def _main(port):
base_model = ShuffleNetV2(32)
base_predictor = 'cortexA76cpu_tflite21'
transf = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip()
]
normalize = [
transforms.ToTensor(),
transforms.Normalize([0.49139968, 0.48215827, 0.44653124], [0.24703233, 0.24348505, 0.26158768])
]
train_dataset = serialize(CIFAR10, 'data', train=True, download=True, transform=transforms.Compose(transf + normalize))
test_dataset = serialize(CIFAR10, 'data', train=False, transform=transforms.Compose(normalize))
trainer = pl.Classification(train_dataloader=pl.DataLoader(train_dataset, batch_size=64),
val_dataloaders=pl.DataLoader(test_dataset, batch_size=64),
max_epochs=2, gpus=1)
simple_strategy = strategy.Random(model_filter=LatencyFilter(threshold=100, predictor=base_predictor))
exp = RetiariiExperiment(base_model, trainer, strategy=simple_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.trial_concurrency = 2
exp_config.max_trial_number = 2
exp_config.trial_gpu_number = 1
exp_config.training_service.use_active_gpu = False
exp_config.execution_engine = 'base'
exp_config.dummy_input = [1, 3, 32, 32]
exp.run(exp_config, port)
print('Exported models:')
for model in exp.export_top_models(formatter='dict'):
print(model)
def _test_strategy(strategy_, support_value_choice=True):
to_test = [
# (model, evaluator), support_or_net
(_mnist_net('simple'), True),
(_mnist_net('simple_value_choice'), support_value_choice),
(_mnist_net('value_choice'), support_value_choice),
(_mnist_net('repeat'), False), # no strategy supports repeat currently
(_mnist_net('custom_op'), False), # this is definitely a NO
(_multihead_attention_net(), support_value_choice),
]
for (base_model, evaluator), support_or_not in to_test:
if isinstance(strategy_, BaseStrategy):
strategy = strategy_
else:
strategy = strategy_(base_model, evaluator)
print('Testing:',
type(strategy).__name__,
type(base_model).__name__,
type(evaluator).__name__, support_or_not)
experiment = RetiariiExperiment(base_model,
evaluator,
strategy=strategy)
config = RetiariiExeConfig()
config.execution_engine = 'oneshot'
if support_or_not:
experiment.run(config)
assert isinstance(experiment.export_top_models()[0], dict)
else:
with pytest.raises(TypeError, match='not supported'):
experiment.run(config)
def test_oneshot_experiment():
base_model = Net()
evaluator = get_mnist_evaluator()
search_strategy = strategy.RandomOneShot()
exp = RetiariiExperiment(base_model, evaluator, strategy=search_strategy)
exp_config = RetiariiExeConfig()
exp_config.execution_engine = 'oneshot'
exp.run(exp_config)
assert isinstance(exp.export_top_models()[0], dict)
def _main(port):
base_model = ShuffleNetV2OneShot(32)
base_predictor = 'cortexA76cpu_tflite21'
transf = [
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip()
]
normalize = [
transforms.ToTensor(),
transforms.Normalize([0.49139968, 0.48215827, 0.44653124],
[0.24703233, 0.24348505, 0.26158768])
]
# FIXME
# CIFAR10 is used here temporarily.
# Actually we should load weight from supernet and evaluate on imagenet.
train_dataset = serialize(CIFAR10,
'data',
train=True,
download=True,
transform=transforms.Compose(transf + normalize))
test_dataset = serialize(CIFAR10,
'data',
train=False,
transform=transforms.Compose(normalize))
trainer = pl.Classification(train_dataloader=pl.DataLoader(train_dataset,
batch_size=64),
val_dataloaders=pl.DataLoader(test_dataset,
batch_size=64),
max_epochs=2,
gpus=1)
simple_strategy = strategy.RegularizedEvolution(model_filter=LatencyFilter(
threshold=100, predictor=base_predictor),
sample_size=1,
population_size=2,
cycles=2)
exp = RetiariiExperiment(base_model, trainer, strategy=simple_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.trial_concurrency = 2
# exp_config.max_trial_number = 2
exp_config.trial_gpu_number = 1
exp_config.training_service.use_active_gpu = False
exp_config.execution_engine = 'base'
exp_config.dummy_input = [1, 3, 32, 32]
exp.run(exp_config, port)
print('Exported models:')
for i, model in enumerate(exp.export_top_models(formatter='dict')):
print(model)
with open(f'architecture_final_{i}.json', 'w') as f:
json.dump(get_archchoice_by_model(model), f, indent=4)
def test_multitrial_experiment(pytestconfig):
base_model = Net()
evaluator = get_mnist_evaluator()
search_strategy = strategy.Random()
exp = RetiariiExperiment(base_model, evaluator, strategy=search_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.trial_concurrency = 1
exp_config.max_trial_number = 1
exp_config._trial_command_params = nas_experiment_trial_params(pytestconfig.rootpath)
exp.run(exp_config)
ensure_success(exp)
assert isinstance(exp.export_top_models()[0], dict)
exp.stop()
def _test_experiment_in_separate_process(rootpath):
try:
base_model, evaluator = _mnist_net('simple', {'max_epochs': 1})
search_strategy = strategy.Random()
exp = RetiariiExperiment(base_model, evaluator, strategy=search_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'mnist_unittest'
exp_config.trial_concurrency = 1
exp_config.max_trial_number = 1
exp_config._trial_command_params = nas_experiment_trial_params(rootpath)
exp.run(exp_config)
ensure_success(exp)
assert isinstance(exp.export_top_models()[0], dict)
finally:
# https://stackoverflow.com/questions/34506638/how-to-register-atexit-function-in-pythons-multiprocessing-subprocess
import atexit
atexit._run_exitfuncs()
def test_multi_trial(model, pytestconfig):
evaluator_kwargs = {
'max_epochs': 1
}
base_model, evaluator = _mnist_net(model, evaluator_kwargs)
search_strategy = strategy.Random()
exp = RetiariiExperiment(base_model, evaluator, strategy=search_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'mnist_unittest'
exp_config.trial_concurrency = 1
exp_config.max_trial_number = 1
exp_config._trial_command_params = nas_experiment_trial_params(pytestconfig.rootpath)
exp.run(exp_config)
ensure_success(exp)
assert isinstance(exp.export_top_models()[0], dict)
exp.stop()
def _test_strategy(strategy_, support_value_choice=True, multi_gpu=False):
evaluator_kwargs = {'max_epochs': 1}
if multi_gpu:
evaluator_kwargs.update(strategy='ddp',
accelerator='gpu',
devices=torch.cuda.device_count())
to_test = [
# (model, evaluator), support_or_net
(_mnist_net('simple', evaluator_kwargs), True),
(_mnist_net('simple_value_choice',
evaluator_kwargs), support_value_choice),
(_mnist_net('value_choice', evaluator_kwargs), support_value_choice),
(_mnist_net('repeat', evaluator_kwargs),
support_value_choice), # no strategy supports repeat currently
(_mnist_net('custom_op',
evaluator_kwargs), False), # this is definitely a NO
(_multihead_attention_net(evaluator_kwargs), support_value_choice),
]
for (base_model, evaluator), support_or_not in to_test:
if isinstance(strategy_, BaseStrategy):
strategy = strategy_
else:
strategy = strategy_(base_model, evaluator)
print('Testing:',
type(strategy).__name__,
type(base_model).__name__,
type(evaluator).__name__, support_or_not)
experiment = RetiariiExperiment(base_model,
evaluator,
strategy=strategy)
config = RetiariiExeConfig()
config.execution_engine = 'oneshot'
if support_or_not:
experiment.run(config)
assert isinstance(experiment.export_top_models()[0], dict)
else:
with pytest.raises(TypeError, match='not supported'):
experiment.run(config)
best_val_ppl
) # reports best validation ppl to nni as final result of one trial
if __name__ == "__main__":
train_iter = WikiText2(split='train')
tokenizer = get_tokenizer('basic_english')
vocab = build_vocab_from_iterator(map(tokenizer, train_iter),
specials=['<unk>'])
vocab.set_default_index(vocab['<unk>'])
n_token = len(vocab)
base_model = Transformer(n_token)
evaluator = FunctionalEvaluator(fit)
exp = RetiariiExperiment(base_model, evaluator, [], strategy.Random())
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'transformer tuning'
exp_config.trial_concurrency = 3 # please change configurations accordingly
exp_config.max_trial_number = 25
exp_config.trial_gpu_number = 1
exp_config.training_service.use_active_gpu = False
export_formatter = 'dict'
exp.run(exp_config, 8081)
print('Final model:')
for model_code in exp.export_top_models(optimize_mode='minimize',
formatter=export_formatter):
print(model_code)
transform=transform)
trainer = pl.Classification(train_dataloader=pl.DataLoader(train_dataset,
batch_size=100),
val_dataloaders=pl.DataLoader(test_dataset,
batch_size=100),
max_epochs=2,
gpus=1,
limit_train_batches=0.1,
limit_val_batches=0.1)
simple_strategy = strategy.Random()
exp = RetiariiExperiment(base_model, trainer, [], simple_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'mnist_search'
exp_config.trial_concurrency = 2
exp_config.max_trial_number = args.budget
exp_config.trial_gpu_number = 1
exp_config.training_service.use_active_gpu = True # Integration test GPU has a Xorg running
export_formatter = 'dict'
if args.exec == 'graph':
exp_config.execution_engine = 'base'
export_formatter = 'code'
exp.run(exp_config, args.port)
print('Final model:')
for model_code in exp.export_top_models(formatter=export_formatter):
print(model_code)
model = model_cls()
# dump the model into an onnx
if 'NNI_OUTPUT_DIR' in os.environ:
dummy_input = torch.zeros(1, 3, 32, 32)
torch.onnx.export(model, (dummy_input, ),
Path(os.environ['NNI_OUTPUT_DIR']) / 'model.onnx')
evaluate_model(model_cls)
# %%
# Relaunch the experiment, and a button is shown on Web portal.
#
# .. image:: ../../img/netron_entrance_webui.png
#
# Export Top Models
# -----------------
#
# Users can export top models after the exploration is done using ``export_top_models``.
for model_dict in exp.export_top_models(formatter='dict'):
print(model_dict)
# %%
# The output is ``json`` object which records the mutation actions of the top model.
# If users want to output source code of the top model,
# they can use :ref:`graph-based execution engine <graph-based-execution-engine>` for the experiment,
# by simply adding the following two lines.
exp_config.execution_engine = 'base'
export_formatter = 'code'
transform=transform)
test_dataset = serialize(MNIST,
root='data/mnist',
train=False,
download=True,
transform=transform)
trainer = pl.Classification(train_dataloader=pl.DataLoader(train_dataset,
batch_size=100),
val_dataloaders=pl.DataLoader(test_dataset,
batch_size=100),
max_epochs=2)
# uncomment the following two lines to debug a generated model
#debug_mutated_model(base_model, trainer, [])
#exit(0)
simple_strategy = strategy.Random()
exp = RetiariiExperiment(base_model, trainer, [], simple_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.experiment_name = 'mnist_search'
exp_config.trial_concurrency = 2
exp_config.max_trial_number = 2
exp_config.training_service.use_active_gpu = False
exp.run(exp_config, 8081 + random.randint(0, 100))
print('Final model:')
for model_code in exp.export_top_models():
print(model_code)
def _main():
parser = argparse.ArgumentParser("SPOS Evolutional Search")
parser.add_argument("--port", type=int, default=8084)
parser.add_argument("--imagenet-dir", type=str, default="./data/imagenet")
parser.add_argument("--checkpoint",
type=str,
default="./data/checkpoint-150000.pth.tar")
parser.add_argument(
"--spos-preprocessing",
action="store_true",
default=False,
help="When true, image values will range from 0 to 255 and use BGR "
"(as in original repo).")
parser.add_argument("--seed", type=int, default=42)
parser.add_argument("--workers", type=int, default=6)
parser.add_argument("--train-batch-size", type=int, default=128)
parser.add_argument("--train-iters", type=int, default=200)
parser.add_argument("--test-batch-size", type=int, default=512)
parser.add_argument("--log-frequency", type=int, default=10)
parser.add_argument("--label-smoothing", type=float, default=0.1)
parser.add_argument("--evolution-sample-size", type=int, default=10)
parser.add_argument("--evolution-population-size", type=int, default=50)
parser.add_argument("--evolution-cycles", type=int, default=10)
parser.add_argument(
"--latency-filter",
type=str,
default=None,
help="Apply latency filter by calling the name of the applied hardware."
)
parser.add_argument("--latency-threshold", type=float, default=100)
args = parser.parse_args()
# use a fixed set of image will improve the performance
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
np.random.seed(args.seed)
random.seed(args.seed)
torch.backends.cudnn.deterministic = True
assert torch.cuda.is_available()
base_model = ShuffleNetV2OneShot()
criterion = CrossEntropyLabelSmooth(1000, args.label_smoothing)
if args.latency_filter:
latency_filter = LatencyFilter(threshold=args.latency_threshold,
predictor=args.latency_filter)
else:
latency_filter = None
evaluator = FunctionalEvaluator(evaluate_acc,
criterion=criterion,
args=args)
evolution_strategy = strategy.RegularizedEvolution(
model_filter=latency_filter,
sample_size=args.evolution_sample_size,
population_size=args.evolution_population_size,
cycles=args.evolution_cycles)
exp = RetiariiExperiment(base_model,
evaluator,
strategy=evolution_strategy)
exp_config = RetiariiExeConfig('local')
exp_config.trial_concurrency = 2
exp_config.trial_gpu_number = 1
exp_config.max_trial_number = args.evolution_cycles
exp_config.training_service.use_active_gpu = False
exp_config.execution_engine = 'base'
exp_config.dummy_input = [1, 3, 224, 224]
exp.run(exp_config, args.port)
print('Exported models:')
for i, model in enumerate(exp.export_top_models(formatter='dict')):
print(model)
with open(f'architecture_final_{i}.json', 'w') as f:
json.dump(get_archchoice_by_model(model), f, indent=4)