def testSimpleCodeView(self):
ops.reset_default_graph()
outfile = os.path.join(test.get_temp_dir(), 'dump')
# TODO(xpan): Test 'micros'. Since the execution time changes each run,
# it's a bit difficult to test it now.
opts = (builder(
builder.trainable_variables_parameter()).with_file_output(
outfile).with_accounted_types(['.*']).with_node_names(
show_name_regexes=['.*model_analyzer_testlib.*']).
account_displayed_op_only(False).select([
'bytes', 'params', 'float_ops', 'num_hidden_ops', 'device',
'input_shapes'
]).build())
with session.Session() as sess:
x = lib.BuildSmallModel()
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.profile(sess.graph,
run_meta,
cmd='code',
options=opts)
with gfile.Open(outfile, 'r') as f:
# pylint: disable=line-too-long
self.assertEqual(
'node name | requested bytes | # parameters | # float_ops | assigned devices | in',
lib.CheckAndRemoveDoc(f.read())[0:80])
def testComplexCodeView(self):
ops.reset_default_graph()
outfile = os.path.join(test.get_temp_dir(), 'dump')
opts = (builder(builder.trainable_variables_parameter())
.with_file_output(outfile)
.with_accounted_types(['.*'])
.with_node_names(show_name_regexes=
['.*model_analyzer_testlib.py.*'])
.account_displayed_op_only(False)
.select(['params', 'float_ops']).build())
with profile_context.ProfileContext(test.get_temp_dir(),
trace_steps=[],
dump_steps=[]) as pctx:
with session.Session() as sess:
x = lib.BuildFullModel()
sess.run(variables.global_variables_initializer())
pctx.trace_next_step()
_ = sess.run(x)
tfprof_node = pctx.profiler.profile_python(options=opts)
# pylint: disable=line-too-long
with gfile.Open(outfile, 'r') as f:
lines = f.read().split('\n')
self.assertGreater(len(lines), 5)
result = '\n'.join([l[:min(len(l), 80)] for l in lines])
self.assertTrue(
compat.as_text(lib.CheckAndRemoveDoc(result))
.startswith('node name | # parameters | # float_ops'))
self.assertLess(0, tfprof_node.total_exec_micros)
self.assertEqual(2844, tfprof_node.total_parameters)
self.assertLess(168800, tfprof_node.total_float_ops)
self.assertEqual(8, len(tfprof_node.children))
self.assertEqual('_TFProfRoot', tfprof_node.name)
self.assertEqual(
'model_analyzer_testlib.py:63:BuildFullModel',
tfprof_node.children[0].name)
self.assertEqual(
'model_analyzer_testlib.py:63:BuildFullModel (gradient)',
tfprof_node.children[1].name)
self.assertEqual(
'model_analyzer_testlib.py:67:BuildFullModel',
tfprof_node.children[2].name)
self.assertEqual(
'model_analyzer_testlib.py:67:BuildFullModel (gradient)',
tfprof_node.children[3].name)
self.assertEqual(
'model_analyzer_testlib.py:69:BuildFullModel',
tfprof_node.children[4].name)
self.assertEqual(
'model_analyzer_testlib.py:70:BuildFullModel',
tfprof_node.children[5].name)
self.assertEqual(
'model_analyzer_testlib.py:70:BuildFullModel (gradient)',
tfprof_node.children[6].name)
self.assertEqual(
'model_analyzer_testlib.py:72:BuildFullModel',
tfprof_node.children[7].name)
def testOpView(self):
ops.reset_default_graph()
outfile = os.path.join(test.get_temp_dir(), 'dump')
opts = (builder(builder.trainable_variables_parameter(
)).with_file_output(outfile).with_accounted_types(
['.*']).with_min_occurrence(10).order_by('occurrence').select([
'params', 'micros', 'bytes', 'peak_bytes', 'residual_bytes',
'output_bytes', 'occurrence', 'input_shapes'
]).build())
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.profile(sess.graph,
run_meta,
cmd='op',
options=opts)
with gfile.Open(outfile, 'r') as f:
# pylint: disable=line-too-long
self.assertEqual(
'nodename|requestedbytes|peakbytes|residualbytes|outputbytes|totalexecutiontime|acceleratorexecutiontime|cpuexecutiontime|#parameters|opoccurrence(run|defined)|inputshapes',
lib.CheckAndRemoveDoc(f.read()).replace('\t', '').replace(
' ', '')[0:170])
# 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.assertGreater(input_shapes, 0)
def testComplexCodeView(self):
ops.reset_default_graph()
outfile = os.path.join(test.get_temp_dir(), 'dump')
opts = (builder(
builder.trainable_variables_parameter()).with_file_output(
outfile).with_accounted_types(['.*']).with_node_names(
show_name_regexes=['.*model_analyzer_testlib.py.*']).
account_displayed_op_only(False).select(
['params', 'float_ops']).build())
with profile_context.ProfileContext(test.get_temp_dir(),
trace_steps=[],
dump_steps=[]) as pctx:
with session.Session() as sess:
x = lib.BuildFullModel()
sess.run(variables.global_variables_initializer())
pctx.trace_next_step()
_ = sess.run(x)
tfprof_node = pctx.profiler.profile_python(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(
compat.as_bytes(
'node name | # parameters | # float_ops\n_TFProfRoot (--/2.84k params, --/168.86k flops)\n model_analyzer_testlib.py:63:BuildFullModel (0/1.80k params, 0/45.37k flops)\n model_analyzer_testlib.py:40:BuildSmallModel (0/0 params, 0/0 flops)\n model_analyzer_testlib.py:44:BuildSmallModel (0/4 params, 0/8 flops)\n model_analyzer_testlib.py:48:BuildSmallModel (0/648 params, 0/1.30k flops)\n model_analyzer_testlib.py:49:BuildSmallModel (0/0 params, 0/23.33k flops)\n model_analyzer_testlib.py:53:BuildSmallModel (0/1.15k params, 0/2.30k flops)\n model_analyzer_testlib.py:54:BuildSmallModel (0/0 params, 0/18.43k flops)\n model_analyzer_testlib.py:63:BuildFullModel (gradient) (0/0 params, 0/67.39k f\n model_analyzer_testlib.py:49:BuildSmallModel (gradient) (0/0 params, 0/46.66\n model_analyzer_testlib.py:54:BuildSmallModel (gradient) (0/0 params, 0/20.74\n model_analyzer_testlib.py:67:BuildFullModel (0/1.04k params, 0/18.58k flops)\n model_analyzer_testlib.py:67:BuildFullModel (gradient) (0/0 params, 0/37.00k f\n model_analyzer_testlib.py:69:BuildFullModel (0/0 params, 0/0 flops)\n model_analyzer_testlib.py:70:BuildFullModel (0/0 params, 0/258 flops)\n model_analyzer_testlib.py:70:BuildFullModel (gradient) (0/0 params, 0/129 flop\n model_analyzer_testlib.py:72:BuildFullModel (0/0 params, 0/141 flops)\n'
), compat.as_bytes(lib.CheckAndRemoveDoc(result)))
self.assertLess(0, tfprof_node.total_exec_micros)
self.assertEqual(2844, tfprof_node.total_parameters)
self.assertEqual(168863, tfprof_node.total_float_ops)
self.assertEqual(8, len(tfprof_node.children))
self.assertEqual('_TFProfRoot', tfprof_node.name)
self.assertEqual('model_analyzer_testlib.py:63:BuildFullModel',
tfprof_node.children[0].name)
self.assertEqual(
'model_analyzer_testlib.py:63:BuildFullModel (gradient)',
tfprof_node.children[1].name)
self.assertEqual('model_analyzer_testlib.py:67:BuildFullModel',
tfprof_node.children[2].name)
self.assertEqual(
'model_analyzer_testlib.py:67:BuildFullModel (gradient)',
tfprof_node.children[3].name)
self.assertEqual('model_analyzer_testlib.py:69:BuildFullModel',
tfprof_node.children[4].name)
self.assertEqual('model_analyzer_testlib.py:70:BuildFullModel',
tfprof_node.children[5].name)
self.assertEqual(
'model_analyzer_testlib.py:70:BuildFullModel (gradient)',
tfprof_node.children[6].name)
self.assertEqual('model_analyzer_testlib.py:72:BuildFullModel',
tfprof_node.children[7].name)
def testDumpToFile(self):
ops.reset_default_graph()
outfile = os.path.join(test.get_temp_dir(), 'dump')
opts = builder(builder.trainable_variables_parameter()
).with_file_output(outfile).build()
with session.Session() as sess:
_ = lib.BuildSmallModel()
model_analyzer.profile(sess.graph, options=opts)
with gfile.Open(outfile, 'r') as f:
self.assertEqual(u'node name | # parameters\n'
'_TFProfRoot (--/451 params)\n'
' DW (3x3x3x6, 162/162 params)\n'
' DW2 (2x2x6x12, 288/288 params)\n'
' ScalarW (1, 1/1 params)\n',
lib.CheckAndRemoveDoc(f.read()))
def testSelectEverythingDetail(self):
ops.reset_default_graph()
dev = '/device:GPU:0' if test.is_gpu_available() else '/device:CPU:0'
outfile = os.path.join(test.get_temp_dir(), 'dump')
opts = (builder(
builder.trainable_variables_parameter()).with_file_output(
outfile).with_accounted_types(['.*']).select([
'micros', 'bytes', 'params', 'float_ops', 'occurrence',
'device', 'op_types', 'input_shapes'
]).build())
with profile_context.ProfileContext(test.get_temp_dir(),
trace_steps=[],
dump_steps=[]) as pctx:
with session.Session() as sess, ops.device(dev):
x = lib.BuildSmallModel()
sess.run(variables.global_variables_initializer())
pctx.trace_next_step()
pctx.dump_next_step()
_ = sess.run(x)
pctx.profiler.profile_name_scope(options=opts)
with gfile.Open(outfile, 'r') as f:
# pylint: disable=line-too-long
dump_str = lib.CheckAndRemoveDoc(f.read())
outputs = dump_str.split('\n')
self.assertEqual(
outputs[0],
'node name | # parameters | # float_ops | requested bytes | total execution time | accelerator execution time | cpu execution time | assigned devices | op types | op count (run|defined) | input shapes'
)
for o in outputs[1:]:
if o.find('Conv2D ') > 0:
metrics = o[o.find('(') + 1:o.find(')')].split(',')
# Make sure time is profiled.
gap = 1 if test.is_gpu_available() else 2
for i in range(3, 6, gap):
mat = re.search('(.*)[um]s/(.*)[um]s',
metrics[i])
self.assertGreater(float(mat.group(1)), 0.0)
self.assertGreater(float(mat.group(2)), 0.0)
# Make sure device is profiled.
if test.is_gpu_available():
self.assertTrue(metrics[6].find('gpu') > 0)
self.assertFalse(metrics[6].find('cpu') > 0)
else:
self.assertFalse(metrics[6].find('gpu') > 0)
self.assertTrue(metrics[6].find('cpu') > 0)
# Make sure float_ops is profiled.
mat = re.search('(.*)k/(.*)k flops',
metrics[1].strip())
self.assertGreater(float(mat.group(1)), 0.0)
self.assertGreater(float(mat.group(2)), 0.0)
# Make sure op_count is profiled.
self.assertEqual(metrics[8].strip(), '1/1|1/1')
# Make sure input_shapes is profiled.
self.assertEqual(metrics[9].strip(),
'0:2x6x6x3|1:3x3x3x6')
if o.find('DW (3x3x3x6') > 0:
metrics = o[o.find('(') + 1:o.find(')')].split(',')
mat = re.search('(.*)/(.*) params',
metrics[1].strip())
self.assertGreater(float(mat.group(1)), 0.0)
self.assertGreater(float(mat.group(2)), 0.0)
# pylint: enable=line-too-long
# Test that profiler restored from profile file gives the same result.
gfile.Remove(outfile)
profile_file = os.path.join(test.get_temp_dir(), 'profile_1')
with lib.ProfilerFromFile(profile_file) as profiler:
profiler.profile_name_scope(options=opts)
with gfile.Open(outfile, 'r') as f:
self.assertEqual(dump_str, lib.CheckAndRemoveDoc(f.read()))