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# Copyright 2016 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.
# ==============================================================================
"""test the RunMetadata proto."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import defaultdict
import six
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.client import session
from tensorflow.python.framework import ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
# pylint: disable=g-bad-import-order
# XXX: this depends on pywrap_tensorflow and must come later
from tensorflow.contrib.tfprof.python.tools.tfprof import model_analyzer
from tensorflow.contrib.tfprof.python.tools.tfprof.internal import model_analyzer_testlib as lib
SIZE = 1300
def _extract_node(run_meta, node_name):
ret = defaultdict(list)
for dev_stat in run_meta.step_stats.dev_stats:
dev = dev_stat.device
for node_stat in dev_stat.node_stats:
if node_stat.node_name == node_name:
ret[dev].append(node_stat)
return ret
def _run_model():
x = random_ops.random_normal(shape=[1, SIZE])
w = random_ops.random_normal(shape=[SIZE, 2 * SIZE])
y = math_ops.matmul(x, w)
with session.Session() as sess:
run_metadata = config_pb2.RunMetadata()
opts = model_analyzer.PRINT_ALL_TIMING_MEMORY
opts['min_micros'] = 0
opts['min_bytes'] = 0
_ = sess.run(y,
options=config_pb2.RunOptions(
trace_level=config_pb2.RunOptions.FULL_TRACE),
run_metadata=run_metadata)
tfprof_node = model_analyzer.print_model_analysis(
sess.graph,
run_meta=run_metadata,
tfprof_options=opts)
return tfprof_node, run_metadata
def _run_loop_model():
with session.Session() as sess:
x = lib.BuildFullModel()
sess.run(variables.global_variables_initializer())
run_meta = config_pb2.RunMetadata()
_ = sess.run(x,
options=config_pb2.RunOptions(
trace_level=config_pb2.RunOptions.FULL_TRACE),
run_metadata=run_meta)
tfprof_node = model_analyzer.print_model_analysis(
sess.graph, run_meta,
tfprof_options=model_analyzer.PRINT_ALL_TIMING_MEMORY)
return tfprof_node, run_meta
class RunMetadataTest(test.TestCase):
def testGPU(self):
if not test.is_gpu_available():
return
ops.reset_default_graph()
with ops.device('/gpu:0'):
tfprof_node, run_meta = _run_model()
self.assertEqual(tfprof_node.children[0].name, 'MatMul')
self.assertGreater(tfprof_node.children[0].exec_micros, 10)
ret = _extract_node(run_meta, 'MatMul')
self.assertEqual(len(ret), 1)
self.assertTrue('/job:localhost/replica:0/task:0/gpu:0' in ret)
ret = _extract_node(run_meta, 'MatMul:MatMul')
self.assertEqual(len(ret), 2)
has_all_stream = False
for k, _ in six.iteritems(ret):
self.assertTrue('gpu:0/stream' in k)
if 'gpu:0/stream:all' in k:
has_all_stream = True
self.assertTrue(has_all_stream)
def testCPU(self):
ops.reset_default_graph()
with ops.device('/cpu:0'):
tfprof_node, run_meta = _run_model()
self.assertEqual(tfprof_node.children[0].name, 'MatMul')
self.assertGreater(tfprof_node.children[0].exec_micros, 10)
ret = _extract_node(run_meta, 'MatMul')
self.assertEqual(len(ret), 1)
self.assertTrue('/job:localhost/replica:0/task:0/cpu:0' in ret)
ret = _extract_node(run_meta, 'MatMul:MatMul')
self.assertEqual(len(ret), 0)
def testLoopCPU(self):
ops.reset_default_graph()
with ops.device('/cpu:0'):
tfprof_node, run_meta = _run_loop_model()
# The while-loop caused a node to appear 4 times in scheduling.
ret = _extract_node(run_meta,
'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
self.assertEqual(len(ret['/job:localhost/replica:0/task:0/cpu:0']), 4)
total_cpu_execs = 0
for node in ret['/job:localhost/replica:0/task:0/cpu:0']:
total_cpu_execs += node.op_end_rel_micros
mm_node = lib.SearchTFProfNode(
tfprof_node,
'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
self.assertEqual(mm_node.run_count, 4)
self.assertEqual(mm_node.cpu_exec_micros, total_cpu_execs)
self.assertEqual(mm_node.exec_micros, total_cpu_execs)
def testLoopGPU(self):
if not test.is_gpu_available():
return
ops.reset_default_graph()
with ops.device('/gpu:0'):
tfprof_node, run_meta = _run_loop_model()
# The while-loop caused a node to appear 4 times in scheduling.
ret = _extract_node(run_meta,
'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
self.assertEqual(len(ret['/job:localhost/replica:0/task:0/gpu:0']), 4)
total_cpu_execs = 0
for node in ret['/job:localhost/replica:0/task:0/gpu:0']:
total_cpu_execs += node.op_end_rel_micros
ret = _extract_node(
run_meta,
'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul:MatMul')
self.assertGreaterEqual(len(ret['/gpu:0/stream:all']), 4)
total_accelerator_execs = 0
for node in ret['/gpu:0/stream:all']:
total_accelerator_execs += node.op_end_rel_micros
mm_node = lib.SearchTFProfNode(
tfprof_node,
'rnn/while/rnn/basic_rnn_cell/basic_rnn_cell/MatMul')
self.assertEqual(mm_node.run_count, 4)
self.assertEqual(mm_node.accelerator_exec_micros, total_accelerator_execs)
self.assertEqual(mm_node.cpu_exec_micros, total_cpu_execs)
self.assertEqual(mm_node.exec_micros,
total_cpu_execs + total_accelerator_execs)
if __name__ == '__main__':
test.main()
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