def kernel(hmc_state, step_size_state, step, seed):
if not self._is_on_jax:
hmc_seed = _test_seed()
else:
hmc_seed, seed = util.split_seed(seed, 2)
hmc_state, hmc_extra = fun_mcmc.hamiltonian_monte_carlo(
hmc_state,
step_size=tf.exp(step_size_state.state),
num_integrator_steps=num_leapfrog_steps,
target_log_prob_fn=target_log_prob_fn,
seed=hmc_seed)
rate = fun_mcmc.prefab._polynomial_decay( # pylint: disable=protected-access
step=step,
step_size=self._constant(0.01),
power=0.5,
decay_steps=num_adapt_steps,
final_step_size=0.)
mean_p_accept = tf.reduce_mean(
tf.exp(tf.minimum(self._constant(0.), hmc_extra.log_accept_ratio)))
loss_fn = fun_mcmc.make_surrogate_loss_fn(
lambda _: (0.9 - mean_p_accept, ()))
step_size_state, _ = fun_mcmc.adam_step(
step_size_state, loss_fn, learning_rate=rate)
return ((hmc_state, step_size_state, step + 1, seed),
(hmc_state.state_extra[0], hmc_extra.log_accept_ratio))
def kernel(hmc_state, seed):
if not self._is_on_jax:
hmc_seed = _test_seed()
else:
hmc_seed, seed = util.split_seed(seed, 2)
hmc_state, hmc_extra = fun_mcmc.hamiltonian_monte_carlo(
hmc_state,
step_size=step_size,
num_integrator_steps=num_leapfrog_steps,
target_log_prob_fn=target_log_prob_fn,
seed=hmc_seed)
return (hmc_state, seed), hmc_extra
def kernel(hmc_state, seed):
if backend.get_backend() == backend.TENSORFLOW:
hmc_seed = tfp_test_util.test_seed()
else:
hmc_seed, seed = util.split_seed(seed, 2)
hmc_state, hmc_extra = fun_mcmc.hamiltonian_monte_carlo(
hmc_state,
step_size=step_size,
num_integrator_steps=num_leapfrog_steps,
target_log_prob_fn=target_log_prob_fn,
seed=hmc_seed)
return (hmc_state, seed), hmc_extra
def trace():
kernel = lambda state: fun_mcmc.hamiltonian_monte_carlo(
state,
step_size=0.1,
num_integrator_steps=3,
target_log_prob_fn=target_log_prob_fn,
seed=tfp_test_util.test_seed())
fun_mcmc.trace(state=fun_mcmc.HamiltonianMonteCarloState(
tf.zeros([1])),
fn=kernel,
num_steps=4,
trace_fn=lambda *args: ())
def kernel(hmc_state, raac_state, seed):
if not self._is_on_jax:
hmc_seed = _test_seed()
else:
hmc_seed, seed = util.split_seed(seed, 2)
hmc_state, hmc_extra = fun_mcmc.hamiltonian_monte_carlo(
hmc_state,
step_size=step_size,
num_integrator_steps=num_leapfrog_steps,
target_log_prob_fn=target_log_prob_fn,
seed=hmc_seed)
raac_state, _ = fun_mcmc.running_approximate_auto_covariance_step(
raac_state, hmc_state.state, axis=aggregation)
return (hmc_state, raac_state, seed), hmc_extra
def trace():
# pylint: disable=g-long-lambda
kernel = lambda state: fun_mcmc.hamiltonian_monte_carlo(
state,
step_size=0.1,
num_integrator_steps=3,
target_log_prob_fn=target_log_prob_fn,
seed=self._make_seed(tfp_test_util.test_seed()))
fun_mcmc.trace(state=fun_mcmc.hamiltonian_monte_carlo_init(
state=tf.zeros([1]), target_log_prob_fn=target_log_prob_fn),
fn=kernel,
num_steps=4,
trace_fn=lambda *args: ())
def trace():
kernel = lambda state: fun_mcmc.hamiltonian_monte_carlo(
state,
step_size=self._constant(0.1),
num_integrator_steps=3,
target_log_prob_fn=target_log_prob_fn,
seed=_test_seed())
fun_mcmc.trace(
state=fun_mcmc.hamiltonian_monte_carlo_init(
tf.zeros([1], dtype=self._dtype), target_log_prob_fn),
fn=kernel,
num_steps=4,
trace_fn=lambda *args: ())
def testPreconditionedHMC(self):
step_size = 0.2
num_steps = 2000
num_leapfrog_steps = 10
state = tf.ones([16, 2])
base_mean = [1., 0]
base_cov = [[1, 0.5], [0.5, 1]]
bijector = tfb.Softplus()
base_dist = tfd.MultivariateNormalFullCovariance(
loc=base_mean, covariance_matrix=base_cov)
target_dist = bijector(base_dist)
def orig_target_log_prob_fn(x):
return target_dist.log_prob(x), ()
target_log_prob_fn, state = fun_mcmc.transform_log_prob_fn(
orig_target_log_prob_fn, bijector, state)
# pylint: disable=g-long-lambda
kernel = tf.function(lambda state: fun_mcmc.hamiltonian_monte_carlo(
state,
step_size=step_size,
num_integrator_steps=num_leapfrog_steps,
target_log_prob_fn=target_log_prob_fn,
seed=_test_seed()))
_, chain = fun_mcmc.trace(
state=fun_mcmc.hamiltonian_monte_carlo_init(state, target_log_prob_fn),
fn=kernel,
num_steps=num_steps,
trace_fn=lambda state, extra: state.state_extra[0])
# Discard the warmup samples.
chain = chain[1000:]
sample_mean = tf.reduce_mean(chain, axis=[0, 1])
sample_cov = tfp.stats.covariance(chain, sample_axis=[0, 1])
true_samples = target_dist.sample(4096, seed=_test_seed())
true_mean = tf.reduce_mean(true_samples, axis=0)
true_cov = tfp.stats.covariance(chain, sample_axis=[0, 1])
self.assertAllClose(true_mean, sample_mean, rtol=0.1, atol=0.1)
self.assertAllClose(true_cov, sample_cov, rtol=0.1, atol=0.1)
def kernel(hmc_state, step_size, step):
"""HMC kernel."""
hmc_state, hmc_extra = fun_mcmc.hamiltonian_monte_carlo(
hmc_state,
step_size=step_size,
num_integrator_steps=FLAGS.mcmc_leapfrog_steps,
momentum_sample_fn=create_momentum_sample_fn(hmc_state.state),
target_log_prob_fn=log_prob_non_transformed)
mean_p_accept = tf.reduce_mean(
tf.exp(tf.minimum(0., hmc_extra.log_accept_ratio)))
if FLAGS.mcmc_adapt_step_size:
step_size = fun_mcmc.sign_adaptation(step_size,
output=mean_p_accept,
set_point=0.9)
return (hmc_state, step_size, step + 1), hmc_extra
def kernel(hmc_state, step_size, step):
hmc_state, hmc_extra = fun_mcmc.hamiltonian_monte_carlo(
hmc_state,
step_size=step_size,
num_integrator_steps=num_leapfrog_steps,
target_log_prob_fn=target_log_prob_fn)
rate = tf.compat.v1.train.polynomial_decay(
0.01,
global_step=step,
power=0.5,
decay_steps=num_adapt_steps,
end_learning_rate=0.)
mean_p_accept = tf.reduce_mean(
tf.exp(tf.minimum(0., hmc_extra.log_accept_ratio)))
step_size = fun_mcmc.sign_adaptation(step_size,
output=mean_p_accept,
set_point=0.9,
adaptation_rate=rate)
return (hmc_state, step_size, step + 1), hmc_extra
def kernel(hmc_state, step_size_state, step):
hmc_state, hmc_extra = fun_mcmc.hamiltonian_monte_carlo(
hmc_state,
step_size=tf.exp(step_size_state.state),
num_integrator_steps=num_leapfrog_steps,
target_log_prob_fn=target_log_prob_fn)
rate = tf.compat.v1.train.polynomial_decay(
0.01,
global_step=step,
power=0.5,
decay_steps=num_adapt_steps,
end_learning_rate=0.)
mean_p_accept = tf.reduce_mean(
tf.exp(tf.minimum(0., hmc_extra.log_accept_ratio)))
loss_fn = fun_mcmc.make_surrogate_loss_fn(
lambda _: (0.9 - mean_p_accept, ()))
step_size_state, _ = fun_mcmc.adam_step(
step_size_state, loss_fn, learning_rate=rate)
return ((hmc_state, step_size_state, step + 1),
(hmc_state.state_extra[0], hmc_extra.log_accept_ratio))
