def __init__(self,
by_epoch: bool = True,
profile_iters: int = 1,
activities: List[str] = ['cpu', 'cuda'],
schedule: Optional[dict] = None,
on_trace_ready: Optional[Union[Callable, dict]] = None,
record_shapes: bool = False,
profile_memory: bool = False,
with_stack: bool = False,
with_flops: bool = False,
json_trace_path: Optional[str] = None) -> None:
try:
from torch import profiler # torch version >= 1.8.1
except ImportError:
raise ImportError('profiler is the new feature of torch1.8.1, '
f'but your version is {torch.__version__}')
assert isinstance(by_epoch, bool), '``by_epoch`` should be a boolean.'
self.by_epoch = by_epoch
if profile_iters < 1:
raise ValueError('profile_iters should be greater than 0, but got '
f'{profile_iters}')
self.profile_iters = profile_iters
if not isinstance(activities, list):
raise ValueError(
f'activities should be list, but got {type(activities)}')
self.activities = []
for activity in activities:
activity = activity.lower()
if activity == 'cpu':
self.activities.append(profiler.ProfilerActivity.CPU)
elif activity == 'cuda':
self.activities.append(profiler.ProfilerActivity.CUDA)
else:
raise ValueError(
f'activity should be "cpu" or "cuda", but got {activity}')
if schedule is not None:
self.schedule = profiler.schedule(**schedule)
else:
self.schedule = None
self.on_trace_ready = on_trace_ready
self.record_shapes = record_shapes
self.profile_memory = profile_memory
self.with_stack = with_stack
self.with_flops = with_flops
self.json_trace_path = json_trace_path
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 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)
#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)
# (such as training loops). Tracing all of the execution can be # slow and result in very large trace files. To avoid this, use optional # arguments: # # - ``schedule`` - specifies a function that takes an integer argument (step number) # as an input and returns an action for the profiler, the best way to use this parameter # is to use ``torch.profiler.schedule`` helper function that can generate a schedule for you; # - ``on_trace_ready`` - specifies a function that takes a reference to the profiler as # an input and is called by the profiler each time the new trace is ready. # # To illustrate how the API works, let's first consider the following example with # ``torch.profiler.schedule`` helper function: from torch.profiler import schedule my_schedule = schedule(skip_first=10, wait=5, warmup=1, active=3, repeat=2) ###################################################################### # Profiler assumes that the long-running job is composed of steps, numbered # starting from zero. The example above defines the following sequence of actions # for the profiler: # # 1. Parameter ``skip_first`` tells profiler that it should ignore the first 10 steps # (default value of ``skip_first`` is zero); # 2. After the first ``skip_first`` steps, profiler starts executing profiler cycles; # 3. Each cycle consists of three phases: # # - idling (``wait=5`` steps), during this phase profiler is not active; # - warming up (``warmup=1`` steps), during this phase profiler starts tracing, but # the results are discarded; this phase is used to discard the samples obtained by # the profiler at the beginning of the trace since they are usually skewed by an extra
