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Switch Python indentation from 2 spaces to 4 spaces. (#726)
* 4 space indentation for actor.py. * 4 space indentation for worker.py. * 4 space indentation for more files. * 4 space indentation for some test files. * Check indentation in Travis. * 4 space indentation for some rl files. * Fix failure test. * Fix multi_node_test. * 4 space indentation for more files. * 4 space indentation for remaining files. * Fixes.
This commit is contained in:
Robert Nishihara
committed by
Philipp Moritz
Philipp Moritz
parent
310ba82131
commit
e0867c8845
+167
-160
@@ -10,218 +10,225 @@ import ray
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def make_linear_network(w_name=None, b_name=None):
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# Define the inputs.
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x_data = tf.placeholder(tf.float32, shape=[100])
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y_data = tf.placeholder(tf.float32, shape=[100])
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# Define the weights and computation.
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w = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name=w_name)
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b = tf.Variable(tf.zeros([1]), name=b_name)
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y = w * x_data + b
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# Return the loss and weight initializer.
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return (tf.reduce_mean(tf.square(y - y_data)),
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tf.global_variables_initializer(), x_data, y_data)
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# Define the inputs.
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x_data = tf.placeholder(tf.float32, shape=[100])
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y_data = tf.placeholder(tf.float32, shape=[100])
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# Define the weights and computation.
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w = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name=w_name)
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b = tf.Variable(tf.zeros([1]), name=b_name)
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y = w * x_data + b
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# Return the loss and weight initializer.
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return (tf.reduce_mean(tf.square(y - y_data)),
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tf.global_variables_initializer(), x_data, y_data)
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class NetActor(object):
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def __init__(self):
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# Uses a separate graph for each network.
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with tf.Graph().as_default():
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# Create the network.
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loss, init, _, _ = make_linear_network()
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sess = tf.Session()
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# Additional code for setting and getting the weights.
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variables = ray.experimental.TensorFlowVariables(loss, sess)
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# Return all of the data needed to use the network.
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self.values = [variables, init, sess]
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sess.run(init)
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def __init__(self):
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# Uses a separate graph for each network.
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with tf.Graph().as_default():
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# Create the network.
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loss, init, _, _ = make_linear_network()
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sess = tf.Session()
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# Additional code for setting and getting the weights.
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variables = ray.experimental.TensorFlowVariables(loss, sess)
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# Return all of the data needed to use the network.
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self.values = [variables, init, sess]
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sess.run(init)
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def set_and_get_weights(self, weights):
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self.values[0].set_weights(weights)
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return self.values[0].get_weights()
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def set_and_get_weights(self, weights):
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self.values[0].set_weights(weights)
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return self.values[0].get_weights()
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def get_weights(self):
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return self.values[0].get_weights()
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def get_weights(self):
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return self.values[0].get_weights()
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class TrainActor(object):
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def __init__(self):
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# Almost the same as above, but now returns the placeholders and gradient.
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with tf.Graph().as_default():
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loss, init, x_data, y_data = make_linear_network()
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sess = tf.Session()
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variables = ray.experimental.TensorFlowVariables(loss, sess)
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optimizer = tf.train.GradientDescentOptimizer(0.9)
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grads = optimizer.compute_gradients(loss)
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train = optimizer.apply_gradients(grads)
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self.values = [loss, variables, init, sess, grads, train, [x_data, y_data]]
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sess.run(init)
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def __init__(self):
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# Almost the same as above, but now returns the placeholders and
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# gradient.
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with tf.Graph().as_default():
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loss, init, x_data, y_data = make_linear_network()
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sess = tf.Session()
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variables = ray.experimental.TensorFlowVariables(loss, sess)
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optimizer = tf.train.GradientDescentOptimizer(0.9)
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grads = optimizer.compute_gradients(loss)
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train = optimizer.apply_gradients(grads)
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self.values = [loss, variables, init, sess, grads, train,
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[x_data, y_data]]
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sess.run(init)
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def training_step(self, weights):
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_, variables, _, sess, grads, _, placeholders = self.values
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variables.set_weights(weights)
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return sess.run([grad[0] for grad in grads],
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feed_dict=dict(zip(placeholders, [[1] * 100, [2] * 100])))
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def training_step(self, weights):
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_, variables, _, sess, grads, _, placeholders = self.values
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variables.set_weights(weights)
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return sess.run([grad[0] for grad in grads],
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feed_dict=dict(zip(placeholders,
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[[1] * 100, [2] * 100])))
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def get_weights(self):
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return self.values[1].get_weights()
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def get_weights(self):
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return self.values[1].get_weights()
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class TensorFlowTest(unittest.TestCase):
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def testTensorFlowVariables(self):
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ray.init(num_workers=2)
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def testTensorFlowVariables(self):
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ray.init(num_workers=2)
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sess = tf.Session()
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loss, init, _, _ = make_linear_network()
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sess.run(init)
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sess = tf.Session()
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loss, init, _, _ = make_linear_network()
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sess.run(init)
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variables = ray.experimental.TensorFlowVariables(loss, sess)
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weights = variables.get_weights()
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variables = ray.experimental.TensorFlowVariables(loss, sess)
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weights = variables.get_weights()
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for (name, val) in weights.items():
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weights[name] += 1.0
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for (name, val) in weights.items():
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weights[name] += 1.0
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variables.set_weights(weights)
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self.assertEqual(weights, variables.get_weights())
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variables.set_weights(weights)
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self.assertEqual(weights, variables.get_weights())
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loss2, init2, _, _ = make_linear_network("w", "b")
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sess.run(init2)
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loss2, init2, _, _ = make_linear_network("w", "b")
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sess.run(init2)
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variables2 = ray.experimental.TensorFlowVariables(loss2, sess)
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weights2 = variables2.get_weights()
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variables2 = ray.experimental.TensorFlowVariables(loss2, sess)
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weights2 = variables2.get_weights()
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for (name, val) in weights2.items():
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weights2[name] += 2.0
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for (name, val) in weights2.items():
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weights2[name] += 2.0
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variables2.set_weights(weights2)
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self.assertEqual(weights2, variables2.get_weights())
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variables2.set_weights(weights2)
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self.assertEqual(weights2, variables2.get_weights())
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flat_weights = variables2.get_flat() + 2.0
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variables2.set_flat(flat_weights)
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assert_almost_equal(flat_weights, variables2.get_flat())
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flat_weights = variables2.get_flat() + 2.0
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variables2.set_flat(flat_weights)
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assert_almost_equal(flat_weights, variables2.get_flat())
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variables3 = ray.experimental.TensorFlowVariables(loss2)
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self.assertEqual(variables3.sess, None)
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sess = tf.Session()
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variables3.set_session(sess)
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self.assertEqual(variables3.sess, sess)
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variables3 = ray.experimental.TensorFlowVariables(loss2)
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self.assertEqual(variables3.sess, None)
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sess = tf.Session()
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variables3.set_session(sess)
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self.assertEqual(variables3.sess, sess)
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ray.worker.cleanup()
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ray.worker.cleanup()
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# Test that the variable names for the two different nets are not
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# modified by TensorFlow to be unique (i.e. they should already
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# be unique because of the variable prefix).
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def testVariableNameCollision(self):
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ray.init(num_workers=2)
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# Test that the variable names for the two different nets are not
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# modified by TensorFlow to be unique (i.e. they should already
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# be unique because of the variable prefix).
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def testVariableNameCollision(self):
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ray.init(num_workers=2)
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net1 = NetActor()
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net2 = NetActor()
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net1 = NetActor()
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net2 = NetActor()
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# This is checking that the variable names of the two nets are the same,
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# i.e. that the names in the weight dictionaries are the same
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net1.values[0].set_weights(net2.values[0].get_weights())
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# This is checking that the variable names of the two nets are the
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# same, i.e. that the names in the weight dictionaries are the same
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net1.values[0].set_weights(net2.values[0].get_weights())
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ray.worker.cleanup()
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ray.worker.cleanup()
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# Test that different networks on the same worker are independent and
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# we can get/set their weights without any interaction.
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def testNetworksIndependent(self):
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# Note we use only one worker to ensure that all of the remote functions
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# run on the same worker.
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ray.init(num_workers=1)
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net1 = NetActor()
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net2 = NetActor()
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# Test that different networks on the same worker are independent and
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# we can get/set their weights without any interaction.
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def testNetworksIndependent(self):
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# Note we use only one worker to ensure that all of the remote
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# functions run on the same worker.
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ray.init(num_workers=1)
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net1 = NetActor()
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net2 = NetActor()
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# Make sure the two networks have different weights. TODO(rkn): Note that
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# equality comparisons of numpy arrays normally does not work. This only
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# works because at the moment they have size 1.
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weights1 = net1.get_weights()
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weights2 = net2.get_weights()
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self.assertNotEqual(weights1, weights2)
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# Make sure the two networks have different weights. TODO(rkn): Note
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# that equality comparisons of numpy arrays normally does not work.
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# This only works because at the moment they have size 1.
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weights1 = net1.get_weights()
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weights2 = net2.get_weights()
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self.assertNotEqual(weights1, weights2)
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# Set the weights and get the weights, and make sure they are unchanged.
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new_weights1 = net1.set_and_get_weights(weights1)
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new_weights2 = net2.set_and_get_weights(weights2)
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self.assertEqual(weights1, new_weights1)
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self.assertEqual(weights2, new_weights2)
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# Set the weights and get the weights, and make sure they are
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# unchanged.
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new_weights1 = net1.set_and_get_weights(weights1)
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new_weights2 = net2.set_and_get_weights(weights2)
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self.assertEqual(weights1, new_weights1)
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self.assertEqual(weights2, new_weights2)
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# Swap the weights.
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new_weights1 = net2.set_and_get_weights(weights1)
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new_weights2 = net1.set_and_get_weights(weights2)
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self.assertEqual(weights1, new_weights1)
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self.assertEqual(weights2, new_weights2)
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# Swap the weights.
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new_weights1 = net2.set_and_get_weights(weights1)
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new_weights2 = net1.set_and_get_weights(weights2)
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self.assertEqual(weights1, new_weights1)
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self.assertEqual(weights2, new_weights2)
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ray.worker.cleanup()
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ray.worker.cleanup()
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# This test creates an additional network on the driver so that the
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# tensorflow variables on the driver and the worker differ.
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def testNetworkDriverWorkerIndependent(self):
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ray.init(num_workers=1)
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# This test creates an additional network on the driver so that the
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# tensorflow variables on the driver and the worker differ.
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def testNetworkDriverWorkerIndependent(self):
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ray.init(num_workers=1)
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# Create a network on the driver locally.
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sess1 = tf.Session()
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loss1, init1, _, _ = make_linear_network()
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ray.experimental.TensorFlowVariables(loss1, sess1)
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sess1.run(init1)
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# Create a network on the driver locally.
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sess1 = tf.Session()
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loss1, init1, _, _ = make_linear_network()
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ray.experimental.TensorFlowVariables(loss1, sess1)
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sess1.run(init1)
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net2 = ray.remote(NetActor).remote()
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weights2 = ray.get(net2.get_weights.remote())
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net2 = ray.remote(NetActor).remote()
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weights2 = ray.get(net2.get_weights.remote())
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new_weights2 = ray.get(net2.set_and_get_weights.remote(
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net2.get_weights.remote()))
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self.assertEqual(weights2, new_weights2)
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new_weights2 = ray.get(net2.set_and_get_weights.remote(
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net2.get_weights.remote()))
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self.assertEqual(weights2, new_weights2)
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ray.worker.cleanup()
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ray.worker.cleanup()
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def testVariablesControlDependencies(self):
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ray.init(num_workers=1)
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def testVariablesControlDependencies(self):
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ray.init(num_workers=1)
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# Creates a network and appends a momentum optimizer.
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sess = tf.Session()
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loss, init, _, _ = make_linear_network()
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minimizer = tf.train.MomentumOptimizer(0.9, 0.9).minimize(loss)
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net_vars = ray.experimental.TensorFlowVariables(minimizer, sess)
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sess.run(init)
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# Creates a network and appends a momentum optimizer.
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sess = tf.Session()
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loss, init, _, _ = make_linear_network()
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minimizer = tf.train.MomentumOptimizer(0.9, 0.9).minimize(loss)
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net_vars = ray.experimental.TensorFlowVariables(minimizer, sess)
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sess.run(init)
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# Tests if all variables are properly retrieved, 2 variables and 2 momentum
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# variables.
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self.assertEqual(len(net_vars.variables.items()), 4)
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# Tests if all variables are properly retrieved, 2 variables and 2
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# momentum variables.
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self.assertEqual(len(net_vars.variables.items()), 4)
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ray.worker.cleanup()
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ray.worker.cleanup()
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def testRemoteTrainingStep(self):
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ray.init(num_workers=1)
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def testRemoteTrainingStep(self):
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ray.init(num_workers=1)
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net = ray.remote(TrainActor).remote()
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ray.get(net.training_step.remote(net.get_weights.remote()))
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net = ray.remote(TrainActor).remote()
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ray.get(net.training_step.remote(net.get_weights.remote()))
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ray.worker.cleanup()
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ray.worker.cleanup()
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def testRemoteTrainingLoss(self):
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ray.init(num_workers=2)
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def testRemoteTrainingLoss(self):
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ray.init(num_workers=2)
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net = ray.remote(TrainActor).remote()
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loss, variables, _, sess, grads, train, placeholders = TrainActor().values
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net = ray.remote(TrainActor).remote()
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(loss, variables, _, sess, grads,
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train, placeholders) = TrainActor().values
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before_acc = sess.run(loss, feed_dict=dict(zip(placeholders,
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[[2] * 100, [4] * 100])))
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before_acc = sess.run(loss,
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feed_dict=dict(zip(placeholders,
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[[2] * 100, [4] * 100])))
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for _ in range(3):
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gradients_list = ray.get(
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[net.training_step.remote(variables.get_weights())
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for _ in range(2)])
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mean_grads = [sum([gradients[i] for gradients in gradients_list]) /
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len(gradients_list) for i in range(len(gradients_list[0]))]
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feed_dict = {grad[0]: mean_grad for (grad, mean_grad)
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in zip(grads, mean_grads)}
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sess.run(train, feed_dict=feed_dict)
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after_acc = sess.run(loss, feed_dict=dict(zip(placeholders,
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[[2] * 100, [4] * 100])))
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self.assertTrue(before_acc < after_acc)
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ray.worker.cleanup()
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for _ in range(3):
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gradients_list = ray.get(
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[net.training_step.remote(variables.get_weights())
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for _ in range(2)])
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mean_grads = [sum([gradients[i] for gradients in gradients_list]) /
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len(gradients_list) for i
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in range(len(gradients_list[0]))]
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feed_dict = {grad[0]: mean_grad for (grad, mean_grad)
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in zip(grads, mean_grads)}
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sess.run(train, feed_dict=feed_dict)
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after_acc = sess.run(loss, feed_dict=dict(zip(placeholders,
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[[2] * 100, [4] * 100])))
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self.assertTrue(before_acc < after_acc)
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ray.worker.cleanup()
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if __name__ == "__main__":
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unittest.main(verbosity=2)
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unittest.main(verbosity=2)
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