def test_multi_steps(self):
batch_size = 32
x_size = 7
# Parameters should be updated only every `k_steps` optimisation steps.
k_steps = 4
data = jnp.ones([batch_size, x_size])
def get_loss(x):
loss = jnp.sum(hk.Linear(10)(x)**2)
return loss
loss_init, loss_apply = hk.without_apply_rng(hk.transform(get_loss))
params = loss_init(jax.random.PRNGKey(1915), data)
ms_opt = wrappers.MultiSteps(alias.adam(1e-4), k_steps)
opt_init, opt_update = ms_opt.gradient_transformation()
# Put the training in one function, to check that the update is indeed
# jittable.
def train_step(data, opt_state, params):
grad = jax.grad(loss_apply)(params, data)
updates, opt_state = opt_update(grad, opt_state, params)
return updates, opt_state
opt_state = opt_init(params)
prev_loss = loss_apply(params, data)
for idx in range(5 * k_steps):
updates, opt_state = self.variant(train_step)(data, opt_state,
params)
new_params = update.apply_updates(params, updates)
new_loss = loss_apply(new_params, data)
if idx % k_steps < k_steps - 1:
# The parameters should not have changed and the loss should be
# constant.
jax.tree_multimap(np.testing.assert_array_equal, new_params,
params)
np.testing.assert_equal(new_loss, prev_loss)
self.assertFalse(ms_opt.has_updated(opt_state))
else:
# This is a step where parameters should actually have been updated, and
# the loss should accordingly go down.
np.testing.assert_array_less(new_loss, prev_loss)
prev_loss = new_loss
self.assertTrue(ms_opt.has_updated(opt_state))
params = new_params
def test_multi_steps_every_k_schedule(self):
# Test a non-trivial schedule which varies over time.
ms_opt = wrappers.MultiSteps(
alias.sgd(1e-4), lambda grad_step: jnp.where(grad_step < 2, 1, 3))
opt_init, opt_update = ms_opt.gradient_transformation()
params = dict(a=jnp.zeros([]))
opt_state = opt_init(params)
grad = dict(a=jnp.zeros([]))
self.assertFalse(ms_opt.has_updated(opt_state))
# First two steps have 1 mini-step per update.
for _ in range(2):
_, opt_state = opt_update(grad, opt_state, params)
self.assertTrue(ms_opt.has_updated(opt_state))
# Subsequently, mini-steps should have 3 mini-steps per update.
for _ in range(5):
for _ in range(2):
_, opt_state = opt_update(grad, opt_state, params)
self.assertFalse(ms_opt.has_updated(opt_state))
_, opt_state = opt_update(grad, opt_state, params)
self.assertTrue(ms_opt.has_updated(opt_state))
