def test_kineto_profiler_api(self):
called_num = [0]
use_cuda = torch.profiler.ProfilerActivity.CUDA in supported_activities(
)
with profile(activities=supported_activities()):
self.payload(use_cuda=use_cuda)
def trace_handler(p):
output = p.key_averages().table(
sort_by="self_cuda_time_total"
if use_cuda else "self_cpu_time_total",
row_limit=-1)
# print(output)
# p.export_chrome_trace("/tmp/test_trace_" + str(called_num[0]) + ".json")
called_num[0] += 1
with profile(activities=supported_activities(),
schedule=torch.profiler.schedule(wait=1,
warmup=1,
active=2),
on_trace_ready=trace_handler) as p:
for idx in range(8):
self.payload(use_cuda=use_cuda)
p.step()
self.assertEqual(called_num[0], 2)
# case without schedule
with profile(activities=supported_activities()) as p:
self.payload(use_cuda=use_cuda)
self.payload(use_cuda=use_cuda)
output = p.key_averages().table(sort_by="self_cuda_time_total"
if use_cuda else "self_cpu_time_total",
row_limit=-1)
def _test_profiler_tracing(self, use_kineto):
with _profile(use_kineto=use_kineto) as prof:
t1, t2 = torch.ones(1), torch.ones(1)
torch.add(t1, t2)
with TemporaryFileName(mode="w+") as fname:
prof.export_chrome_trace(fname)
# read the trace and expect valid json
# if the JSON generated by export_chrome_trace is not valid, this will throw and fail the test.
with io.open(fname, 'r') as f:
json.load(f)
# test empty trace
with _profile(use_kineto=use_kineto) as prof:
pass
# saving an empty trace
with TemporaryFileName(mode="w+") as fname:
prof.export_chrome_trace(fname)
# Same test but for cuda.
use_cuda = torch.profiler.ProfilerActivity.CUDA in supported_activities(
)
if not use_cuda:
return
device = torch.device("cuda:0")
with _profile(use_cuda=True, use_kineto=use_kineto) as prof:
t1, t2 = torch.ones(1, device=device), torch.ones(1, device=device)
torch.add(t1, t2)
with TemporaryFileName(mode="w+") as fname:
prof.export_chrome_trace(fname)
# Now validate the json
with io.open(fname, 'r') as f:
json.load(f)
def test_kineto(self):
use_cuda = torch.profiler.ProfilerActivity.CUDA in supported_activities(
)
with _profile(use_cuda=use_cuda, use_kineto=True):
self.payload(use_cuda=use_cuda)
# rerun to avoid initial start overhead
with _profile(use_cuda=use_cuda, use_kineto=True) as p:
self.payload(use_cuda=use_cuda)
output = p.key_averages().table(sort_by="self_cuda_time_total"
if use_cuda else "self_cpu_time_total",
row_limit=-1)
# print(output)
found_gemm = False
found_memcpy = False
found_mm = False
for e in p.function_events:
if "aten::mm" in e.name:
found_mm = True
if "gemm" in e.name:
found_gemm = True
if "Memcpy" in e.name or "memcpy" in e.name:
found_memcpy = True
if use_cuda:
self.assertTrue(found_gemm)
self.assertTrue(found_memcpy)
else:
self.assertTrue(found_mm)
def test_execution_graph_start_stop(self):
use_cuda = torch.profiler.ProfilerActivity.CUDA in supported_activities(
)
# Create a temp file to save execution graph data.
fp = tempfile.NamedTemporaryFile('w+t', suffix='.json', delete=False)
fp.close()
expected_loop_events = 0
eg = ExecutionGraphObserver()
eg.register_callback(fp.name)
for idx in range(10):
if idx == 3:
eg.start()
elif idx == 5:
eg.stop()
elif idx == 8:
eg.start()
elif idx == 9:
eg.stop()
eg.unregister_callback()
if eg._execution_graph_running:
expected_loop_events += 1
with record_function(f"## LOOP {idx} ##"):
self.payload(use_cuda=use_cuda)
assert fp.name == eg.get_output_file_path()
nodes = self.get_execution_graph_root(fp.name)
loop_count = 0
for n in nodes:
assert "name" in n
if "[pytorch|profiler|execution_graph|process]" in n["name"]:
found_root_node = True
if n["name"].startswith("## LOOP "):
loop_count += 1
assert found_root_node
assert loop_count == expected_loop_events
def test_tensorboard_trace_handler(self):
use_cuda = torch.profiler.ProfilerActivity.CUDA in supported_activities(
)
with _profile(use_cuda=use_cuda, use_kineto=True):
self.payload(use_cuda=use_cuda)
with TemporaryDirectoryName() as dname:
with profile(
activities=[torch.profiler.ProfilerActivity.CPU] +
([torch.profiler.ProfilerActivity.CUDA] if use_cuda else []),
schedule=torch.profiler.schedule(wait=1,
warmup=1,
active=2,
repeat=3),
on_trace_ready=torch.profiler.tensorboard_trace_handler(
dname)) as p:
for _ in range(18):
self.payload(use_cuda=use_cuda)
p.step()
self.assertTrue(os.path.exists(dname))
file_num = 0
for file_name in os.listdir(dname):
parts = file_name.split('.')
self.assertTrue(len(parts) > 4)
self.assertTrue(parts[-4].isdigit() and int(parts[-4]) > 0,
"Wrong tracing file name pattern")
self.assertEqual(parts[-3:], ['pt', 'trace', 'json'])
file_num += 1
self.assertEqual(file_num, 3)
# test case for gzip file format
with TemporaryDirectoryName() as dname:
p = profile(
activities=[torch.profiler.ProfilerActivity.CPU] +
([torch.profiler.ProfilerActivity.CUDA] if use_cuda else []),
schedule=torch.profiler.schedule(wait=1,
warmup=1,
active=2,
repeat=3),
on_trace_ready=torch.profiler.tensorboard_trace_handler(
dname, use_gzip=True))
p.start()
for _ in range(18):
self.payload(use_cuda=use_cuda)
p.step()
p.stop()
self.assertTrue(os.path.exists(dname))
file_num = 0
for file_name in os.listdir(dname):
parts = file_name.split('.')
self.assertTrue(len(parts) > 4)
self.assertTrue(parts[-5].isdigit() and int(parts[-5]) > 0,
"Wrong tracing file name pattern")
self.assertEqual(parts[-4:], ['pt', 'trace', 'json', 'gz'])
file_num += 1
self.assertEqual(file_num, 3)
def test_execution_graph_with_kineto(self):
trace_called_num = 0
def trace_handler(p):
nonlocal trace_called_num
trace_called_num += 1
use_cuda = torch.profiler.ProfilerActivity.CUDA in supported_activities(
)
# Create a temp file to save execution graph data.
fp = tempfile.NamedTemporaryFile('w+t', suffix='.json', delete=False)
fp.close()
expected_loop_events = 0
eg = ExecutionGraphObserver()
eg.register_callback(fp.name)
with profile(
activities=supported_activities(),
schedule=torch.profiler.schedule(skip_first=3,
wait=1,
warmup=1,
active=2),
on_trace_ready=trace_handler,
) as p:
eg.start()
for idx in range(10):
expected_loop_events += 1
with record_function(f"## LOOP {idx} ##"):
self.payload(use_cuda=use_cuda)
p.step()
eg.stop()
eg.unregister_callback()
assert trace_called_num == 2
assert fp.name == eg.get_output_file_path()
nodes = self.get_execution_graph_root(fp.name)
loop_count = 0
for n in nodes:
assert "name" in n
if "[pytorch|profiler|execution_graph|process]" in n["name"]:
found_root_node = True
if n["name"].startswith("## LOOP "):
loop_count += 1
assert found_root_node
assert loop_count == expected_loop_events
