def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument('config', metavar='FILE', help='config file')
parser.add_argument('--run-dir', metavar='DIR', help='run directory')
parser.add_argument('--pdb', action='store_true', help='pdb')
parser.add_argument('--gpu', type=str, help='gpu ids', default=None)
args, opts = parser.parse_known_args()
configs.load(args.config, recursive=True)
configs.update(opts)
if configs.debug.pdb or args.pdb:
pdb.set_trace()
if args.gpu is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if configs.debug.set_seed:
torch.manual_seed(configs.debug.seed)
np.random.seed(configs.debug.seed)
if configs.run.device == 'gpu':
device = torch.device('cuda')
elif configs.run.device == 'cpu':
device = torch.device('cpu')
else:
raise ValueError(configs.run.device)
logger.info(' '.join([sys.executable] + sys.argv))
logger.info(f'Profiling started: "{args.run_dir}".' + '\n' + f'{configs}')
inputs = torch.tensor(torch.rand(configs.run.bsz, 1, 28, 28),
device=device)
model = builder.make_model()
model.to(device)
with profiler.profile(record_shapes=True) as prof:
with profiler.record_function("model_inference"):
for _ in range(3):
time.sleep(0.5)
model(inputs)
# for _ in range(3):
# model(inputs)
prof.export_chrome_trace("part1_static.json")
def main() -> None:
torch.backends.cudnn.benchmark = True
parser = argparse.ArgumentParser()
parser.add_argument('config', metavar='FILE', help='config file')
parser.add_argument('--ckpt-dir', metavar='DIR', help='run directory')
parser.add_argument('--pdb', action='store_true', help='pdb')
parser.add_argument('--gpu', type=str, help='gpu ids', default=None)
parser.add_argument('--print-configs',
action='store_true',
help='print ALL configs')
args, opts = parser.parse_known_args()
configs.load(args.config, recursive=True)
configs.update(opts)
if configs.debug.pdb or args.pdb:
pdb.set_trace()
if configs.debug.set_seed:
torch.manual_seed(configs.debug.seed)
np.random.seed(configs.debug.seed)
if args.print_configs:
print_conf = configs
else:
print_conf = get_cared_configs(configs, 'train')
logger.info(f"Generate subnet started: \n {print_conf}")
model = builder.make_model()
sampler = ArchSampler(
model,
strategy=configs.model.sampler.strategy,
n_layers_per_block=configs.model.arch.n_layers_per_block)
arch_all = []
for _ in range(100):
arch = sampler.get_random_sample_arch()
arch_all.append(arch)
arch_all.sort(key=lambda x: x[-1])
for arch in arch_all:
logger.info(f"blk {arch[-1]} arch: {arch}")
def main() -> None:
dist.init()
torch.backends.cudnn.benchmark = True
torch.cuda.set_device(dist.local_rank())
parser = argparse.ArgumentParser()
parser.add_argument('config', metavar='FILE', help='config file')
parser.add_argument('--run-dir', metavar='DIR', help='run directory')
args, opts = parser.parse_known_args()
configs.load(args.config, recursive=True)
configs.update(opts)
if args.run_dir is None:
args.run_dir = auto_set_run_dir()
else:
set_run_dir(args.run_dir)
logger.info(' '.join([sys.executable] + sys.argv))
logger.info(f'Experiment started: "{args.run_dir}".' + '\n' + f'{configs}')
dataset = builder.make_dataset()
dataflow = {}
for split in dataset:
sampler = torch.utils.data.DistributedSampler(
dataset[split],
num_replicas=dist.size(),
rank=dist.rank(),
shuffle=(split == 'train'),
)
dataflow[split] = torch.utils.data.DataLoader(
dataset[split],
batch_size=configs.batch_size // dist.size(),
sampler=sampler,
num_workers=configs.workers_per_gpu,
pin_memory=True,
)
model = builder.make_model()
model = torch.nn.parallel.DistributedDataParallel(
model.cuda(),
device_ids=[dist.local_rank()],
)
criterion = builder.make_criterion()
optimizer = builder.make_optimizer(model)
scheduler = builder.make_scheduler(optimizer)
trainer = ClassificationTrainer(
model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
amp_enabled=configs.amp.enabled,
)
trainer.train_with_defaults(
dataflow['train'],
num_epochs=configs.num_epochs,
callbacks=[
SaverRestore(),
InferenceRunner(
dataflow['test'],
callbacks=[
TopKCategoricalAccuracy(k=1, name='acc/top1'),
TopKCategoricalAccuracy(k=5, name='acc/top5'),
],
),
MaxSaver('acc/top1'),
Saver(),
],
)
def main() -> None:
# dist.init()
torch.backends.cudnn.benchmark = True
# torch.cuda.set_device(dist.local_rank())
parser = argparse.ArgumentParser()
parser.add_argument('config', metavar='FILE', help='config file')
parser.add_argument('--run-dir', metavar='DIR', help='run directory')
parser.add_argument('--pdb', action='store_true', help='pdb')
parser.add_argument('--gpu', type=str, help='gpu ids', default=None)
args, opts = parser.parse_known_args()
configs.load(args.config, recursive=True)
configs.update(opts)
if configs.debug.pdb or args.pdb:
pdb.set_trace()
if args.gpu is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if configs.debug.set_seed:
torch.manual_seed(configs.debug.seed)
np.random.seed(configs.debug.seed)
if configs.run.device == 'gpu':
device = torch.device('cuda')
elif configs.run.device == 'cpu':
device = torch.device('cpu')
else:
raise ValueError(configs.run.device)
if isinstance(configs.optimizer.lr, str):
configs.optimizer.lr = eval(configs.optimizer.lr)
if args.run_dir is None:
args.run_dir = auto_set_run_dir()
else:
set_run_dir(args.run_dir)
logger.info(' '.join([sys.executable] + sys.argv))
logger.info(f'Experiment started: "{args.run_dir}".' + '\n' + f'{configs}')
dataset = builder.make_dataset()
dataflow = dict()
for split in dataset:
sampler = torch.utils.data.RandomSampler(dataset[split])
dataflow[split] = torch.utils.data.DataLoader(
dataset[split],
batch_size=configs.run.bsz,
sampler=sampler,
num_workers=configs.run.workers_per_gpu,
pin_memory=True)
model = builder.make_model()
model.to(device)
# model = torch.nn.parallel.DistributedDataParallel(
# model.cuda(),
# device_ids=[dist.local_rank()],
# find_unused_parameters=True)
total_params = sum(p.numel() for p in model.parameters())
logger.info(f'Model Size: {total_params}')
# logger.info(f'Model MACs: {profile_macs(model, inputs)}')
criterion = builder.make_criterion()
optimizer = builder.make_optimizer(model)
scheduler = builder.make_scheduler(optimizer)
trainer = LayerRegressionTrainer(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler)
trainer.train_with_defaults(
dataflow['train'],
num_epochs=configs.run.n_epochs,
callbacks=[
# SaverRestore(),
InferenceRunner(dataflow=dataflow['valid'], callbacks=[]),
MaxSaver('loss/valid'),
# Saver(),
])
def main() -> None:
# dist.init()
torch.backends.cudnn.benchmark = True
# torch.cuda.set_device(dist.local_rank())
parser = argparse.ArgumentParser()
parser.add_argument('config', metavar='FILE', help='config file')
parser.add_argument('--ckpt-dir', metavar='DIR', help='run directory')
parser.add_argument('--pdb', action='store_true', help='pdb')
parser.add_argument('--gpu', type=str, help='gpu ids', default=None)
parser.add_argument('--print-configs',
action='store_true',
help='print ALL configs')
args, opts = parser.parse_known_args()
configs.load(args.config, recursive=True)
configs.update(opts)
if configs.debug.pdb or args.pdb:
pdb.set_trace()
if args.gpu is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if configs.debug.set_seed:
torch.manual_seed(configs.debug.seed)
np.random.seed(configs.debug.seed)
if configs.run.device == 'gpu':
device = torch.device('cuda')
elif configs.run.device == 'cpu':
device = torch.device('cpu')
else:
raise ValueError(configs.run.device)
if isinstance(configs.optimizer.lr, str):
configs.optimizer.lr = eval(configs.optimizer.lr)
# set the run dir according to config file's name
args.run_dir = 'runs/' + args.config.replace('/', '.').replace(
'examples.', '').replace('.yml', '').replace('configs.', '')
set_run_dir(args.run_dir)
logger.info(' '.join([sys.executable] + sys.argv))
if args.print_configs:
print_conf = configs
else:
print_conf = get_cared_configs(configs, 'train')
logger.info(f'Training started: "{args.run_dir}".' + '\n' +
f'{print_conf}')
dataset = builder.make_dataset()
dataflow = dict()
# for split in dataset:
# sampler = torch.utils.data.distributed.DistributedSampler(
# dataset[split],
# num_replicas=dist.size(),
# rank=dist.rank(),
# shuffle=(split == 'train'))
# dataflow[split] = torch.utils.data.DataLoader(
# dataset[split],
# batch_size=configs.run.bsz // dist.size(),
# sampler=sampler,
# num_workers=configs.run.workers_per_gpu,
# pin_memory=True)
for split in dataset:
if split == 'train':
sampler = torch.utils.data.RandomSampler(dataset[split])
batch_size = configs.run.bsz
else:
# for valid and test, use SequentialSampler to make the train.py
# and eval.py results consistent
sampler = torch.utils.data.SequentialSampler(dataset[split])
batch_size = getattr(configs.run, 'eval_bsz', configs.run.bsz)
dataflow[split] = torch.utils.data.DataLoader(
dataset[split],
batch_size=batch_size,
sampler=sampler,
num_workers=configs.run.workers_per_gpu,
pin_memory=True)
model = builder.make_model()
state_dict = {}
solution = None
score = None
if configs.ckpt.load_ckpt:
logger.warning('Loading checkpoint!')
state_dict = io.load(os.path.join(args.ckpt_dir, configs.ckpt.name),
map_location='cpu')
if getattr(state_dict, 'model_arch', None) is not None:
model_load = state_dict['model_arch']
for module_load, module in zip(model_load.modules(),
model.modules()):
if isinstance(module, tq.RandomLayer):
# random layer, need to restore the architecture
module.rebuild_random_layer_from_op_list(
n_ops_in=module_load.n_ops,
wires_in=module_load.wires,
op_list_in=module_load.op_list,
)
if not configs.ckpt.weight_from_scratch:
model.load_state_dict(state_dict['model'], strict=False)
else:
logger.warning(f"DO NOT load weight, train weights from scratch!")
if 'solution' in state_dict.keys():
solution = state_dict['solution']
logger.info(f"Loading the solution {solution}")
logger.info(f"Original score: {state_dict['score']}")
model.set_sample_arch(solution['arch'])
score = state_dict['score']
if 'v_c_reg_mapping' in state_dict.keys():
try:
model.measure.set_v_c_reg_mapping(
state_dict['v_c_reg_mapping'])
except AttributeError:
logger.warning(f"Cannot set v_c_reg_mapping.")
if configs.model.load_op_list:
assert state_dict['q_layer_op_list'] is not None
logger.warning(f"Loading the op_list, will replace the q_layer in "
f"the original model!")
q_layer = build_module_from_op_list(state_dict['q_layer_op_list'])
model.q_layer = q_layer
if configs.model.transpile_before_run:
# transpile the q_layer
logger.warning(f"Transpile the q_layer to basis gate set before "
f"training, will replace the q_layer!")
processor = builder.make_qiskit_processor()
if getattr(model, 'q_layer', None) is not None:
circ = tq2qiskit(model.q_device, model.q_layer)
"""
add measure because the transpile process may permute the wires,
so we need to get the final q reg to c reg mapping
"""
circ.measure(list(range(model.q_device.n_wires)),
list(range(model.q_device.n_wires)))
logger.info("Transpiling circuit...")
if solution is not None:
processor.set_layout(solution['layout'])
logger.warning(
f"Set layout {solution['layout']} for transpile!")
circ_transpiled = processor.transpile(circs=circ)
q_layer = qiskit2tq(circ=circ_transpiled)
model.measure.set_v_c_reg_mapping(
get_v_c_reg_mapping(circ_transpiled))
model.q_layer = q_layer
if configs.trainer.add_noise:
# noise-aware training
noise_model_tq = builder.make_noise_model_tq()
noise_model_tq.is_add_noise = True
noise_model_tq.v_c_reg_mapping = get_v_c_reg_mapping(
circ_transpiled)
noise_model_tq.p_c_reg_mapping = get_p_c_reg_mapping(
circ_transpiled)
noise_model_tq.p_v_reg_mapping = get_p_v_reg_mapping(
circ_transpiled)
model.set_noise_model_tq(noise_model_tq)
elif getattr(model, 'nodes', None) is not None:
# every node has a noise model because it is possible that
# different nodes run on different QC
for node in model.nodes:
circ = tq2qiskit(node.q_device, node.q_layer)
circ.measure(list(range(node.q_device.n_wires)),
list(range(node.q_device.n_wires)))
circ_transpiled = processor.transpile(circs=circ)
q_layer = qiskit2tq(circ=circ_transpiled)
node.measure.set_v_c_reg_mapping(
get_v_c_reg_mapping(circ_transpiled))
node.q_layer = q_layer
if configs.trainer.add_noise:
# noise-aware training
noise_model_tq = builder.make_noise_model_tq()
noise_model_tq.is_add_noise = True
noise_model_tq.v_c_reg_mapping = get_v_c_reg_mapping(
circ_transpiled)
noise_model_tq.p_c_reg_mapping = get_p_c_reg_mapping(
circ_transpiled)
noise_model_tq.p_v_reg_mapping = get_p_v_reg_mapping(
circ_transpiled)
node.set_noise_model_tq(noise_model_tq)
if getattr(configs.model.arch, 'sample_arch', None) is not None and \
not configs.model.transpile_before_run:
sample_arch = configs.model.arch.sample_arch
logger.warning(f"Setting sample arch {sample_arch} from config file!")
if isinstance(sample_arch, str):
# this is the name of arch
sample_arch = get_named_sample_arch(model.arch_space, sample_arch)
logger.warning(f"Decoded sample arch: {sample_arch}")
model.set_sample_arch(sample_arch)
if configs.trainer.name == 'pruning_trainer':
"""
in pruning, convert the super layers to module list, otherwise the
pruning ratio is difficulty to set
"""
logger.warning(f"Convert sampled layer to module list layer!")
model.q_layer = build_module_from_op_list(
build_module_op_list(model.q_layer))
model.to(device)
# model = torch.nn.parallel.DistributedDataParallel(
# model.cuda(),
# device_ids=[dist.local_rank()],
# find_unused_parameters=True)
if getattr(model, 'sample_arch', None) is not None and \
not configs.model.transpile_before_run and \
not configs.trainer.name == 'pruning_trainer':
n_params = model.count_sample_params()
logger.info(f"Number of sampled params: {n_params}")
total_params = sum(p.numel() for p in model.parameters())
logger.info(f'Model Size: {total_params}')
# logger.info(f'Model MACs: {profile_macs(model, inputs)}')
criterion = builder.make_criterion()
optimizer = builder.make_optimizer(model)
scheduler = builder.make_scheduler(optimizer)
trainer = builder.make_trainer(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler)
trainer.solution = solution
trainer.score = score
# trainer state_dict will be loaded in a callback
callbacks = builder.make_callbacks(dataflow, state_dict)
trainer.train_with_defaults(dataflow['train'],
num_epochs=configs.run.n_epochs,
callbacks=callbacks)
def main() -> None:
dist.init()
torch.backends.cudnn.benchmark = True
torch.cuda.set_device(dist.local_rank())
parser = argparse.ArgumentParser()
parser.add_argument('config', metavar='FILE', help='config file')
parser.add_argument('--run-dir', metavar='DIR', help='run directory')
parser.add_argument('--name', type=str, help='model name')
args, opts = parser.parse_known_args()
configs.load(args.config, recursive=True)
configs.update(opts)
if args.run_dir is None:
args.run_dir = auto_set_run_dir()
else:
set_run_dir(args.run_dir)
logger.info(' '.join([sys.executable] + sys.argv))
logger.info(f'Experiment started: "{args.run_dir}".' + '\n' + f'{configs}')
dataset = builder.make_dataset()
dataflow = dict()
for split in dataset:
sampler = torch.utils.data.distributed.DistributedSampler(
dataset[split],
num_replicas=dist.size(),
rank=dist.rank(),
shuffle=(split == 'train'))
dataflow[split] = torch.utils.data.DataLoader(
dataset[split],
batch_size=configs.batch_size if split == 'train' else 1,
sampler=sampler,
num_workers=configs.workers_per_gpu,
pin_memory=True,
collate_fn=dataset[split].collate_fn)
if 'spvnas' in args.name.lower():
model = spvnas_specialized(args.name)
elif 'spvcnn' in args.name.lower():
model = spvcnn(args.name)
elif 'mink' in args.name.lower():
model = minkunet(args.name)
else:
raise NotImplementedError
#model = builder.make_model()
model = torch.nn.parallel.DistributedDataParallel(
model.cuda(),
device_ids=[dist.local_rank()],
find_unused_parameters=True)
model.eval()
criterion = builder.make_criterion()
optimizer = builder.make_optimizer(model)
scheduler = builder.make_scheduler(optimizer)
meter = MeanIoU(configs.data.num_classes,
configs.data.ignore_label)
trainer = SemanticKITTITrainer(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
num_workers=configs.workers_per_gpu,
seed=configs.train.seed
)
callbacks=Callbacks([
SaverRestore(),
MeanIoU(
configs.data.num_classes,
configs.data.ignore_label
)
])
callbacks._set_trainer(trainer)
trainer.callbacks = callbacks
trainer.dataflow = dataflow['test']
trainer.before_train()
trainer.before_epoch()
# important
model.eval()
for feed_dict in tqdm(dataflow['test'], desc='eval'):
_inputs = dict()
for key, value in feed_dict.items():
if not 'name' in key:
_inputs[key] = value.cuda()
inputs = _inputs['lidar']
targets = feed_dict['targets'].F.long().cuda(non_blocking=True)
outputs = model(inputs)
invs = feed_dict['inverse_map']
all_labels = feed_dict['targets_mapped']
_outputs = []
_targets = []
for idx in range(invs.C[:, -1].max()+1):
cur_scene_pts = (inputs.C[:, -1] == idx).cpu().numpy()
cur_inv = invs.F[invs.C[:, -1] == idx].cpu().numpy()
cur_label = (all_labels.C[:, -1] == idx).cpu().numpy()
outputs_mapped = outputs[cur_scene_pts][
cur_inv].argmax(1)
targets_mapped = all_labels.F[cur_label]
_outputs.append(outputs_mapped)
_targets.append(targets_mapped)
outputs = torch.cat(_outputs, 0)
targets = torch.cat(_targets, 0)
output_dict = {
'outputs': outputs,
'targets': targets
}
trainer.after_step(output_dict)
trainer.after_epoch()
def main() -> None:
torch.backends.cudnn.benchmark = True
parser = argparse.ArgumentParser()
parser.add_argument('config', metavar='FILE', help='config file')
parser.add_argument('--run-dir', metavar='DIR', help='run directory')
parser.add_argument('--pdb', action='store_true', help='pdb')
parser.add_argument('--gpu', type=str, help='gpu ids', default=None)
parser.add_argument('--jobs',
type=int,
default=None,
help='max parallel job on qiskit')
parser.add_argument('--print-configs',
action='store_true',
help='print ALL configs')
args, opts = parser.parse_known_args()
configs.load(os.path.join(args.run_dir, 'metainfo', 'configs.yaml'))
configs.load(args.config, recursive=True)
configs.update(opts)
if configs.debug.pdb or args.pdb:
pdb.set_trace()
if args.gpu is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.jobs is not None:
configs.qiskit.max_jobs = args.jobs
# if use qiskit, then not need to estimate success rate
assert not (configs.es.est_success_rate and configs.qiskit.use_qiskit)
if configs.run.device == 'gpu':
device = torch.device('cuda')
elif configs.run.device == 'cpu':
device = torch.device('cpu')
else:
raise ValueError(configs.run.device)
if args.print_configs:
print_conf = configs
else:
print_conf = get_cared_configs(configs, 'es')
logger.info(f'Evolutionary Search started: "{args.run_dir}".' + '\n' +
f'{print_conf}')
if configs.qiskit.use_qiskit:
IBMQ.load_account()
if configs.run.bsz == 'qiskit_max':
provider = get_provider(configs.qiskit.backend_name)
configs.run.bsz = provider.get_backend(
configs.qiskit.backend_name).configuration().max_experiments
dataset = builder.make_dataset()
sampler = torch.utils.data.SequentialSampler(
dataset[configs.dataset.split])
dataflow = torch.utils.data.DataLoader(
dataset[configs.dataset.split],
sampler=sampler,
batch_size=configs.run.bsz,
num_workers=configs.run.workers_per_gpu,
pin_memory=True)
state_dict = io.load(os.path.join(args.run_dir, configs.ckpt.name),
map_location=device)
model_load = state_dict['model_arch']
model = builder.make_model()
for module_load, module in zip(model_load.modules(), model.modules()):
if isinstance(module, tq.RandomLayer):
# random layer, need to restore the architecture
module.rebuild_random_layer_from_op_list(
n_ops_in=module_load.n_ops,
wires_in=module_load.wires,
op_list_in=module_load.op_list,
)
model.load_state_dict(state_dict['model'], strict=False)
if configs.legalization.legalize:
legalize_unitary(model)
model.to(device)
model.eval()
es_dir = 'es_runs/' + args.config.replace('/', '.').replace(
'examples.', '').replace('.yml', '').replace('configs.', '')
io.save(os.path.join(es_dir, 'metainfo/configs.yaml'), configs.dict())
writer = SummaryWriter(os.path.normpath(os.path.join(es_dir, 'tb')))
if configs.qiskit.use_qiskit or configs.es.est_success_rate:
IBMQ.load_account()
provider = get_provider(configs.qiskit.backend_name)
properties = provider.get_backend(
configs.qiskit.backend_name).properties()
if configs.qiskit.backend_name == 'ibmq_qasm_simulator':
if configs.qiskit.noise_model_name == 'ibmq_16_melbourne':
n_available_wires = 15
else:
n_available_wires = len(properties.qubits)
qiskit_processor = builder.make_qiskit_processor()
model.set_qiskit_processor(qiskit_processor)
else:
n_available_wires = model.q_device.n_wires
total_params = sum(p.numel() for p in model.parameters())
logger.info(f'Model Size: {total_params}')
es_engine = EvolutionEngine(
population_size=configs.es.population_size,
parent_size=configs.es.parent_size,
mutation_size=configs.es.mutation_size,
mutation_prob=configs.es.mutation_prob,
crossover_size=configs.es.crossover_size,
n_wires=model.q_device.n_wires,
n_available_wires=n_available_wires,
arch_space=model.arch_space,
gene_mask=configs.es.gene_mask,
random_search=configs.es.random_search,
)
evaluator = Evaluator()
logger.info(f"Start Evolution Search")
for k in range(configs.es.n_iterations):
logger.info(f"ES iteration {k}:")
solutions = es_engine.ask()
scores, best_solution_accuracy, best_solution_loss, \
best_solution_success_rate, best_solution_score \
= evaluator.evaluate_all(model, dataflow, solutions, writer, k,
configs.es.population_size)
es_engine.tell(scores)
logger.info(f"Best solution: {es_engine.best_solution}")
logger.info(f"Best score: {es_engine.best_score}")
assert best_solution_score == es_engine.best_score
writer.add_text('es/best_solution_arch', str(es_engine.best_solution),
k)
writer.add_scalar('es/best_solution_accuracy', best_solution_accuracy,
k)
writer.add_scalar('es/best_solution_loss', best_solution_loss, k)
writer.add_scalar('es/best_solution_success_rate',
best_solution_success_rate, k)
writer.add_scalar('es/best_solution_score', es_engine.best_score, k)
# store the model and solution after every iteration
state_dict = dict()
if not configs.qiskit.use_real_qc:
state_dict['model_arch'] = model
state_dict['model'] = model.state_dict()
state_dict['solution'] = es_engine.best_solution
state_dict['score'] = es_engine.best_score
if not configs.dataset.name == 'vqe':
state_dict['encoder_func_list'] = model.encoder.func_list
state_dict['q_layer_op_list'] = build_module_op_list(model.q_layer)
io.save(os.path.join(es_dir, 'checkpoints/best_solution.pt'),
state_dict)
logger.info(f"\n Best solution evaluation on tq:")
# eval the best solution
evaluator.evaluate_all(model, dataflow, [es_engine.best_solution])
def main() -> None:
torch.backends.cudnn.benchmark = True
parser = argparse.ArgumentParser()
parser.add_argument('config', metavar='FILE', help='config file')
parser.add_argument('--run-dir', metavar='DIR', help='run directory')
parser.add_argument('--pdb', action='store_true', help='pdb')
parser.add_argument('--verbose', action='store_true', help='verbose')
parser.add_argument('--gpu', type=str, help='gpu ids', default=None)
parser.add_argument('--print-configs',
action='store_true',
help='print ALL configs')
parser.add_argument('--jobs',
type=int,
default=None,
help='max parallel job on qiskit')
parser.add_argument('--hub', type=str, default=None, help='IBMQ provider')
args, opts = parser.parse_known_args()
configs.load(os.path.join(args.run_dir, 'metainfo', 'configs.yaml'))
configs.load(args.config, recursive=True)
configs.update(opts)
# for eval, always need load weights
configs.ckpt.weight_from_scratch = False
if configs.debug.pdb or args.pdb:
pdb.set_trace()
configs.verbose = args.verbose
configs.qiskit.hub = args.hub
if args.gpu is not None:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.jobs is not None:
configs.qiskit.max_jobs = args.jobs
if configs.run.device == 'gpu':
device = torch.device('cuda')
elif configs.run.device == 'cpu':
device = torch.device('cpu')
else:
raise ValueError(configs.run.device)
if args.print_configs:
print_conf = configs
else:
print_conf = get_cared_configs(configs, 'eval')
logger.info(f'Evaluation started: "{args.run_dir}".' + '\n' +
f'{print_conf}')
eval_config_dir = args.config.replace('/', '.').replace(
'examples.', '').replace('.yml', '').replace('configs.', '')
configs.eval_config_dir = eval_config_dir
configs.run_dir = args.run_dir
# if configs.qiskit.use_qiskit:
# IBMQ.load_account()
# if configs.run.bsz == 'qiskit_max':
# configs.run.bsz = IBMQ.get_provider(hub='ibm-q').get_backend(
# configs.qiskit.backend_name).configuration().max_experiments
dataset = builder.make_dataset()
sampler = torch.utils.data.SequentialSampler(
dataset[configs.dataset.split])
dataflow = torch.utils.data.DataLoader(
dataset[configs.dataset.split],
sampler=sampler,
batch_size=configs.run.bsz,
num_workers=configs.run.workers_per_gpu,
pin_memory=True)
state_dict = io.load(os.path.join(args.run_dir, configs.ckpt.name),
map_location='cpu')
model_load = state_dict['model_arch']
model = builder.make_model()
for module_load, module in zip(model_load.modules(), model.modules()):
if isinstance(module, tq.RandomLayer):
# random layer, need to restore the architecture
module.rebuild_random_layer_from_op_list(
n_ops_in=module_load.n_ops,
wires_in=module_load.wires,
op_list_in=module_load.op_list,
)
model.load_state_dict(state_dict['model'], strict=False)
solution = None
if 'solution' in state_dict.keys():
solution = state_dict['solution']
logger.info(f"Evaluate the solution {solution}")
logger.info(f"Original score: {state_dict['score']}")
model.set_sample_arch(solution['arch'])
if 'v_c_reg_mapping' in state_dict.keys():
if getattr(model, 'q_layer', None) is not None:
try:
model.measure.set_v_c_reg_mapping(
state_dict['v_c_reg_mapping'])
except AttributeError:
logger.warning(f"Cannot set v_c_reg_mapping.")
elif getattr(model, 'nodes', None) is not None:
for k, node in enumerate(model.nodes):
node.measure.set_v_c_reg_mapping(
state_dict['v_c_reg_mapping'][k])
if state_dict.get('q_layer_op_list', None) is not None and not \
configs.model.load_op_list:
logger.warning(f"the model has op_list but is not loaded!!")
if configs.model.load_op_list:
assert state_dict['q_layer_op_list'] is not None
logger.warning(f"Loading the op_list, will replace the q_layer in "
f"the original model!")
if getattr(model, 'q_layer', None) is not None:
q_layer = build_module_from_op_list(
op_list=state_dict['q_layer_op_list'],
remove_ops=configs.prune.eval.remove_ops,
thres=configs.prune.eval.remove_ops_thres)
model.q_layer = q_layer
elif getattr(model, 'nodes', None) is not None:
for k, node in enumerate(model.nodes):
q_layer = build_module_from_op_list(
op_list=state_dict['q_layer_op_list'][k],
remove_ops=configs.prune.eval.remove_ops,
thres=configs.prune.eval.remove_ops_thres)
node.q_layer = q_layer
if state_dict.get('noise_model_tq', None) is not None:
# the readout error is ALSO applied for eval and test so need load
# noise_model_tq
if getattr(model, 'q_layer', None) is not None:
model.set_noise_model_tq(state_dict['noise_model_tq'])
if getattr(configs, 'add_noise', False):
model.noise_model_tq.mode = 'train'
model.noise_model_tq.noise_total_prob = \
configs.noise_total_prob
else:
model.noise_model_tq.mode = 'test'
elif getattr(model, 'nodes', None) is not None:
for k, node in enumerate(model.nodes):
node.set_noise_model_tq(state_dict['noise_model_tq'][k])
if getattr(configs, 'add_noise', False):
node.noise_model_tq.mode = 'train'
node.noise_model_tq.noise_total_prob = \
configs.noise_total_prob
else:
node.noise_model_tq.mode = 'test'
if configs.model.transpile_before_run:
# transpile the q_layer
logger.warning(f"Transpile the q_layer to basis gate set before "
f"evaluation, will replace the q_layer!")
processor = builder.make_qiskit_processor()
circ = tq2qiskit(model.q_device, model.q_layer)
"""
add measure because the transpile process may permute the wires,
so we need to get the final q reg to c reg mapping
"""
circ.measure(list(range(model.q_device.n_wires)),
list(range(model.q_device.n_wires)))
if solution is not None:
processor.set_layout(solution['layout'])
logger.warning(f"Set layout {solution['layout']} for transpile!")
logger.info("Transpiling circuit...")
circ_transpiled = processor.transpile(circs=circ)
q_layer = qiskit2tq(circ=circ_transpiled)
model.measure.set_v_c_reg_mapping(get_v_c_reg_mapping(circ_transpiled))
model.q_layer = q_layer
if configs.legalization.legalize:
legalize_unitary(model)
if configs.act_quant.add_in_eval:
quantizers = []
assert getattr(model, 'nodes', None) is not None
if getattr(configs.act_quant, 'act_quant_bit', None) is not None:
# settings from config file has higher priority
act_quant_bit = configs.act_quant.act_quant_bit
act_quant_ratio = configs.act_quant.act_quant_ratio
act_quant_level = configs.act_quant.act_quant_level
act_quant_lower_bound = configs.act_quant.act_quant_lower_bound
act_quant_upper_bound = configs.act_quant.act_quant_upper_bound
logger.warning(f"Get act_quant setting from config file!")
elif state_dict.get('act_quant', None) is not None:
act_quant_bit = state_dict['act_quant']['act_quant_bit']
act_quant_ratio = state_dict['act_quant']['act_quant_ratio']
act_quant_level = state_dict['act_quant']['act_quant_level']
act_quant_lower_bound = state_dict['act_quant'][
'act_quant_lower_bound']
act_quant_upper_bound = state_dict['act_quant'][
'act_quant_upper_bound']
logger.warning(f"Get act_quant setting from ckpt file!")
elif getattr(configs.trainer, 'act_quant_bit', None) is not None:
# if the act_quant info is not stored in ckpt, use the info from
# training config file
act_quant_bit = configs.trainer.act_quant_bit
act_quant_ratio = configs.trainer.act_quant_ratio
act_quant_level = configs.trainer.act_quant_level
act_quant_lower_bound = configs.trainer.act_quant_lower_bound
act_quant_upper_bound = configs.trainer.act_quant_upper_bound
logger.warning(f"Get act_quant setting from previous training "
f"config file!")
else:
raise NotImplementedError('No act_quant info specified!')
logger.warning(f"act_quant_bit={act_quant_bit}, "
f"act_quant_ratio={act_quant_ratio}, "
f"act_quant_level={act_quant_level}, "
f"act_quant_lower_bound={act_quant_lower_bound}, "
f"act_quant_upper_bound={act_quant_upper_bound}")
for k, node in enumerate(model.nodes):
if configs.trainer.act_quant_skip_last_node and k == len(
model.nodes) - 1:
continue
quantizer = PACTActivationQuantizer(
module=node,
precision=act_quant_bit,
level=act_quant_level,
alpha=1.0,
backprop_alpha=False,
quant_ratio=act_quant_ratio,
device=device,
lower_bound=act_quant_lower_bound,
upper_bound=act_quant_upper_bound,
)
quantizers.append(quantizer)
for quantizer in quantizers:
quantizer.register_hook()
if getattr(configs, 'pre_specified_mean', None) is not None and \
configs.pre_specified_std \
is not None:
for k, node in enumerate(model.nodes):
node.pre_specified_mean_std = {
'mean': configs.pre_specified_mean[k],
'std': configs.pre_specified_std[k],
}
model.to(device)
model.eval()
if configs.qiskit.use_qiskit:
qiskit_processor = builder.make_qiskit_processor()
if configs.qiskit.initial_layout is not None:
layout = configs.qiskit.initial_layout
logger.warning(f"Use layout {layout} from config file")
elif 'solution' in state_dict.keys():
layout = state_dict['solution']['layout']
logger.warning(f"Use layout {layout} from checkpoint file")
else:
layout = None
logger.warning(f"No specified layout")
qiskit_processor.set_layout(layout)
model.set_qiskit_processor(qiskit_processor)
if getattr(configs.model.arch, 'sample_arch', None) is not None:
sample_arch = configs.model.arch.sample_arch
logger.warning(f"Setting sample arch {sample_arch} from config file!")
if isinstance(sample_arch, str):
# this is the name of arch
sample_arch = get_named_sample_arch(model.arch_space, sample_arch)
logger.warning(f"Decoded sample arch: {sample_arch}")
model.set_sample_arch(sample_arch)
if configs.get_n_params:
n_params = model.count_sample_params()
logger.info(f"Number of sampled params: {n_params}")
exit(0)
if configs.qiskit.est_success_rate:
circ_parameterized, params = tq2qiskit_parameterized(
model.q_device, model.encoder.func_list)
circ_fixed = tq2qiskit(model.q_device, model.q_layer)
circ = circ_parameterized + circ_fixed
transpiled_circ = model.qiskit_processor.transpile(circ)
success_rate = get_success_rate(model.qiskit_processor.properties,
transpiled_circ)
logger.info(f"Success rate: {success_rate}")
logger.info(f"Size: {transpiled_circ.size()}")
logger.info(f"Depth: {transpiled_circ.depth()}")
logger.info(f"Width: {transpiled_circ.width()}")
exit(0)
total_params = sum(p.numel() for p in model.parameters())
logger.info(f'Model Size: {total_params}')
if hasattr(model, 'sample_arch') and not configs.model.load_op_list:
n_params = model.count_sample_params()
logger.info(f"Number of sampled params: {n_params}")
with torch.no_grad():
target_all = None
output_all = None
for feed_dict in tqdm.tqdm(dataflow):
if configs.run.device == 'gpu':
inputs = feed_dict[configs.dataset.input_name].cuda(
non_blocking=True)
targets = feed_dict[configs.dataset.target_name].cuda(
non_blocking=True)
else:
inputs = feed_dict[configs.dataset.input_name]
targets = feed_dict[configs.dataset.target_name]
outputs = model(inputs,
verbose=configs.verbose,
use_qiskit=configs.qiskit.use_qiskit)
if target_all is None:
target_all = targets
output_all = outputs
else:
target_all = torch.cat([target_all, targets], dim=0)
output_all = torch.cat([output_all, outputs], dim=0)
# if configs.verbose:
# logger.info(f"Measured log_softmax: {outputs}")
if not configs.dataset.name == 'vqe':
log_acc(output_all, target_all)
logger.info("Final:")
if not configs.dataset.name == 'vqe':
log_acc(output_all, target_all)
else:
logger.info(f"Eigenvalue: {output_all.detach().cpu().numpy()}")
def main() -> None:
dist.init()
torch.backends.cudnn.benchmark = True
torch.cuda.set_device(dist.local_rank())
parser = argparse.ArgumentParser()
parser.add_argument('config', metavar='FILE', help='config file')
parser.add_argument('--run-dir', metavar='DIR', help='run directory')
args, opts = parser.parse_known_args()
configs.load(args.config, recursive=True)
configs.update(opts)
if args.run_dir is None:
args.run_dir = auto_set_run_dir()
else:
set_run_dir(args.run_dir)
logger.info(' '.join([sys.executable] + sys.argv))
logger.info(f'Experiment started: "{args.run_dir}".' + '\n' + f'{configs}')
# seed
if ('seed' not in configs.train) or (configs.train.seed is None):
configs.train.seed = torch.initial_seed() % (2**32 - 1)
seed = configs.train.seed + dist.rank(
) * configs.workers_per_gpu * configs.num_epochs
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
dataset = builder.make_dataset()
dataflow = dict()
for split in dataset:
sampler = torch.utils.data.distributed.DistributedSampler(
dataset[split],
num_replicas=dist.size(),
rank=dist.rank(),
shuffle=(split == 'train'))
dataflow[split] = torch.utils.data.DataLoader(
dataset[split],
batch_size=configs.batch_size,
sampler=sampler,
num_workers=configs.workers_per_gpu,
pin_memory=True,
collate_fn=dataset[split].collate_fn)
model = builder.make_model()
model = torch.nn.parallel.DistributedDataParallel(
model.cuda(),
device_ids=[dist.local_rank()],
find_unused_parameters=True)
criterion = builder.make_criterion()
optimizer = builder.make_optimizer(model)
scheduler = builder.make_scheduler(optimizer)
trainer = SemanticKITTITrainer(model=model,
criterion=criterion,
optimizer=optimizer,
scheduler=scheduler,
num_workers=configs.workers_per_gpu,
seed=seed)
trainer.train_with_defaults(
dataflow['train'],
num_epochs=configs.num_epochs,
callbacks=[
InferenceRunner(dataflow[split],
callbacks=[
MeanIoU(name=f'iou/{split}',
num_classes=configs.data.num_classes,
ignore_label=configs.data.ignore_label)
]) for split in ['test']
] + [
MaxSaver('iou/test'),
Saver(),
])
