wassname/ray
1
0
Fork 0
mirror of https://github.com/wassname/ray.git synced 2026-07-03 13:27:59 +08:00
Code Issues Packages Projects Releases Wiki Activity

Convert some unittests to pytest. (#2779)

Browse Source 
* Convert multi_node_test.py to pytest.

* Convert array_test.py to pytest.

* Convert failure_test.py to pytest.

* Convert microbenchmarks to pytest.

* Convert component_failures_test.py to pytest and some minor quotes changes.

* Convert tensorflow_test.py to pytest.

* Convert actor_test.py to pytest.

* Fix.

* Fix
This commit is contained in:
Robert Nishihara
2018-08-31 11:24:15 -07:00
committed by Philipp Moritz
parent 3813ae34b3
commit eda6ebb87d
10 changed files with 3185 additions and 3205 deletions
Download Patch File Download Diff File Expand all files Collapse all files
test/tensorflow_test.py
+165 -174
View File
@@ -3,8 +3,8 @@ from __future__ import division
from __future__ import print_function
from numpy.testing import assert_almost_equal
import pytest
import tensorflow as tf
import unittest
import ray
@@ -93,178 +93,169 @@ class TrainActor(object):
return self.values[1].get_weights()
class TensorFlowTest(unittest.TestCase):
def tearDown(self):
ray.shutdown()
def testTensorFlowVariables(self):
ray.init(num_workers=2)
sess = tf.Session()
loss, init, _, _ = make_linear_network()
sess.run(init)
variables = ray.experimental.TensorFlowVariables(loss, sess)
weights = variables.get_weights()
for (name, val) in weights.items():
weights[name] += 1.0
variables.set_weights(weights)
assert weights == variables.get_weights()
loss2, init2, _, _ = make_linear_network("w", "b")
sess.run(init2)
variables2 = ray.experimental.TensorFlowVariables(loss2, sess)
weights2 = variables2.get_weights()
for (name, val) in weights2.items():
weights2[name] += 2.0
variables2.set_weights(weights2)
assert weights2 == variables2.get_weights()
flat_weights = variables2.get_flat() + 2.0
variables2.set_flat(flat_weights)
assert_almost_equal(flat_weights, variables2.get_flat())
variables3 = ray.experimental.TensorFlowVariables([loss2])
assert variables3.sess is None
sess = tf.Session()
variables3.set_session(sess)
assert variables3.sess == sess
# Test that the variable names for the two different nets are not
# modified by TensorFlow to be unique (i.e., they should already
# be unique because of the variable prefix).
def testVariableNameCollision(self):
ray.init(num_workers=2)
net1 = NetActor()
net2 = NetActor()
# This is checking that the variable names of the two nets are the
# same, i.e., that the names in the weight dictionaries are the same.
net1.values[0].set_weights(net2.values[0].get_weights())
# Test that TensorFlowVariables can take in addition variables through
# input_variables arg and with no loss.
def testAdditionalVariablesNoLoss(self):
ray.init(num_workers=1)
net = LossActor(use_loss=False)
assert len(net.values[0].variables.items()) == 1
assert len(net.values[0].placeholders.items()) == 1
net.values[0].set_weights(net.values[0].get_weights())
# Test that TensorFlowVariables can take in addition variables through
# input_variables arg and with a loss.
def testAdditionalVariablesWithLoss(self):
ray.init(num_workers=1)
net = LossActor()
assert len(net.values[0].variables.items()) == 3
assert len(net.values[0].placeholders.items()) == 3
net.values[0].set_weights(net.values[0].get_weights())
# Test that different networks on the same worker are independent and
# we can get/set their weights without any interaction.
def testNetworksIndependent(self):
# Note we use only one worker to ensure that all of the remote
# functions run on the same worker.
ray.init(num_workers=1)
net1 = NetActor()
net2 = NetActor()
# Make sure the two networks have different weights. TODO(rkn): Note
# that equality comparisons of numpy arrays normally does not work.
# This only works because at the moment they have size 1.
weights1 = net1.get_weights()
weights2 = net2.get_weights()
assert weights1 != weights2
# Set the weights and get the weights, and make sure they are
# unchanged.
new_weights1 = net1.set_and_get_weights(weights1)
new_weights2 = net2.set_and_get_weights(weights2)
assert weights1 == new_weights1
assert weights2 == new_weights2
# Swap the weights.
new_weights1 = net2.set_and_get_weights(weights1)
new_weights2 = net1.set_and_get_weights(weights2)
assert weights1 == new_weights1
assert weights2 == new_weights2
# This test creates an additional network on the driver so that the
# tensorflow variables on the driver and the worker differ.
def testNetworkDriverWorkerIndependent(self):
ray.init(num_workers=1)
# Create a network on the driver locally.
sess1 = tf.Session()
loss1, init1, _, _ = make_linear_network()
ray.experimental.TensorFlowVariables(loss1, sess1)
sess1.run(init1)
net2 = ray.remote(NetActor).remote()
weights2 = ray.get(net2.get_weights.remote())
new_weights2 = ray.get(
net2.set_and_get_weights.remote(net2.get_weights.remote()))
assert weights2 == new_weights2
def testVariablesControlDependencies(self):
ray.init(num_workers=1)
# Creates a network and appends a momentum optimizer.
sess = tf.Session()
loss, init, _, _ = make_linear_network()
minimizer = tf.train.MomentumOptimizer(0.9, 0.9).minimize(loss)
net_vars = ray.experimental.TensorFlowVariables(minimizer, sess)
sess.run(init)
# Tests if all variables are properly retrieved, 2 variables and 2
# momentum variables.
assert len(net_vars.variables.items()) == 4
def testRemoteTrainingStep(self):
ray.init(num_workers=1)
net = ray.remote(TrainActor).remote()
ray.get(net.training_step.remote(net.get_weights.remote()))
def testRemoteTrainingLoss(self):
ray.init(num_workers=2)
net = ray.remote(TrainActor).remote()
net_values = TrainActor().values
loss, variables, _, sess, grads, train, placeholders = net_values
before_acc = sess.run(
loss, feed_dict=dict(zip(placeholders, [[2] * 100, [4] * 100])))
for _ in range(3):
gradients_list = ray.get([
net.training_step.remote(variables.get_weights())
for _ in range(2)
])
mean_grads = [
sum(gradients[i]
for gradients in gradients_list) / len(gradients_list)
for i in range(len(gradients_list[0]))
]
feed_dict = {
grad[0]: mean_grad
for (grad, mean_grad) in zip(grads, mean_grads)
}
sess.run(train, feed_dict=feed_dict)
after_acc = sess.run(
loss, feed_dict=dict(zip(placeholders, [[2] * 100, [4] * 100])))
assert before_acc < after_acc
@pytest.fixture
def ray_start_regular():
# Start the Ray processes.
ray.init(num_cpus=2)
yield None
# The code after the yield will run as teardown code.
ray.shutdown()
if __name__ == "__main__":
unittest.main(verbosity=2)
def test_tensorflow_variables(ray_start_regular):
sess = tf.Session()
loss, init, _, _ = make_linear_network()
sess.run(init)
variables = ray.experimental.TensorFlowVariables(loss, sess)
weights = variables.get_weights()
for (name, val) in weights.items():
weights[name] += 1.0
variables.set_weights(weights)
assert weights == variables.get_weights()
loss2, init2, _, _ = make_linear_network("w", "b")
sess.run(init2)
variables2 = ray.experimental.TensorFlowVariables(loss2, sess)
weights2 = variables2.get_weights()
for (name, val) in weights2.items():
weights2[name] += 2.0
variables2.set_weights(weights2)
assert weights2 == variables2.get_weights()
flat_weights = variables2.get_flat() + 2.0
variables2.set_flat(flat_weights)
assert_almost_equal(flat_weights, variables2.get_flat())
variables3 = ray.experimental.TensorFlowVariables([loss2])
assert variables3.sess is None
sess = tf.Session()
variables3.set_session(sess)
assert variables3.sess == sess
# Test that the variable names for the two different nets are not
# modified by TensorFlow to be unique (i.e., they should already
# be unique because of the variable prefix).
def test_variable_name_collision(ray_start_regular):
net1 = NetActor()
net2 = NetActor()
# This is checking that the variable names of the two nets are the
# same, i.e., that the names in the weight dictionaries are the same.
net1.values[0].set_weights(net2.values[0].get_weights())
# Test that TensorFlowVariables can take in addition variables through
# input_variables arg and with no loss.
def test_additional_variables_no_loss(ray_start_regular):
net = LossActor(use_loss=False)
assert len(net.values[0].variables.items()) == 1
assert len(net.values[0].placeholders.items()) == 1
net.values[0].set_weights(net.values[0].get_weights())
# Test that TensorFlowVariables can take in addition variables through
# input_variables arg and with a loss.
def test_additional_variables_with_loss(ray_start_regular):
net = LossActor()
assert len(net.values[0].variables.items()) == 3
assert len(net.values[0].placeholders.items()) == 3
net.values[0].set_weights(net.values[0].get_weights())
# Test that different networks on the same worker are independent and
# we can get/set their weights without any interaction.
def test_networks_independent(ray_start_regular):
# Note we use only one worker to ensure that all of the remote
# functions run on the same worker.
net1 = NetActor()
net2 = NetActor()
# Make sure the two networks have different weights. TODO(rkn): Note
# that equality comparisons of numpy arrays normally does not work.
# This only works because at the moment they have size 1.
weights1 = net1.get_weights()
weights2 = net2.get_weights()
assert weights1 != weights2
# Set the weights and get the weights, and make sure they are
# unchanged.
new_weights1 = net1.set_and_get_weights(weights1)
new_weights2 = net2.set_and_get_weights(weights2)
assert weights1 == new_weights1
assert weights2 == new_weights2
# Swap the weights.
new_weights1 = net2.set_and_get_weights(weights1)
new_weights2 = net1.set_and_get_weights(weights2)
assert weights1 == new_weights1
assert weights2 == new_weights2
# This test creates an additional network on the driver so that the
# tensorflow variables on the driver and the worker differ.
def test_network_driver_worker_independent(ray_start_regular):
# Create a network on the driver locally.
sess1 = tf.Session()
loss1, init1, _, _ = make_linear_network()
ray.experimental.TensorFlowVariables(loss1, sess1)
sess1.run(init1)
net2 = ray.remote(NetActor).remote()
weights2 = ray.get(net2.get_weights.remote())
new_weights2 = ray.get(
net2.set_and_get_weights.remote(net2.get_weights.remote()))
assert weights2 == new_weights2
def test_variables_control_dependencies(ray_start_regular):
# Creates a network and appends a momentum optimizer.
sess = tf.Session()
loss, init, _, _ = make_linear_network()
minimizer = tf.train.MomentumOptimizer(0.9, 0.9).minimize(loss)
net_vars = ray.experimental.TensorFlowVariables(minimizer, sess)
sess.run(init)
# Tests if all variables are properly retrieved, 2 variables and 2
# momentum variables.
assert len(net_vars.variables.items()) == 4
def test_remote_training_step(ray_start_regular):
net = ray.remote(TrainActor).remote()
ray.get(net.training_step.remote(net.get_weights.remote()))
def test_remote_training_loss(ray_start_regular):
net = ray.remote(TrainActor).remote()
net_values = TrainActor().values
loss, variables, _, sess, grads, train, placeholders = net_values
before_acc = sess.run(
loss, feed_dict=dict(zip(placeholders, [[2] * 100, [4] * 100])))
for _ in range(3):
gradients_list = ray.get([
net.training_step.remote(variables.get_weights()) for _ in range(2)
])
mean_grads = [
sum(gradients[i]
for gradients in gradients_list) / len(gradients_list)
for i in range(len(gradients_list[0]))
]
feed_dict = {
grad[0]: mean_grad
for (grad, mean_grad) in zip(grads, mean_grads)
}
sess.run(train, feed_dict=feed_dict)
after_acc = sess.run(
loss, feed_dict=dict(zip(placeholders, [[2] * 100, [4] * 100])))
assert before_acc < after_acc