(('iand', ('ieq', 'a@32', 0), ('ieq', 'b@32', 0)), ('ieq', ('ior', a, b), 0), '!options->lower_bitops'),
(('ior', ('ine', 'a@32', 0), ('ine', 'b@32', 0)), ('ine', ('ior', a, b), 0), '!options->lower_bitops'),
+ # This pattern occurs coutresy of __flt64_nonnan in the soft-fp64 code.
+ # The first part of the iand comes from the !__feq64_nonnan.
+ #
+ # The second pattern is a reformulation of the first based on the relation
+ # (a == 0 || y == 0) <=> umin(a, y) == 0, where b in the first equation
+ # happens to be y == 0.
+ (('iand', ('inot', ('iand', ('ior', ('ieq', a, 0), b), c)), ('ilt', a, 0)),
+ ('iand', ('inot', ('iand', b , c)), ('ilt', a, 0))),
+ (('iand', ('inot', ('iand', ('ieq', ('umin', a, b), 0), c)), ('ilt', a, 0)),
+ ('iand', ('inot', ('iand', ('ieq', b , 0), c)), ('ilt', a, 0))),
+
# These patterns can result when (a < b || a < c) => (a < min(b, c))
# transformations occur before constant propagation and loop-unrolling.
(('~flt', a, ('fmax', b, a)), ('flt', a, b)),
(('~fexp2', ('fmul', ('flog2', a), b)), ('fpow', a, b), '!options->lower_fpow'), # 2^(lg2(a)*b) = a^b
(('~fexp2', ('fadd', ('fmul', ('flog2', a), b), ('fmul', ('flog2', c), d))),
('~fmul', ('fpow', a, b), ('fpow', c, d)), '!options->lower_fpow'), # 2^(lg2(a) * b + lg2(c) + d) = a^b * c^d
+ (('~fexp2', ('fmul', ('flog2', a), 0.5)), ('fsqrt', a)),
(('~fexp2', ('fmul', ('flog2', a), 2.0)), ('fmul', a, a)),
(('~fexp2', ('fmul', ('flog2', a), 4.0)), ('fmul', ('fmul', a, a), ('fmul', a, a))),
(('~fpow', a, 1.0), a),
optimizations.append(((x2yN, (b2x, a)), (b2y, a)))
# Optimize away x2xN(a@N)
-for t in ['int', 'uint', 'float']:
+for t in ['int', 'uint', 'float', 'bool']:
for N in type_sizes(t):
x2xN = '{0}2{0}{1}'.format(t[0], N)
aN = 'a@{0}'.format(N)
# The N == M case is handled by other optimizations
pass
+# Downcast operations should be able to see through pack
+for t in ['i', 'u']:
+ for N in [8, 16, 32]:
+ x2xN = '{0}2{0}{1}'.format(t, N)
+ optimizations += [
+ ((x2xN, ('pack_64_2x32_split', a, b)), (x2xN, a)),
+ ((x2xN, ('pack_64_2x32_split', a, b)), (x2xN, a)),
+ ]
+
# Optimize comparisons with up-casts
for t in ['int', 'uint', 'float']:
for N, M in itertools.product(type_sizes(t), repeat=2):