Source code for sympy.simplify.cse_main

""" Tools for doing common subexpression elimination.
from __future__ import print_function, division

from sympy.core import Basic, Mul, Add, Pow, sympify, Symbol
from sympy.core.containers import Tuple, OrderedSet
from sympy.core.singleton import S
from sympy.core.function import _coeff_isneg
from sympy.core.exprtools import factor_terms
from sympy.core.compatibility import iterable, range
from sympy.utilities.iterables import filter_symbols, \
    numbered_symbols, sift, topological_sort, ordered

from . import cse_opts

# (preprocessor, postprocessor) pairs which are commonly useful. They should
# each take a sympy expression and return a possibly transformed expression.
# When used in the function ``cse()``, the target expressions will be transformed
# by each of the preprocessor functions in order. After the common
# subexpressions are eliminated, each resulting expression will have the
# postprocessor functions transform them in *reverse* order in order to undo the
# transformation if necessary. This allows the algorithm to operate on
# a representation of the expressions that allows for more optimization
# opportunities.
# ``None`` can be used to specify no transformation for either the preprocessor or
# postprocessor.

basic_optimizations = [(cse_opts.sub_pre, cse_opts.sub_post),
                       (factor_terms, None)]

# sometimes we want the output in a different format; non-trivial
# transformations can be put here for users
# ===============================================================

def reps_toposort(r):
    """Sort replacements `r` so (k1, v1) appears before (k2, v2)
    if k2 is in v1's free symbols. This orders items in the
    way that cse returns its results (hence, in order to use the
    replacements in a substitution option it would make sense
    to reverse the order).


    >>> from sympy.simplify.cse_main import reps_toposort
    >>> from import x, y
    >>> from sympy import Eq
    >>> for l, r in reps_toposort([(x, y + 1), (y, 2)]):
    ...     print(Eq(l, r))
    Eq(y, 2)
    Eq(x, y + 1)

    r = sympify(r)
    E = []
    for c1, (k1, v1) in enumerate(r):
        for c2, (k2, v2) in enumerate(r):
            if k1 in v2.free_symbols:
                E.append((c1, c2))
    return [r[i] for i in topological_sort((range(len(r)), E))]

def cse_separate(r, e):
    """Move expressions that are in the form (symbol, expr) out of the
    expressions and sort them into the replacements using the reps_toposort.


    >>> from sympy.simplify.cse_main import cse_separate
    >>> from import x, y, z
    >>> from sympy import cos, exp, cse, Eq, symbols
    >>> x0, x1 = symbols('x:2')
    >>> eq = (x + 1 + exp((x + 1)/(y + 1)) + cos(y + 1))
    >>> cse([eq, Eq(x, z + 1), z - 2], postprocess=cse_separate) in [
    ... [[(x0, y + 1), (x, z + 1), (x1, x + 1)],
    ...  [x1 + exp(x1/x0) + cos(x0), z - 2]],
    ... [[(x1, y + 1), (x, z + 1), (x0, x + 1)],
    ...  [x0 + exp(x0/x1) + cos(x1), z - 2]]]
    d = sift(e, lambda w: w.is_Equality and w.lhs.is_Symbol)
    r = r + [w.args for w in d[True]]
    e = d[False]
    return [reps_toposort(r), e]

# ====end of cse postprocess idioms===========================

def preprocess_for_cse(expr, optimizations):
    """ Preprocess an expression to optimize for common subexpression

    expr : sympy expression
        The target expression to optimize.
    optimizations : list of (callable, callable) pairs
        The (preprocessor, postprocessor) pairs.

    expr : sympy expression
        The transformed expression.
    for pre, post in optimizations:
        if pre is not None:
            expr = pre(expr)
    return expr

def postprocess_for_cse(expr, optimizations):
    """ Postprocess an expression after common subexpression elimination to
    return the expression to canonical sympy form.

    expr : sympy expression
        The target expression to transform.
    optimizations : list of (callable, callable) pairs, optional
        The (preprocessor, postprocessor) pairs.  The postprocessors will be
        applied in reversed order to undo the effects of the preprocessors

    expr : sympy expression
        The transformed expression.
    for pre, post in reversed(optimizations):
        if post is not None:
            expr = post(expr)
    return expr

class FuncArgTracker(object):
    A class which manages a mapping from functions to arguments and an inverse
    mapping from arguments to functions.

    def __init__(self, funcs):
        # To minimize the number of symbolic comparisons, all function arguments
        # get assigned a value number.
        self.value_numbers = {}
        self.value_number_to_value = []

        # Both of these maps use integer indices for arguments / functions.
        self.arg_to_funcset = []
        self.func_to_argset = []

        for func_i, func in enumerate(funcs):
            func_argset = OrderedSet()

            for func_arg in func.args:
                arg_number = self.get_or_add_value_number(func_arg)


    def get_args_in_value_order(self, argset):
        Return the list of arguments in sorted order according to their value
        return [self.value_number_to_value[argn] for argn in sorted(argset)]

    def get_or_add_value_number(self, value):
        Return the value number for the given argument.
        nvalues = len(self.value_numbers)
        value_number = self.value_numbers.setdefault(value, nvalues)
        if value_number == nvalues:
        return value_number

    def stop_arg_tracking(self, func_i):
        Remove the function func_i from the argument to function mapping.
        for arg in self.func_to_argset[func_i]:

    def get_common_arg_candidates(self, argset, min_func_i=0):
        """Return a dict whose keys are function numbers. The entries of the dict are
        the number of arguments said function has in common with
        `argset`. Entries have at least 2 items in common.  All keys have
        value at least `min_func_i`.
        from collections import defaultdict
        count_map = defaultdict(lambda: 0)

        funcsets = [self.arg_to_funcset[arg] for arg in argset]
        # As an optimization below, we handle the largest funcset separately from
        # the others.
        largest_funcset = max(funcsets, key=len)

        for funcset in funcsets:
            if largest_funcset is funcset:
            for func_i in funcset:
                if func_i >= min_func_i:
                    count_map[func_i] += 1

        # We pick the smaller of the two containers (count_map, largest_funcset)
        # to iterate over to reduce the number of iterations needed.
         larger_funcs_container) = sorted(
                 [largest_funcset, count_map],

        for func_i in smaller_funcs_container:
            # Not already in count_map? It can't possibly be in the output, so
            # skip it.
            if count_map[func_i] < 1:

            if func_i in larger_funcs_container:
                count_map[func_i] += 1

        return dict((k, v) for k, v in count_map.items() if v >= 2)

    def get_subset_candidates(self, argset, restrict_to_funcset=None):
        Return a set of functions each of which whose argument list contains
        `argset`, optionally filtered only to contain functions in
        iarg = iter(argset)

        indices = OrderedSet(
            fi for fi in self.arg_to_funcset[next(iarg)])

        if restrict_to_funcset is not None:
            indices &= restrict_to_funcset

        for arg in iarg:
            indices &= self.arg_to_funcset[arg]

        return indices

    def update_func_argset(self, func_i, new_argset):
        Update a function with a new set of arguments.
        new_args = OrderedSet(new_argset)
        old_args = self.func_to_argset[func_i]

        for deleted_arg in old_args - new_args:
        for added_arg in new_args - old_args:


class Unevaluated(object):

    def __init__(self, func, args):
        self.func = func
        self.args = args

    def __str__(self):
        return "Uneval<{}>({})".format(
                self.func, ", ".join(str(a) for a in self.args))

    def as_unevaluated_basic(self):
        return self.func(*self.args, evaluate=False)

    def free_symbols(self):
        return set().union(*[a.free_symbols for a in self.args])

    __repr__ = __str__

def match_common_args(func_class, funcs, opt_subs):
    Recognize and extract common subexpressions of function arguments within a
    set of function calls. For instance, for the following function calls::

        x + z + y
        sin(x + y)

    this will extract a common subexpression of `x + y`::

        w = x + y
        w + z

    The function we work with is assumed to be associative and commutative.

    :arg func_class: The function class (e.g. Add, Mul)
    :arg funcs: A list of function calls
    :arg opt_subs: A dictionary of substitutions which this function may update

    # Sort to ensure that whole-function subexpressions come before the items
    # that use them.
    funcs = sorted(funcs, key=lambda f: len(f.args))
    arg_tracker = FuncArgTracker(funcs)

    changed = OrderedSet()

    for i in range(len(funcs)):
        common_arg_candidates_counts = arg_tracker.get_common_arg_candidates(
                arg_tracker.func_to_argset[i], min_func_i=i + 1)

        # Sort the candidates in order of match size.
        # This makes us try combining smaller matches first.
        common_arg_candidates = OrderedSet(sorted(
                key=lambda k: (common_arg_candidates_counts[k], k)))

        while common_arg_candidates:
            j = common_arg_candidates.pop(last=False)

            com_args = arg_tracker.func_to_argset[i].intersection(

            if len(com_args) <= 1:
                # This may happen if a set of common arguments was already
                # combined in a previous iteration.

            # For all sets, replace the common symbols by the function
            # over them, to allow recursive matches.

            diff_i = arg_tracker.func_to_argset[i].difference(com_args)
            if diff_i:
                # com_func needs to be unevaluated to allow for recursive matches.
                com_func = Unevaluated(
                        func_class, arg_tracker.get_args_in_value_order(com_args))
                com_func_number = arg_tracker.get_or_add_value_number(com_func)
                arg_tracker.update_func_argset(i, diff_i | OrderedSet([com_func_number]))
                # Treat the whole expression as a CSE.
                # The reason this needs to be done is somewhat subtle. Within
                # tree_cse(), to_eliminate only contains expressions that are
                # seen more than once. The problem is unevaluated expressions
                # do not compare equal to the evaluated equivalent. So
                # tree_cse() won't mark funcs[i] as a CSE if we use an
                # unevaluated version.
                com_func = funcs[i]
                com_func_number = arg_tracker.get_or_add_value_number(funcs[i])

            diff_j = arg_tracker.func_to_argset[j].difference(com_args)
            arg_tracker.update_func_argset(j, diff_j | OrderedSet([com_func_number]))

            for k in arg_tracker.get_subset_candidates(
                    com_args, common_arg_candidates):
                diff_k = arg_tracker.func_to_argset[k].difference(com_args)
                arg_tracker.update_func_argset(k, diff_k | OrderedSet([com_func_number]))

        if i in changed:
            opt_subs[funcs[i]] = Unevaluated(func_class,


[docs]def opt_cse(exprs, order='canonical'): """Find optimization opportunities in Adds, Muls, Pows and negative coefficient Muls Parameters ---------- exprs : list of sympy expressions The expressions to optimize. order : string, 'none' or 'canonical' The order by which Mul and Add arguments are processed. For large expressions where speed is a concern, use the setting order='none'. Returns ------- opt_subs : dictionary of expression substitutions The expression substitutions which can be useful to optimize CSE. Examples ======== >>> from sympy.simplify.cse_main import opt_cse >>> from import x >>> opt_subs = opt_cse([x**-2]) >>> k, v = list(opt_subs.keys())[0], list(opt_subs.values())[0] >>> print((k, v.as_unevaluated_basic())) (x**(-2), 1/(x**2)) """ from sympy.matrices.expressions import MatAdd, MatMul, MatPow opt_subs = dict() adds = OrderedSet() muls = OrderedSet() seen_subexp = set() def _find_opts(expr): if not isinstance(expr, (Basic, Unevaluated)): return if expr.is_Atom or expr.is_Order: return if iterable(expr): list(map(_find_opts, expr)) return if expr in seen_subexp: return expr seen_subexp.add(expr) list(map(_find_opts, expr.args)) if _coeff_isneg(expr): neg_expr = -expr if not neg_expr.is_Atom: opt_subs[expr] = Unevaluated(Mul, (S.NegativeOne, neg_expr)) seen_subexp.add(neg_expr) expr = neg_expr if isinstance(expr, (Mul, MatMul)): muls.add(expr) elif isinstance(expr, (Add, MatAdd)): adds.add(expr) elif isinstance(expr, (Pow, MatPow)): base, exp = expr.base, expr.exp if _coeff_isneg(exp): opt_subs[expr] = Unevaluated(Pow, (Pow(base, -exp), -1)) for e in exprs: if isinstance(e, (Basic, Unevaluated)): _find_opts(e) # split muls into commutative commutative_muls = OrderedSet() for m in muls: c, nc = m.args_cnc(cset=False) if c: c_mul = m.func(*c) if nc: if c_mul == 1: new_obj = m.func(*nc) else: new_obj = m.func(c_mul, m.func(*nc), evaluate=False) opt_subs[m] = new_obj if len(c) > 1: commutative_muls.add(c_mul) match_common_args(Add, adds, opt_subs) match_common_args(Mul, commutative_muls, opt_subs) return opt_subs
[docs]def tree_cse(exprs, symbols, opt_subs=None, order='canonical', ignore=()): """Perform raw CSE on expression tree, taking opt_subs into account. Parameters ========== exprs : list of sympy expressions The expressions to reduce. symbols : infinite iterator yielding unique Symbols The symbols used to label the common subexpressions which are pulled out. opt_subs : dictionary of expression substitutions The expressions to be substituted before any CSE action is performed. order : string, 'none' or 'canonical' The order by which Mul and Add arguments are processed. For large expressions where speed is a concern, use the setting order='none'. ignore : iterable of Symbols Substitutions containing any Symbol from ``ignore`` will be ignored. """ from sympy.matrices.expressions import MatrixExpr, MatrixSymbol, MatMul, MatAdd if opt_subs is None: opt_subs = dict() ## Find repeated sub-expressions to_eliminate = set() seen_subexp = set() excluded_symbols = set() def _find_repeated(expr): if not isinstance(expr, (Basic, Unevaluated)): return if isinstance(expr, Basic) and (expr.is_Atom or expr.is_Order): if expr.is_Symbol: excluded_symbols.add(expr) return if iterable(expr): args = expr else: if expr in seen_subexp: for ign in ignore: if ign in expr.free_symbols: break else: to_eliminate.add(expr) return seen_subexp.add(expr) if expr in opt_subs: expr = opt_subs[expr] args = expr.args list(map(_find_repeated, args)) for e in exprs: if isinstance(e, Basic): _find_repeated(e) ## Rebuild tree # Remove symbols from the generator that conflict with names in the expressions. symbols = (symbol for symbol in symbols if symbol not in excluded_symbols) replacements = [] subs = dict() def _rebuild(expr): if not isinstance(expr, (Basic, Unevaluated)): return expr if not expr.args: return expr if iterable(expr): new_args = [_rebuild(arg) for arg in expr] return expr.func(*new_args) if expr in subs: return subs[expr] orig_expr = expr if expr in opt_subs: expr = opt_subs[expr] # If enabled, parse Muls and Adds arguments by order to ensure # replacement order independent from hashes if order != 'none': if isinstance(expr, (Mul, MatMul)): c, nc = expr.args_cnc() if c == [1]: args = nc else: args = list(ordered(c)) + nc elif isinstance(expr, (Add, MatAdd)): args = list(ordered(expr.args)) else: args = expr.args else: args = expr.args new_args = list(map(_rebuild, args)) if isinstance(expr, Unevaluated) or new_args != args: new_expr = expr.func(*new_args) else: new_expr = expr if orig_expr in to_eliminate: try: sym = next(symbols) except StopIteration: raise ValueError("Symbols iterator ran out of symbols.") if isinstance(orig_expr, MatrixExpr): sym = MatrixSymbol(, orig_expr.rows, orig_expr.cols) subs[orig_expr] = sym replacements.append((sym, new_expr)) return sym else: return new_expr reduced_exprs = [] for e in exprs: if isinstance(e, Basic): reduced_e = _rebuild(e) else: reduced_e = e reduced_exprs.append(reduced_e) return replacements, reduced_exprs
[docs]def cse(exprs, symbols=None, optimizations=None, postprocess=None, order='canonical', ignore=()): """ Perform common subexpression elimination on an expression. Parameters ========== exprs : list of sympy expressions, or a single sympy expression The expressions to reduce. symbols : infinite iterator yielding unique Symbols The symbols used to label the common subexpressions which are pulled out. The ``numbered_symbols`` generator is useful. The default is a stream of symbols of the form "x0", "x1", etc. This must be an infinite iterator. optimizations : list of (callable, callable) pairs The (preprocessor, postprocessor) pairs of external optimization functions. Optionally 'basic' can be passed for a set of predefined basic optimizations. Such 'basic' optimizations were used by default in old implementation, however they can be really slow on larger expressions. Now, no pre or post optimizations are made by default. postprocess : a function which accepts the two return values of cse and returns the desired form of output from cse, e.g. if you want the replacements reversed the function might be the following lambda: lambda r, e: return reversed(r), e order : string, 'none' or 'canonical' The order by which Mul and Add arguments are processed. If set to 'canonical', arguments will be canonically ordered. If set to 'none', ordering will be faster but dependent on expressions hashes, thus machine dependent and variable. For large expressions where speed is a concern, use the setting order='none'. ignore : iterable of Symbols Substitutions containing any Symbol from ``ignore`` will be ignored. Returns ======= replacements : list of (Symbol, expression) pairs All of the common subexpressions that were replaced. Subexpressions earlier in this list might show up in subexpressions later in this list. reduced_exprs : list of sympy expressions The reduced expressions with all of the replacements above. Examples ======== >>> from sympy import cse, SparseMatrix >>> from import x, y, z, w >>> cse(((w + x + y + z)*(w + y + z))/(w + x)**3) ([(x0, y + z), (x1, w + x)], [(w + x0)*(x0 + x1)/x1**3]) Note that currently, y + z will not get substituted if -y - z is used. >>> cse(((w + x + y + z)*(w - y - z))/(w + x)**3) ([(x0, w + x)], [(w - y - z)*(x0 + y + z)/x0**3]) List of expressions with recursive substitutions: >>> m = SparseMatrix([x + y, x + y + z]) >>> cse([(x+y)**2, x + y + z, y + z, x + z + y, m]) ([(x0, x + y), (x1, x0 + z)], [x0**2, x1, y + z, x1, Matrix([ [x0], [x1]])]) Note: the type and mutability of input matrices is retained. >>> isinstance(_[1][-1], SparseMatrix) True The user may disallow substitutions containing certain symbols: >>> cse([y**2*(x + 1), 3*y**2*(x + 1)], ignore=(y,)) ([(x0, x + 1)], [x0*y**2, 3*x0*y**2]) """ from sympy.matrices import (MatrixBase, Matrix, ImmutableMatrix, SparseMatrix, ImmutableSparseMatrix) if isinstance(exprs, (int, float)): exprs = sympify(exprs) # Handle the case if just one expression was passed. if isinstance(exprs, (Basic, MatrixBase)): exprs = [exprs] copy = exprs temp = [] for e in exprs: if isinstance(e, (Matrix, ImmutableMatrix)): temp.append(Tuple(*e._mat)) elif isinstance(e, (SparseMatrix, ImmutableSparseMatrix)): temp.append(Tuple(*e._smat.items())) else: temp.append(e) exprs = temp del temp if optimizations is None: optimizations = list() elif optimizations == 'basic': optimizations = basic_optimizations # Preprocess the expressions to give us better optimization opportunities. reduced_exprs = [preprocess_for_cse(e, optimizations) for e in exprs] if symbols is None: symbols = numbered_symbols(cls=Symbol) else: # In case we get passed an iterable with an __iter__ method instead of # an actual iterator. symbols = iter(symbols) # Find other optimization opportunities. opt_subs = opt_cse(reduced_exprs, order) # Main CSE algorithm. replacements, reduced_exprs = tree_cse(reduced_exprs, symbols, opt_subs, order, ignore) # Postprocess the expressions to return the expressions to canonical form. exprs = copy for i, (sym, subtree) in enumerate(replacements): subtree = postprocess_for_cse(subtree, optimizations) replacements[i] = (sym, subtree) reduced_exprs = [postprocess_for_cse(e, optimizations) for e in reduced_exprs] # Get the matrices back for i, e in enumerate(exprs): if isinstance(e, (Matrix, ImmutableMatrix)): reduced_exprs[i] = Matrix(e.rows, e.cols, reduced_exprs[i]) if isinstance(e, ImmutableMatrix): reduced_exprs[i] = reduced_exprs[i].as_immutable() elif isinstance(e, (SparseMatrix, ImmutableSparseMatrix)): m = SparseMatrix(e.rows, e.cols, {}) for k, v in reduced_exprs[i]: m[k] = v if isinstance(e, ImmutableSparseMatrix): m = m.as_immutable() reduced_exprs[i] = m if postprocess is None: return replacements, reduced_exprs return postprocess(replacements, reduced_exprs)