forked from HIPS/autograd
/
test_util.py
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/
test_util.py
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from functools import partial
from itertools import product
from .core import make_vjp, make_jvp, vspace
from .util import subvals
from .wrap_util import unary_to_nary, get_name
TOL = 1e-6
RTOL = 1e-6
def scalar_close(a, b):
return abs(a - b) < TOL or abs(a - b) / abs(a + b) < RTOL
EPS = 1e-6
def make_numerical_jvp(f, x):
y = f(x)
x_vs, y_vs = vspace(x), vspace(y)
def jvp(v):
# (f(x + v*eps/2) - f(x - v*eps/2)) / eps
f_x_plus = f(x_vs.add(x, x_vs.scalar_mul(v, EPS/2)))
f_x_minus = f(x_vs.add(x, x_vs.scalar_mul(v, -EPS/2)))
neg_f_x_minus = y_vs.scalar_mul(f_x_minus, -1.0)
return y_vs.scalar_mul(y_vs.add(f_x_plus, neg_f_x_minus), 1.0 / EPS)
return jvp
def check_vjp(f, x):
vjp, y = make_vjp(f, x)
jvp = make_numerical_jvp(f, x)
x_vs, y_vs = vspace(x), vspace(y)
x_v, y_v = x_vs.randn(), y_vs.randn()
vjp_y = x_vs.covector(vjp(y_vs.covector(y_v)))
assert vspace(vjp_y) == x_vs
vjv_exact = x_vs.inner_prod(x_v, vjp_y)
vjv_numeric = y_vs.inner_prod(y_v, jvp(x_v))
assert scalar_close(vjv_numeric, vjv_exact), \
("Derivative (VJP) check of {} failed with arg {}:\n"
"analytic: {}\nnumeric: {}".format(
get_name(f), x, vjv_exact, vjv_numeric))
def check_jvp(f, x):
jvp = make_jvp(f, x)
jvp_numeric = make_numerical_jvp(f, x)
x_v = vspace(x).randn()
check_equivalent(jvp(x_v)[1], jvp_numeric(x_v))
def check_equivalent(x, y):
x_vs, y_vs = vspace(x), vspace(y)
assert x_vs == y_vs, "VSpace mismatch:\nx: {}\ny: {}".format(x_vs, y_vs)
v = x_vs.randn()
assert scalar_close(x_vs.inner_prod(x, v), x_vs.inner_prod(y, v)), \
"Value mismatch:\nx: {}\ny: {}".format(x, y)
@unary_to_nary
def check_grads(f, x, modes=['fwd', 'rev'], order=2):
assert all(m in ['fwd', 'rev'] for m in modes)
if 'fwd' in modes:
check_jvp(f, x)
if order > 1:
grad_f = lambda x, v: make_jvp(f, x)(v)[1]
grad_f.__name__ = 'jvp_{}'.format(get_name(f))
v = vspace(x).randn()
check_grads(grad_f, (0, 1), modes, order=order-1)(x, v)
if 'rev' in modes:
check_vjp(f, x)
if order > 1:
grad_f = lambda x, v: make_vjp(f, x)[0](v)
grad_f.__name__ = 'vjp_{}'.format(get_name(f))
v = vspace(f(x)).randn()
check_grads(grad_f, (0, 1), modes, order=order-1)(x, v)
def combo_check(fun, *args, **kwargs):
# Tests all combinations of args and kwargs given.
_check_grads = lambda f: check_grads(f, *args, **kwargs)
def _combo_check(*args, **kwargs):
kwarg_key_vals = [[(k, x) for x in xs] for k, xs in kwargs.items()]
for _args in product(*args):
for _kwargs in product(*kwarg_key_vals):
_check_grads(fun)(*_args, **dict(_kwargs))
return _combo_check