Move math-related optimizations to theano.tensor.math_opt

doc/library/tensor/math_opt.txt

+===================================================================
+:mod:`tensor.math_opt` --  Tensor Optimizations for Math Operations
+===================================================================
+
+.. module:: tensor.math_opt
+   :platform: Unix, Windows
+   :synopsis: Tensor Optimizations for Math Operations
+.. moduleauthor:: LISA, PyMC Developers
+
+.. automodule:: theano.tensor.math_opt
+    :members:

doc/optimizations.txt

@@ -143,7 +143,7 @@ Optimization                                              o4             o3  o2
 
             (a+b+c+...) - (z + x + y + ....)
 
-        See :class:`Canonizer`, :attr:`local_add_canonizer`
+        See :class:`AlgebraicCanonizer`, :attr:`local_add_canonizer`
 
     mul canonicalization
         Rearrange expressions of multiplication and division to a canonical
@@ -153,7 +153,7 @@ Optimization                                              o4             o3  o2
 
             \frac{a * b * c * ...}{z * x * y * ....}
 
-        See :class:`Canonizer`, :attr:`local_mul_canonizer`
+        See :class:`AlgebraicCanonizer`, :attr:`local_mul_canonizer`
 
     dot22
         This simple optimization replaces dot(matrix, matrix) with a special
@@ -288,4 +288,3 @@ Optimization                                              o4             o3  o2
         Use this optimization if you are sure everything is valid in your graph.
 
 	See :ref:`unsafe_optimization`
-

tests/tensor/test_opt.py

@@ -47,11 +47,8 @@ from theano.tensor.basic_opt import (
     MakeVector,
     ShapeFeature,
     assert_op,
-    local_add_specialize,
     local_canonicalize_alloc,
     local_dimshuffle_lift,
-    local_greedy_distributor,
-    local_lift_transpose_through_dot,
     local_merge_alloc,
     local_reshape_to_dimshuffle,
     local_useless_alloc,
@@ -59,7 +56,6 @@ from theano.tensor.basic_opt import (
     local_useless_elemwise,
     local_useless_reshape,
     make_vector,
-    mul_canonizer,
     register_specialize,
 )
 from theano.tensor.blas import Dot22, Gemv
@@ -109,6 +105,12 @@ from theano.tensor.math import round as tt_round
 from theano.tensor.math import sgn, sin, sinh, sqr, sqrt, sub
 from theano.tensor.math import sum as tt_sum
 from theano.tensor.math import tan, tanh, true_div, xor
+from theano.tensor.math_opt import (
+    local_add_specialize,
+    local_greedy_distributor,
+    local_lift_transpose_through_dot,
+    mul_canonizer,
+)
 from theano.tensor.nnet.sigm import softplus
 from theano.tensor.shape import Reshape, Shape_i, SpecifyShape, reshape, specify_shape
 from theano.tensor.subtensor import (
@@ -465,7 +467,7 @@ class TestCanonize:
         print(pprint(g.outputs[0]))
 
     def test_elemwise_multiple_inputs_optimisation(self):
-        # verify that the Canonizer merge sequential Elemwise({mul,add}) part 1
+        # verify that the AlgebraicCanonizer merge sequential Elemwise({mul,add}) part 1
         #
         # This part are that case that is done, but don't include case
         # that are not implemented but are supposed to be.
@@ -574,8 +576,8 @@ class TestCanonize:
         ]  # [10:11]
         # print cases
 
-        # We must be sure that the Canonizer is working, but that we don't have other
-        # optimisation that could hide bug in the Canonizer as local_elemwise_fusion
+        # We must be sure that the AlgebraicCanonizer is working, but that we don't have other
+        # optimisation that could hide bug in the AlgebraicCanonizer as local_elemwise_fusion
         mode = get_default_mode()
         opt = Query(["canonicalize"])
         opt = opt.excluding("local_elemwise_fusion")
@@ -595,11 +597,11 @@ class TestCanonize:
             assert out_dtype == out.dtype
 
     @pytest.mark.skip(
-        reason="Current implementation of Canonizer does not "
+        reason="Current implementation of AlgebraicCanonizer does not "
         "implement all cases. Skip the corresponding test."
     )
     def test_elemwise_multiple_inputs_optimisation2(self):
-        # verify that the Canonizer merge sequential Elemwise({mul,add}) part 2.
+        # verify that the AlgebraicCanonizer merge sequential Elemwise({mul,add}) part 2.
         # This part are that case that should have been done, but that are not implemented.
         # Test with and without DimShuffle
 
@@ -709,8 +711,8 @@ class TestCanonize:
         ]  # [10:11]
         # print cases
 
-        # We must be sure that the Canonizer is working, but that we don't have other
-        # optimisation that could hide bug in the Canonizer as local_elemwise_fusion
+        # We must be sure that the AlgebraicCanonizer is working, but that we don't have other
+        # optimisation that could hide bug in the AlgebraicCanonizer as local_elemwise_fusion
         mode = get_default_mode()
         mode._optimizer = Query(["canonicalize"])
         mode._optimizer = mode._optimizer.excluding("local_elemwise_fusion")
@@ -728,7 +730,7 @@ class TestCanonize:
 
     @pytest.mark.slow
     def test_multiple_case(self):
-        # test those case take from the comment in Canonizer
+        # test those case take from the comment in AlgebraicCanonizer
         # x / x -> 1
         # (x * y) / x -> y
         # x / y / x -> 1 / y
@@ -756,8 +758,8 @@ class TestCanonize:
         dwv = _asarray(np.random.rand(*shp), dtype="float64")
         dvv = _asarray(np.random.rand(shp[0]), dtype="float64").reshape(1, shp[0])
 
-        # We must be sure that the Canonizer is working, but that we don't have other
-        # optimisation that could hide bug in the Canonizer as local_elemwise_fusion
+        # We must be sure that the AlgebraicCanonizer is working, but that we don't have other
+        # optimisation that could hide bug in the AlgebraicCanonizer as local_elemwise_fusion
         mode = get_default_mode()
 
         opt = Query(["canonicalize"])
@@ -1109,7 +1111,7 @@ class TestCanonize:
         assert f.maker.fgraph.toposort()[0].op == sgn
 
     @pytest.mark.skip(
-        reason="Current implementation of Canonizer does not "
+        reason="Current implementation of AlgebraicCanonizer does not "
         "implement all cases. Skip the corresponding test."
     )
     def test_multiple_case_that_fail(self):
@@ -1123,8 +1125,8 @@ class TestCanonize:
         dyv = _asarray(np.random.rand(*shp), dtype="float32")
         dzv = _asarray(np.random.rand(*shp), dtype="float32")
         # fvv = _asarray(np.random.rand(shp[0]), dtype='float32').reshape(1, shp[0])
-        # We must be sure that the Canonizer is working, but that we don't have other
-        # optimisation that could hide bug in the Canonizer as local_elemwise_fusion
+        # We must be sure that the AlgebraicCanonizer is working, but that we don't have other
+        # optimisation that could hide bug in the AlgebraicCanonizer as local_elemwise_fusion
         mode = get_default_mode()
 
         opt = Query(["canonicalize"])

theano/scan/opt.py

@@ -86,7 +86,7 @@ from theano.scan.utils import (
     scan_args,
     scan_can_remove_outs,
 )
-from theano.tensor import basic_opt
+from theano.tensor import basic_opt, math_opt
 from theano.tensor.basic import Alloc, AllocEmpty, get_scalar_constant_value
 from theano.tensor.elemwise import DimShuffle, Elemwise
 from theano.tensor.exceptions import NotScalarConstantError
@@ -118,8 +118,8 @@ __copyright__ = "(c) 2010, Universite de Montreal"
 _logger = logging.getLogger("theano.scan.opt")
 
 list_opt_slice = [
-    basic_opt.local_abs_merge,
-    basic_opt.local_mul_switch_sink,
+    math_opt.local_abs_merge,
+    math_opt.local_mul_switch_sink,
     basic_opt.local_upcast_elemwise_constant_inputs,
     basic_opt.local_useless_switch,
     basic_opt.constant_folding,

theano/tensor/nnet/basic.py

@@ -31,7 +31,7 @@ from theano.scalar import UnaryScalarOp
 
 # Work-around for Python 3.6 issue that prevents `import theano.tensor as tt`
 from theano.tensor import basic as tt
-from theano.tensor import basic_opt, extra_ops
+from theano.tensor import extra_ops, math_opt
 from theano.tensor.basic import ARange, as_tensor_variable
 from theano.tensor.basic_opt import (
     register_canonicalize,
@@ -985,7 +985,7 @@ def softmax_simplifier(numerators, denominators):
     return numerators, denominators
 
 
-basic_opt.local_mul_canonizer.add_simplifier(softmax_simplifier, "softmax_simplifier")
+math_opt.local_mul_canonizer.add_simplifier(softmax_simplifier, "softmax_simplifier")
 
 
 class CrossentropySoftmaxArgmax1HotWithBias(COp):

theano/tensor/nnet/sigm.py

@@ -1085,7 +1085,7 @@ def local_1msigmoid(fgraph, node):
 
 register_local_1msigmoid = False
 # This is False because the Stabilize pattern above
-# is looking for 1-sigm.  Also Canonizer turns neg into *(-1) and so
+# is looking for 1-sigm.  Also AlgebraicCanonizer turns neg into *(-1) and so
 # this optimization might set off an unwanted chain of things.
 # OTH - this transformation can be seen as pushing normal arithmetic either  below or above the
 # sigmoidal nonlinearity... so if the canonicalized form had anything to say about that then it