def testAdvisor(self):
ops.reset_default_graph()
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)
advice_pb = model_analyzer.advise(sess.graph, run_meta)
self.assertTrue('AcceleratorUtilizationChecker' in advice_pb.checkers)
self.assertTrue('ExpensiveOperationChecker' in advice_pb.checkers)
self.assertTrue('OperationChecker' in advice_pb.checkers)
checker = advice_pb.checkers['AcceleratorUtilizationChecker']
if test.is_gpu_available():
self.assertGreater(len(checker.reports), 0)
else:
self.assertEqual(len(checker.reports), 0)
checker = advice_pb.checkers['ExpensiveOperationChecker']
self.assertGreater(len(checker.reports), 0)
def testTimeline(self):
ops.reset_default_graph()
opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
outfile = os.path.join(test.get_temp_dir(), 'timeline')
opts['output'] = 'timeline:outfile=' + outfile
opts['account_type_regexes'] = ['.*']
opts['max_depth'] = 100000
opts['step'] = 0
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)
_ = model_analyzer.print_model_analysis(
sess.graph, run_meta, tfprof_cmd='graph', tfprof_options=opts)
with gfile.Open(outfile, 'r') as f:
# Test that a json file is created.
# TODO(xpan): tfprof Timeline isn't quite correct on Windows.
# Investigate why.
if os.name != 'nt':
self.assertLess(1000, len(f.read()))
else:
self.assertLess(1, len(f.read()))
def testOpView(self):
ops.reset_default_graph()
opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS
outfile = os.path.join(test.get_temp_dir(), 'dump')
opts['output'] = 'file:outfile=' + outfile
opts['account_type_regexes'] = ['.*']
opts['min_occurrence'] = 10
opts['select'] = ['params', 'micros', 'occurrence', 'input_shapes']
opts['order_by'] = 'occurrence'
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_cmd='op', tfprof_options=opts)
with gfile.Open(outfile, 'r') as f:
# pylint: disable=line-too-long
self.assertEqual(
'nodename|totalexecutiontime|acceleratorexecutiontime|cpuexecutiontime|#parameters|opoccurrence|input',
f.read().replace('\t', '').replace(' ', '')[0:100])
# pylint: enable=line-too-long
total_children = 0
last_occurrence = 1e32
input_shapes = 0
last_total_micros = tfprof_node.total_exec_micros
last_micros = tfprof_node.exec_micros
while tfprof_node.children:
for gnode in tfprof_node.graph_nodes:
input_shapes += len(gnode.input_shapes)
self.assertEqual(len(tfprof_node.children), 1)
tfprof_node = tfprof_node.children[0]
self.assertEqual(last_total_micros,
tfprof_node.total_exec_micros + last_micros)
last_total_micros = tfprof_node.total_exec_micros
last_micros = tfprof_node.exec_micros
total_children += 1
self.assertLessEqual(len(tfprof_node.graph_nodes),
last_occurrence)
last_occurrence = len(tfprof_node.graph_nodes)
self.assertEqual(total_children, 15)
self.assertGreater(input_shapes, 0)
def testComplexCodeView(self):
ops.reset_default_graph()
opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
outfile = os.path.join(test.get_temp_dir(), 'dump')
opts['output'] = 'file:outfile=' + outfile
opts['account_type_regexes'] = ['.*']
opts['show_name_regexes'] = ['.*model_analyzer_testlib.py.*']
opts['account_displayed_op_only'] = False
opts['select'] = ['params', 'float_ops']
with session.Session() as sess, ops.device('/cpu:0'):
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_cmd='code', tfprof_options=opts)
# pylint: disable=line-too-long
with gfile.Open(outfile, 'r') as f:
lines = f.read().split('\n')
result = '\n'.join([l[:min(len(l), 80)] for l in lines])
self.assertEqual(
'node name | # parameters | # float_ops\n_TFProfRoot (--/2.84k params, --/54.08k flops)\n model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_... (0/1.80k para\n model_analyzer_testlib.py:35:BuildSmallModel:image = array_ops... (0/0 param\n model_analyzer_testlib.py:39:BuildSmallModel:initializer=init_... (0/4 param\n model_analyzer_testlib.py:43:BuildSmallModel:initializer=init_... (0/648 par\n model_analyzer_testlib.py:44:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n model_analyzer_testlib.py:48:BuildSmallModel:initializer=init_... (0/1.15k p\n model_analyzer_testlib.py:49:BuildSmallModel:x = nn_ops.conv2d... (0/0 param\n model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c... (0/1.04k para\n model_analyzer_testlib.py:64:BuildFullModel:target = array_op... (0/0 params, \n model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_... (0/0 params, \n model_analyzer_testlib.py:67:BuildFullModel:return sgd_op.min... (0/0 params, \n',
result)
self.assertLess(0, tfprof_node.total_exec_micros)
self.assertEqual(2844, tfprof_node.total_parameters)
self.assertEqual(54080, tfprof_node.total_float_ops)
self.assertEqual(5, len(tfprof_node.children))
self.assertEqual('_TFProfRoot', tfprof_node.name)
self.assertEqual(
'model_analyzer_testlib.py:58:BuildFullModel:seq.append(array_...',
tfprof_node.children[0].name)
self.assertEqual(
'model_analyzer_testlib.py:62:BuildFullModel:cell, array_ops.c...',
tfprof_node.children[1].name)
self.assertEqual(
'model_analyzer_testlib.py:64:BuildFullModel:target = array_op...',
tfprof_node.children[2].name)
self.assertEqual(
'model_analyzer_testlib.py:65:BuildFullModel:loss = nn_ops.l2_...',
tfprof_node.children[3].name)
self.assertEqual(
'model_analyzer_testlib.py:67:BuildFullModel:return sgd_op.min...',
tfprof_node.children[4].name)
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
def testProfileBasic(self):
ops.reset_default_graph()
opts = model_analyzer.TRAINABLE_VARS_PARAMS_STAT_OPTIONS.copy()
opts['account_type_regexes'] = ['.*']
opts['select'] = ['params', 'float_ops', 'micros', 'bytes',
'device', 'op_types', 'occurrence']
outfile = os.path.join(test.get_temp_dir(), 'dump')
opts['output'] = 'file:outfile=' + outfile
# Test the output without run_meta.
sess = session.Session()
r = lib.BuildFullModel()
sess.run(variables.global_variables_initializer())
profiler = model_analyzer.Profiler(sess.graph)
profiler.profile_name_scope(opts)
with gfile.Open(outfile, 'r') as f:
profiler_str = f.read()
model_analyzer.print_model_analysis(
sess.graph, tfprof_cmd='scope', tfprof_options=opts)
with gfile.Open(outfile, 'r') as f:
pma_str = f.read()
self.assertEqual(pma_str, profiler_str)
# Test the output with run_meta.
run_meta = config_pb2.RunMetadata()
_ = sess.run(r,
options=config_pb2.RunOptions(
trace_level=config_pb2.RunOptions.FULL_TRACE),
run_metadata=run_meta)
profiler.add_step(1, run_meta)
profiler.profile_graph(opts)
with gfile.Open(outfile, 'r') as f:
profiler_str = f.read()
model_analyzer.print_model_analysis(
sess.graph, tfprof_cmd='graph', run_meta=run_meta, tfprof_options=opts)
with gfile.Open(outfile, 'r') as f:
pma_str = f.read()
self.assertEqual(pma_str, profiler_str)
profiler.profile_python_codes(opts)
with gfile.Open(outfile, 'r') as f:
profiler_str = f.read()
model_analyzer.print_model_analysis(
sess.graph, tfprof_cmd='code', run_meta=run_meta, tfprof_options=opts)
with gfile.Open(outfile, 'r') as f:
pma_str = f.read()
self.assertEqual(pma_str, profiler_str)
profiler.profile_operations(opts)
with gfile.Open(outfile, 'r') as f:
profiler_str = f.read()
model_analyzer.print_model_analysis(
sess.graph, tfprof_cmd='op', run_meta=run_meta, tfprof_options=opts)
with gfile.Open(outfile, 'r') as f:
pma_str = f.read()
self.assertEqual(pma_str, profiler_str)
# Test the output difference between multi-step profile and 1-step profile.
_ = sess.run(r,
options=config_pb2.RunOptions(
trace_level=config_pb2.RunOptions.FULL_TRACE),
run_metadata=run_meta)
profiler.add_step(2, run_meta)
profiler.profile_name_scope(opts)
with gfile.Open(outfile, 'r') as f:
profiler_str = f.read()
model_analyzer.print_model_analysis(
sess.graph, tfprof_cmd='scope', run_meta=run_meta, tfprof_options=opts)
with gfile.Open(outfile, 'r') as f:
pma_str = f.read()
self.assertNotEqual(pma_str, profiler_str)
opts2 = opts.copy()
opts2['select'] = ['params', 'float_ops']
profiler.profile_name_scope(opts2)
with gfile.Open(outfile, 'r') as f:
profiler_str = f.read()
model_analyzer.print_model_analysis(
sess.graph, tfprof_cmd='scope', run_meta=run_meta, tfprof_options=opts2)
with gfile.Open(outfile, 'r') as f:
pma_str = f.read()
self.assertEqual(pma_str, profiler_str)
