def chooser_taylor_rule(primals_in, series_in, **params):
operand, = primals_in
gs, = series_in
primal_out = chooser_fun(operand, **params)
axes = params.pop("axes", None)
primal_dtype = gs[0].dtype
shape = [1 if i in axes else d for i, d in enumerate(operand.shape)]
location_indicators = lax.convert_element_type(
lax._eq_meet(operand, lax.reshape(primal_out, shape)), primal_dtype)
counts = lax._reduce_sum(location_indicators, axes)
def _reduce_chooser_taylor_rule(g):
return lax.div(lax._reduce_sum(lax.mul(g, location_indicators), axes), counts)
series_out = [_reduce_chooser_taylor_rule(g) for g in gs]
return primal_out, series_out
def _reduce_chooser_taylor_rule(g):
return lax.div(
lax._reduce_sum(lax.mul(g, location_indicators), axes), counts)
def _psum_serial_pmap_rule(vals, axes):
val, = vals
axis, = axes
return lax._reduce_sum(val, [axis]), None
def f(x, y):
z = lax.mul(x, y)
w = lax.sin(z)
u = lax._reduce_sum(w, [0])
return (u, )