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_kineto_profiler_api(self):
called_num = [0]
with _profile(use_cuda=True, use_kineto=True):
self.payload()
def trace_handler(p):
print(p.key_averages().table(sort_by="self_cuda_time_total",
row_limit=-1))
# p.export_chrome_trace("/tmp/test_trace_" + str(called_num[0]) + ".json")
called_num[0] += 1
with profile(activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA
],
schedule=torch.profiler.schedule(wait=1,
warmup=1,
active=2),
on_trace_ready=trace_handler) as p:
for idx in range(8):
self.payload()
p.step()
self.assertEqual(called_num[0], 2)
# case without enable_pred
with profile(activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA
]) as p:
self.payload()
self.payload()
print(p.key_averages().table(sort_by="self_cuda_time_total",
row_limit=-1))
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_extra_fields(self):
with profile(with_stack=True, profile_memory=True) as p:
_ = torch.ones((1, ))
def find_ones(nodes):
for n in nodes:
if n.name() == "aten::ones":
return n
result = find_ones(n.children)
if result:
return result
node = find_ones(p.profiler.kineto_results.experimental_event_tree())
self.assertIsNotNone(node)
self.assertIsInstance(node.extra_fields,
torch._C._autograd._ExtraFields_TorchOp)
self.assertIsInstance(node.parent.extra_fields,
torch._C._autograd._ExtraFields_PyCCall)
self.assertEqual(node.children[0].name(), "aten::empty")
self.assertEqual(node.children[0].children[0].name(), "[memory]")
self.assertIsInstance(node.children[0].children[0].extra_fields,
torch._C._autograd._ExtraFields_Allocation)
def test_kineto_multigpu(self):
with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA
]) as prof:
for gpu_id in [0, 1]:
x = torch.randn(10, 10).cuda(gpu_id)
y = torch.randn(10, 10).cuda(gpu_id)
z = x.matmul(y)
found_gemm_0 = False
found_gemm_1 = False
found_cuda = False
for evt in prof.events():
if "gemm" in evt.name.lower(
) and evt.device_type == DeviceType.CUDA:
if evt.device_index == 0:
found_gemm_0 = True
elif evt.device_index == 1:
found_gemm_1 = True
if "cuda" in evt.name.lower(
) and evt.device_type == DeviceType.CPU:
found_cuda = True
self.assertTrue(found_gemm_0)
self.assertTrue(found_gemm_1)
self.assertTrue(found_cuda)
def test_flops(self):
model = torch.nn.Sequential(
nn.Conv2d(16, 33, 18),
nn.ReLU(),
nn.Linear(243, 243),
nn.ReLU(),
)
inputs = torch.randn(40, 16, 18, 260)
with _profile(record_shapes=True,
with_flops=True,
use_kineto=kineto_available()) as prof:
model(inputs)
profiler_output = prof.key_averages(group_by_input_shape=True).table(
sort_by="cpu_time_total", row_limit=10)
self.assertIn("FLOPS", profiler_output)
if not (kineto_available() and torch.cuda.is_available()):
return
with profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA
],
record_shapes=True,
with_flops=True,
) as kineto_profiler:
model(inputs)
profiler_output = kineto_profiler.key_averages().table(
sort_by="self_cuda_time_total", row_limit=-1)
self.assertIn("FLOPS", profiler_output)
def test_tensorboard_trace_handler(self):
with _profile(use_cuda=True, use_kineto=True):
self.payload()
with TemporaryDirectoryName() as dname:
with profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA
],
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()
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)
def inference(self, model, device):
with torch.no_grad():
# for data, target in zip(self.x_train, self.y_train):
if self.params["profile_pytorch"]:
try:
from torch.profiler import profile
with profile(record_shapes=True) as prof:
self.inner_loop(model)
profile_data = prof.key_averages().table(
sort_by="cpu_time_total",
row_limit=20,
)
print(profile_data)
except Exception:
from .torchprof import Profile
# Profile using torchprof (TODO:profile_per_batch for all batches and epochs)
profile_cuda = self.device.type == "cuda"
with Profile(model, use_cuda=profile_cuda) as prof:
self.inner_loop(model)
data = prof.display(show_events=False)
profile_data = json.dumps(data,
indent=4,
separators=(",", ": "))
self.params["profile_data"] = profile_data
else:
self.inner_loop(model)
if self.params["nb_gpus"] > 0:
torch.cuda.synchronize()
def test_utils_compute_queue_depth_when_no_cuda_events(self):
# For traces with only cpu events, we expect empty queue depth list
x = torch.ones((1024, 1024))
with profile() as prof:
for _ in range(5):
x = x @ x
basic_evaluation = _utils.BasicEvaluation(prof.profiler)
self.assertFalse(basic_evaluation.compute_queue_depth())
def profile_cuda_kernels(fn, args, string_id="Model time"):
print("################################################")
print(f"#### Profiling for {string_id} starts #########")
print("################################################")
warmup = 50
old_args = args[:]
n_repeats = 1
n_layers = 1
ref = fn(*old_args)
gO = torch.rand_like(ref)
for _ in range(0, warmup // n_layers):
args = list(old_args[:])
ref = fn(*args)
ref.backward(gO)
torch.cuda.synchronize()
# Forward profile
def fwd_run():
for _ in range(0, n_repeats // n_layers):
args = list(old_args[:])
for arg in args:
if isinstance(arg, torch.Tensor):
arg.grad = None
ref = fn(*args)
print(f"###### Forward profile for {string_id} starts #####")
with profile(activities=[ProfilerActivity.CUDA],
record_shapes=True) as prof:
with record_function("baseline"):
fwd_run()
print(prof.key_averages().table(sort_by="cuda_time_total", row_limit=30))
print(f"###### Forward profile for {string_id} ends #####")
# Backward profile
def bwd_run():
for _ in range(0, n_repeats // n_layers):
args = list(old_args[:])
for arg in args:
if isinstance(arg, torch.Tensor):
arg.grad = None
ref = fn(*args)
print(f"###### Backward profile for {string_id} starts #####")
torch.cuda.synchronize()
with profile(activities=[ProfilerActivity.CUDA],
record_shapes=True) as prof:
with record_function("baseline"):
ref.backward(gO)
print(prof.key_averages().table(sort_by="cuda_time_total",
row_limit=30))
torch.cuda.synchronize()
print(f"###### Backward profile for {string_id} ends #####")
bwd_run()
print("################################################")
print(f"#### Profiling for {string_id} ends #########")
print("################################################\n\n\n\n")
def dump_chrome_trace(f,
input,
trace_filename,
optimize_ctx,
activities,
num_runs=1,
devices=None,
kwargs_for_f=None,
kwargs_for_profiler=None):
"""
Output the chrome trace of running f(input, **kwargs_for_f) with [optimize_ctx]
[num_runs] times to [trace_filename].
[activities] are the activities that the profiler will record, e.g. ProfilerActivity.CUDA.
Return total runtime without the profiler
Outputs to trace_filename
"""
if devices is None:
devices = ["cuda"]
global synchronize
if devices != ["cpu"] and torch.cuda.is_available():
synchronize = torch.cuda.synchronize
if kwargs_for_f is None:
kwargs_for_f = {}
if kwargs_for_profiler is None:
kwargs_for_profiler = {}
with optimize_ctx:
torch.manual_seed(1337)
for _ in range(5): # warmup runs
f(input, **kwargs_for_f)
synchronize()
torch.manual_seed(1337)
t0 = time.perf_counter()
for _ in range(num_runs):
f(input, **kwargs_for_f)
synchronize()
t1 = time.perf_counter()
timing = t1 - t0
with profile(activities=activities, **kwargs_for_profiler) as prof:
with optimize_ctx:
synchronize()
torch.manual_seed(1337)
for _ in range(num_runs):
f(input, **kwargs_for_f)
synchronize()
prof.export_chrome_trace(trace_filename)
return timing
def test_profiler_type(self):
profiler_type = torch._C._autograd._profiler_type
ActiveProfilerType = torch._C._autograd.ActiveProfilerType
self.assertEqual(profiler_type(), ActiveProfilerType.NONE)
# Autograd profiler
with _profile_legacy():
self.assertEqual(profiler_type(), ActiveProfilerType.LEGACY)
# Kineto profiler
with profile():
self.assertEqual(profiler_type(), ActiveProfilerType.KINETO)
def profile_it(f, inp):
for _ in range(5):
f(inp)
itr = 5
with profile(activities=[ProfilerActivity.CUDA], record_shapes=True) as prof:
for _ in range(itr):
f(inp)
timing = prof.key_averages()
cuda_time_total = 0
for e in timing:
cuda_time_total = cuda_time_total + e.cuda_time_total
return cuda_time_total / itr
def test_profiler_metadata(self):
t1, t2 = torch.ones(1), torch.ones(1)
with profile() as prof:
torch.add(t1, t2)
prof.add_metadata("test_key1", "test_value1")
prof.add_metadata_json("test_key2", "[1,2,3]")
with TemporaryFileName(mode="w+") as fname:
prof.export_chrome_trace(fname)
with io.open(fname, 'r') as f:
trace = json.load(f)
assert "test_key1" in trace
assert trace["test_key1"] == "test_value1"
assert "test_key2" in trace
assert trace["test_key2"] == [1, 2, 3]
def __init__(self,
record_func_name='inference',
activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
record_shapes=False,
profile_memory=True,
scheduler=schedule(wait=1, warmup=1, active=2),
trace_handler=tensorboard_trace_handler('./log')):
self.activities = activities
self.profile = profile(activities=activities,
record_shapes=record_shapes,
profile_memory=profile_memory,
with_flops=True,
schedule=scheduler,
on_trace_ready=trace_handler)
self.record_function = record_function(record_func_name)
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
def bwd_run():
for _ in range(0, n_repeats // n_layers):
args = list(old_args[:])
for arg in args:
if isinstance(arg, torch.Tensor):
arg.grad = None
ref = fn(*args)
print(f"###### Backward profile for {string_id} starts #####")
torch.cuda.synchronize()
with profile(activities=[ProfilerActivity.CUDA],
record_shapes=True) as prof:
with record_function("baseline"):
ref.backward(gO)
print(prof.key_averages().table(sort_by="cuda_time_total",
row_limit=30))
torch.cuda.synchronize()
print(f"###### Backward profile for {string_id} ends #####")
def test_utils_compute_self_time(self):
with profile() as prof:
t1, t2 = torch.ones(1, requires_grad=True), torch.ones(
1, requires_grad=True)
z = torch.add(t1, t2)
y = torch.ones(1)
loss = torch.nn.functional.binary_cross_entropy_with_logits(z, y)
loss.backward()
basic_eval = _utils.BasicEvaluation(prof.profiler)
metrics = basic_eval.metrics
self.assertTrue(len(metrics) > 0)
for event_key, event_metrics in metrics.items():
self.assertEqual(
event_metrics.self_time_ns,
event_key.event.duration_time_ns - sum([
child.duration_time_ns
for child in event_key.event.children
]))
def profile_conv_runtimes(model, filename):
model = model.cuda()
inputs = torch.randn(32, 3, 224, 224).cuda()
with profile(activities=[ProfilerActivity.CUDA],
profile_memory=True,
record_shapes=True) as prof:
with record_function("model_inference"):
model(inputs)
print(
prof.key_averages(group_by_input_shape=True).table(
sort_by="cpu_time_total", row_limit=10))
print(
prof.key_averages(group_by_input_shape=True).table(
sort_by="cuda_time_total", row_limit=10))
print(
prof.key_averages(group_by_input_shape=True).table(
sort_by="cuda_memory_usage", row_limit=10))
prof.export_chrome_trace(filename + '.json')
def train_func():
from ray.train.torch import TorchWorkerProfiler
from torch.profiler import profile, record_function, schedule
twp = TorchWorkerProfiler()
with profile(
activities=[],
schedule=schedule(wait=0, warmup=0, active=1),
on_trace_ready=twp.trace_handler,
) as p:
for epoch in range(num_epochs):
with record_function("test_function"):
pass
p.step()
profile_results = twp.get_and_clear_profile_traces()
train.report(epoch=epoch, **profile_results)
def train_func():
twp = TorchWorkerProfiler()
with profile(
activities=[],
schedule=schedule(wait=0, warmup=0, active=1),
on_trace_ready=twp.trace_handler,
) as p:
# Setup model.
model = torch.nn.Linear(1, 1)
model = train.torch.prepare_model(model)
loss_fn = torch.nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-2)
# Setup data.
input = torch.randn(1000, 1)
labels = input * 2
dataset = torch.utils.data.TensorDataset(input, labels)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=32)
dataloader = train.torch.prepare_data_loader(dataloader)
# Train.
for epoch in range(5):
with record_function("train_epoch"):
for X, y in dataloader:
pred = model(X)
loss = loss_fn(pred, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
with record_function("train_checkpoint"):
state_dict = model.state_dict()
consume_prefix_in_state_dict_if_present(state_dict, "module.")
train.save_checkpoint(epoch=epoch, model_weights=state_dict)
p.step()
with record_function("train_report"):
profile_results = twp.get_and_clear_profile_traces()
train.report(epoch=epoch, **profile_results)
def test_utils_compute_queue_depth(self):
x = torch.ones((8096, 8096), device="cuda")
with profile() as prof:
# First half we want it to be compute bound
for _ in range(5):
y = torch.mm(x, x)
# Second half we want it to be overhead bound
# So we are synchronize and sleeping
torch.cuda.synchronize()
for _ in range(3):
y[0] += 1
time.sleep(0.1)
basic_evaluation = _utils.BasicEvaluation(prof.profiler)
# We can assume golden because mm is compute intensive,
# so kernel will queued up.
# But later tensor indexing is overhead bound, and there
# is sleep to make sure kernel finished before next dispatch.
golden_queue_depth_list = [1, 2, 3, 4, 5, 1, 1, 1]
for entry, golden in zip(basic_evaluation.compute_queue_depth(),
golden_queue_depth_list):
self.assertTrue(entry.queue_depth == golden)
def wrap_forward(*args, **kwargs):
"""The forward pass is decorated and profiled here
"""
# only torch 1.8.1+ is supported
torch_version = torch.__version__
if torch_version <= '1.8.1':
raise NotImplementedError(
"Profiler requires at least torch 1.8.1")
# profile the forward pass
with torch_profiler.profile(
activities=[
torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
], profile_memory=self.profile_memory) as prof:
res = _forward(*args, **kwargs)
event_list = prof.events()
# each profile call should be contained in its own list
self.trace_profile_events[path].append(event_list)
return res
def test_profiler_correlation_id(self):
'''
We expect the correlation_id to be unique across multiple invokation of the profiler,
So we will reuse id_uniqueness_set.
'''
id_uniqueness_set = set()
model = torch.nn.Sequential(
nn.Conv2d(16, 33, 18),
nn.ReLU(),
nn.Linear(243, 243),
nn.ReLU(),
)
inputs = torch.randn(40, 16, 18, 260)
uint32_max = 2**32 - 1
for i in range(5):
with profile() as prof:
model(inputs)
for event in prof.profiler.kineto_results.events():
corr_id = event.correlation_id()
if (corr_id):
self.assertTrue(corr_id not in id_uniqueness_set)
id_uniqueness_set.add(corr_id)
self.assertTrue(corr_id < uint32_max)
def profile(args, model, model_info, device):
"""
Profile.
:param model:
:param model_info:
:return:
"""
import copy
from torch.profiler import profile, record_function, ProfilerActivity
model = copy.deepcopy(model)
model = model.to(device)
model.eval()
inputs = tuple(
torch.ones((args.batch_size, ) + model_info['input_shapes'][k][1:],
dtype=torch.float32).to(device)
for k in model_info['input_names'])
for x in inputs:
print(x.shape, x.device)
def trace_handler(p):
output = p.key_averages().table(sort_by="self_cuda_time_total",
row_limit=50)
print(output)
p.export_chrome_trace("/tmp/trace_" + str(p.step_num) + ".json")
with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
schedule=torch.profiler.schedule(wait=2,
warmup=2,
active=6,
repeat=2),
on_trace_ready=trace_handler) as p:
for idx in range(100):
model(*inputs)
p.step()
#p.outShape = (1, 64, 1088, 1920)
getMemUsed = lambda i: torch.cuda.memory_stats(i)['reserved_bytes.all.peak']
t = torch.randn(shape, dtype=config.dtype(), device=config.device()) # pylint: disable=E1101
load = shape[-1] * shape[-2] * shape[0]
m = getMemUsed(config.device()) if config.cuda else None
print(config.dtype(), config.device(), m)
if config.cuda:
p(t)
#doCrop(p, t)
getMemUsed(config.device())
start = perf_counter()
p(t)
#doCrop(p, t).mean().cpu()
print('time elpased: {}'.format(perf_counter() - start))
m = getMemUsed(config.device())
else:
schedule1 = schedule(
wait=1,
warmup=1,
active=1)
with profile(
activities=[ProfilerActivity.CPU],
schedule=schedule1, profile_memory=True) as pro:
for _ in range(3):
p(t)
pro.step()
avg = pro.key_averages()
avg.sort(key=lambda o: o.cpu_memory_usage, reverse=True)
m = avg[0].cpu_memory_usage
print(m, m / load, load)
def test_memory_profiler(self):
def run_profiler(tensor_creation_fn):
# collecting allocs / deallocs
with _profile(profile_memory=True,
record_shapes=True,
use_kineto=kineto_available()) as prof:
x = None
with record_function("test_user_scope_alloc"):
x = tensor_creation_fn()
with record_function("test_user_scope_dealloc"):
del x
return prof.key_averages(group_by_input_shape=True)
def check_metrics(stats, metric, allocs=None, deallocs=None):
stat_metrics = {}
for stat in stats:
stat_metrics[stat.key] = getattr(stat, metric)
if allocs is not None:
for alloc_fn in allocs:
self.assertTrue(alloc_fn in stat_metrics)
self.assertTrue(stat_metrics[alloc_fn] > 0)
if deallocs is not None:
for dealloc_fn in deallocs:
self.assertTrue(dealloc_fn in stat_metrics)
self.assertTrue(stat_metrics[dealloc_fn] < 0)
def create_cpu_tensor():
return torch.rand(10, 10)
def create_cuda_tensor():
return torch.rand(10, 10).cuda()
def create_mkldnn_tensor():
return torch.rand(10, 10, dtype=torch.float32).to_mkldnn()
stats = run_profiler(create_cpu_tensor)
check_metrics(stats,
"cpu_memory_usage",
allocs=[
"aten::empty",
"aten::rand",
"test_user_scope_alloc",
],
deallocs=[
"test_user_scope_dealloc",
])
if kineto_available():
with TemporaryFileName(mode="w+") as fname:
with profile(profile_memory=True) as prof:
x = None
with record_function("test_user_scope_alloc"):
x = create_cpu_tensor()
with record_function("test_user_scope_dealloc"):
del x
prof.export_chrome_trace(fname)
with io.open(fname, 'r') as f:
trace = json.load(f)
assert "traceEvents" in trace
events = trace["traceEvents"]
found_memory_events = False
for evt in events:
assert "name" in evt
if evt["name"] == "[memory]":
found_memory_events = True
assert "args" in evt
assert "Device Type" in evt["args"]
assert "Device Id" in evt["args"]
assert "Bytes" in evt["args"]
assert found_memory_events
if torch.cuda.is_available():
create_cuda_tensor()
stats = run_profiler(create_cuda_tensor)
check_metrics(stats,
"cuda_memory_usage",
allocs=[
"test_user_scope_alloc",
"aten::to",
"aten::empty_strided",
],
deallocs=[
"test_user_scope_dealloc",
])
check_metrics(stats,
"cpu_memory_usage",
allocs=[
"aten::rand",
"aten::empty",
])
if torch._C.has_mkldnn:
create_mkldnn_tensor()
stats = run_profiler(create_mkldnn_tensor)
check_metrics(stats,
"cpu_memory_usage",
allocs=[
"test_user_scope_alloc",
"aten::rand",
"aten::empty",
"aten::to_mkldnn",
],
deallocs=[
"test_user_scope_dealloc",
])
# check top-level memory events
with _profile(profile_memory=True,
use_kineto=kineto_available()) as prof:
x = torch.rand(10, 10)
del x
if torch.cuda.is_available():
y = torch.rand(10, 10).cuda()
del y
gc.collect()
stats = prof.key_averages(group_by_input_shape=True)
check_metrics(stats,
"cpu_memory_usage",
allocs=["aten::rand", "aten::empty"],
deallocs=["[memory]"])
if torch.cuda.is_available():
check_metrics(stats, "cuda_memory_usage", deallocs=["[memory]"])
def inference(model, dataloader, datatype, args):
batch_time = AverageMeter('Time', ':6.3f')
batch_size = args.batch_size
warmup_iters = args.warmup_iterations
max_iters = args.max_iterations if dataloader is None else len(dataloader)
model.eval()
coco = get_coco_api_from_dataset(dataloader.dataset)
iou_types = ["bbox"]
iou_types.append("segm")
coco_evaluator = CocoEvaluator(coco, iou_types)
if args.ipex:
import intel_extension_for_pytorch as ipex
model = model.to(memory_format=torch.channels_last)
model = ipex.optimize(model,
dtype=datatype,
level="O1",
conv_bn_folding=False,
replace_dropout_with_identity=False)
model.backbone = ipex.optimize(model.backbone,
dtype=datatype,
level="O1")
else:
if args.jit:
model = model.to(memory_format=torch.channels_last)
else:
from torch.utils import mkldnn as mkldnn_utils
model = mkldnn_utils.to_mkldnn(model, dtype=datatype)
if args.jit:
x = torch.randn(batch_size, 3, 1200,
1200).to(memory_format=torch.channels_last)
if args.precision == "bf16":
with torch.cpu.amp.autocast(), torch.no_grad():
model.backbone = torch.jit.trace(model.backbone,
x,
strict=False)
model.backbone = torch.jit.freeze(model.backbone)
else:
with torch.no_grad():
model.backbone = torch.jit.trace(model.backbone,
x,
strict=False)
model.backbone = torch.jit.freeze(model.backbone)
with torch.no_grad():
if dataloader is None:
print(
"Models for detection tasks need to use real dataset. You need to specify coco dataset. "
)
exit(1)
else:
for i, batch in enumerate(dataloader):
images = batch[0]
if not args.ipex and not args.jit:
images = list(img.to(datatype) for img in images)
if args.ipex and args.precision == "bf16":
with torch.cpu.amp.autocast():
if i == warmup_iters:
with profile(
activities=[ProfilerActivity.CPU],
record_shapes=True
) as prof, record_function("model_inference"):
output = model(images)
else:
output = model(images)
else:
if i == warmup_iters:
with profile(
activities=[ProfilerActivity.CPU],
record_shapes=True) as prof, record_function(
"model_inference"):
output = model(images)
else:
output = model(images)
if i > warmup_iters:
break
for i, batch in enumerate(dataloader):
images = batch[0]
end = time.time()
if not args.ipex and not args.jit:
images = list(img.to(datatype) for img in images)
if args.ipex and args.precision == "bf16":
with torch.cpu.amp.autocast():
output = model(images)
else:
output = model(images)
batch_time.update(time.time() - end)
output = [{k: v.to(torch.float32)
for k, v in t.items()} for t in output]
res = {
target["image_id"].item(): output
for target, output in zip(batch[1], output)
}
coco_evaluator.update(res)
if max_iters != -1 and i >= max_iters:
break
print(prof.key_averages().table(sort_by="cpu_time_total", row_limit=-1))
latency = batch_time.avg / batch_size * 1000
perf = batch_size / batch_time.avg
coco_evaluator.synchronize_between_processes()
coco_evaluator.accumulate()
coco_evaluator.summarize()
print("Bbox AP: {:.5f} ".format(coco_evaluator.coco_eval['bbox'].stats[0]))
print("Segm AP: {:.5f} ".format(coco_evaluator.coco_eval['segm'].stats[0]))
print('Latency: %.3f ms' % latency)
print("Throughput: {:.3f} fps".format(perf))
def train(args):
local_rank = int(os.environ['LOCAL_RANK'])
verbose = local_rank == 0
if verbose:
print('Using PyTorch version:', torch.__version__)
print(torch.__config__.show())
dist.init_process_group(backend='nccl')
world_size = dist.get_world_size()
torch.manual_seed(0)
torch.cuda.set_device(local_rank)
# Set up standard model.
if verbose:
print(f'Using {args.model} model')
model = getattr(models, args.model)()
model = model.cuda()
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), 1e-4)
model = DistributedDataParallel(model, device_ids=[local_rank])
train_dataset = dataset_from_datadir(args.datadir, verbose=verbose)
train_sampler = DistributedSampler(train_dataset)
train_loader = DataLoader(dataset=train_dataset,
batch_size=args.batchsize,
shuffle=False,
num_workers=args.workers,
pin_memory=True,
sampler=train_sampler)
if args.profiler:
th = None
if args.profiler_format == 'tb':
th = torch.profiler.tensorboard_trace_handler('./logs/profiler')
prof = profile(
schedule=torch.profiler.schedule(
wait=1, # number of steps steps not active
warmup=1, # warmup steps (tracing, but results discarded)
active=10, # tracing steps
repeat=1), # repeat procedure this many times
on_trace_ready=th,
activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
record_shapes=True,
with_stack=True)
prof.start()
total_step = args.steps if args.steps is not None else len(train_loader)
# For each block of printed steps
last_start = datetime.now()
last_images = 0
# For final average
avg_images = 0
avg_start = None
tot_steps = 0
for epoch in range(args.epochs):
for i, (images, labels) in enumerate(train_loader):
images = images.cuda(non_blocking=True)
labels = labels.cuda(non_blocking=True)
outputs = model(images)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if args.profiler:
prof.step()
li = len(images)
last_images += li
tot_steps += 1
if tot_steps == args.warmup_steps:
avg_start = datetime.now()
elif tot_steps > args.warmup_steps:
avg_images += li
if (i + 1) % args.print_steps == 0 and verbose:
now = datetime.now()
last_secs = (now - last_start).total_seconds()
print(
f'Epoch [{epoch+1}/{args.epochs}], Step [{i+1}/{total_step}], '
f'Loss: {loss.item():.4f}, '
f'Images/sec: {last_images*world_size/last_secs:.2f} '
f'(last {args.print_steps} steps)')
last_start = now
last_images = 0
if args.steps is not None and tot_steps >= args.steps:
break
if args.profiler:
if args.profiler_format == 'json' and verbose:
trace_datetime = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
trace_fname = f"profiler-trace-{trace_datetime}.json"
print(f'Writing profiler trace to {trace_fname}')
prof.export_chrome_trace(trace_fname)
prof.stop()
if verbose:
if avg_start is None:
print(
"WARNING: stopped before warmup steps done, not printing stats."
)
else:
dur = datetime.now() - avg_start
print(f"Training completed in: {dur}")
print(
f"Images/sec: {avg_images*world_size/dur.total_seconds():.2f} "
f"(average, skipping {args.warmup_steps} warmup steps)")
def main(args):
_logger.info('args:\n - %s',
'\n - '.join(str(it) for it in args.__dict__.items()))
if args.file_fraction < 1:
_logger.warning(
'Use of `file-fraction` is not recommended in general -- prefer using `data-fraction` instead.'
)
# classification/regression mode
if args.regression_mode:
_logger.info('Running in regression mode')
from utils.nn.tools import train_regression as train
from utils.nn.tools import evaluate_regression as evaluate
else:
_logger.info('Running in classification mode')
from utils.nn.tools import train_classification as train
from utils.nn.tools import evaluate_classification as evaluate
# training/testing mode
training_mode = not args.predict
# device
if args.gpus:
gpus = [int(i) for i in args.gpus.split(',')]
dev = torch.device(gpus[0])
else:
gpus = None
dev = torch.device('cpu')
# load data
if training_mode:
train_loader, val_loader, data_config, train_input_names, train_label_names = train_load(
args)
else:
test_loaders, data_config = test_load(args)
if args.io_test:
data_loader = train_loader if training_mode else list(
test_loaders.values())[0]()
iotest(args, data_loader)
return
model, model_info, network_module, network_options = model_setup(
args, data_config)
if args.print:
return
if args.profile:
profile(args, model, model_info, device=dev)
return
# export to ONNX
if args.export_onnx:
onnx(args, model, data_config, model_info)
return
if args.tensorboard:
from utils.nn.tools import TensorboardHelper
tb = TensorboardHelper(tb_comment=args.tensorboard,
tb_custom_fn=args.tensorboard_custom_fn)
else:
tb = None
# note: we should always save/load the state_dict of the original model, not the one wrapped by nn.DataParallel
# so we do not convert it to nn.DataParallel now
orig_model = model
if training_mode:
model = orig_model.to(dev)
# loss function
try:
loss_func = network_module.get_loss(data_config, **network_options)
_logger.info('Using loss function %s with options %s' %
(loss_func, network_options))
except AttributeError:
loss_func = torch.nn.CrossEntropyLoss()
_logger.warning(
'Loss function not defined in %s. Will use `torch.nn.CrossEntropyLoss()` by default.',
args.network_config)
# optimizer & learning rate
opt, scheduler = optim(args, model, dev)
# multi-gpu
if gpus is not None and len(gpus) > 1:
# model becomes `torch.nn.DataParallel` w/ model.module being the original `torch.nn.Module`
model = torch.nn.DataParallel(model, device_ids=gpus)
model = model.to(dev)
# lr finder: keep it after all other setups
if args.lr_finder is not None:
start_lr, end_lr, num_iter = args.lr_finder.replace(' ',
'').split(',')
from utils.lr_finder import LRFinder
lr_finder = LRFinder(model,
opt,
loss_func,
device=dev,
input_names=train_input_names,
label_names=train_label_names)
lr_finder.range_test(train_loader,
start_lr=float(start_lr),
end_lr=float(end_lr),
num_iter=int(num_iter))
lr_finder.plot(output='lr_finder.png'
) # to inspect the loss-learning rate graph
return
if args.use_amp:
from torch.cuda.amp import GradScaler
scaler = GradScaler()
else:
scaler = None
# training loop
best_valid_metric = np.inf if args.regression_mode else 0
for epoch in range(args.num_epochs):
if args.load_epoch is not None:
if epoch <= args.load_epoch:
continue
print('-' * 50)
_logger.info('Epoch #%d training' % epoch)
train(model,
loss_func,
opt,
scheduler,
train_loader,
dev,
epoch,
steps_per_epoch=args.steps_per_epoch,
grad_scaler=scaler,
tb_helper=tb)
if args.model_prefix:
dirname = os.path.dirname(args.model_prefix)
if dirname and not os.path.exists(dirname):
os.makedirs(dirname)
state_dict = model.module.state_dict() if isinstance(
model, torch.nn.DataParallel) else model.state_dict()
torch.save(state_dict,
args.model_prefix + '_epoch-%d_state.pt' % epoch)
torch.save(
opt.state_dict(),
args.model_prefix + '_epoch-%d_optimizer.pt' % epoch)
_logger.info('Epoch #%d validating' % epoch)
valid_metric = evaluate(model,
val_loader,
dev,
epoch,
loss_func=loss_func,
steps_per_epoch=args.steps_per_epoch_val,
tb_helper=tb)
is_best_epoch = (valid_metric < best_valid_metric
) if args.regression_mode else (
valid_metric > best_valid_metric)
if is_best_epoch:
best_valid_metric = valid_metric
if args.model_prefix:
shutil.copy2(
args.model_prefix + '_epoch-%d_state.pt' % epoch,
args.model_prefix + '_best_epoch_state.pt')
torch.save(model,
args.model_prefix + '_best_epoch_full.pt')
_logger.info(
'Epoch #%d: Current validation metric: %.5f (best: %.5f)' %
(epoch, valid_metric, best_valid_metric),
color='bold')
if args.data_test:
if training_mode:
del train_loader, val_loader
test_loaders, data_config = test_load(args)
if not args.model_prefix.endswith('.onnx'):
model = orig_model.to(dev)
model_path = args.model_prefix if args.model_prefix.endswith(
'.pt') else args.model_prefix + '_best_epoch_state.pt'
_logger.info('Loading model %s for eval' % model_path)
model.load_state_dict(torch.load(model_path, map_location=dev))
if gpus is not None and len(gpus) > 1:
model = torch.nn.DataParallel(model, device_ids=gpus)
model = model.to(dev)
for name, get_test_loader in test_loaders.items():
test_loader = get_test_loader()
# run prediction
if args.model_prefix.endswith('.onnx'):
_logger.info('Loading model %s for eval' % args.model_prefix)
from utils.nn.tools import evaluate_onnx
test_metric, scores, labels, observers = evaluate_onnx(
args.model_prefix, test_loader)
else:
test_metric, scores, labels, observers = evaluate(
model,
test_loader,
dev,
epoch=None,
for_training=False,
tb_helper=tb)
_logger.info('Test metric %.5f' % test_metric, color='bold')
del test_loader
if args.predict_output:
if '/' not in args.predict_output:
args.predict_output = os.path.join(
os.path.dirname(args.model_prefix), 'predict_output',
args.predict_output)
os.makedirs(os.path.dirname(args.predict_output),
exist_ok=True)
if name == '':
output_path = args.predict_output
else:
base, ext = os.path.splitext(args.predict_output)
output_path = base + '_' + name + ext
if output_path.endswith('.root'):
save_root(args, output_path, data_config, scores, labels,
observers)
else:
save_awk(args, output_path, scores, labels, observers)
_logger.info('Written output to %s' % output_path,
color='bold')