def test_grad_ob_aux_return(self):
class Test(bp.Base):
def __init__(self):
super(Test, self).__init__()
self.a = bm.TrainVar(bm.ones(10))
self.b = bm.TrainVar(bm.random.randn(10))
self.c = bm.TrainVar(bm.random.uniform(size=10))
def __call__(self):
return bm.sum(self.a + self.b + self.c), (bm.sin(100), bm.exp(0.1))
bm.random.seed(0)
t = Test()
f_grad = bm.grad(t, grad_vars=[t.a, t.b], dyn_vars=t.vars(),
has_aux=True, return_value=True)
grads, returns, aux = f_grad()
for g in grads: assert (g == 1.).all()
assert returns == bm.sum(t.a + t.b + t.c)
assert aux[0] == bm.sin(100)
assert aux[1] == bm.exp(0.1)
t = Test()
f_grad = bm.grad(t, grad_vars=t.a, dyn_vars=t.vars(),
has_aux=True, return_value=True)
grads, returns, aux = f_grad()
assert (grads == 1.).all()
assert returns == bm.sum(t.a + t.b + t.c)
assert aux[0] == bm.sin(100)
assert aux[1] == bm.exp(0.1)
def test_grad_ob_argnums_aux_return(self):
class Test(bp.Base):
def __init__(self):
super(Test, self).__init__()
self.a = bm.TrainVar(bm.ones(10))
self.b = bm.TrainVar(bm.random.randn(10))
self.c = bm.TrainVar(bm.random.uniform(size=10))
def __call__(self, d):
return bm.sum(self.a + self.b + self.c + 2 * d), (bm.sin(100), bm.exp(0.1))
bm.random.seed(0)
t = Test()
f_grad = bm.grad(t, grad_vars=t.vars(), argnums=0, has_aux=True, return_value=True)
d = bm.random.random(10)
(var_grads, arg_grads), loss, aux = f_grad(d)
for g in var_grads.values(): assert (g == 1.).all()
assert (arg_grads == 2.).all()
assert aux[0] == bm.sin(100)
assert aux[1] == bm.exp(0.1)
assert loss == t(d)[0]
t = Test()
f_grad = bm.grad(t, grad_vars=t.vars(), argnums=[0], has_aux=True, return_value=True)
d = bm.random.random(10)
(var_grads, arg_grads), loss, aux = f_grad(d)
for g in var_grads.values(): assert (g == 1.).all()
assert (arg_grads[0] == 2.).all()
assert aux[0] == bm.sin(100)
assert aux[1] == bm.exp(0.1)
assert loss == t(d)[0]
t = Test()
f_grad = bm.grad(t, dyn_vars=t.vars(), argnums=0, has_aux=True, return_value=True)
d = bm.random.random(10)
arg_grads, loss, aux = f_grad(d)
assert (arg_grads == 2.).all()
assert aux[0] == bm.sin(100)
assert aux[1] == bm.exp(0.1)
assert loss == t(d)[0]
t = Test()
f_grad = bm.grad(t, dyn_vars=t.vars(), argnums=[0], has_aux=True, return_value=True)
d = bm.random.random(10)
arg_grads, loss, aux = f_grad(d)
assert (arg_grads[0] == 2.).all()
assert aux[0] == bm.sin(100)
assert aux[1] == bm.exp(0.1)
assert loss == t(d)[0]
def test_jacfwd_and_aux_nested(self):
def f(x):
jac, aux = _jacfwd(lambda x: (x ** 3, [x ** 3]), has_aux=True)(x)
return aux[0]
f2 = lambda x: x ** 3
self.assertEqual(_jacfwd(f)(4.), _jacfwd(f2)(4.))
self.assertEqual(bm.jit(_jacfwd(f))(4.), _jacfwd(f2)(4.))
self.assertEqual(bm.jit(_jacfwd(bm.jit(f)))(4.), _jacfwd(f2)(4.))
self.assertEqual(_jacfwd(f)(bm.asarray(4.)), _jacfwd(f2)(bm.asarray(4.)))
self.assertEqual(bm.jit(_jacfwd(f))(bm.asarray(4.)), _jacfwd(f2)(bm.asarray(4.)))
self.assertEqual(bm.jit(_jacfwd(bm.jit(f)))(bm.asarray(4.)), _jacfwd(f2)(bm.asarray(4.)))
def f(x):
jac, aux = _jacfwd(lambda x: (x ** 3, [x ** 3]), has_aux=True)(x)
return aux[0] * bm.sin(x)
f2 = lambda x: x ** 3 * bm.sin(x)
self.assertEqual(_jacfwd(f)(4.), _jacfwd(f2)(4.))
self.assertEqual(bm.jit(_jacfwd(f))(4.), _jacfwd(f2)(4.))
self.assertEqual(bm.jit(_jacfwd(bm.jit(f)))(4.), _jacfwd(f2)(4.))
self.assertEqual(_jacfwd(f)(bm.asarray(4.)), _jacfwd(f2)(bm.asarray(4.)))
self.assertEqual(bm.jit(_jacfwd(f))(bm.asarray(4.)), _jacfwd(f2)(bm.asarray(4.)))
self.assertEqual(bm.jit(_jacfwd(bm.jit(f)))(bm.asarray(4.)), _jacfwd(f2)(bm.asarray(4.)))
def test_grad_pure_func_aux2(self):
def call(a, b, c):
return bm.sum(a + b + c), (bm.sin(100), bm.exp(0.1))
bm.random.seed(1)
f_grad = bm.grad(call, argnums=[0, 1, 2], has_aux=True)
grads, aux = f_grad(bm.ones(10), bm.random.randn(10), bm.random.uniform(size=10))
for g in grads: assert (g == 1.).all()
assert aux[0] == bm.sin(100)
assert aux[1] == bm.exp(0.1)
def test_grad_func_return_aux1(self):
def call(a, b, c):
return bm.sum(a + b + c), (bm.sin(100), bm.exp(0.1))
bm.random.seed(1)
a = bm.ones(10)
b = bm.random.randn(10)
c = bm.random.uniform(size=10)
f_grad = bm.grad(call, return_value=True, has_aux=True)
grads, returns, aux = f_grad(a, b, c)
assert (grads == 1.).all()
assert returns == bm.sum(a + b + c)
assert aux[0] == bm.sin(100)
assert aux[1] == bm.exp(0.1)
def call(a, b, c): return bm.sum(a + b + c), (bm.sin(100), bm.exp(0.1))
def f(x): jac, aux = _jacfwd(lambda x: (x ** 3, [x ** 3]), has_aux=True)(x) return aux[0] * bm.sin(x)
def __call__(self, d): return bm.sum(self.a + self.b + self.c + 2 * d), (bm.sin(100), bm.exp(0.1))
