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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests l2hmc fit to 2D strongly correlated Gaussian executed eagerly."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import time
import numpy.random as npr
import tensorflow as tf
import tensorflow.contrib.eager as tfe
from tensorflow.contrib.eager.python.examples.l2hmc import l2hmc
def get_default_hparams():
return tf.contrib.training.HParams(
x_dim=2,
n_samples=200,
n_steps=10,
eps=.1,
n_iters=10,
learning_rate=.0003,
n_warmup_iters=3)
def warmup(dynamics,
optimizer,
n_iters=1,
n_samples=200,
loss_fn=l2hmc.compute_loss):
"""Warmup optimization to reduce overhead."""
samples = tf.random_normal(
shape=[n_samples, dynamics.x_dim], dtype=tf.float32)
for _ in range(n_iters):
_, grads, samples, _ = l2hmc.loss_and_grads(
dynamics, samples, loss_fn=loss_fn)
optimizer.apply_gradients(zip(grads, dynamics.variables))
def fit(dynamics,
samples,
optimizer,
loss_fn=l2hmc.compute_loss,
n_iters=5000,
verbose=True,
logdir=None):
"""Fit L2HMC sampler with given log-likelihood function."""
if logdir:
summary_writer = tf.contrib.summary.create_file_writer(logdir)
for i in range(n_iters):
loss, grads, samples, _ = l2hmc.loss_and_grads(
dynamics, samples, loss_fn=loss_fn)
optimizer.apply_gradients(zip(grads, dynamics.variables))
if verbose:
print("Iteration %d: loss %.4f" % (i, loss))
if logdir:
with summary_writer.as_default():
with tf.contrib.summary.always_record_summaries():
tf.contrib.summary.scalar("loss", loss)
class L2hmcTest(tf.test.TestCase):
"""Unit tests for l2hmc in both eager and graph mode."""
def test_apply_transition(self):
"""Testing function `Dynamics.apply_transition` in graph and eager mode."""
# Eager mode testing
hparams = get_default_hparams()
energy_fn, _, _ = l2hmc.get_scg_energy_fn()
dynamics = l2hmc.Dynamics(
x_dim=hparams.x_dim,
minus_loglikelihood_fn=energy_fn,
n_steps=hparams.n_steps,
eps=hparams.eps)
samples = tf.random_normal(shape=[hparams.n_samples, hparams.x_dim])
x_, v_, x_accept_prob, x_out = dynamics.apply_transition(samples)
self.assertEqual(x_.shape, v_.shape)
self.assertEqual(x_out.shape, samples.shape)
self.assertEqual(x_.shape, x_out.shape)
self.assertEqual(x_accept_prob.shape, (hparams.n_samples,))
# Graph mode testing
with tf.Graph().as_default():
energy_fn, _, _ = l2hmc.get_scg_energy_fn()
dynamics = l2hmc.Dynamics(
x_dim=hparams.x_dim,
minus_loglikelihood_fn=energy_fn,
n_steps=hparams.n_steps,
eps=hparams.eps)
x = tf.placeholder(tf.float32, shape=[None, hparams.x_dim])
x_, v_, x_accept_prob, x_out = dynamics.apply_transition(x)
samples = npr.normal(size=[hparams.n_samples, hparams.x_dim])
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
np_x_, np_v_, np_x_accept_prob, np_x_out = sess.run(
[x_, v_, x_accept_prob, x_out], feed_dict={x: samples})
self.assertEqual(np_x_.shape, np_v_.shape)
self.assertEqual(samples.shape, np_x_out.shape)
self.assertEqual(np_x_.shape, np_x_out.shape)
self.assertEqual(np_x_accept_prob.shape, (hparams.n_samples,))
class L2hmcBenchmark(tf.test.Benchmark):
"""Eager and graph benchmarks for l2hmc."""
def benchmark_graph(self):
"""Benchmark Graph performance."""
hparams = get_default_hparams()
tf.reset_default_graph()
with tf.Graph().as_default():
energy_fn, _, _ = l2hmc.get_scg_energy_fn()
dynamics = l2hmc.Dynamics(
x_dim=hparams.x_dim,
minus_loglikelihood_fn=energy_fn,
n_steps=hparams.n_steps,
eps=hparams.eps)
x = tf.placeholder(tf.float32, shape=[None, hparams.x_dim])
loss, x_out, _ = l2hmc.compute_loss(dynamics, x)
global_step = tf.Variable(0., name="global_step", trainable=False)
learning_rate = tf.train.exponential_decay(
hparams.learning_rate, global_step, 1000, 0.96, staircase=True)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss, global_step=global_step)
# Single thread; fairer comparison against eager
session_conf = tf.ConfigProto(
intra_op_parallelism_threads=1, inter_op_parallelism_threads=1)
with tf.Session(config=session_conf) as sess:
sess.run(tf.global_variables_initializer())
# Warmup to reduce initialization effect when timing
samples = npr.normal(size=[hparams.n_samples, hparams.x_dim])
for _ in range(hparams.n_warmup_iters):
_, _, _, _ = sess.run(
[x_out, loss, train_op, learning_rate], feed_dict={x: samples})
# Training
start_time = time.time()
for i in range(hparams.n_iters):
samples, loss_np, _, _ = sess.run(
[x_out, loss, train_op, learning_rate], feed_dict={x: samples})
print("Iteration %d: loss %.4f" % (i, loss_np))
wall_time = time.time() - start_time
examples_per_sec = hparams.n_samples / wall_time
self.report_benchmark(
name="graph_train_%s" % ("gpu"
if tf.test.is_gpu_available() else "cpu"),
iters=hparams.n_iters,
extras={"examples_per_sec": examples_per_sec},
wall_time=wall_time)
def benchmark_eager(self):
self._benchmark_eager()
def benchmark_eager_defun(self):
self._benchmark_eager(defun=True)
def _benchmark_eager(self, defun=False):
"""Benchmark Eager performance."""
hparams = get_default_hparams()
energy_fn, _, _ = l2hmc.get_scg_energy_fn()
dynamics = l2hmc.Dynamics(
x_dim=hparams.x_dim,
minus_loglikelihood_fn=energy_fn,
n_steps=hparams.n_steps,
eps=hparams.eps)
optimizer = tf.train.AdamOptimizer(learning_rate=hparams.learning_rate)
loss_fn = tfe.defun(l2hmc.compute_loss) if defun else l2hmc.compute_loss
# Warmup to reduce initialization effect when timing
warmup(dynamics, optimizer, n_iters=hparams.n_warmup_iters, loss_fn=loss_fn)
# Training
samples = tf.random_normal(
shape=[hparams.n_samples, hparams.x_dim], dtype=tf.float32)
start_time = time.time()
fit(dynamics, samples, optimizer, loss_fn=loss_fn, n_iters=hparams.n_iters)
wall_time = time.time() - start_time
examples_per_sec = hparams.n_samples / wall_time
self.report_benchmark(
name="eager_train_%s%s" % ("gpu" if tf.test.is_gpu_available() else
"cpu", "_defun" if defun else ""),
iters=hparams.n_iters,
extras={"examples_per_sec": examples_per_sec},
wall_time=wall_time)
del dynamics
if __name__ == "__main__":
tf.enable_eager_execution()
tf.test.main()
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