def _pow_taylor(primals_in, series_in):
u_, r_ = primals_in
x, series = jet(lambda x, y: lax.mul(y, lax.log(x)), primals_in, series_in)
u = [x] + series
v = [u_ ** r_] + [None] * len(series)
for k in range(1, len(v)):
v[k] = fact(k-1) * sum([_scale(k, j)* v[k-j] * u[j] for j in range(1, k+1)])
primal_out, *series_out = v
return primal_out, series_out
def _erf_inv_rule(primals_in, series_in):
x, = primals_in
series, = series_in
u = [x] + series
primal_out = lax.erf_inv(x)
v = [primal_out] + [None] * len(series)
# derivative on co-domain for caching purposes
deriv_const = np.sqrt(np.pi) / 2.
deriv_y = lambda y: lax.mul(deriv_const, lax.exp(lax.square(y)))
# manually propagate through deriv_y since we don't have lazy evaluation of sensitivities
c = [deriv_y(primal_out)] + [None] * (len(series) - 1)
tmp_sq = [lax.square(v[0])] + [None] * (len(series) - 1)
tmp_exp = [lax.exp(tmp_sq[0])] + [None] * (len(series) - 1)
for k in range(1, len(series)):
# we know c[:k], we compute c[k]
# propagate c to get v
v[k] = fact(k - 1) * sum(
_scale(k, j) * c[k - j] * u[j] for j in range(1, k + 1))
# propagate v to get next c
# square
tmp_sq[k] = fact(k) * sum(
_scale2(k, j) * v[k - j] * v[j] for j in range(k + 1))
# exp
tmp_exp[k] = fact(k - 1) * sum(
_scale(k, j) * tmp_exp[k - j] * tmp_sq[j] for j in range(1, k + 1))
# const
c[k] = deriv_const * tmp_exp[k]
# we can't, and don't need, to compute c[k+1], just need to get the last v[k]
k = len(series)
v[k] = fact(k - 1) * sum(
_scale(k, j) * c[k - j] * u[j] for j in range(1, k + 1))
primal_out, *series_out = v
return primal_out, series_out
def _reduce_chooser_taylor_rule(g):
return lax.div(
lax._reduce_sum(lax.mul(g, location_indicators), axes), counts)
x, = primals_in
series, = series_in
primal_out = prim.bind(x)
c0, cs = jet(deriv, primals_in, series_in)
c = [c0] + cs
u = [x] + series
v = [primal_out] + [None] * len(series)
for k in range(1, len(v)):
v[k] = fact(k - 1) * sum(
_scale(k, j) * c[k - j] * u[j] for j in range(1, k + 1))
primal_out, *series_out = v
return primal_out, series_out
def_deriv(
lax.erf_p, lambda x: lax.mul(lax._const(x, 2. / np.sqrt(np.pi)),
lax.exp(lax.neg(lax.square(x)))))
def def_comp(prim, comp):
"""
Define the jet rule for a primitive in terms of a composition of simpler primitives.
"""
jet_rules[prim] = partial(jet, comp)
def_comp(lax.expm1_p, lambda x: lax.exp(x) - 1)
def_comp(lax.log1p_p, lambda x: lax.log(1 + x))
def_comp(lax.sqrt_p, lambda x: x**0.5)
def_comp(lax.rsqrt_p, lambda x: x**-0.5)
def_comp(lax.asinh_p, lambda x: lax.log(x + lax.sqrt(lax.square(x) + 1)))
def_comp(lax.acosh_p, lambda x: lax.log(x + lax.sqrt(lax.square(x) - 1)))