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hunk.file_path: doc/source/contact.rst
hunk.type: replace
file: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from numpy.testing import assert_almost_equal
  import pytest
  import tensorflow as tf
  
  import ray
  
  
  def make_linear_network(w_name=None, b_name=None):
      # Define the inputs.
      x_data = tf.placeholder(tf.float32, shape=[100])
      y_data = tf.placeholder(tf.float32, shape=[100])
      # Define the weights and computation.
      w = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name=w_name)
      b = tf.Variable(tf.zeros([1]), name=b_name)
      y = w * x_data + b
      # Return the loss and weight initializer.
      return (tf.reduce_mean(tf.square(y - y_data)),
              tf.global_variables_initializer(), x_data, y_data)
  
  
  class LossActor(object):
      def __init__(self, use_loss=True):
          # Uses a separate graph for each network.
          with tf.Graph().as_default():
              # Create the network.
              var = [tf.Variable(1)]
              loss, init, _, _ = make_linear_network()
              sess = tf.Session()
              # Additional code for setting and getting the weights.
              weights = ray.experimental.TensorFlowVariables(
                  loss if use_loss else None, sess, input_variables=var)
          # Return all of the data needed to use the network.
          self.values = [weights, init, sess]
          sess.run(init)
  
      def set_and_get_weights(self, weights):
          self.values[0].set_weights(weights)
          return self.values[0].get_weights()
  
      def get_weights(self):
          return self.values[0].get_weights()
  
  
  class NetActor(object):
      def __init__(self):
          # Uses a separate graph for each network.
          with tf.Graph().as_default():
              # Create the network.
              loss, init, _, _ = make_linear_network()
              sess = tf.Session()
              # Additional code for setting and getting the weights.
              variables = ray.experimental.TensorFlowVariables(loss, sess)
          # Return all of the data needed to use the network.
          self.values = [variables, init, sess]
          sess.run(init)
  
      def set_and_get_weights(self, weights):
          self.values[0].set_weights(weights)
          return self.values[0].get_weights()
  
      def get_weights(self):
          return self.values[0].get_weights()
  
  
  class TrainActor(object):
      def __init__(self):
          # Almost the same as above, but now returns the placeholders and
          # gradient.
          with tf.Graph().as_default():
              loss, init, x_data, y_data = make_linear_network()
              sess = tf.Session()
              variables = ray.experimental.TensorFlowVariables(loss, sess)
              optimizer = tf.train.GradientDescentOptimizer(0.9)
              grads = optimizer.compute_gradients(loss)
              train = optimizer.apply_gradients(grads)
          self.values = [
              loss, variables, init, sess, grads, train, [x_data, y_data]
          ]
          sess.run(init)
  
      def training_step(self, weights):
          _, variables, _, sess, grads, _, placeholders = self.values
          variables.set_weights(weights)
          return sess.run(
              [grad[0] for grad in grads],
              feed_dict=dict(zip(placeholders, [[1] * 100, [2] * 100])))
  
      def get_weights(self):
          return self.values[1].get_weights()
  
  
  @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()
  
  
  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
  