX).reshape(2, 3)])
@pytest.mark.parametrize("axis", [None, 0, 1])
def test_logsoftmax(axis):
x = matrix("x")
- x.tag.test_value = np.arange(6, dtype=config.floatX).reshape(2, 3)
out = log_softmax(x, axis=axis)
fgraph = FunctionGraph([x], [out])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, [np.arange(6, dtype=config.floatX).reshape(2, 3)])
@pytest.mark.parametrize("axis", [None, 0, 1])
def test_softmax_grad(axis):
dy = matrix("dy")
- dy.tag.test_value = np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
sm = matrix("sm")
- sm.tag.test_value = np.arange(6, dtype=config.floatX).reshape(2, 3)
+ test_value = [
+ np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX),
+ np.arange(6, dtype=config.floatX).reshape(2, 3),
+ ]
out = SoftmaxGrad(axis=axis)(dy, sm)
fgraph = FunctionGraph([dy, sm], [out])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, list(test_value))
@pytest.mark.parametrize("size", [(10, 10), (1000, 1000), (10000, 10000)])
diff --git a/tests/link/jax/test_extra_ops.py b/tests/link/jax/test_extra_ops.py
index 1427413379..ddb72a0e4c 100644
--- a/tests/link/jax/test_extra_ops.py
+++ b/tests/link/jax/test_extra_ops.py
@@ -4,7 +4,6 @@
import pytensor.tensor.basic as ptb
from pytensor.configdefaults import config
from pytensor.graph.fg import FunctionGraph
-from pytensor.graph.op import get_test_value
from pytensor.tensor import extra_ops as pt_extra_ops
from pytensor.tensor.type import matrix, tensor
from tests.link.jax.test_basic import compare_jax_and_py
@@ -52,7 +51,9 @@ def test_extra_ops():
out = pt_extra_ops.unravel_index(indices, (3, 4), order="C")
fgraph = FunctionGraph([], out)
compare_jax_and_py(
- fgraph, [get_test_value(i) for i in fgraph.inputs], must_be_device_array=False
+ fgraph,
+ [],
+ must_be_device_array=False,
)
diff --git a/tests/link/jax/test_random.py b/tests/link/jax/test_random.py
index f9ae5d00c1..840045537a 100644
--- a/tests/link/jax/test_random.py
+++ b/tests/link/jax/test_random.py
@@ -474,17 +474,19 @@ def test_random_RandomVariable(rv_op, dist_params, base_size, cdf_name, params_c
"""
rng = shared(np.random.default_rng(29403))
+ test_values = {k: v for d in dist_params for k, v in d.items() if d}
+ dist_params = list(test_values.keys())
g = rv_op(*dist_params, size=(10000, *base_size), rng=rng)
g_fn = compile_random_function(dist_params, g, mode=jax_mode)
samples = g_fn(
*[
- i.tag.test_value
- for i in g_fn.maker.fgraph.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
]
)
- bcast_dist_args = np.broadcast_arrays(*[i.tag.test_value for i in dist_params])
+ bcast_dist_args = np.broadcast_arrays(*[test_values[i] for i in test_values])
for idx in np.ndindex(*base_size):
cdf_params = params_conv(*(arg[idx] for arg in bcast_dist_args))
diff --git a/tests/link/jax/test_scalar.py b/tests/link/jax/test_scalar.py
index 0469301791..4eac6b625b 100644
--- a/tests/link/jax/test_scalar.py
+++ b/tests/link/jax/test_scalar.py
@@ -5,7 +5,6 @@
import pytensor.tensor as pt
from pytensor.configdefaults import config
from pytensor.graph.fg import FunctionGraph
-from pytensor.graph.op import get_test_value
from pytensor.scalar.basic import Composite
from pytensor.tensor import as_tensor
from pytensor.tensor.elemwise import Elemwise
@@ -80,11 +79,11 @@ def test_second_constant_scalar():
def test_identity():
a = scalar("a")
- a.tag.test_value = 10
+ a_test_value = 10
out = ps.identity(a)
fgraph = FunctionGraph([a], [out])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, [a_test_value])
@pytest.mark.parametrize(
@@ -238,34 +237,36 @@ def test_log1mexp():
def test_nnet():
x = vector("x")
- x.tag.test_value = np.r_[1.0, 2.0].astype(config.floatX)
+ x_test_value = np.r_[1.0, 2.0].astype(config.floatX)
out = sigmoid(x)
fgraph = FunctionGraph([x], [out])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, [x_test_value])
out = softplus(x)
fgraph = FunctionGraph([x], [out])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, [x_test_value])
def test_jax_variadic_Scalar():
mu = vector("mu", dtype=config.floatX)
- mu.tag.test_value = np.r_[0.1, 1.1].astype(config.floatX)
tau = vector("tau", dtype=config.floatX)
- tau.tag.test_value = np.r_[1.0, 2.0].astype(config.floatX)
+ test_values = {
+ mu: np.r_[0.1, 1.1].astype(config.floatX),
+ tau: np.r_[1.0, 2.0].astype(config.floatX),
+ }
res = -tau * mu
fgraph = FunctionGraph([mu, tau], [res])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, [test_values[i] for i in test_values])
res = -tau * (tau - mu) ** 2
fgraph = FunctionGraph([mu, tau], [res])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, [test_values[i] for i in test_values])
def test_add_scalars():
@@ -306,27 +307,31 @@ def test_mod_scalars():
def test_jax_multioutput():
x = vector("x")
- x.tag.test_value = np.r_[1.0, 2.0].astype(config.floatX)
y = vector("y")
- y.tag.test_value = np.r_[3.0, 4.0].astype(config.floatX)
+ test_values = {
+ x: np.r_[1.0, 2.0].astype(config.floatX),
+ y: np.r_[3.0, 4.0].astype(config.floatX),
+ }
w = cosh(x**2 + y / 3.0)
v = cosh(x / 3.0 + y**2)
fgraph = FunctionGraph([x, y], [w, v])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, [test_values[i] for i in test_values])
def test_jax_logp():
mu = vector("mu")
- mu.tag.test_value = np.r_[0.0, 0.0].astype(config.floatX)
tau = vector("tau")
- tau.tag.test_value = np.r_[1.0, 1.0].astype(config.floatX)
sigma = vector("sigma")
- sigma.tag.test_value = (1.0 / get_test_value(tau)).astype(config.floatX)
value = vector("value")
- value.tag.test_value = np.r_[0.1, -10].astype(config.floatX)
+ test_values = {
+ mu: np.r_[0.0, 0.0].astype(config.floatX),
+ tau: np.r_[1.0, 1.0].astype(config.floatX),
+ sigma: (1.0 / np.r_[1.0, 1.0].astype(config.floatX)).astype(config.floatX),
+ value: np.r_[0.1, -10].astype(config.floatX),
+ }
logp = (-tau * (value - mu) ** 2 + log(tau / np.pi / 2.0)) / 2.0
conditions = [sigma > 0]
@@ -335,4 +340,4 @@ def test_jax_logp():
fgraph = FunctionGraph([mu, tau, sigma, value], [normal_logp])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, [test_values[i] for i in test_values])
diff --git a/tests/link/jax/test_tensor_basic.py b/tests/link/jax/test_tensor_basic.py
index 0ee4a236d9..183e3f08ba 100644
--- a/tests/link/jax/test_tensor_basic.py
+++ b/tests/link/jax/test_tensor_basic.py
@@ -11,7 +11,6 @@
import pytensor.tensor.basic as ptb
from pytensor.configdefaults import config
from pytensor.graph.fg import FunctionGraph
-from pytensor.graph.op import get_test_value
from pytensor.tensor.type import iscalar, matrix, scalar, vector
from tests.link.jax.test_basic import compare_jax_and_py
from tests.tensor.test_basic import TestAlloc
@@ -81,12 +80,12 @@ def test_arange_nonconcrete():
"""JAX cannot JIT-compile `jax.numpy.arange` when arguments are not concrete values."""
a = scalar("a")
- a.tag.test_value = 10
+ a_test_value = 10
out = ptb.arange(a)
with pytest.raises(NotImplementedError):
fgraph = FunctionGraph([a], [out])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, [a_test_value])
def test_jax_Join():
@@ -230,9 +229,7 @@ def test_tri_nonconcrete():
scalar("n", dtype="int64"),
scalar("k", dtype="int64"),
)
- m.tag.test_value = 10
- n.tag.test_value = 10
- k.tag.test_value = 0
+ test_values = {m: 10, n: 10, k: 0}
out = ptb.tri(m, n, k)
@@ -240,4 +237,4 @@ def test_tri_nonconcrete():
# the error handler raises an Attribute error first, so that's what this test needs to pass
with pytest.raises(AttributeError):
fgraph = FunctionGraph([m, n, k], [out])
- compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
+ compare_jax_and_py(fgraph, [test_values[i] for i in test_values])
diff --git a/tests/link/numba/test_basic.py b/tests/link/numba/test_basic.py
index cfbc61eaca..22591570ba 100644
--- a/tests/link/numba/test_basic.py
+++ b/tests/link/numba/test_basic.py
@@ -24,7 +24,7 @@
from pytensor.compile.sharedvalue import SharedVariable
from pytensor.graph.basic import Apply, Constant
from pytensor.graph.fg import FunctionGraph
-from pytensor.graph.op import Op, get_test_value
+from pytensor.graph.op import Op
from pytensor.graph.rewriting.db import RewriteDatabaseQuery
from pytensor.graph.type import Type
from pytensor.ifelse import ifelse
@@ -129,8 +129,8 @@ def perform(self, node, inputs, outputs):
def set_test_value(x, v):
- x.tag.test_value = v
- return x
+ test_values_dict = {x: v}
+ return test_values_dict
def compare_shape_dtype(x, y):
@@ -384,7 +384,7 @@ def test_Shape(x, i):
@pytest.mark.parametrize(
- "v, shape, ndim",
+ "test_values, shape, ndim",
[
(set_test_value(pt.vector(), np.array([4], dtype=config.floatX)), (), 0),
(set_test_value(pt.vector(), np.arange(4, dtype=config.floatX)), (2, 2), 2),
@@ -395,14 +395,18 @@ def test_Shape(x, i):
),
],
)
-def test_Reshape(v, shape, ndim):
+def test_Reshape(test_values, shape, ndim):
+ v = next(iter(test_values.keys()))
+ if isinstance(shape, dict):
+ test_values.update(shape)
+ shape = next(iter(shape.keys()))
g = Reshape(ndim)(v, shape)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -410,21 +414,21 @@ def test_Reshape(v, shape, ndim):
def test_Reshape_scalar():
v = pt.vector()
- v.tag.test_value = np.array([1.0], dtype=config.floatX)
+ test_values = {v: np.array([1.0], dtype=config.floatX)}
g = Reshape(1)(v[0], (1,))
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "v, shape, fails",
+ "test_values, shape, fails",
[
(
set_test_value(pt.matrix(), np.array([[1.0]], dtype=config.floatX)),
@@ -443,7 +447,8 @@ def test_Reshape_scalar():
),
],
)
-def test_SpecifyShape(v, shape, fails):
+def test_SpecifyShape(test_values, shape, fails):
+ v = next(iter(test_values.keys()))
g = SpecifyShape()(v, *shape)
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if not fails else pytest.raises(AssertionError)
@@ -451,34 +456,35 @@ def test_SpecifyShape(v, shape, fails):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "v",
+ "test_values",
[
set_test_value(pt.vector(), np.arange(4, dtype=config.floatX)),
],
)
-def test_ViewOp(v):
+def test_ViewOp(test_values):
+ v = next(iter(test_values.keys()))
g = ViewOp()(v)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "inputs, op, exc",
+ "test_values, op, exc",
[
(
[
@@ -502,7 +508,9 @@ def test_ViewOp(v):
),
],
)
-def test_perform(inputs, op, exc):
+def test_perform(test_values, op, exc):
+ test_values = {k: v for d in test_values for k, v in d.items()}
+ inputs = list(test_values.keys())
g = op()(*inputs)
if isinstance(g, list):
@@ -515,8 +523,8 @@ def test_perform(inputs, op, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -526,7 +534,8 @@ def test_perform_params():
"""This tests for `Op.perform` implementations that require the `params` arguments."""
x = pt.vector()
- x.tag.test_value = np.array([1.0, 2.0], dtype=config.floatX)
+
+ test_values = {x: np.array([1.0, 2.0], dtype=config.floatX)}
out = assert_op(x, np.array(True))
@@ -534,7 +543,7 @@ def test_perform_params():
out = [out]
out_fg = FunctionGraph([x], out)
- compare_numba_and_py(out_fg, [get_test_value(i) for i in out_fg.inputs])
+ compare_numba_and_py(out_fg, [test_values[i] for i in test_values])
def test_perform_type_convert():
@@ -545,7 +554,8 @@ def test_perform_type_convert():
"""
x = pt.vector()
- x.tag.test_value = np.array([1.0, 2.0], dtype=config.floatX)
+
+ test_values = {x: np.array([1.0, 2.0], dtype=config.floatX)}
out = assert_op(x.sum(), np.array(True))
@@ -553,40 +563,55 @@ def test_perform_type_convert():
out = [out]
out_fg = FunctionGraph([x], out)
- compare_numba_and_py(out_fg, [get_test_value(i) for i in out_fg.inputs])
+ compare_numba_and_py(out_fg, [test_values[i] for i in test_values])
@pytest.mark.parametrize(
- "x, y, exc",
+ "inputs, exc",
[
(
- set_test_value(pt.matrix(), rng.random(size=(3, 2)).astype(config.floatX)),
- set_test_value(pt.vector(), rng.random(size=(2,)).astype(config.floatX)),
+ [
+ set_test_value(
+ pt.matrix(), rng.random(size=(3, 2)).astype(config.floatX)
+ ),
+ set_test_value(
+ pt.vector(), rng.random(size=(2,)).astype(config.floatX)
+ ),
+ ],
None,
),
(
- set_test_value(
- pt.matrix(dtype="float64"), rng.random(size=(3, 2)).astype("float64")
- ),
- set_test_value(
- pt.vector(dtype="float32"), rng.random(size=(2,)).astype("float32")
- ),
+ [
+ set_test_value(
+ pt.matrix(dtype="float64"),
+ rng.random(size=(3, 2)).astype("float64"),
+ ),
+ set_test_value(
+ pt.vector(dtype="float32"), rng.random(size=(2,)).astype("float32")
+ ),
+ ],
None,
),
(
- set_test_value(pt.lmatrix(), rng.poisson(size=(3, 2))),
- set_test_value(pt.fvector(), rng.random(size=(2,)).astype("float32")),
+ [
+ set_test_value(pt.lmatrix(), rng.poisson(size=(3, 2))),
+ set_test_value(pt.fvector(), rng.random(size=(2,)).astype("float32")),
+ ],
None,
),
(
- set_test_value(pt.lvector(), rng.random(size=(2,)).astype(np.int64)),
- set_test_value(pt.lvector(), rng.random(size=(2,)).astype(np.int64)),
+ [
+ set_test_value(pt.lvector(), rng.random(size=(2,)).astype(np.int64)),
+ set_test_value(pt.lvector(), rng.random(size=(2,)).astype(np.int64)),
+ ],
None,
),
],
)
-def test_Dot(x, y, exc):
- g = ptm.Dot()(x, y)
+def test_Dot(inputs, exc):
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ inputs = list(test_values.keys())
+ g = ptm.Dot()(*inputs)
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.warns(exc)
@@ -594,15 +619,15 @@ def test_Dot(x, y, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, exc",
+ "test_values, exc",
[
(
set_test_value(ps.float64(), np.array(0.0, dtype="float64")),
@@ -630,7 +655,8 @@ def test_Dot(x, y, exc):
),
],
)
-def test_Softplus(x, exc):
+def test_Softplus(test_values, exc):
+ x = next(iter(test_values))
g = psm.Softplus(ps.upgrade_to_float)(x)
g_fg = FunctionGraph(outputs=[g])
@@ -639,42 +665,48 @@ def test_Softplus(x, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, y, exc",
+ "inputs, exc",
[
(
- set_test_value(
- pt.dtensor3(),
- rng.random(size=(2, 3, 3)).astype("float64"),
- ),
- set_test_value(
- pt.dtensor3(),
- rng.random(size=(2, 3, 3)).astype("float64"),
- ),
+ [
+ set_test_value(
+ pt.dtensor3(),
+ rng.random(size=(2, 3, 3)).astype("float64"),
+ ),
+ set_test_value(
+ pt.dtensor3(),
+ rng.random(size=(2, 3, 3)).astype("float64"),
+ ),
+ ],
None,
),
(
- set_test_value(
- pt.dtensor3(),
- rng.random(size=(2, 3, 3)).astype("float64"),
- ),
- set_test_value(
- pt.ltensor3(),
- rng.poisson(size=(2, 3, 3)).astype("int64"),
- ),
+ [
+ set_test_value(
+ pt.dtensor3(),
+ rng.random(size=(2, 3, 3)).astype("float64"),
+ ),
+ set_test_value(
+ pt.ltensor3(),
+ rng.poisson(size=(2, 3, 3)).astype("int64"),
+ ),
+ ],
None,
),
],
)
-def test_BatchedDot(x, y, exc):
- g = blas.BatchedDot()(x, y)
+def test_BatchedDot(inputs, exc):
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ inputs = list(test_values.keys())
+ g = blas.BatchedDot()(*inputs)
if isinstance(g, list):
g_fg = FunctionGraph(outputs=g)
@@ -686,8 +718,8 @@ def test_BatchedDot(x, y, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -804,6 +836,8 @@ def test_shared_updates():
],
)
def test_IfElse(inputs, cond_fn, true_vals, false_vals):
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ inputs = list(test_values.keys())
out = ifelse(cond_fn(*inputs), true_vals, false_vals)
if not isinstance(out, list):
@@ -811,7 +845,7 @@ def test_IfElse(inputs, cond_fn, true_vals, false_vals):
out_fg = FunctionGraph(inputs, out)
- compare_numba_and_py(out_fg, [get_test_value(i) for i in out_fg.inputs])
+ compare_numba_and_py(out_fg, [test_values[i] for i in test_values])
@pytest.mark.xfail(reason="https://github.com/numba/numba/issues/7409")
diff --git a/tests/link/numba/test_elemwise.py b/tests/link/numba/test_elemwise.py
index 4c13004409..6e4bcd468f 100644
--- a/tests/link/numba/test_elemwise.py
+++ b/tests/link/numba/test_elemwise.py
@@ -146,7 +146,7 @@ def test_elemwise_speed(benchmark):
@pytest.mark.parametrize(
- "v, new_order",
+ "test_values, new_order",
[
# `{'drop': [], 'shuffle': [], 'augment': [0, 1]}`
(
@@ -204,14 +204,15 @@ def test_elemwise_speed(benchmark):
),
],
)
-def test_Dimshuffle(v, new_order):
+def test_Dimshuffle(test_values, new_order):
+ v = next(iter(test_values.keys()))
g = v.dimshuffle(new_order)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -237,7 +238,7 @@ def test_Dimshuffle_non_contiguous():
@pytest.mark.parametrize(
- "careduce_fn, axis, v",
+ "careduce_fn, axis, test_values",
[
(
lambda x, axis=None, dtype=None, acc_dtype=None: Sum(
@@ -375,15 +376,16 @@ def test_Dimshuffle_non_contiguous():
),
],
)
-def test_CAReduce(careduce_fn, axis, v):
+def test_CAReduce(careduce_fn, axis, test_values):
+ v = next(iter(test_values.keys()))
g = careduce_fn(v, axis=axis)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -401,36 +403,50 @@ def test_scalar_Elemwise_Clip():
@pytest.mark.parametrize(
- "dy, sm, axis, exc",
+ "inputs, axis, exc",
[
(
- set_test_value(
- pt.matrix(), np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
- ),
- set_test_value(pt.matrix(), rng.random(size=(2, 3)).astype(config.floatX)),
+ [
+ set_test_value(
+ pt.matrix(), np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
+ ),
+ set_test_value(
+ pt.matrix(), rng.random(size=(2, 3)).astype(config.floatX)
+ ),
+ ],
None,
None,
),
(
- set_test_value(
- pt.matrix(), np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
- ),
- set_test_value(pt.matrix(), rng.random(size=(2, 3)).astype(config.floatX)),
+ [
+ set_test_value(
+ pt.matrix(), np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
+ ),
+ set_test_value(
+ pt.matrix(), rng.random(size=(2, 3)).astype(config.floatX)
+ ),
+ ],
0,
None,
),
(
- set_test_value(
- pt.matrix(), np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
- ),
- set_test_value(pt.matrix(), rng.random(size=(2, 3)).astype(config.floatX)),
+ [
+ set_test_value(
+ pt.matrix(), np.array([[1, 1, 1], [0, 0, 0]], dtype=config.floatX)
+ ),
+ set_test_value(
+ pt.matrix(), rng.random(size=(2, 3)).astype(config.floatX)
+ ),
+ ],
1,
None,
),
],
)
-def test_SoftmaxGrad(dy, sm, axis, exc):
- g = SoftmaxGrad(axis=axis)(dy, sm)
+def test_SoftmaxGrad(inputs, axis, exc):
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ inputs = list(test_values.keys())
+ g = SoftmaxGrad(axis=axis)(*inputs)
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.warns(exc)
@@ -438,8 +454,8 @@ def test_SoftmaxGrad(dy, sm, axis, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -456,7 +472,7 @@ def test_SoftMaxGrad_constant_dy():
@pytest.mark.parametrize(
- "x, axis, exc",
+ "test_values, axis, exc",
[
(
set_test_value(pt.vector(), rng.random(size=(2,)).astype(config.floatX)),
@@ -475,7 +491,8 @@ def test_SoftMaxGrad_constant_dy():
),
],
)
-def test_Softmax(x, axis, exc):
+def test_Softmax(test_values, axis, exc):
+ x = next(iter(test_values.keys()))
g = Softmax(axis=axis)(x)
g_fg = FunctionGraph(outputs=[g])
@@ -484,15 +501,15 @@ def test_Softmax(x, axis, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, axis, exc",
+ "test_values, axis, exc",
[
(
set_test_value(pt.vector(), rng.random(size=(2,)).astype(config.floatX)),
@@ -511,7 +528,8 @@ def test_Softmax(x, axis, exc):
),
],
)
-def test_LogSoftmax(x, axis, exc):
+def test_LogSoftmax(test_values, axis, exc):
+ x = next(iter(test_values.keys()))
g = LogSoftmax(axis=axis)(x)
g_fg = FunctionGraph(outputs=[g])
@@ -520,15 +538,15 @@ def test_LogSoftmax(x, axis, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, axes, exc",
+ "test_values, axes, exc",
[
(
set_test_value(pt.dscalar(), np.array(0.0, dtype="float64")),
@@ -552,7 +570,8 @@ def test_LogSoftmax(x, axis, exc):
),
],
)
-def test_Max(x, axes, exc):
+def test_Max(test_values, axes, exc):
+ x = next(iter(test_values.keys()))
g = ptm.Max(axes)(x)
if isinstance(g, list):
@@ -565,15 +584,15 @@ def test_Max(x, axes, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, axes, exc",
+ "test_values, axes, exc",
[
(
set_test_value(pt.dscalar(), np.array(0.0, dtype="float64")),
@@ -597,7 +616,8 @@ def test_Max(x, axes, exc):
),
],
)
-def test_Argmax(x, axes, exc):
+def test_Argmax(test_values, axes, exc):
+ x = next(iter(test_values.keys()))
g = ptm.Argmax(axes)(x)
if isinstance(g, list):
@@ -610,8 +630,8 @@ def test_Argmax(x, axes, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
diff --git a/tests/link/numba/test_extra_ops.py b/tests/link/numba/test_extra_ops.py
index e61862ffdf..e39ce180c1 100644
--- a/tests/link/numba/test_extra_ops.py
+++ b/tests/link/numba/test_extra_ops.py
@@ -16,20 +16,21 @@
@pytest.mark.parametrize(
- "val",
+ "test_values",
[
set_test_value(pt.lscalar(), np.array(6, dtype="int64")),
],
)
-def test_Bartlett(val):
+def test_Bartlett(test_values):
+ val = next(iter(test_values.keys()))
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
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
assert_fn=lambda x, y: np.testing.assert_allclose(x, y, atol=1e-15),
@@ -37,7 +38,7 @@ def test_Bartlett(val):
@pytest.mark.parametrize(
- "val, axis, mode",
+ "test_values, axis, mode",
[
(
set_test_value(
@@ -97,22 +98,23 @@ def test_Bartlett(val):
),
],
)
-def test_CumOp(val, axis, mode):
+def test_CumOp(test_values, axis, mode):
+ val = next(iter(test_values.keys()))
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
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "a, val",
+ "inputs",
[
(
set_test_value(pt.lmatrix(), np.zeros((10, 2), dtype="int64")),
@@ -120,22 +122,26 @@ def test_CumOp(val, axis, mode):
)
],
)
-def test_FillDiagonal(a, val):
- g = extra_ops.FillDiagonal()(a, val)
+def test_FillDiagonal(inputs):
+ print(inputs)
+ # assert 0
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ inputs = list(test_values.keys())
+ g = extra_ops.FillDiagonal()(*inputs)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "a, val, offset",
+ "inputs",
[
(
set_test_value(pt.lmatrix(), np.zeros((10, 2), dtype="int64")),
@@ -154,15 +160,17 @@ def test_FillDiagonal(a, val):
),
],
)
-def test_FillDiagonalOffset(a, val, offset):
- g = extra_ops.FillDiagonalOffset()(a, val, offset)
+def test_FillDiagonalOffset(inputs):
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ inputs = list(test_values.keys())
+ g = extra_ops.FillDiagonalOffset()(*inputs)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -238,6 +246,10 @@ def test_FillDiagonalOffset(a, val, offset):
],
)
def test_RavelMultiIndex(arr, shape, mode, order, exc):
+ test_values = {k: v for d in arr for k, v in d.items()}
+ arr = tuple(test_values.keys())
+ test_values.update(shape)
+ shape = next(iter(shape.keys()))
g = extra_ops.RavelMultiIndex(mode, order)(*((*arr, shape)))
g_fg = FunctionGraph(outputs=[g])
@@ -246,15 +258,15 @@ def test_RavelMultiIndex(arr, shape, mode, order, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, repeats, axis, exc",
+ "test_values, repeats, axis, exc",
[
(
set_test_value(pt.lscalar(), np.array(1, dtype="int64")),
@@ -282,7 +294,10 @@ def test_RavelMultiIndex(arr, shape, mode, order, exc):
),
],
)
-def test_Repeat(x, repeats, axis, exc):
+def test_Repeat(test_values, repeats, axis, exc):
+ x = next(iter(test_values.keys()))
+ test_values.update(repeats)
+ repeats = next(iter(repeats.keys()))
g = extra_ops.Repeat(axis)(x, repeats)
g_fg = FunctionGraph(outputs=[g])
@@ -291,15 +306,15 @@ def test_Repeat(x, repeats, axis, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, axis, return_index, return_inverse, return_counts, exc",
+ "test_values, axis, return_index, return_inverse, return_counts, exc",
[
(
set_test_value(pt.lscalar(), np.array(1, dtype="int64")),
@@ -347,7 +362,8 @@ def test_Repeat(x, repeats, axis, exc):
),
],
)
-def test_Unique(x, axis, return_index, return_inverse, return_counts, exc):
+def test_Unique(test_values, axis, return_index, return_inverse, return_counts, exc):
+ x = next(iter(test_values.keys()))
g = extra_ops.Unique(return_index, return_inverse, return_counts, axis)(x)
if isinstance(g, list):
@@ -360,15 +376,15 @@ def test_Unique(x, axis, return_index, return_inverse, return_counts, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "arr, shape, order, exc",
+ "test_values, shape, order, exc",
[
(
set_test_value(pt.lvector(), np.array([9, 15, 1], dtype="int64")),
@@ -390,7 +406,8 @@ def test_Unique(x, axis, return_index, return_inverse, return_counts, exc):
),
],
)
-def test_UnravelIndex(arr, shape, order, exc):
+def test_UnravelIndex(test_values, shape, order, exc):
+ arr = next(iter(test_values.keys()))
g = extra_ops.UnravelIndex(order)(arr, shape)
if isinstance(g, list):
@@ -403,53 +420,71 @@ def test_UnravelIndex(arr, shape, order, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "a, v, side, sorter, exc",
+ "inputs, side, sorter, exc",
[
(
- set_test_value(pt.vector(), np.array([1.0, 2.0, 3.0], dtype=config.floatX)),
- set_test_value(pt.matrix(), rng.random((3, 2)).astype(config.floatX)),
+ [
+ set_test_value(
+ pt.vector(), np.array([1.0, 2.0, 3.0], dtype=config.floatX)
+ ),
+ set_test_value(pt.matrix(), rng.random((3, 2)).astype(config.floatX)),
+ ],
"left",
None,
None,
),
pytest.param(
- set_test_value(
- pt.vector(),
- np.array([0.29769574, 0.71649186, 0.20475563]).astype(config.floatX),
- ),
- set_test_value(
- pt.matrix(),
- np.array(
- [
- [0.18847123, 0.39659508],
- [0.56220006, 0.57428752],
- [0.86720994, 0.44522637],
- ]
- ).astype(config.floatX),
- ),
+ [
+ set_test_value(
+ pt.vector(),
+ np.array([0.29769574, 0.71649186, 0.20475563]).astype(
+ config.floatX
+ ),
+ ),
+ set_test_value(
+ pt.matrix(),
+ np.array(
+ [
+ [0.18847123, 0.39659508],
+ [0.56220006, 0.57428752],
+ [0.86720994, 0.44522637],
+ ]
+ ).astype(config.floatX),
+ ),
+ ],
"left",
None,
None,
),
(
- set_test_value(pt.vector(), np.array([1.0, 2.0, 3.0], dtype=config.floatX)),
- set_test_value(pt.matrix(), rng.random((3, 2)).astype(config.floatX)),
+ [
+ set_test_value(
+ pt.vector(), np.array([1.0, 2.0, 3.0], dtype=config.floatX)
+ ),
+ set_test_value(pt.matrix(), rng.random((3, 2)).astype(config.floatX)),
+ ],
"right",
set_test_value(pt.lvector(), np.array([0, 2, 1])),
UserWarning,
),
],
)
-def test_Searchsorted(a, v, side, sorter, exc):
- g = extra_ops.SearchsortedOp(side)(a, v, sorter)
+def test_Searchsorted(inputs, side, sorter, exc):
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ inputs = list(test_values.keys())
+ if isinstance(sorter, dict):
+ test_values.update(sorter)
+ sorter = next(iter(sorter.keys()))
+ inputs.append(sorter)
+ g = extra_ops.SearchsortedOp(side)(*inputs)
g_fg = FunctionGraph(outputs=[g])
cm = contextlib.suppress() if exc is None else pytest.warns(exc)
@@ -457,8 +492,8 @@ def test_Searchsorted(a, v, side, sorter, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
diff --git a/tests/link/numba/test_nlinalg.py b/tests/link/numba/test_nlinalg.py
index 6fbb6e6c58..334c189b6b 100644
--- a/tests/link/numba/test_nlinalg.py
+++ b/tests/link/numba/test_nlinalg.py
@@ -15,32 +15,38 @@
@pytest.mark.parametrize(
- "A, x, lower, exc",
+ "inputs, lower, exc",
[
(
- set_test_value(
- pt.dmatrix(),
- (lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
- ),
- set_test_value(pt.dvector(), rng.random(size=(3,)).astype("float64")),
+ [
+ set_test_value(
+ pt.dmatrix(),
+ (lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype("float64")),
+ ),
+ set_test_value(pt.dvector(), rng.random(size=(3,)).astype("float64")),
+ ],
"gen",
None,
),
(
- set_test_value(
- pt.lmatrix(),
- (lambda x: x.T.dot(x))(
- rng.integers(1, 10, size=(3, 3)).astype("int64")
+ [
+ set_test_value(
+ pt.lmatrix(),
+ (lambda x: x.T.dot(x))(
+ rng.integers(1, 10, size=(3, 3)).astype("int64")
+ ),
),
- ),
- set_test_value(pt.dvector(), rng.random(size=(3,)).astype("float64")),
+ set_test_value(pt.dvector(), rng.random(size=(3,)).astype("float64")),
+ ],
"gen",
None,
),
],
)
-def test_Solve(A, x, lower, exc):
- g = slinalg.Solve(lower=lower, b_ndim=1)(A, x)
+def test_Solve(inputs, lower, exc):
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ inputs = list(test_values.keys())
+ g = slinalg.Solve(lower=lower, b_ndim=1)(*inputs)
if isinstance(g, list):
g_fg = FunctionGraph(outputs=g)
@@ -52,15 +58,15 @@ def test_Solve(A, x, lower, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, exc",
+ "test_values, exc",
[
(
set_test_value(
@@ -78,7 +84,8 @@ def test_Solve(A, x, lower, exc):
),
],
)
-def test_Det(x, exc):
+def test_Det(test_values, exc):
+ x = next(iter(test_values.keys()))
g = nlinalg.Det()(x)
g_fg = FunctionGraph(outputs=[g])
@@ -87,15 +94,15 @@ def test_Det(x, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, exc",
+ "test_values, exc",
[
(
set_test_value(
@@ -113,7 +120,8 @@ def test_Det(x, exc):
),
],
)
-def test_SLogDet(x, exc):
+def test_SLogDet(test_values, exc):
+ x = next(iter(test_values.keys()))
g = nlinalg.SLogDet()(x)
g_fg = FunctionGraph(outputs=g)
@@ -122,8 +130,8 @@ def test_SLogDet(x, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -154,7 +162,7 @@ def test_SLogDet(x, exc):
@pytest.mark.parametrize(
- "x, exc",
+ "test_values, exc",
[
(
set_test_value(
@@ -181,7 +189,8 @@ def test_SLogDet(x, exc):
),
],
)
-def test_Eig(x, exc):
+def test_Eig(test_values, exc):
+ x = next(iter(test_values.keys()))
g = nlinalg.Eig()(x)
if isinstance(g, list):
@@ -194,15 +203,15 @@ def test_Eig(x, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, uplo, exc",
+ "test_values, uplo, exc",
[
(
set_test_value(
@@ -224,7 +233,8 @@ def test_Eig(x, exc):
),
],
)
-def test_Eigh(x, uplo, exc):
+def test_Eigh(test_values, uplo, exc):
+ x = next(iter(test_values.keys()))
g = nlinalg.Eigh(uplo)(x)
if isinstance(g, list):
@@ -237,15 +247,15 @@ def test_Eigh(x, uplo, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "op, x, exc, op_args",
+ "op, test_values, exc, op_args",
[
(
nlinalg.MatrixInverse,
@@ -289,7 +299,8 @@ def test_Eigh(x, uplo, exc):
),
],
)
-def test_matrix_inverses(op, x, exc, op_args):
+def test_matrix_inverses(op, test_values, exc, op_args):
+ x = next(iter(test_values.keys()))
g = op(*op_args)(x)
g_fg = FunctionGraph(outputs=[g])
@@ -298,15 +309,15 @@ def test_matrix_inverses(op, x, exc, op_args):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, mode, exc",
+ "test_values, mode, exc",
[
(
set_test_value(
@@ -346,7 +357,8 @@ def test_matrix_inverses(op, x, exc, op_args):
),
],
)
-def test_QRFull(x, mode, exc):
+def test_QRFull(test_values, mode, exc):
+ x = next(iter(test_values.keys()))
g = nlinalg.QRFull(mode)(x)
if isinstance(g, list):
@@ -359,15 +371,15 @@ def test_QRFull(x, mode, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "x, full_matrices, compute_uv, exc",
+ "test_values, full_matrices, compute_uv, exc",
[
(
set_test_value(
@@ -411,7 +423,8 @@ def test_QRFull(x, mode, exc):
),
],
)
-def test_SVD(x, full_matrices, compute_uv, exc):
+def test_SVD(test_values, full_matrices, compute_uv, exc):
+ x = next(iter(test_values.keys()))
g = nlinalg.SVD(full_matrices, compute_uv)(x)
if isinstance(g, list):
@@ -424,8 +437,8 @@ def test_SVD(x, full_matrices, compute_uv, exc):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
diff --git a/tests/link/numba/test_random.py b/tests/link/numba/test_random.py
index b966ed2870..5dbbdefcd8 100644
--- a/tests/link/numba/test_random.py
+++ b/tests/link/numba/test_random.py
@@ -327,7 +327,7 @@ def test_multivariate_normal():
np.array(1.0, dtype=np.float64),
),
],
- pt.as_tensor(tuple(set_test_value(pt.lscalar(), v) for v in [3, 2])),
+ [set_test_value(pt.lscalar(), v) for v in [3, 2]],
),
(
ptr.poisson,
@@ -523,15 +523,22 @@ def test_multivariate_normal():
)
def test_aligned_RandomVariable(rv_op, dist_args, size):
"""Tests for Numba samplers that are one-to-one with PyTensor's/NumPy's samplers."""
+ inputs = {k: v for d in dist_args for k, v in d.items()}
+ dist_args = list(inputs.keys())
rng = shared(np.random.default_rng(29402))
+ test_values = {}
+ if isinstance(size, list):
+ size = {k: v for d in size for k, v in d.items()}
+ test_values.update(size)
+ size = pt.as_tensor(tuple(size.keys()))
+ test_values.update(inputs)
g = rv_op(*dist_args, size=size, rng=rng)
g_fg = FunctionGraph(outputs=[g])
-
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
eval_obj_mode=False, # No python impl
@@ -577,18 +584,20 @@ def test_aligned_RandomVariable(rv_op, dist_args, size):
)
def test_unaligned_RandomVariable(rv_op, dist_args, base_size, cdf_name, params_conv):
"""Tests for Numba samplers that are not one-to-one with PyTensor's/NumPy's samplers."""
+ test_values = {k: v for d in dist_args for k, v in d.items()}
+ dist_args = list(test_values.keys())
rng = shared(np.random.default_rng(29402))
g = rv_op(*dist_args, size=(2000, *base_size), rng=rng)
g_fn = function(dist_args, g, mode=numba_mode)
samples = g_fn(
*[
- i.tag.test_value
- for i in g_fn.maker.fgraph.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
]
)
- bcast_dist_args = np.broadcast_arrays(*[i.tag.test_value for i in dist_args])
+ bcast_dist_args = np.broadcast_arrays(*[test_values[i] for i in test_values])
for idx in np.ndindex(*base_size):
cdf_params = params_conv(*(arg[idx] for arg in bcast_dist_args))
@@ -599,7 +608,7 @@ def test_unaligned_RandomVariable(rv_op, dist_args, base_size, cdf_name, params_
@pytest.mark.parametrize(
- "a, size, cm",
+ "test_values, size, cm",
[
pytest.param(
set_test_value(
@@ -636,13 +645,14 @@ def test_unaligned_RandomVariable(rv_op, dist_args, base_size, cdf_name, params_
),
],
)
-def test_DirichletRV(a, size, cm):
+def test_DirichletRV(test_values, size, cm):
+ a = next(iter(test_values.keys()))
rng = shared(np.random.default_rng(29402))
g = ptr.dirichlet(a, size=size, rng=rng)
g_fn = function([a], g, mode=numba_mode)
with cm:
- a_val = a.tag.test_value
+ a_val = test_values[a]
all_samples = []
for i in range(1000):
diff --git a/tests/link/numba/test_scalar.py b/tests/link/numba/test_scalar.py
index 437956bdc0..8588188da9 100644
--- a/tests/link/numba/test_scalar.py
+++ b/tests/link/numba/test_scalar.py
@@ -17,49 +17,58 @@
@pytest.mark.parametrize(
- "x, y",
+ "inputs",
[
(
- set_test_value(pt.lvector(), np.arange(4, dtype="int64")),
- set_test_value(pt.dvector(), np.arange(4, dtype="float64")),
+ [
+ set_test_value(pt.lvector(), np.arange(4, dtype="int64")),
+ set_test_value(pt.dvector(), np.arange(4, dtype="float64")),
+ ]
),
(
- set_test_value(pt.dmatrix(), np.arange(4, dtype="float64").reshape((2, 2))),
- set_test_value(pt.lscalar(), np.array(4, dtype="int64")),
+ [
+ set_test_value(
+ pt.dmatrix(), np.arange(4, dtype="float64").reshape((2, 2))
+ ),
+ set_test_value(pt.lscalar(), np.array(4, dtype="int64")),
+ ]
),
],
)
-def test_Second(x, y):
+def test_Second(inputs):
# We use the `Elemwise`-wrapped version of `Second`
- g = pt.second(x, y)
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ inputs = list(test_values.keys())
+ g = pt.second(*inputs)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "v, min, max",
+ "test_values, min, max",
[
(set_test_value(pt.scalar(), np.array(10, dtype=config.floatX)), 3.0, 7.0),
(set_test_value(pt.scalar(), np.array(1, dtype=config.floatX)), 3.0, 7.0),
(set_test_value(pt.scalar(), np.array(10, dtype=config.floatX)), 7.0, 3.0),
],
)
-def test_Clip(v, min, max):
+def test_Clip(test_values, min, max):
+ v = next(iter(test_values.keys()))
g = ps.clip(v, min, max)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -104,39 +113,41 @@ def test_Composite(inputs, input_values, scalar_fn):
@pytest.mark.parametrize(
- "v, dtype",
+ "test_values, dtype",
[
(set_test_value(pt.fscalar(), np.array(1.0, dtype="float32")), psb.float64),
(set_test_value(pt.dscalar(), np.array(1.0, dtype="float64")), psb.float32),
],
)
-def test_Cast(v, dtype):
+def test_Cast(test_values, dtype):
+ v = next(iter(test_values.keys()))
g = psb.Cast(dtype)(v)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "v, dtype",
+ "test_values, dtype",
[
(set_test_value(pt.iscalar(), np.array(10, dtype="int32")), psb.float64),
],
)
-def test_reciprocal(v, dtype):
+def test_reciprocal(test_values, dtype):
+ v = next(iter(test_values.keys()))
g = psb.reciprocal(v)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
diff --git a/tests/link/numba/test_scan.py b/tests/link/numba/test_scan.py
index 5db0f24222..4d2ba6900d 100644
--- a/tests/link/numba/test_scan.py
+++ b/tests/link/numba/test_scan.py
@@ -248,10 +248,8 @@ def seir_one_step(ct0, dt0, st0, et0, it0, logp_c, logp_d, beta, gamma, delta):
benchmark(scan_fn, *test_input_vals)
-@config.change_flags(compute_test_value="raise")
def test_scan_tap_output():
a_pt = pt.scalar("a")
- a_pt.tag.test_value = 10.0
b_pt = pt.arange(11).astype(config.floatX)
b_pt.name = "b"
diff --git a/tests/link/numba/test_tensor_basic.py b/tests/link/numba/test_tensor_basic.py
index 269fc57940..62555f4f4b 100644
--- a/tests/link/numba/test_tensor_basic.py
+++ b/tests/link/numba/test_tensor_basic.py
@@ -36,14 +36,18 @@
],
)
def test_Alloc(v, shape):
+ test_values = {}
+ if isinstance(v, dict):
+ test_values = v
+ v = next(iter(v.keys()))
g = pt.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
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -64,56 +68,59 @@ def test_AllocEmpty():
@pytest.mark.parametrize(
- "v", [set_test_value(ps.float64(), np.array(1.0, dtype="float64"))]
+ "test_values", [set_test_value(ps.float64(), np.array(1.0, dtype="float64"))]
)
-def test_TensorFromScalar(v):
+def test_TensorFromScalar(test_values):
+ v = next(iter(test_values.keys()))
g = ptb.TensorFromScalar()(v)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "v",
+ "test_values",
[
set_test_value(pt.scalar(), np.array(1.0, dtype=config.floatX)),
],
)
-def test_ScalarFromTensor(v):
+def test_ScalarFromTensor(test_values):
+ v = next(iter(test_values))
g = ptb.ScalarFromTensor()(v)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
def test_Unbroadcast():
- v = set_test_value(pt.row(), np.array([[1.0, 2.0]], dtype=config.floatX))
+ test_values = set_test_value(pt.row(), np.array([[1.0, 2.0]], dtype=config.floatX))
+ v = next(iter(test_values))
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
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "vals, dtype",
+ "inputs, dtype",
[
(
(
@@ -140,47 +147,53 @@ def test_Unbroadcast():
),
],
)
-def test_MakeVector(vals, dtype):
+def test_MakeVector(inputs, dtype):
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ vals = list(test_values.keys())
g = ptb.MakeVector(dtype)(*vals)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "start, stop, step, dtype",
+ "inputs, dtype",
[
(
- set_test_value(pt.lscalar(), np.array(1)),
- set_test_value(pt.lscalar(), np.array(10)),
- set_test_value(pt.lscalar(), np.array(3)),
+ [
+ set_test_value(pt.lscalar(), np.array(1)),
+ set_test_value(pt.lscalar(), np.array(10)),
+ set_test_value(pt.lscalar(), np.array(3)),
+ ],
config.floatX,
),
],
)
-def test_ARange(start, stop, step, dtype):
- g = ptb.ARange(dtype)(start, stop, step)
+def test_ARange(inputs, dtype):
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ inputs = list(test_values.keys())
+ g = ptb.ARange(dtype)(*inputs)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "vals, axis",
+ "inputs, axis",
[
(
(
@@ -228,25 +241,28 @@ def test_ARange(start, stop, step, dtype):
),
],
)
-def test_Join(vals, axis):
+def test_Join(inputs, axis):
+ test_values = {k: v for d in inputs for k, v in d.items()}
+ vals = list(test_values.keys())
g = pt.join(axis, *vals)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
def test_Join_view():
- vals = (
- set_test_value(pt.matrix(), rng.normal(size=(2, 2)).astype(config.floatX)),
- set_test_value(pt.matrix(), rng.normal(size=(2, 2)).astype(config.floatX)),
- )
+ test_values = {
+ pt.matrix(): rng.normal(size=(2, 2)).astype(config.floatX),
+ pt.matrix(): rng.normal(size=(2, 2)).astype(config.floatX),
+ }
+ vals = list(test_values.keys())
g = ptb.Join(view=1)(1, *vals)
g_fg = FunctionGraph(outputs=[g])
@@ -254,15 +270,15 @@ def test_Join_view():
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@pytest.mark.parametrize(
- "n_splits, axis, values, sizes",
+ "n_splits, axis, test_values, sizes",
[
(
0,
@@ -304,7 +320,10 @@ def test_Join_view():
),
],
)
-def test_Split(n_splits, axis, values, sizes):
+def test_Split(n_splits, axis, test_values, sizes):
+ values = next(iter(test_values.keys()))
+ test_values.update(sizes)
+ sizes = next(iter(sizes.keys()))
g = pt.split(values, sizes, n_splits, axis=axis)
assert len(g) == n_splits
if n_splits == 0:
@@ -314,8 +333,8 @@ def test_Split(n_splits, axis, values, sizes):
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -346,7 +365,7 @@ def test_Split_view():
@pytest.mark.parametrize(
- "val, offset",
+ "test_values, offset",
[
(
set_test_value(
@@ -366,15 +385,16 @@ def test_Split_view():
),
],
)
-def test_ExtractDiag(val, offset):
+def test_ExtractDiag(test_values, offset):
+ val = next(iter(test_values.keys()))
g = pt.diag(val, offset)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(
g_fg,
[
- i.tag.test_value
- for i in g_fg.inputs
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
@@ -423,14 +443,19 @@ def wrap(x):
],
)
def test_Eye(n, m, k, dtype):
+ test_values = n
+ if m:
+ test_values.update(m)
+ m = next(iter(m))
+ n = next(iter(n))
g = pt.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
+ test_values[i]
+ for i in test_values
if not isinstance(i, SharedVariable | Constant)
],
)
diff --git a/tests/scan/test_basic.py b/tests/scan/test_basic.py
index 7bdf490b68..fac500192f 100644
--- a/tests/scan/test_basic.py
+++ b/tests/scan/test_basic.py
@@ -1426,11 +1426,12 @@ def test_grad_multiple_outs_taps(self):
x0 = vector("x0")
y0 = vector("y0")
- W_in1.tag.test_value = vW_in1
- u1.tag.test_value = v_u1
- u2.tag.test_value = v_u2
- x0.tag.test_value = v_x0
- y0.tag.test_value = v_y0
+ with pytest.warns(FutureWarning):
+ W_in1.tag.test_value = vW_in1
+ u1.tag.test_value = v_u1
+ u2.tag.test_value = v_u2
+ x0.tag.test_value = v_x0
+ y0.tag.test_value = v_y0
def f_rnn_cmpl(u1_t, u2_tm1, u2_t, u2_tp1, x_tm1, y_tm1, y_tm3, W_in1):
return [
@@ -2210,7 +2211,8 @@ def f_py():
def test_compute_test_values():
"""Verify that test values can be used with scan."""
x = vector("x")
- x.tag.test_value = np.ones(3, dtype=config.floatX)
+ with pytest.warns(FutureWarning):
+ x.tag.test_value = np.ones(3, dtype=config.floatX)
y = shared(np.arange(3, dtype=config.floatX), name="y")
@@ -2242,38 +2244,39 @@ def test_compute_test_value_grad():
WEIGHT = np.array([1, 2, 1, 3, 4, 1, 5, 6, 1, 7, 8, 1], dtype="float32")
with config.change_flags(compute_test_value="raise", exception_verbosity="high"):
- W_flat = fvector(name="W")
- W_flat.tag.test_value = WEIGHT
- W = W_flat.reshape((2, 2, 3))
-
- outputs_mi = pt.as_tensor_variable(np.asarray(0, dtype="float32"))
- outputs_mi.tag.test_value = np.asarray(0, dtype="float32")
-
- def loss_mi(mi, sum_mi, W):
- outputs_ti = pt.as_tensor_variable(np.asarray(0, dtype="float32"))
- outputs_ti.tag.test_value = np.asarray(0, dtype="float32")
-
- def loss_ti(ti, sum_ti, mi, W):
- return W.sum().sum().sum() + sum_ti
+ with pytest.warns(FutureWarning):
+ W_flat = fvector(name="W")
+ W_flat.tag.test_value = WEIGHT
+ W = W_flat.reshape((2, 2, 3))
+
+ outputs_mi = pt.as_tensor_variable(np.asarray(0, dtype="float32"))
+ outputs_mi.tag.test_value = np.asarray(0, dtype="float32")
+
+ def loss_mi(mi, sum_mi, W):
+ outputs_ti = pt.as_tensor_variable(np.asarray(0, dtype="float32"))
+ outputs_ti.tag.test_value = np.asarray(0, dtype="float32")
+
+ def loss_ti(ti, sum_ti, mi, W):
+ return W.sum().sum().sum() + sum_ti
+
+ result_ti, _ = scan(
+ fn=loss_ti,
+ outputs_info=outputs_ti,
+ sequences=pt.arange(W.shape[1], dtype="int32"),
+ non_sequences=[mi, W],
+ )
+ lossmi = result_ti[-1]
+ return sum_mi + lossmi
- result_ti, _ = scan(
- fn=loss_ti,
- outputs_info=outputs_ti,
- sequences=pt.arange(W.shape[1], dtype="int32"),
- non_sequences=[mi, W],
+ result_mi, _ = scan(
+ fn=loss_mi,
+ outputs_info=outputs_mi,
+ sequences=pt.arange(W.shape[0], dtype="int32"),
+ non_sequences=[W],
)
- lossmi = result_ti[-1]
- return sum_mi + lossmi
- result_mi, _ = scan(
- fn=loss_mi,
- outputs_info=outputs_mi,
- sequences=pt.arange(W.shape[0], dtype="int32"),
- non_sequences=[W],
- )
-
- loss = result_mi[-1]
- grad(loss, W_flat)
+ loss = result_mi[-1]
+ grad(loss, W_flat)
@pytest.mark.xfail(reason="NominalVariables don't support test values")
@@ -2283,24 +2286,27 @@ def test_compute_test_value_grad_cast():
See https://groups.google.com/d/topic/theano-users/o4jK9xDe5WI/discussion
"""
with config.change_flags(compute_test_value="raise"):
- h = matrix("h")
- h.tag.test_value = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=config.floatX)
+ with pytest.warns(FutureWarning):
+ h = matrix("h")
+ h.tag.test_value = np.array(
+ [[1, 2, 3, 4], [5, 6, 7, 8]], dtype=config.floatX
+ )
- w = shared(
- np.random.default_rng(utt.fetch_seed())
- .random((4, 3))
- .astype(config.floatX),
- name="w",
- )
+ w = shared(
+ np.random.default_rng(utt.fetch_seed())
+ .random((4, 3))
+ .astype(config.floatX),
+ name="w",
+ )
- outputs, _ = scan(
- lambda i, h, w: (dot(h[i], w), i),
- outputs_info=[None, 0],
- non_sequences=[h, w],
- n_steps=3,
- )
+ outputs, _ = scan(
+ lambda i, h, w: (dot(h[i], w), i),
+ outputs_info=[None, 0],
+ non_sequences=[h, w],
+ n_steps=3,
+ )
- grad(outputs[0].sum(), w)
+ grad(outputs[0].sum(), w)
def test_constant_folding_n_steps():
diff --git a/tests/scan/test_utils.py b/tests/scan/test_utils.py
index a26c2cbd4b..ccc6526940 100644
--- a/tests/scan/test_utils.py
+++ b/tests/scan/test_utils.py
@@ -77,7 +77,8 @@ def test_ScanArgs():
with pytest.raises(TypeError):
ScanArgs.from_node(pt.ones(2).owner)
- hmm_model_env = create_test_hmm()
+ with pytest.warns(FutureWarning):
+ hmm_model_env = create_test_hmm()
scan_args = hmm_model_env["scan_args"]
scan_op = hmm_model_env["scan_op"]
@@ -136,15 +137,16 @@ def test_ScanArgs():
def test_ScanArgs_basics_mit_sot():
srng = pt.random.RandomStream()
- N_pt = pt.iscalar("N")
- N_pt.tag.test_value = 10
- M_pt = pt.iscalar("M")
- M_pt.tag.test_value = 2
+ with pytest.warns(FutureWarning):
+ N_pt = pt.iscalar("N")
+ N_pt.tag.test_value = 10
+ M_pt = pt.iscalar("M")
+ M_pt.tag.test_value = 2
- mus_pt = pt.matrix("mus")
- mus_pt.tag.test_value = np.stack(
- [np.arange(0.0, 10), np.arange(0.0, -10, -1)], axis=-1
- ).astype(pytensor.config.floatX)
+ mus_pt = pt.matrix("mus")
+ mus_pt.tag.test_value = np.stack(
+ [np.arange(0.0, 10), np.arange(0.0, -10, -1)], axis=-1
+ ).astype(pytensor.config.floatX)
sigmas_pt = pt.ones((N_pt,))
sigmas_pt.name = "sigmas"
@@ -199,7 +201,8 @@ def scan_fn(mus_t, sigma_t, S_tm2, S_tm1, Gamma_t):
def test_ScanArgs_remove_inner_input():
- hmm_model_env = create_test_hmm()
+ with pytest.warns(FutureWarning):
+ hmm_model_env = create_test_hmm()
scan_args = hmm_model_env["scan_args"]
hmm_model_env["scan_op"]
Y_t = hmm_model_env["Y_t"]
@@ -269,7 +272,8 @@ def test_ScanArgs_remove_inner_input():
def test_ScanArgs_remove_outer_input():
- hmm_model_env = create_test_hmm()
+ with pytest.warns(FutureWarning):
+ hmm_model_env = create_test_hmm()
scan_args = hmm_model_env["scan_args"]
hmm_model_env["scan_op"]
Y_t = hmm_model_env["Y_t"]
@@ -315,7 +319,8 @@ def test_ScanArgs_remove_outer_input():
def test_ScanArgs_remove_inner_output():
- hmm_model_env = create_test_hmm()
+ with pytest.warns(FutureWarning):
+ hmm_model_env = create_test_hmm()
scan_args = hmm_model_env["scan_args"]
hmm_model_env["scan_op"]
Y_t = hmm_model_env["Y_t"]
@@ -353,7 +358,8 @@ def test_ScanArgs_remove_inner_output():
def test_ScanArgs_remove_outer_output():
- hmm_model_env = create_test_hmm()
+ with pytest.warns(FutureWarning):
+ hmm_model_env = create_test_hmm()
scan_args = hmm_model_env["scan_args"]
hmm_model_env["scan_op"]
Y_t = hmm_model_env["Y_t"]
@@ -390,7 +396,8 @@ def test_ScanArgs_remove_outer_output():
def test_ScanArgs_remove_nonseq_outer_input():
- hmm_model_env = create_test_hmm()
+ with pytest.warns(FutureWarning):
+ hmm_model_env = create_test_hmm()
scan_args = hmm_model_env["scan_args"]
hmm_model_env["scan_op"]
Y_t = hmm_model_env["Y_t"]
@@ -427,7 +434,8 @@ def test_ScanArgs_remove_nonseq_outer_input():
def test_ScanArgs_remove_nonseq_inner_input():
- hmm_model_env = create_test_hmm()
+ with pytest.warns(FutureWarning):
+ hmm_model_env = create_test_hmm()
scan_args = hmm_model_env["scan_args"]
hmm_model_env["scan_op"]
hmm_model_env["Y_t"]
@@ -463,7 +471,8 @@ def test_ScanArgs_remove_nonseq_inner_input():
def test_ScanArgs_remove_shared_inner_output():
- hmm_model_env = create_test_hmm()
+ with pytest.warns(FutureWarning):
+ hmm_model_env = create_test_hmm()
scan_args = hmm_model_env["scan_args"]
hmm_model_env["scan_op"]
hmm_model_env["Y_t"]
diff --git a/tests/tensor/random/rewriting/test_basic.py b/tests/tensor/random/rewriting/test_basic.py
index f342d5b81c..7ee0b155de 100644
--- a/tests/tensor/random/rewriting/test_basic.py
+++ b/tests/tensor/random/rewriting/test_basic.py
@@ -146,7 +146,6 @@ def test_inplace_rewrites(rv_op):
assert check_stack_trace(f)
-@config.change_flags(compute_test_value="raise")
@pytest.mark.parametrize(
"dist_op, dist_params, size",
[
@@ -200,14 +199,18 @@ def test_inplace_rewrites(rv_op):
def test_local_rv_size_lift(dist_op, dist_params, size):
rng = shared(np.random.default_rng(1233532), borrow=False)
- new_out, f_inputs, dist_st, f_rewritten = apply_local_rewrite_to_rv(
- local_rv_size_lift,
- lambda rv: rv,
- dist_op,
- dist_params,
- size,
- rng,
- )
+ with pytest.warns(
+ FutureWarning,
+ match="test_value machinery is deprecated and will stop working in the future.",
+ ):
+ new_out, f_inputs, dist_st, f_rewritten = apply_local_rewrite_to_rv(
+ local_rv_size_lift,
+ lambda rv: rv,
+ dist_op,
+ dist_params,
+ size,
+ rng,
+ )
assert new_out.owner.op.size_param(new_out.owner).data is None
@@ -413,18 +416,18 @@ def test_local_rv_size_lift(dist_op, dist_params, size):
),
],
)
-@config.change_flags(compute_test_value_opt="raise", compute_test_value="raise")
def test_DimShuffle_lift(ds_order, lifted, dist_op, dist_params, size, rtol):
rng = shared(np.random.default_rng(1233532), borrow=False)
- new_out, f_inputs, dist_st, f_rewritten = apply_local_rewrite_to_rv(
- local_dimshuffle_rv_lift,
- lambda rv: rv.dimshuffle(ds_order),
- dist_op,
- dist_params,
- size,
- rng,
- )
+ with pytest.warns(FutureWarning):
+ new_out, f_inputs, dist_st, f_rewritten = apply_local_rewrite_to_rv(
+ local_dimshuffle_rv_lift,
+ lambda rv: rv.dimshuffle(ds_order),
+ dist_op,
+ dist_params,
+ size,
+ rng,
+ )
if lifted:
assert isinstance(new_out.owner.op, type(dist_op))
@@ -443,7 +446,8 @@ def test_DimShuffle_lift(ds_order, lifted, dist_op, dist_params, size, rtol):
mode=no_mode,
)
- arg_values = [p.get_test_value() for p in f_inputs]
+ with pytest.warns(FutureWarning):
+ arg_values = [p.get_test_value() for p in f_inputs]
res_base = f_base(*arg_values)
res_rewritten, _ = f_rewritten(*arg_values)
@@ -797,7 +801,6 @@ def rand_bool_mask(shape, rng=None):
),
],
)
-@config.change_flags(compute_test_value_opt="raise", compute_test_value="raise")
def test_Subtensor_lift(indices, lifted, dist_op, dist_params, size):
from pytensor.tensor.subtensor import as_index_constant
@@ -807,17 +810,19 @@ def test_Subtensor_lift(indices, lifted, dist_op, dist_params, size):
for i in indices:
i_pt = as_index_constant(i)
if not isinstance(i_pt, slice):
- i_pt.tag.test_value = i
+ with pytest.warns(FutureWarning):
+ i_pt.tag.test_value = i
indices_pt += (i_pt,)
- new_out, f_inputs, dist_st, f_rewritten = apply_local_rewrite_to_rv(
- local_subtensor_rv_lift,
- lambda rv: rv[indices_pt],
- dist_op,
- dist_params,
- size,
- rng,
- )
+ with pytest.warns(FutureWarning):
+ new_out, f_inputs, dist_st, f_rewritten = apply_local_rewrite_to_rv(
+ local_subtensor_rv_lift,
+ lambda rv: rv[indices_pt],
+ dist_op,
+ dist_params,
+ size,
+ rng,
+ )
def is_subtensor_or_dimshuffle_subtensor(inp) -> bool:
subtensor_ops = Subtensor | AdvancedSubtensor | AdvancedSubtensor1
@@ -846,7 +851,8 @@ def is_subtensor_or_dimshuffle_subtensor(inp) -> bool:
mode=no_mode,
)
- arg_values = [p.get_test_value() for p in f_inputs]
+ with pytest.warns(FutureWarning):
+ arg_values = [p.get_test_value() for p in f_inputs]
res_base = f_base(*arg_values)
res_rewritten, _ = f_rewritten(*arg_values)
@@ -857,7 +863,8 @@ def test_Subtensor_lift_restrictions():
rng = shared(np.random.default_rng(1233532), borrow=False)
std = vector("std")
- std.tag.test_value = np.array([1e-5, 2e-5, 3e-5], dtype=config.floatX)
+ with pytest.warns(FutureWarning):
+ std.tag.test_value = np.array([1e-5, 2e-5, 3e-5], dtype=config.floatX)
x = normal(pt.arange(2), pt.ones(2), rng=rng)
y = x[1]
# The non-`Subtensor` client depends on the RNG state, so we can't perform
@@ -960,14 +967,15 @@ def test_Dimshuffle_lift_restrictions():
def test_Dimshuffle_lift_rename(ds_order, lifted, dist_op, dist_params, size, rtol):
rng = shared(np.random.default_rng(1233532), borrow=False)
- new_out, *_ = apply_local_rewrite_to_rv(
- local_dimshuffle_rv_lift,
- lambda rv: rv.dimshuffle(ds_order),
- dist_op,
- dist_params,
- size,
- rng,
- name="test_name",
- )
+ with pytest.warns(FutureWarning):
+ new_out, *_ = apply_local_rewrite_to_rv(
+ local_dimshuffle_rv_lift,
+ lambda rv: rv.dimshuffle(ds_order),
+ dist_op,
+ dist_params,
+ size,
+ rng,
+ name="test_name",
+ )
assert new_out.name == "test_name_lifted"
diff --git a/tests/tensor/random/test_basic.py b/tests/tensor/random/test_basic.py
index 7d24a49228..6c7fb49593 100644
--- a/tests/tensor/random/test_basic.py
+++ b/tests/tensor/random/test_basic.py
@@ -269,13 +269,14 @@ def test_normal_infer_shape(M, sd, size):
@config.change_flags(compute_test_value="raise")
def test_normal_ShapeFeature():
- M_pt = iscalar("M")
- M_pt.tag.test_value = 3
- sd_pt = scalar("sd")
- sd_pt.tag.test_value = np.array(1.0, dtype=config.floatX)
+ with pytest.warns(FutureWarning):
+ M_pt = iscalar("M")
+ M_pt.tag.test_value = 3
+ sd_pt = scalar("sd")
+ sd_pt.tag.test_value = np.array(1.0, dtype=config.floatX)
- d_rv = normal(pt.ones((M_pt,)), sd_pt, size=(2, M_pt))
- d_rv.tag.test_value
+ d_rv = normal(pt.ones((M_pt,)), sd_pt, size=(2, M_pt))
+ d_rv.tag.test_value
fg = FunctionGraph(
[i for i in graph_inputs([d_rv]) if not isinstance(i, Constant)],
@@ -627,7 +628,8 @@ def test_mvnormal_default_args():
@config.change_flags(compute_test_value="raise")
def test_mvnormal_ShapeFeature():
M_pt = iscalar("M")
- M_pt.tag.test_value = 2
+ with pytest.warns(FutureWarning):
+ M_pt.tag.test_value = 2
d_rv = multivariate_normal(pt.ones((M_pt,)), pt.eye(M_pt), size=2)
@@ -645,12 +647,14 @@ def test_mvnormal_ShapeFeature():
# Test broadcasted shapes
mean = tensor(dtype=config.floatX, shape=(1, None))
- mean.tag.test_value = np.array([[0, 1, 2]], dtype=config.floatX)
+ with pytest.warns(FutureWarning):
+ mean.tag.test_value = np.array([[0, 1, 2]], dtype=config.floatX)
test_covar = np.diag(np.array([1, 10, 100], dtype=config.floatX))
test_covar = np.stack([test_covar, test_covar * 10.0])
cov = pt.as_tensor(test_covar).type()
- cov.tag.test_value = test_covar
+ with pytest.warns(FutureWarning):
+ cov.tag.test_value = test_covar
d_rv = multivariate_normal(mean, cov, size=[2, 3, 2])
@@ -736,24 +740,25 @@ def test_dirichlet_infer_shape(M, size):
fn_inputs, [pt.as_tensor(o) for o in [*rv_shape, rv]], mode=py_mode
)
- *rv_shape_val, rv_val = pytensor_fn(
- *[
- i.tag.test_value
- for i in fn_inputs
- if not isinstance(i, SharedVariable | Constant)
- ]
- )
+ with pytest.warns(FutureWarning):
+ *rv_shape_val, rv_val = pytensor_fn(
+ *[
+ i.tag.test_value
+ for i in fn_inputs
+ if not isinstance(i, SharedVariable | Constant)
+ ]
+ )
assert tuple(rv_shape_val) == tuple(rv_val.shape)
-@config.change_flags(compute_test_value="raise")
def test_dirichlet_ShapeFeature():
"""Make sure `RandomVariable.infer_shape` works with `ShapeFeature`."""
- M_pt = iscalar("M")
- M_pt.tag.test_value = 2
- N_pt = iscalar("N")
- N_pt.tag.test_value = 3
+ with pytest.warns(FutureWarning):
+ M_pt = iscalar("M")
+ M_pt.tag.test_value = 2
+ N_pt = iscalar("N")
+ N_pt.tag.test_value = 3
d_rv = dirichlet(pt.ones((M_pt, N_pt)), name="Gamma")
@@ -1594,7 +1599,6 @@ def test_unnatural_batched_dims(batch_dims_tester):
batch_dims_tester()
-@config.change_flags(compute_test_value="off")
def test_pickle():
# This is an interesting `Op` case, because it has a conditional dtype
sample_a = choice(5, replace=False, size=(2, 3))
diff --git a/tests/tensor/random/test_op.py b/tests/tensor/random/test_op.py
index 8e74b06bd4..6b1ef6c44e 100644
--- a/tests/tensor/random/test_op.py
+++ b/tests/tensor/random/test_op.py
@@ -15,8 +15,9 @@
@pytest.fixture(scope="function", autouse=False)
def strict_test_value_flags():
- with config.change_flags(cxx="", compute_test_value="raise"):
- yield
+ with pytest.warns(FutureWarning):
+ with config.change_flags(cxx="", compute_test_value="raise"):
+ yield
def test_RandomVariable_basics(strict_test_value_flags):
@@ -90,17 +91,17 @@ def test_RandomVariable_bcast(strict_test_value_flags):
rv = RandomVariable("normal", 0, [0, 0], config.floatX, inplace=True)
mu = tensor(dtype=config.floatX, shape=(1, None, None))
- mu.tag.test_value = np.zeros((1, 2, 3)).astype(config.floatX)
- sd = tensor(dtype=config.floatX, shape=(None, None))
- sd.tag.test_value = np.ones((2, 3)).astype(config.floatX)
-
- s1 = iscalar()
- s1.tag.test_value = 1
- s2 = iscalar()
- s2.tag.test_value = 2
- s3 = iscalar()
- s3.tag.test_value = 3
- s3 = Assert("testing")(s3, eq(s1, 1))
+ with pytest.warns(FutureWarning):
+ mu.tag.test_value = np.zeros((1, 2, 3)).astype(config.floatX)
+ sd = tensor(dtype=config.floatX, shape=(None, None))
+ sd.tag.test_value = np.ones((2, 3)).astype(config.floatX)
+ s1 = iscalar()
+ s1.tag.test_value = 1
+ s2 = iscalar()
+ s2.tag.test_value = 2
+ s3 = iscalar()
+ s3.tag.test_value = 3
+ s3 = Assert("testing")(s3, eq(s1, 1))
res = rv(mu, sd, size=(s1, s2, s3))
assert res.broadcastable == (False,) * 3
@@ -119,19 +120,20 @@ def test_RandomVariable_bcast(strict_test_value_flags):
def test_RandomVariable_bcast_specify_shape(strict_test_value_flags):
rv = RandomVariable("normal", 0, [0, 0], config.floatX, inplace=True)
- s1 = pt.as_tensor(1, dtype=np.int64)
- s2 = iscalar()
- s2.tag.test_value = 2
- s3 = iscalar()
- s3.tag.test_value = 3
- s3 = Assert("testing")(s3, eq(s1, 1))
+ with pytest.warns(FutureWarning):
+ s1 = pt.as_tensor(1, dtype=np.int64)
+ s2 = iscalar()
+ s2.tag.test_value = 2
+ s3 = iscalar()
+ s3.tag.test_value = 3
+ s3 = Assert("testing")(s3, eq(s1, 1))
- size = specify_shape(pt.as_tensor([s1, s3, s2, s2, s1]), (5,))
- mu = tensor(dtype=config.floatX, shape=(None, None, 1))
- mu.tag.test_value = np.random.normal(size=(2, 2, 1)).astype(config.floatX)
+ size = specify_shape(pt.as_tensor([s1, s3, s2, s2, s1]), (5,))
+ mu = tensor(dtype=config.floatX, shape=(None, None, 1))
+ mu.tag.test_value = np.random.normal(size=(2, 2, 1)).astype(config.floatX)
- std = tensor(dtype=config.floatX, shape=(None, 1, 1))
- std.tag.test_value = np.ones((2, 1, 1)).astype(config.floatX)
+ std = tensor(dtype=config.floatX, shape=(None, 1, 1))
+ std.tag.test_value = np.ones((2, 1, 1)).astype(config.floatX)
res = rv(mu, std, size=size)
assert res.type.shape == (1, None, None, None, 1)
diff --git a/tests/tensor/random/test_utils.py b/tests/tensor/random/test_utils.py
index 3616b2fd24..453ac18743 100644
--- a/tests/tensor/random/test_utils.py
+++ b/tests/tensor/random/test_utils.py
@@ -17,7 +17,7 @@
def set_pytensor_flags():
rewrites_query = RewriteDatabaseQuery(include=[None], exclude=[])
py_mode = Mode("py", rewrites_query)
- with config.change_flags(mode=py_mode, compute_test_value="warn"):
+ with config.change_flags(mode=py_mode):
yield
@@ -72,16 +72,18 @@ def test_broadcast_params():
# Try it in PyTensor
with config.change_flags(compute_test_value="raise"):
- mean = tensor(dtype=config.floatX, shape=(None, 1))
- mean.tag.test_value = np.array([[0], [10], [100]], dtype=config.floatX)
- cov = matrix()
- cov.tag.test_value = np.diag(np.array([1e-6], dtype=config.floatX))
- params = [mean, cov]
- res = broadcast_params(params, ndims_params)
- assert np.array_equal(res[0].get_test_value(), mean.get_test_value())
- assert np.array_equal(
- res[1].get_test_value(), np.broadcast_to(cov.get_test_value(), (3, 1, 1))
- )
+ with pytest.warns(FutureWarning):
+ mean = tensor(dtype=config.floatX, shape=(None, 1))
+ mean.tag.test_value = np.array([[0], [10], [100]], dtype=config.floatX)
+ cov = matrix()
+ cov.tag.test_value = np.diag(np.array([1e-6], dtype=config.floatX))
+ params = [mean, cov]
+ res = broadcast_params(params, ndims_params)
+ assert np.array_equal(res[0].get_test_value(), mean.get_test_value())
+ assert np.array_equal(
+ res[1].get_test_value(),
+ np.broadcast_to(cov.get_test_value(), (3, 1, 1)),
+ )
class TestSharedRandomStream:
diff --git a/tests/tensor/rewriting/test_elemwise.py b/tests/tensor/rewriting/test_elemwise.py
index 9488a9f688..07ce5d0dc2 100644
--- a/tests/tensor/rewriting/test_elemwise.py
+++ b/tests/tensor/rewriting/test_elemwise.py
@@ -1160,9 +1160,10 @@ def test_test_values(self, test_value):
"""
x, y, z = dmatrices("xyz")
- x.tag.test_value = test_value
- y.tag.test_value = test_value
- z.tag.test_value = test_value
+ with pytest.warns(FutureWarning):
+ x.tag.test_value = test_value
+ y.tag.test_value = test_value
+ z.tag.test_value = test_value
with config.change_flags(
compute_test_value="raise", compute_test_value_opt="raise"
diff --git a/tests/tensor/rewriting/test_subtensor.py b/tests/tensor/rewriting/test_subtensor.py
index 91575bc7da..3855ca926a 100644
--- a/tests/tensor/rewriting/test_subtensor.py
+++ b/tests/tensor/rewriting/test_subtensor.py
@@ -95,7 +95,9 @@
def test_local_replace_AdvancedSubtensor(indices, is_none):
X_val = np.random.normal(size=(4, 4, 4))
X = tensor(dtype=np.float64, shape=(None, None, None), name="X")
- X.tag.test_value = X_val
+
+ with pytest.warns(FutureWarning):
+ X.tag.test_value = X_val
Y = X[indices]
@@ -124,8 +126,12 @@ def test_local_replace_AdvancedSubtensor(indices, is_none):
if v.owner
)
- res_val = res_fn(*[i.tag.test_value for i in inputs])
- exp_res_val = exp_res_fn(*[i.tag.test_value for i in inputs])
+ with pytest.warns(
+ FutureWarning,
+ match="test_value machinery is deprecated and will stop working in the future.",
+ ):
+ res_val = res_fn(*[i.tag.test_value for i in inputs])
+ exp_res_val = exp_res_fn(*[i.tag.test_value for i in inputs])
assert np.array_equal(res_val, exp_res_val)
diff --git a/tests/tensor/test_basic.py b/tests/tensor/test_basic.py
index 323d401f42..6dc7be6488 100644
--- a/tests/tensor/test_basic.py
+++ b/tests/tensor/test_basic.py
@@ -151,8 +151,6 @@
)
-pytestmark = pytest.mark.filterwarnings("error")
-
if config.mode == "FAST_COMPILE":
mode_opt = "FAST_RUN"
else:
@@ -1110,13 +1108,15 @@ class TestNonzero:
def test_nonzero(self):
def check(m):
m_symb = tensor(dtype=m.dtype, shape=(None,) * m.ndim)
- m_symb.tag.test_value = m
+ with pytest.warns(FutureWarning):
+ m_symb.tag.test_value = m
res_tuple_pt = nonzero(m_symb, return_matrix=False)
res_matrix_pt = nonzero(m_symb, return_matrix=True)
res_tuple = tuple(r.tag.test_value for r in res_tuple_pt)
- res_matrix = res_matrix_pt.tag.test_value
+ with pytest.warns(FutureWarning):
+ res_matrix = res_matrix_pt.tag.test_value
assert np.allclose(res_matrix, np.vstack(np.nonzero(m)))
@@ -1139,11 +1139,13 @@ def check(m):
def test_flatnonzero(self):
def check(m):
m_symb = tensor(dtype=m.dtype, shape=(None,) * m.ndim)
- m_symb.tag.test_value = m
+ with pytest.warns(FutureWarning):
+ m_symb.tag.test_value = m
res_pt = flatnonzero(m_symb)
- result = res_pt.tag.test_value
+ with pytest.warns(FutureWarning):
+ result = res_pt.tag.test_value
assert np.allclose(result, np.flatnonzero(m))
rand0d = np.empty(())
@@ -1168,11 +1170,13 @@ def check(m):
def test_nonzero_values(self):
def check(m):
m_symb = tensor(dtype=m.dtype, shape=(None,) * m.ndim)
- m_symb.tag.test_value = m
+ with pytest.warns(FutureWarning):
+ m_symb.tag.test_value = m
res_pt = nonzero_values(m_symb)
- result = res_pt.tag.test_value
+ with pytest.warns(FutureWarning):
+ result = res_pt.tag.test_value
assert np.allclose(result, m[np.nonzero(m)], equal_nan=True)
rand0d = np.empty(())
@@ -3545,11 +3549,11 @@ def test_assert(self):
a = Assert()(c, c > 1)
assert get_underlying_scalar_constant_value(a) == 2
- with config.change_flags(compute_test_value="off"):
- # condition is always False
- a = Assert()(c, c > 2)
- with pytest.raises(NotScalarConstantError):
- get_underlying_scalar_constant_value(a)
+ # with config.change_flags(compute_test_value="off"):
+ # condition is always False
+ a = Assert()(c, c > 2)
+ with pytest.raises(NotScalarConstantError):
+ get_underlying_scalar_constant_value(a)
# condition is not constant
a = Assert()(c, c > x)
diff --git a/tests/tensor/test_extra_ops.py b/tests/tensor/test_extra_ops.py
index 0da714c3bf..7d853f43d1 100644
--- a/tests/tensor/test_extra_ops.py
+++ b/tests/tensor/test_extra_ops.py
@@ -63,7 +63,8 @@
def set_test_value(x, v):
- x.tag.test_value = v
+ with pytest.warns(FutureWarning):
+ x.tag.test_value = v
return x
@@ -1202,14 +1203,16 @@ def test_broadcast_shape_constants():
def test_broadcast_shape_symbolic(s1_vals, s2_vals, exp_res):
s1s = pt.lscalars(len(s1_vals))
eval_point = {}
- for s, s_val in zip(s1s, s1_vals):
- eval_point[s] = s_val
- s.tag.test_value = s_val
+ with pytest.warns(FutureWarning):
+ for s, s_val in zip(s1s, s1_vals):
+ eval_point[s] = s_val
+ s.tag.test_value = s_val
s2s = pt.lscalars(len(s2_vals))
- for s, s_val in zip(s2s, s2_vals):
- eval_point[s] = s_val
- s.tag.test_value = s_val
+ with pytest.warns(FutureWarning):
+ for s, s_val in zip(s2s, s2_vals):
+ eval_point[s] = s_val
+ s.tag.test_value = s_val
res = broadcast_shape(s1s, s2s, arrays_are_shapes=True)
res = pt.as_tensor(res)
diff --git a/tests/tensor/test_math_scipy.py b/tests/tensor/test_math_scipy.py
index 6ca9279bca..bf7331cb8e 100644
--- a/tests/tensor/test_math_scipy.py
+++ b/tests/tensor/test_math_scipy.py
@@ -226,7 +226,7 @@ def scipy_special_gammal(k, x):
good=_good_broadcast_unary_gammaln,
grad=_grad_broadcast_unary_gammaln,
mode=mode_no_scipy,
- eps=1e-5,
+ eps=1e-4,
)
TestGammaInplaceBroadcast = makeBroadcastTester(
op=inplace.gamma_inplace,
diff --git a/tests/tensor/test_nlinalg.py b/tests/tensor/test_nlinalg.py
index 1a13992011..550fe81f48 100644
--- a/tests/tensor/test_nlinalg.py
+++ b/tests/tensor/test_nlinalg.py
@@ -528,7 +528,8 @@ def test_numpy_compare(self, n):
config.floatX
)
A = matrix("A", dtype=config.floatX)
- A.tag.test_value = a
+ with pytest.warns(FutureWarning):
+ A.tag.test_value = a
Q = matrix_power(A, n)
n_p = np.linalg.matrix_power(a, n)
assert np.allclose(n_p, Q.get_test_value())
diff --git a/tests/tensor/test_shape.py b/tests/tensor/test_shape.py
index 2ffcb25fe5..0e22c0ff3f 100644
--- a/tests/tensor/test_shape.py
+++ b/tests/tensor/test_shape.py
@@ -620,10 +620,11 @@ def test_reshape(self):
@config.change_flags(compute_test_value="raise")
def test_nonstandard_shapes():
- a = tensor3(config.floatX)
- a.tag.test_value = np.random.random((2, 3, 4)).astype(config.floatX)
- b = tensor3(config.floatX)
- b.tag.test_value = np.random.random((2, 3, 4)).astype(config.floatX)
+ with pytest.warns(FutureWarning):
+ a = tensor3(config.floatX)
+ a.tag.test_value = np.random.random((2, 3, 4)).astype(config.floatX)
+ b = tensor3(config.floatX)
+ b.tag.test_value = np.random.random((2, 3, 4)).astype(config.floatX)
tl = make_list([a, b])
tl_shape = shape(tl)
diff --git a/tests/tensor/test_subtensor.py b/tests/tensor/test_subtensor.py
index d02880f543..08cc13512d 100644
--- a/tests/tensor/test_subtensor.py
+++ b/tests/tensor/test_subtensor.py
@@ -400,7 +400,6 @@ def test_err_invalid(self):
with pytest.raises(IndexError):
n.__getitem__(0)
- @config.change_flags(compute_test_value="off")
def test_err_bounds(self):
n = self.shared(np.ones(3, dtype=self.dtype))
t = n[7]
@@ -481,7 +480,6 @@ def test_ok_strided(self):
assert tval.shape == (2,)
assert (tval == [0.0, 2.0]).all()
- @config.change_flags(compute_test_value="off")
def test_err_bounds0(self):
n = self.shared(np.ones((2, 3), dtype=self.dtype) * 5)
for idx in [(0, 4), (0, -4)]:
@@ -497,7 +495,6 @@ def test_err_bounds0(self):
finally:
_logger.setLevel(oldlevel)
- @config.change_flags(compute_test_value="off")
def test_err_bounds1(self):
n = self.shared(np.ones((2, 3), dtype=self.dtype) * 5)
t = n[4:5, 3]
@@ -719,71 +716,70 @@ def numpy_inc_subtensor(x, idx, a):
inc_subtensor(n4[test_array > 2, ..., 0, 1], 1).eval(),
)
- with config.change_flags(compute_test_value="off"):
- # the boolean mask should have the correct shape
- # - too large, padded with True
- mask = np.array([True, False, True])
- with pytest.raises(IndexError):
- test_array[mask].eval()
- with pytest.raises(IndexError):
- test_array[mask, ...].eval()
- with pytest.raises(IndexError):
- inc_subtensor(test_array[mask], 1).eval()
- with pytest.raises(IndexError):
- inc_subtensor(test_array[mask, ...], 1).eval()
- mask = np.array([[True, False, False, True], [False, True, False, True]])
- with pytest.raises(IndexError):
- test_array[mask].eval()
- with pytest.raises(IndexError):
- inc_subtensor(test_array[mask], 1).eval()
- # - too large, padded with False (this works in NumPy < 0.13.0)
- mask = np.array([True, False, False])
- with pytest.raises(IndexError):
- test_array[mask].eval()
- with pytest.raises(IndexError):
- test_array[mask, ...].eval()
- with pytest.raises(IndexError):
- inc_subtensor(test_array[mask], 1).eval()
- with pytest.raises(IndexError):
- inc_subtensor(test_array[mask, ...], 1).eval()
- mask = np.array([[True, False, False, False], [False, True, False, False]])
- with pytest.raises(IndexError):
- test_array[mask].eval()
- with pytest.raises(IndexError):
- inc_subtensor(test_array[mask], 1).eval()
- # - mask too small (this works in NumPy < 0.13.0)
- mask = np.array([True])
- with pytest.raises(IndexError):
- test_array[mask].eval()
- with pytest.raises(IndexError):
- test_array[mask, ...].eval()
- with pytest.raises(IndexError):
- inc_subtensor(test_array[mask], 1).eval()
- with pytest.raises(IndexError):
- inc_subtensor(test_array[mask, ...], 1).eval()
- mask = np.array([[True], [True]])
- with pytest.raises(IndexError):
- test_array[mask].eval()
- with pytest.raises(IndexError):
- inc_subtensor(test_array[mask], 1).eval()
- # - too many dimensions
- mask = np.array([[[True, False, False], [False, True, False]]])
- with pytest.raises(IndexError):
- test_array.__getitem__(mask)
- with pytest.raises(IndexError):
- test_array.__getitem__(mask)
-
- # special cases: Python bools and bools nested in Python arrays are not supported
- with pytest.raises(TypeError):
- test_array.__getitem__((True,))
- with pytest.raises(TypeError):
- test_array.__getitem__((False,))
- with pytest.raises(TypeError):
- test_array.__getitem__((True, False))
- with pytest.raises(TypeError):
- test_array.__getitem__(([0, 1], [0, False]))
- with pytest.raises(TypeError):
- test_array.__getitem__(([0, 1], [0, pytensor.shared(True)]))
+ # the boolean mask should have the correct shape
+ # - too large, padded with True
+ mask = np.array([True, False, True])
+ with pytest.raises(IndexError):
+ test_array[mask].eval()
+ with pytest.raises(IndexError):
+ test_array[mask, ...].eval()
+ with pytest.raises(IndexError):
+ inc_subtensor(test_array[mask], 1).eval()
+ with pytest.raises(IndexError):
+ inc_subtensor(test_array[mask, ...], 1).eval()
+ mask = np.array([[True, False, False, True], [False, True, False, True]])
+ with pytest.raises(IndexError):
+ test_array[mask].eval()
+ with pytest.raises(IndexError):
+ inc_subtensor(test_array[mask], 1).eval()
+ # - too large, padded with False (this works in NumPy < 0.13.0)
+ mask = np.array([True, False, False])
+ with pytest.raises(IndexError):
+ test_array[mask].eval()
+ with pytest.raises(IndexError):
+ test_array[mask, ...].eval()
+ with pytest.raises(IndexError):
+ inc_subtensor(test_array[mask], 1).eval()
+ with pytest.raises(IndexError):
+ inc_subtensor(test_array[mask, ...], 1).eval()
+ mask = np.array([[True, False, False, False], [False, True, False, False]])
+ with pytest.raises(IndexError):
+ test_array[mask].eval()
+ with pytest.raises(IndexError):
+ inc_subtensor(test_array[mask], 1).eval()
+ # - mask too small (this works in NumPy < 0.13.0)
+ mask = np.array([True])
+ with pytest.raises(IndexError):
+ test_array[mask].eval()
+ with pytest.raises(IndexError):
+ test_array[mask, ...].eval()
+ with pytest.raises(IndexError):
+ inc_subtensor(test_array[mask], 1).eval()
+ with pytest.raises(IndexError):
+ inc_subtensor(test_array[mask, ...], 1).eval()
+ mask = np.array([[True], [True]])
+ with pytest.raises(IndexError):
+ test_array[mask].eval()
+ with pytest.raises(IndexError):
+ inc_subtensor(test_array[mask], 1).eval()
+ # - too many dimensions
+ mask = np.array([[[True, False, False], [False, True, False]]])
+ with pytest.raises(IndexError):
+ test_array.__getitem__(mask)
+ with pytest.raises(IndexError):
+ test_array.__getitem__(mask)
+
+ # special cases: Python bools and bools nested in Python arrays are not supported
+ with pytest.raises(TypeError):
+ test_array.__getitem__((True,))
+ with pytest.raises(TypeError):
+ test_array.__getitem__((False,))
+ with pytest.raises(TypeError):
+ test_array.__getitem__((True, False))
+ with pytest.raises(TypeError):
+ test_array.__getitem__(([0, 1], [0, False]))
+ with pytest.raises(TypeError):
+ test_array.__getitem__(([0, 1], [0, pytensor.shared(True)]))
def test_grad_1d(self):
subi = 0
@@ -2486,18 +2482,18 @@ def test_AdvancedSubtensor(self):
AdvancedSubtensor,
)
- admat.tag.test_value = admat_val
- aivec.tag.test_value = aivec_val
- bivec.tag.test_value = bivec_val
+ with pytest.warns(FutureWarning):
+ admat.tag.test_value = admat_val
+ aivec.tag.test_value = aivec_val
+ bivec.tag.test_value = bivec_val
# Make sure it doesn't complain about test values
- with config.change_flags(compute_test_value="raise"):
- self._compile_and_check(
- [admat, aivec],
- [admat[1:3, aivec]],
- [admat_val, aivec_val],
- AdvancedSubtensor,
- )
+ self._compile_and_check(
+ [admat, aivec],
+ [admat[1:3, aivec]],
+ [admat_val, aivec_val],
+ AdvancedSubtensor,
+ )
def test_AdvancedSubtensor_bool(self):
n = dmatrix()
diff --git a/tests/tensor/test_variable.py b/tests/tensor/test_variable.py
index 50c36a05fc..86f38d80fa 100644
--- a/tests/tensor/test_variable.py
+++ b/tests/tensor/test_variable.py
@@ -405,6 +405,7 @@ def test_take(self):
indices = [1, 0, 1]
assert_array_equal(X.take(indices, 1).eval({X: x}), x.take(indices, 1))
indices = np.array([-10, 5, 12], dtype="int32")
+
assert_array_equal(
X.take(indices, 1, mode="wrap").eval({X: x}),
x.take(indices, 1, mode="wrap"),
diff --git a/tests/test_gradient.py b/tests/test_gradient.py
index c45d07662d..12198beb8c 100644
--- a/tests/test_gradient.py
+++ b/tests/test_gradient.py
@@ -910,7 +910,6 @@ def test_grad_scale():
assert np.allclose(out, (8, 4))
-@config.change_flags(compute_test_value="off")
def test_undefined_grad_opt():
# Make sure that undefined grad get removed in optimized graph.
random = RandomStream(np.random.default_rng().integers(1, 2147462579))
diff --git a/tests/test_ifelse.py b/tests/test_ifelse.py
index d506d96df6..afb59e1f6f 100644
--- a/tests/test_ifelse.py
+++ b/tests/test_ifelse.py
@@ -500,11 +500,11 @@ def test_merge_ifs_true_false(self):
def test_grad_test_values(self):
# Regression test for test values of `ifelse` gradient.
- with pytensor.config.change_flags(compute_test_value="raise"):
- x = scalar("x")
+ x = scalar("x")
+ with pytest.warns(FutureWarning):
x.tag.test_value = 1
- # Used to crash due to undefined test value.
- pytensor.grad(ifelse(0, x, x), x)
+ # Used to crash due to undefined test value.
+ pytensor.grad(ifelse(0, x, x), x)
def test_grad_int_value(self):
w = pytensor.shared(np.random.random(10))