def eager_reduce_exp(op, arg, reduced_vars):
# x.exp().reduce(ops.add) == x.reduce(ops.logaddexp).exp()
log_result = arg.arg.reduce(ops.logaddexp, reduced_vars)
if log_result is not normalize(Reduce, ops.logaddexp, arg.arg,
reduced_vars):
return log_result.exp()
return None
def normalize_contraction_commutative_canonical_order(red_op, bin_op,
reduced_vars, *terms):
# when bin_op is commutative, put terms into a canonical order for pattern matching
new_terms = tuple(v for i, v in sorted(
enumerate(terms),
key=lambda t: (ORDERING.get(type(t[1]).__origin__, -1), t[0])))
if any(v is not vv for v, vv in zip(terms, new_terms)):
return Contraction(red_op, bin_op, reduced_vars, *new_terms)
return normalize(Contraction, red_op, bin_op, reduced_vars, new_terms)
def eager_contraction_generic_recursive(red_op, bin_op, reduced_vars, terms):
# push down leaf reductions
terms, reduced_vars, leaf_reduced = list(terms), frozenset(
reduced_vars), False
for i, v in enumerate(terms):
unique_vars = reduced_vars.intersection(v.inputs) - \
frozenset().union(*(reduced_vars.intersection(vv.inputs) for vv in terms if vv is not v))
if unique_vars:
result = v.reduce(red_op, unique_vars)
if result is not normalize(Contraction, red_op, nullop,
unique_vars, (v, )):
terms[i] = result
reduced_vars -= unique_vars
leaf_reduced = True
if leaf_reduced:
return Contraction(red_op, bin_op, reduced_vars, *terms)
# exploit associativity to recursively evaluate this contraction
# a bit expensive, but handles interpreter-imposed directionality constraints
terms = tuple(terms)
# return reduce(bin_op, terms).reduce(red_op, reduced_vars)
# for i, (lhs, rhs) in enumerate(zip(terms[0:-1], terms[1:])):
for i, lhs in enumerate(terms[0:-1]):
for j_, rhs in enumerate(terms[i + 1:]):
j = i + j_ + 1
unique_vars = reduced_vars.intersection(lhs.inputs, rhs.inputs) - \
frozenset().union(*(reduced_vars.intersection(vv.inputs)
for vv in terms[:i] + terms[i+1:j] + terms[j+1:]))
result = Contraction(red_op, bin_op, unique_vars, lhs, rhs)
if result is not normalize(Contraction, red_op, bin_op,
unique_vars,
(lhs, rhs)): # did we make progress?
# pick the first evaluable pair
reduced_vars -= unique_vars
new_terms = terms[:i] + (result, ) + terms[i + 1:j] + terms[j +
1:]
return Contraction(red_op, bin_op, reduced_vars, *new_terms)
return None
def normalize_contraction_generic_args(red_op, bin_op, reduced_vars, *terms):
return normalize(Contraction, red_op, bin_op, reduced_vars, tuple(terms))
