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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 the graph placer."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from tensorflow.core.protobuf import device_properties_pb2
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import meta_graph
from tensorflow.python.framework import ops as tf_ops
from tensorflow.python.grappler import cluster
from tensorflow.python.grappler import graph_placer
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variable_scope
from tensorflow.python.platform import test
class GraphPlacerTest(test.TestCase):
@staticmethod
def _buildMnist(batch_size=128,
input_size=256,
num_classes=1024,
num_layers=10,
hidden_size=256,
name='mnist'):
g = tf_ops.get_default_graph()
with g.as_default():
ops = {}
x = random_ops.random_uniform(
[batch_size, input_size], -0.1, 0.1, dtype=dtypes.float32)
for layer_id in range(num_layers):
with variable_scope.variable_scope('layer_{}'.format(layer_id)):
a = input_size if layer_id == 0 else hidden_size
b = hidden_size if layer_id < num_layers - 1 else num_classes
w = variable_scope.get_variable('w', [a, b])
x = math_ops.matmul(x, w)
x = nn_ops.relu(x)
ops['y_preds'] = math_ops.argmax(x, axis=1)
train_op = g.get_collection_ref(tf_ops.GraphKeys.TRAIN_OP)
train_op.append(ops['y_preds'])
return g
@staticmethod
def _buildCluster(num_cpus=1, num_gpus=1):
devices = []
if num_gpus > 0:
device_properties = device_properties_pb2.DeviceProperties(
type='GPU',
vendor='NVidia',
model='GeForce GTX TITAN X',
frequency=1076,
num_cores=24,
environment={'architecture': '5.2',
'cuda': '8000',
'cudnn': '6021'},
num_registers=65536,
l1_cache_size=24576,
l2_cache_size=3145728,
shared_memory_size_per_multiprocessor=98304,
memory_size=12783648768,
bandwidth=336480000)
for i in range(num_gpus):
devices.append(
device_properties_pb2.NamedDevice(
properties=device_properties, name='/GPU:' + str(i)))
assert num_cpus > 0
device_properties = device_properties_pb2.DeviceProperties(
type='CPU',
frequency=2000,
num_cores=4,
l1_cache_size=32768,
l2_cache_size=262144,
l3_cache_size=12582912)
for i in range(num_cpus):
devices.append(
device_properties_pb2.NamedDevice(
properties=device_properties, name='/CPU:' + str(i)))
return cluster.Cluster(devices=devices)
def testBasic(self):
"""Place a trivial graph."""
a = constant_op.constant(10, name='a')
b = constant_op.constant(20, name='b')
c = math_ops.add_n([a, b], name='c')
d = math_ops.add_n([b, c], name='d')
train_op = tf_ops.get_collection_ref(tf_ops.GraphKeys.TRAIN_OP)
train_op.append(d)
mg = meta_graph.create_meta_graph_def(graph=tf_ops.get_default_graph())
gcluster = cluster.Cluster()
placed_mg = graph_placer.PlaceGraph(mg, allotted_time=15, cluster=gcluster)
self.assertEqual(4, len(placed_mg.graph_def.node))
self.assertItemsEqual([node.name for node in placed_mg.graph_def.node],
[node.name for node in mg.graph_def.node])
available_devices = [device.name for device in gcluster.ListDevices()]
for node in placed_mg.graph_def.node:
# The constant nodes are optimized away before the placer is run, and
# therefore won't be placed.
self.assertTrue(not node.device or node.device in available_devices)
def testMNIST(self):
graph = GraphPlacerTest._buildMnist()
mg = meta_graph.create_meta_graph_def(graph=graph)
gcluster = GraphPlacerTest._buildCluster(num_gpus=1)
# Spend 15 seconds trying to optimize the placement of the model. This
# should give us enough time to exercise the code, but not enough to find
# a good placement, so we'll just check for legality.
placed_mg = graph_placer.PlaceGraph(mg, allotted_time=15, cluster=gcluster)
self.assertEqual(len(placed_mg.graph_def.node), len(mg.graph_def.node))
self.assertItemsEqual([node.name for node in placed_mg.graph_def.node],
[node.name for node in mg.graph_def.node])
available_devices = [device.name for device in gcluster.ListDevices()]
for node in placed_mg.graph_def.node:
self.assertTrue(not node.device or node.device in available_devices)
if __name__ == '__main__':
test.main()
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