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test_keras.py
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test_keras.py
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# Copyright 2018 Uber Technologies, Inc. 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 for horovod.keras."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import keras
from keras import backend as K
import numpy as np
import os
import tempfile
import tensorflow as tf
import warnings
import horovod.keras as hvd
class KerasTests(tf.test.TestCase):
"""
Tests for ops in horovod.keras.
"""
def __init__(self, *args, **kwargs):
super(KerasTests, self).__init__(*args, **kwargs)
warnings.simplefilter('module')
hvd.init()
self.config = tf.ConfigProto()
self.config.gpu_options.allow_growth = True
self.config.gpu_options.visible_device_list = str(hvd.local_rank())
def test_sparse_as_dense(self):
with self.test_session(config=self.config) as sess:
K.set_session(sess)
opt = keras.optimizers.RMSprop(lr=0.0001)
opt = hvd.DistributedOptimizer(opt, sparse_as_dense=True)
model = keras.models.Sequential()
model.add(keras.layers.Embedding(1000, 64, input_length=10))
model.compile(loss=keras.losses.MSE,
optimizer=opt)
x = np.random.randint(1000, size=(32, 10))
y = np.random.random((32, 10, 64))
# No assertions, we just need to verify that it doesn't hang
model.train_on_batch(x, y)
def test_load_model(self):
with self.test_session(config=self.config) as sess:
K.set_session(sess)
opt = keras.optimizers.RMSprop(lr=0.0001)
opt = hvd.DistributedOptimizer(opt)
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
model.compile(loss=keras.losses.MSE,
optimizer=opt,
metrics=[keras.metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
_, fname = tempfile.mkstemp('.h5')
model.save(fname)
new_model = hvd.load_model(fname)
new_opt = new_model.optimizer
os.remove(fname)
self.assertEqual(type(new_opt).__module__, 'horovod._keras')
self.assertEqual(type(new_opt).__name__, 'RMSprop')
self.assertEqual(K.get_value(opt.lr), K.get_value(new_opt.lr))
self.assertEqual(len(opt.get_weights()), len(new_opt.get_weights()))
for weights, new_weights in zip(opt.get_weights(),
new_opt.get_weights()):
self.assertListEqual(weights.tolist(), new_weights.tolist())
def test_load_model_custom_optimizers(self):
class TestOptimizer(keras.optimizers.RMSprop):
def __init__(self, **kwargs):
super(TestOptimizer, self).__init__(**kwargs)
with self.test_session(config=self.config) as sess:
K.set_session(sess)
opt = TestOptimizer(lr=0.0001)
opt = hvd.DistributedOptimizer(opt)
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
model.compile(loss=keras.losses.MSE,
optimizer=opt,
metrics=[keras.metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
_, fname = tempfile.mkstemp('.h5')
model.save(fname)
custom_optimizers = [TestOptimizer]
new_model = hvd.load_model(fname, custom_optimizers=custom_optimizers)
new_opt = new_model.optimizer
os.remove(fname)
self.assertEqual(type(new_opt).__module__, 'horovod._keras')
self.assertEqual(type(new_opt).__name__, 'TestOptimizer')
self.assertEqual(K.get_value(opt.lr), K.get_value(new_opt.lr))
self.assertEqual(len(opt.get_weights()), len(new_opt.get_weights()))
for weights, new_weights in zip(opt.get_weights(),
new_opt.get_weights()):
self.assertListEqual(weights.tolist(), new_weights.tolist())
def test_load_model_custom_objects(self):
class TestOptimizer(keras.optimizers.RMSprop):
def __init__(self, **kwargs):
super(TestOptimizer, self).__init__(**kwargs)
with self.test_session(config=self.config) as sess:
K.set_session(sess)
opt = TestOptimizer(lr=0.0001)
opt = hvd.DistributedOptimizer(opt)
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
model.compile(loss=keras.losses.MSE,
optimizer=opt,
metrics=[keras.metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
_, fname = tempfile.mkstemp('.h5')
model.save(fname)
custom_objects = {
'TestOptimizer': lambda **kwargs: hvd.DistributedOptimizer(
TestOptimizer(**kwargs))
}
new_model = hvd.load_model(fname, custom_objects=custom_objects)
new_opt = new_model.optimizer
os.remove(fname)
self.assertEqual(type(new_opt).__module__, 'horovod._keras')
self.assertEqual(type(new_opt).__name__, 'TestOptimizer')
self.assertEqual(K.get_value(opt.lr), K.get_value(new_opt.lr))
self.assertEqual(len(opt.get_weights()), len(new_opt.get_weights()))
for weights, new_weights in zip(opt.get_weights(),
new_opt.get_weights()):
self.assertListEqual(weights.tolist(), new_weights.tolist())
def test_load_model_broadcast(self):
def create_model():
opt = keras.optimizers.SGD(lr=0.01 * hvd.size(), momentum=0.9)
opt = hvd.DistributedOptimizer(opt)
model = keras.models.Sequential()
model.add(keras.layers.Dense(2, input_shape=(3,)))
model.add(keras.layers.RepeatVector(3))
model.add(keras.layers.TimeDistributed(keras.layers.Dense(3)))
model.compile(loss=keras.losses.MSE,
optimizer=opt,
metrics=[keras.metrics.categorical_accuracy],
sample_weight_mode='temporal')
return model
with self.test_session(config=self.config) as sess:
K.set_session(sess)
model = create_model()
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
if hvd.rank() == 0:
_, fname = tempfile.mkstemp('.h5')
model.save(fname)
K.clear_session()
with self.test_session(config=self.config) as sess:
K.set_session(sess)
if hvd.rank() == 0:
model = hvd.load_model(fname)
os.remove(fname)
else:
model = create_model()
def generator():
while 1:
yield (x, y)
if hvd.rank() == 0:
self.assertEqual(len(model.optimizer.weights), 5)
else:
self.assertEqual(len(model.optimizer.weights), 0)
# No assertions, we just need to verify that it doesn't hang
callbacks = [hvd.callbacks.BroadcastGlobalVariablesCallback(0)]
model.fit_generator(generator(),
steps_per_epoch=10,
callbacks=callbacks,
epochs=0,
verbose=0,
workers=4,
initial_epoch=1)
self.assertEqual(len(model.optimizer.weights), 5)