Remove mentions of unimplemented reoptimize_unpickled_function option

aesara/configdefaults.py

@@ -378,13 +378,6 @@ def add_basic_configvars():
         in_c_key=False,
     )
 
-    config.add(
-        "reoptimize_unpickled_function",
-        "Re-optimize the graph when an Aesara function is unpickled from the disk.",
-        BoolParam(False, mutable=True),
-        in_c_key=False,
-    )
-
 
 def _is_gt_0(x):
     return x > 0

doc/faq.rst

@@ -72,15 +72,6 @@ detection algorithm. If the graph is big enough,we suggest that you use
 this flag instead of ``optimizer_excluding=inplace``. It will result in a
 computation time that is in between fast compile and fast run.
 
-Aesara flag `reoptimize_unpickled_function` controls if an unpickled
-aesara function should reoptimize its graph or not. Aesara users can
-use the standard python pickle tools to save a compiled aesara
-function. When pickling, both graph before and after the optimization
-are saved, including shared variables. When set to True, the graph is
-reoptimized when being unpickled. Otherwise, skip the graph
-optimization and use directly the optimized graph from the pickled
-file. The default is False.
-
 Faster Aesara function
 ----------------------
 

doc/library/config.rst

@@ -820,12 +820,6 @@ import ``aesara`` and print the config variable, as in:
     If ``'True'``, Aesara will include the test values in a variable's
     ``__str__`` output.
 
-.. attribute:: reoptimize_unpickled_function
-
-    Bool value, default: False
-
-    When this option is set to ``True``, a graph is re-optimized when unpickled.
-
 .. attribute:: exception_verbosity
 
     String Value: ``'low'``, ``'high'``.

tests/compile/function/test_function.py

@@ -3,7 +3,6 @@ import pickle
 import re
 import shutil
 import tempfile
-from collections import OrderedDict
 
 import numpy as np
 import pytest
@@ -12,13 +11,11 @@ from aesara.compile import shared
 from aesara.compile.function import function, function_dump
 from aesara.compile.io import In
 from aesara.configdefaults import config
-from aesara.tensor.math import sum as at_sum
 from aesara.tensor.type import (
     bscalar,
     bvector,
     dscalar,
     dvector,
-    fmatrix,
     fscalar,
     fvector,
     vector,
@@ -236,68 +233,3 @@ class TestFunctionIn:
         # If allow_downcast is None, like False
         with pytest.raises(TypeError):
             f(z, z, [0.1])
-
-
-def test_pickle_unpickle_with_reoptimization():
-    mode = config.mode
-    if mode in ["DEBUG_MODE", "DebugMode"]:
-        mode = "FAST_RUN"
-    x1 = fmatrix("x1")
-    x2 = fmatrix("x2")
-    x3 = shared(np.ones((10, 10), dtype=floatX))
-    x4 = shared(np.ones((10, 10), dtype=floatX))
-    y = at_sum(at_sum(at_sum(x1**2 + x2) + x3) + x4)
-
-    updates = OrderedDict()
-    updates[x3] = x3 + 1
-    updates[x4] = x4 + 1
-    f = function([x1, x2], y, updates=updates, mode=mode)
-
-    # now pickle the compiled aesara fn
-    string_pkl = pickle.dumps(f, -1)
-
-    in1 = np.ones((10, 10), dtype=floatX)
-    in2 = np.ones((10, 10), dtype=floatX)
-
-    # test unpickle with optimization
-    default = config.reoptimize_unpickled_function
-    try:
-        # the default is True
-        config.reoptimize_unpickled_function = True
-        f_ = pickle.loads(string_pkl)
-        assert f(in1, in2) == f_(in1, in2)
-    finally:
-        config.reoptimize_unpickled_function = default
-
-
-def test_pickle_unpickle_without_reoptimization():
-    mode = config.mode
-    if mode in ["DEBUG_MODE", "DebugMode"]:
-        mode = "FAST_RUN"
-    x1 = fmatrix("x1")
-    x2 = fmatrix("x2")
-    x3 = shared(np.ones((10, 10), dtype=floatX))
-    x4 = shared(np.ones((10, 10), dtype=floatX))
-    y = at_sum(at_sum(at_sum(x1**2 + x2) + x3) + x4)
-
-    updates = OrderedDict()
-    updates[x3] = x3 + 1
-    updates[x4] = x4 + 1
-    f = function([x1, x2], y, updates=updates, mode=mode)
-
-    # now pickle the compiled aesara fn
-    string_pkl = pickle.dumps(f, -1)
-
-    # compute f value
-    in1 = np.ones((10, 10), dtype=floatX)
-    in2 = np.ones((10, 10), dtype=floatX)
-
-    # test unpickle without optimization
-    default = config.reoptimize_unpickled_function
-    try:
-        # the default is True
-        config.reoptimize_unpickled_function = False
-        f_ = pickle.loads(string_pkl)
-        assert f(in1, in2) == f_(in1, in2)
-    finally:
-        config.reoptimize_unpickled_function = default