class AliasTest(chex.TestCase):
def setUp(self):
super(AliasTest, self).setUp()
self.init_params = (jnp.array([1., 2.]), jnp.array([3., 4.]))
self.per_step_updates = (jnp.array([500., 5.]), jnp.array([300., 3.]))
@chex.all_variants()
@parameterized.named_parameters(
('sgd', alias.sgd(LR, 0.0), optimizers.sgd(LR), 1e-5),
('adam', alias.adam(LR, 0.9, 0.999,
1e-8), optimizers.adam(LR, 0.9, 0.999), 1e-4),
('rmsprop', alias.rmsprop(LR, .9, 0.1), optimizers.rmsprop(
LR, .9, 0.1), 1e-5),
('adagrad', alias.adagrad(
LR,
0.,
0.,
), optimizers.adagrad(LR, 0.), 1e-5),
)
def test_jax_optimizer_equivalent(self, optax_optimizer, jax_optimizer,
rtol):
# experimental/optimizers.py
jax_params = self.init_params
opt_init, opt_update, get_params = jax_optimizer
state = opt_init(jax_params)
for i in range(STEPS):
state = opt_update(i, self.per_step_updates, state)
jax_params = get_params(state)
# optax
optax_params = self.init_params
state = optax_optimizer.init(optax_params)
@self.variant
def step(updates, state):
return optax_optimizer.update(updates, state)
for _ in range(STEPS):
updates, state = step(self.per_step_updates, state)
optax_params = update.apply_updates(optax_params, updates)
# Check equivalence.
chex.assert_tree_all_close(jax_params, optax_params, rtol=rtol)
@parameterized.named_parameters(
('sgd', alias.sgd(1e-2, 0.0)),
('adam', alias.adam(1e-1)),
('adamw', alias.adamw(1e-1)),
('lamb', alias.adamw(1e-1)),
('rmsprop', alias.rmsprop(1e-1)),
('fromage', transform.scale_by_fromage(-1e-2)),
('adabelief', alias.adabelief(1e-1)),
)
def test_parabel(self, opt):
initial_params = jnp.array([-1.0, 10.0, 1.0])
final_params = jnp.array([1.0, -1.0, 1.0])
@jax.grad
def get_updates(params):
return jnp.sum((params - final_params)**2)
@jax.jit
def step(params, state):
updates, state = opt.update(get_updates(params), state, params)
params = update.apply_updates(params, updates)
return params, state
params = initial_params
state = opt.init(params)
for _ in range(1000):
params, state = step(params, state)
chex.assert_tree_all_close(params, final_params, rtol=1e-2, atol=1e-2)
@parameterized.named_parameters(
('sgd', alias.sgd(2e-3, 0.2)),
('adam', alias.adam(1e-1)),
('adamw', alias.adamw(1e-1)),
('lamb', alias.adamw(1e-1)),
('rmsprop', alias.rmsprop(5e-3)),
('fromage', transform.scale_by_fromage(-5e-3)),
('adabelief', alias.adabelief(1e-1)),
)
def test_rosenbrock(self, opt):
a = 1.0
b = 100.0
initial_params = jnp.array([0.0, 0.0])
final_params = jnp.array([a, a**2])
@jax.grad
def get_updates(params):
return (a - params[0])**2 + b * (params[1] - params[0]**2)**2
@jax.jit
def step(params, state):
updates, state = opt.update(get_updates(params), state, params)
params = update.apply_updates(params, updates)
return params, state
params = initial_params
state = opt.init(params)
for _ in range(10000):
params, state = step(params, state)
chex.assert_tree_all_close(params, final_params, rtol=3e-2, atol=3e-2)
class AliasTest(chex.TestCase):
@parameterized.parameters(
('sgd', lambda: alias.sgd(1e-2, 0.0)),
('adam', lambda: alias.adam(1e-1)),
('adamw', lambda: alias.adamw(1e-1)),
('lamb', lambda: alias.adamw(1e-1)),
('rmsprop', lambda: alias.rmsprop(1e-1)),
('rmsprop_momentum', lambda: alias.rmsprop(5e-2, momentum=0.9)),
('fromage', lambda: alias.fromage(1e-2)),
('adabelief', lambda: alias.adabelief(1e-1)),
('radam', lambda: alias.radam(1e-1)), ('sm3', lambda: alias.sm3(1.0)),
('yogi', lambda: alias.yogi(1.0)),
('dpsgd', lambda: alias.dpsgd(1e-2, 10.0, 0.001, 0)))
def test_parabel(self, opt_name, opt):
opt = opt()
initial_params = jnp.array([-1.0, 10.0, 1.0])
final_params = jnp.array([1.0, -1.0, 1.0])
@jax.grad
def get_updates(params):
return jnp.sum((params - final_params)**2)
@jax.jit
def step(params, state):
updates = get_updates(params)
if opt_name == 'dpsgd': updates = updates[None]
updates, state = opt.update(updates, state, params)
params = update.apply_updates(params, updates)
return params, state
params = initial_params
state = opt.init(params)
for _ in range(1000):
params, state = step(params, state)
chex.assert_tree_all_close(params, final_params, rtol=1e-2, atol=1e-2)
@parameterized.parameters(
('sgd', lambda: alias.sgd(2e-3, 0.2)),
('adam', lambda: alias.adam(1e-1)),
('adamw', lambda: alias.adamw(1e-1)),
('lamb', lambda: alias.adamw(1e-1)),
('rmsprop', lambda: alias.rmsprop(5e-3)),
('rmsprop_momentum', lambda: alias.rmsprop(5e-3, momentum=0.9)),
('fromage', lambda: alias.fromage(5e-3)),
('adabelief', lambda: alias.adabelief(1e-1)),
('radam', lambda: alias.radam(1e-3)), ('sm3', lambda: alias.sm3(1.0)),
('yogi', lambda: alias.yogi(1.0)),
('dpsgd', lambda: alias.dpsgd(2e-3, 10., 0.001, 0, 0.2)))
def test_rosenbrock(self, opt_name, opt):
opt = opt()
a = 1.0
b = 100.0
initial_params = jnp.array([0.0, 0.0])
final_params = jnp.array([a, a**2])
@jax.grad
def get_updates(params):
return (a - params[0])**2 + b * (params[1] - params[0]**2)**2
@jax.jit
def step(params, state):
updates = get_updates(params)
if opt_name == 'dpsgd': updates = updates[None]
updates, state = opt.update(updates, state, params)
params = update.apply_updates(params, updates)
return params, state
params = initial_params
state = opt.init(params)
for _ in range(10000):
params, state = step(params, state)
chex.assert_tree_all_close(params, final_params, rtol=3e-2, atol=3e-2)
class AliasTest(chex.TestCase):
@parameterized.product(
(
dict(opt_name='sgd', opt=lambda: alias.sgd(1e-3, 0.9)),
dict(opt_name='adafactor', opt=lambda: alias.adafactor(5e-3)),
dict(opt_name='adagrad', opt=lambda: alias.adagrad(1.0)),
dict(opt_name='adam', opt=lambda: alias.adam(1e-1)),
dict(opt_name='adamw', opt=lambda: alias.adamw(1e-1)),
dict(opt_name='lars', opt=lambda: alias.lars(1.0)),
dict(opt_name='lamb', opt=lambda: alias.lamb(1e-3)),
dict(opt_name='noisy_sgd',
opt=lambda: alias.noisy_sgd(1e-3, eta=1e-4)),
dict(opt_name='rmsprop', opt=lambda: alias.rmsprop(5e-3)),
dict(opt_name='rmsprop_momentum',
opt=lambda: alias.rmsprop(5e-3, momentum=0.9)),
dict(opt_name='fromage', opt=lambda: alias.fromage(5e-3)),
dict(opt_name='adabelief', opt=lambda: alias.adabelief(1e-2)),
dict(opt_name='radam', opt=lambda: alias.radam(5e-3)),
dict(opt_name='sm3', opt=lambda: alias.sm3(1.0)),
dict(opt_name='yogi', opt=lambda: alias.yogi(1e-1)),
dict(opt_name='dpsgd',
opt=lambda: alias.dpsgd(1e-3, 10.0, 0.001, 0, 0.2)),
),
target=(_setup_parabola, _setup_rosenbrock),
dtype=(jnp.float32, jnp.complex64),
)
def test_optimization(self, opt_name, opt, target, dtype):
if (opt_name in ('fromage', 'noisy_sgd', 'sm3')
and jnp.iscomplexobj(dtype)):
raise absltest.SkipTest(
f'{opt_name} does not support complex parameters.')
opt = opt()
initial_params, final_params, get_updates = target(dtype)
@jax.jit
def step(params, state):
updates = get_updates(params)
if opt_name == 'dpsgd':
updates = updates[None]
# Complex gradients need to be conjugated before being added to parameters
# https://gist.github.com/wdphy16/118aef6fb5f82c49790d7678cf87da29
updates = jax.tree_map(lambda x: x.conj(), updates)
updates, state = opt.update(updates, state, params)
params = update.apply_updates(params, updates)
return params, state
params = initial_params
state = opt.init(params)
for _ in range(10000):
params, state = step(params, state)
chex.assert_tree_all_close(params, final_params, rtol=3e-2, atol=3e-2)
@parameterized.named_parameters([
('float32', 'float32'),
('bfloat16', 'bfloat16'),
('complex64', 'complex64'),
('None', None),
])
def test_explicit_dtype(self, dtype):
expected_dtype = jax.dtypes.canonicalize_dtype(
dtype) # None -> float32
tx = alias.sgd(0.1, momentum=0.9, accumulator_dtype=dtype)
trace_state, _ = tx.init(jnp.array([0.0, 0.0]))
self.assertEqual(expected_dtype, trace_state.trace.dtype)
tx = alias.adam(0.1, mu_dtype=dtype)
adam_state, _ = tx.init(jnp.array([0.0, 0.0]))
self.assertEqual(expected_dtype, adam_state.mu.dtype)
tx = alias.adamw(0.1, mu_dtype=dtype)
adam_state, _, _ = tx.init(jnp.array([0.0, 0.0]))
self.assertEqual(expected_dtype, adam_state.mu.dtype)