def test_can_restore_binary_mask_on_magnitude_algo_resume():
config = get_empty_config()
config['compression'] = {
"algorithm": "magnitude_sparsity",
"weight_importance": "abs",
"params": {
"schedule": "multistep",
"sparsity_levels": [0.3, 0.5]
}
}
magnitude_algo = create_compression_algorithm(MagnitudeTestModel(), config)
sparse_model = magnitude_algo.model
with torch.no_grad():
sparse_model(torch.ones([1, 1, 10, 10]))
config = get_empty_config()
config["compression"] = {"algorithm": "const_sparsity"}
const_algo = create_compression_algorithm(MagnitudeTestModel(), config)
const_sparse_model = const_algo.model
load_state(const_sparse_model, sparse_model.state_dict())
op = const_sparse_model.conv1.pre_ops['0']
check_equal(ref_mask_1, op.operand.binary_mask)
op = const_sparse_model.conv2.pre_ops['0']
check_equal(ref_mask_2, op.operand.binary_mask)
def test_can_restore_binary_mask_on_magnitude_quant_algo_resume():
config = get_empty_config()
config["compression"] = [
{"algorithm": "magnitude_sparsity", "weight_importance": "abs",
"params": {"schedule": "multistep", "sparsity_levels": [0.3, 0.5]}},
{"algorithm": "quantization"}]
reset_context('orig')
reset_context('quantized_graphs')
magnitude_quant_algo = create_compression_algorithm(MagnitudeTestModel(), config)
# load_state doesn't support CPU + Quantization
sparse_model = torch.nn.DataParallel(magnitude_quant_algo.model)
sparse_model.cuda()
with torch.no_grad():
sparse_model(torch.ones([1, 1, 10, 10]))
reset_context('orig')
reset_context('quantized_graphs')
config = get_empty_config()
config["compression"] = [{"algorithm": "const_sparsity"}, {"algorithm": "quantization"}]
const_algo = create_compression_algorithm(MagnitudeTestModel(), config)
const_sparse_model = const_algo.model
load_state(const_sparse_model, sparse_model.state_dict())
op = const_sparse_model.module.conv1.pre_ops['0']
check_equal(ref_mask_1, op.operand.binary_mask)
op = const_sparse_model.module.conv2.pre_ops['0']
check_equal(ref_mask_2, op.operand.binary_mask)
def test_can_not_create_magnitude_algo__without_steps():
model = MagnitudeTestModel()
config = get_basic_magnitude_sparsity_config()
config['compression']['params'] = {
'schedule': 'multistep',
'sparsity_levels': [0.1]
}
with pytest.raises(AttributeError):
create_compression_algorithm(model, config)
def test_can_create_rb_algo__with_adaptive_scheduler():
config = get_empty_config()
config['compression']['algorithm'] = 'rb_sparsity'
config['compression']["params"]["schedule"] = 'adaptive'
compression_algo = create_compression_algorithm(BasicConvTestModel(),
config)
assert isinstance(compression_algo.scheduler, AdaptiveSparsityScheduler)
def test_can_choose_scheduler(algo, schedule_type, scheduler_class):
config = get_empty_config()
config['compression']['algorithm'] = algo
config['compression']["params"]["schedule"] = schedule_type
compression_algo = create_compression_algorithm(BasicConvTestModel(),
config)
assert isinstance(compression_algo.scheduler, scheduler_class)
def test_sparse_algo_can_collect_sparse_layers():
model = TwoConvTestModel()
config = get_basic_sparsity_config()
compression_algo = create_compression_algorithm(model, config)
assert len(compression_algo.sparsified_module_info) == 2
def test_magnitude_scheduler_can_do_epoch_step__with_norm():
model = MagnitudeTestModel()
config = get_multistep_normed_abs_config()
compression_algo = create_compression_algorithm(model, config)
scheduler = compression_algo.scheduler
assert isinstance(scheduler, MultiStepSparsityScheduler)
assert pytest.approx(compression_algo.sparsity_level) == 0.1
assert compression_algo.threshold == pytest.approx(0.219, 0.01)
assert scheduler.prev_ind == 0
scheduler.epoch_step()
assert compression_algo.sparsity_level == 0.5
assert compression_algo.threshold == pytest.approx(0.243, 0.01)
assert scheduler.prev_ind == 1
scheduler.epoch_step()
assert compression_algo.sparsity_level == 0.5
assert compression_algo.threshold == pytest.approx(0.243, 0.01)
assert scheduler.prev_ind == 1
scheduler.epoch_step()
assert compression_algo.sparsity_level == 0.9
assert compression_algo.threshold == pytest.approx(0.371, 0.01)
assert scheduler.prev_ind == 2
def test_can_load_quant_algo__with_defaults():
model = BasicConvTestModel()
config = get_basic_quantization_config()
reset_context('orig')
reset_context('quantized_graphs')
compression_algo = create_compression_algorithm(deepcopy(model), config)
assert isinstance(compression_algo, Quantization)
quant_model = compression_algo.model
model_conv = get_all_modules_by_type(model, 'Conv2d')
quant_model_conv = get_all_modules_by_type(
quant_model.get_nncf_wrapped_module(), 'NNCFConv2d')
assert len(model_conv) == len(quant_model_conv)
for module_name in model_conv:
scope = module_name.split('/')
scope[-1] = scope[-1].replace('Conv2d', 'NNCFConv2d')
quant_module_name = '/'.join(scope)
assert quant_module_name in quant_model_conv
store = []
for op in quant_model_conv[quant_module_name].pre_ops.values():
if isinstance(op, (UpdateInputs, UpdateWeight)) and isinstance(
op.operand, SymmetricQuantizer):
assert op.__class__.__name__ not in store
store.append(op.__class__.__name__)
assert UpdateWeight.__name__ in store
def test_can_create_magnitude_sparse_algo__with_defaults():
model = MagnitudeTestModel()
config = get_basic_magnitude_sparsity_config()
config['compression']['params'] = \
{'schedule': 'multistep'}
compression_algo = create_compression_algorithm(deepcopy(model), config)
assert isinstance(compression_algo, MagnitudeSparsity)
sparse_model = compression_algo.model
assert compression_algo.sparsity_level == approx(0.1)
assert len(list(sparse_model.modules())) == 11
model_conv = get_all_modules_by_type(model, 'Conv2d')
sparse_model_conv = get_all_modules_by_type(sparse_model, 'NNCFConv2d')
assert len(model_conv) == len(sparse_model_conv)
i = 0
for module_name in model_conv:
scope = module_name.split('/')
scope[-1] = scope[-1].replace('Conv2d', 'NNCFConv2d')
sparse_module_name = '/'.join(scope)
assert sparse_module_name in sparse_model_conv
store = []
sparse_module = sparse_model_conv[sparse_module_name]
ref_mask = torch.ones_like(sparse_module.weight) if i == 0 else ref_mask_2
i += 1
for op in sparse_module.pre_ops.values():
if isinstance(op, UpdateWeight) and isinstance(op.operand, BinaryMask):
assert compression_algo.threshold == approx(0.24, 0.1)
assert torch.allclose(op.operand.binary_mask, ref_mask)
assert isinstance(compression_algo.weight_importance, type(normed_magnitude))
assert op.__class__.__name__ not in store
store.append(op.__class__.__name__)
def test_can_create_const_sparse_algo__with_default():
model = BasicConvTestModel()
config = get_empty_config()
config["compression"] = {"algorithm": "const_sparsity"}
compression_algo = create_compression_algorithm(deepcopy(model), config)
assert isinstance(compression_algo, ConstSparsity)
sparse_model = compression_algo.model
assert len(list(sparse_model.modules())) == 6
model_conv = get_all_modules_by_type(model, 'Conv2d')
sparse_model_conv = get_all_modules_by_type(sparse_model, 'NNCFConv2d')
assert len(model_conv) == len(sparse_model_conv)
for module_name in model_conv:
scope = module_name.split('/')
scope[-1] = scope[-1].replace('Conv2d', 'NNCFConv2d')
sparse_module_name = '/'.join(scope)
assert sparse_module_name in sparse_model_conv
store = []
sparse_module = sparse_model_conv[sparse_module_name]
for op in sparse_module.pre_ops.values():
if isinstance(op, UpdateWeight) and isinstance(
op.operand, BinaryMask):
ref_mask = torch.ones_like(sparse_module.weight)
assert torch.allclose(op.operand.binary_mask, ref_mask)
assert op.__class__.__name__ not in store
store.append(op.__class__.__name__)
def test_can_not_set_sparsity_more_than_one_for_magnitude_sparse_algo():
model = MagnitudeTestModel()
config = get_basic_magnitude_sparsity_config()
compression_algo = create_compression_algorithm(model, config)
with pytest.raises(AttributeError):
compression_algo.set_sparsity_level(1)
compression_algo.set_sparsity_level(1.2)
def create_compressed_model(model: Module,
config: Config,
dummy_forward_fn: Callable[[Module], Any] = None):
"""dummy_forward_fn will be used instead of a *forward* function call to build
the graph - useful when the original training pipeline has special formats of
data loader output or has additional *forward* arguments other than input tensors.
Otherwise, the *forward* call of the model will be made with a single Tensor with
a shape and type specified in config."""
if dummy_forward_fn is None:
input_info_list = create_input_infos(config)
graph_builder = GraphBuilder(
custom_forward_fn=create_dummy_forward_fn(input_info_list))
else:
graph_builder = GraphBuilder(custom_forward_fn=dummy_forward_fn)
if is_main_process():
print(*get_all_modules(model).keys(), sep="\n")
reset_context('create_model')
graph = graph_builder.build_graph(model, 'create_model')
graph.dump_graph(osp.join(config.log_dir, "original_graph.dot"))
compression_algo = create_compression_algorithm(model, config,
dummy_forward_fn)
compressed_model = compression_algo.model
if is_main_process() and not isinstance(compression_algo,
NoCompressionAlgorithm):
context_name = 'create_compressed_graph'
if isinstance(compressed_model, QuantizedNetwork):
context_name = compressed_model.get_context_name()
graph = graph_builder.build_graph(compression_algo.model, context_name)
graph.dump_graph(osp.join(config.log_dir, "compressed_graph.dot"))
return compression_algo, compressed_model
def create_compressed_model(model, config):
input_args = (next(model.parameters()).new_empty(
config['input_sample_size']), )
if is_main_process():
print(*get_all_modules(model).keys(), sep="\n")
ctx = build_graph(model,
input_args, {},
'create_model',
reset_context=True)
dump_graph(ctx, osp.join(config.log_dir, "original_graph.dot"))
compression_algo = create_compression_algorithm(model, config)
if is_main_process():
if hasattr(compression_algo.model, "build_graph"):
ctx = compression_algo.model.build_graph()
else:
ctx = build_graph(compression_algo.model,
input_args, {},
"create_model_compressed",
reset_context=True)
dump_graph(ctx, osp.join(config.log_dir, "compressed_graph.dot"))
model = compression_algo.model
return compression_algo, model
def test_quantization_configs__custom():
model = BasicConvTestModel()
config = get_basic_quantization_config()
config['compression'].update({
"weights": {
"mode": "asymmetric",
"per_channel": True,
"signed": False,
"bits": 4
},
"activations": {
"mode": "asymmetric",
"bits": 4,
"signed": True,
},
})
reset_context('orig')
reset_context('quantized_graphs')
compression_algo = create_compression_algorithm(model, config)
weight_quantizers, activation_quantizers = split_quantizers(
compression_algo.model)
ref_weight_qconfig = QuantizerConfig(4, QuantizationMode.ASYMMETRIC, None,
True, None, True)
for wq in weight_quantizers:
compare_qconfigs(ref_weight_qconfig, wq.config)
ref_activation_qconfig = QuantizerConfig(4, QuantizationMode.ASYMMETRIC,
True, False, None, False)
for wq in activation_quantizers:
compare_qconfigs(ref_activation_qconfig, wq.config)
def test_can_quantize_inputs_for_sparsity_plus_quantization():
reset_context('orig')
reset_context('quantized_graphs')
reset_context('test')
model = BasicConvTestModel()
config = get_basic_sparsity_plus_quantization_config()
compression_algo = create_compression_algorithm(model, config)
assert isinstance(compression_algo, CompositeCompressionAlgorithm)
sparse_quantized_model = compression_algo.model
sparse_quantized_model_conv = get_all_modules_by_type(
sparse_quantized_model, 'NNCFConv2d')
nncf_module = next(iter(sparse_quantized_model_conv.values()))
assert len(
nncf_module.pre_ops
) == 3 # 1x weight sparsifier + 1x weight quantizer + 1x input quantizer
assert isinstance(nncf_module.pre_ops['0'], UpdateWeight)
assert isinstance(nncf_module.pre_ops['0'].op, RBSparsifyingWeight)
assert isinstance(nncf_module.pre_ops['1'], UpdateWeight)
assert isinstance(nncf_module.pre_ops['1'].op, SymmetricQuantizer)
assert isinstance(nncf_module.pre_ops['2'], UpdateInputs)
assert isinstance(nncf_module.pre_ops['2'].op, SymmetricQuantizer)
def test_can_load_sparse_algo__with_defaults():
model = BasicConvTestModel()
config = get_basic_sparsity_config()
compression_algo = create_compression_algorithm(deepcopy(model), config)
assert isinstance(compression_algo, RBSparsity)
sparse_model = compression_algo.model
model_conv = get_all_modules_by_type(model, 'Conv2d')
sparse_model_conv = get_all_modules_by_type(sparse_model, 'NNCFConv2d')
assert len(model_conv) == len(sparse_model_conv)
for module_name in model_conv:
scope = module_name.split('/')
scope[-1] = scope[-1].replace('Conv2d', 'NNCFConv2d')
sparse_module_name = '/'.join(scope)
assert sparse_module_name in sparse_model_conv
store = []
sparse_module = sparse_model_conv[sparse_module_name]
for op in sparse_module.pre_ops.values():
if isinstance(op, UpdateWeight) and isinstance(
op.operand, RBSparsifyingWeight):
assert torch.allclose(op.operand.binary_mask,
torch.ones_like(sparse_module.weight))
assert op.operand.sparsify
assert op.__class__.__name__ not in store
store.append(op.__class__.__name__)
def test_magnitude_algo_binary_masks_are_applied():
model = BasicConvTestModel()
config = get_empty_config()
config['compression']['algorithm'] = "magnitude_sparsity"
compression_algo = create_compression_algorithm(model, config)
compressed_model = compression_algo.model
minfo_list = compression_algo.sparsified_module_info # type: List[SparseModuleInfo]
minfo = minfo_list[0] # type: SparseModuleInfo
minfo.operand.binary_mask = torch.ones_like(minfo.module.weight) # 1x1x2x2
input_ = torch.ones(size=(1, 1, 5, 5))
ref_output_1 = -4 * torch.ones(size=(2, 4, 4))
output_1 = compressed_model(input_)
assert torch.all(torch.eq(output_1, ref_output_1))
minfo.operand.binary_mask[0][0][0][1] = 0
minfo.operand.binary_mask[1][0][1][0] = 0
ref_output_2 = - 3 * torch.ones_like(ref_output_1)
output_2 = compressed_model(input_)
assert torch.all(torch.eq(output_2, ref_output_2))
minfo.operand.binary_mask[1][0][0][1] = 0
ref_output_3 = ref_output_2.clone()
ref_output_3[1] = -2 * torch.ones_like(ref_output_1[1])
output_3 = compressed_model(input_)
assert torch.all(torch.eq(output_3, ref_output_3))
def test_can_set_sparse_layers_to_loss():
model = BasicConvTestModel()
config = get_basic_sparsity_config()
config['compression']['train_phase'] = ''
compression_algo = create_compression_algorithm(model, config)
loss = compression_algo.loss
assert isinstance(loss, SparseLoss)
for layer in loss.sparse_layers:
assert isinstance(layer, RBSparsifyingWeight)
def start_evaluation(args):
"""Launches the evaluation process"""
if args.dataset == 'vgg':
dataset = VGGFace2(args.val,
args.v_list,
args.v_land,
landmarks_training=True)
elif args.dataset == 'celeb':
dataset = CelebA(args.val, args.v_land, test=True)
else:
dataset = NDG(args.val, args.v_land)
if dataset.have_landmarks:
log.info('Use alignment for the train data')
dataset.transform = t.Compose(
[Rescale((48, 48)), ToTensor(switch_rb=True)])
else:
exit()
val_loader = DataLoader(dataset,
batch_size=args.val_batch_size,
num_workers=4,
shuffle=False,
pin_memory=True)
model = models_landmarks['landnet']()
assert args.snapshot is not None
if args.compr_config:
config = Config.from_json(args.compr_config)
compression_algo = create_compression_algorithm(model, config)
model = compression_algo.model
log.info('Testing snapshot ' + args.snapshot + ' ...')
model = load_model_state(model,
args.snapshot,
args.device,
eval_state=True)
model.eval()
cudnn.benchmark = True
model = torch.nn.DataParallel(
model,
device_ids=[args.device],
)
log.info('Face landmarks model:')
log.info(model)
avg_err, per_point_avg_err, failures_rate = evaluate(val_loader, model)
log.info('Avg RMSE error: {}'.format(avg_err))
log.info('Per landmark RMSE error: {}'.format(per_point_avg_err))
log.info('Failure rate: {}'.format(failures_rate))
if args.compr_config and "sparsity_level" in compression_algo.statistics():
log.info("Sparsity level: {0:.2f}".format(
compression_algo.statistics()
['sparsity_rate_for_sparsified_modules']))
def test_magnitude_sparse_algo_sets_threshold(weight_importance, sparsity_level, threshold):
model = MagnitudeTestModel()
config = get_basic_magnitude_sparsity_config()
config['compression']['weight_importance'] = weight_importance
config['compression']['params'] = {'schedule': 'multistep'}
compression_algo = create_compression_algorithm(model, config)
if sparsity_level:
compression_algo.set_sparsity_level(sparsity_level)
assert compression_algo.threshold == pytest.approx(threshold, 0.01)
def test_can_create_sparse_scheduler__with_defaults(self, algo):
config = get_empty_config()
config['compression']['algorithm'] = algo
config['compression']["params"]["schedule"] = 'polynomial'
compression_algo = create_compression_algorithm(BasicConvTestModel(), config)
scheduler = compression_algo.scheduler
assert scheduler.initial_sparsity == 0
assert scheduler.max_sparsity == 0.5
assert scheduler.max_step == 90
assert scheduler.sparsity_training_steps == 100
def test_sparse_algo_can_calc_sparsity_rate__for_2_conv_model():
model = TwoConvTestModel()
config = get_basic_sparsity_config()
compression_algo = create_compression_algorithm(model, config)
assert compression_algo.sparsified_weights_count == model.weights_num
assert compression_algo.sparsity_rate_for_model == (
1 - (model.nz_weights_num + model.nz_bias_num) /
(model.weights_num + model.bias_num))
assert compression_algo.sparsity_rate_for_sparsified_modules == 1 - model.nz_weights_num / model.weights_num
def test_scale_and_sign_init_for_quant_algo():
model = TwoConvTestModel()
config = get_empty_config()
config['compression'] = {
'algorithm': 'quantization',
'initializer': {
'num_init_steps': 1
}
}
reset_context('orig')
reset_context('quantized_graphs')
compression_algo = create_compression_algorithm(model, config)
model = compression_algo.model
input_sample_size = config.input_sample_size
class OnesDatasetMock:
def __init__(self, input_size):
self.input_size = input_size
super().__init__()
def __getitem__(self, index):
return torch.ones(self.input_size), torch.ones(1)
def __len__(self):
return 1
data_loader = torch.utils.data.DataLoader(OnesDatasetMock(
input_sample_size[1:]),
batch_size=1,
num_workers=1,
shuffle=False)
compression_algo.initialize(data_loader)
model_conv = get_all_modules_by_type(model, 'Quantize')
ref_table = {
'.*Sequential\\[0\\].*UpdateWeight.*': (True, 1),
'.*Sequential\\[1\\].*UpdateWeight. *': (False, 1),
'.*activation_quantizers.*Sequential\\[0\\].*': (True, 4),
'.*activation_quantizers.*Sequential\\[1\\].*': (True, 24)
}
for name, module in model_conv.items():
for pattern, ref_values in ref_table.items():
match = re.search(pattern, name)
if match:
assert isinstance(module, Quantize)
assert module.signed == ref_values[
0], 'sign is not matched for {}'.format(name)
assert module.scale == ref_values[
1], 'scale is not matched for {}'.format(name)
def activation_quantizers_dumping_worker(current_gpu, config, tmp_path):
model = resnet50(pretrained=False)
reset_context('orig')
reset_context('quantized_graphs')
algo = create_compression_algorithm(model, config)
model = algo.model
path = get_path_to_keys(tmp_path, current_gpu)
print(path)
with open(path, 'w') as f:
f.writelines("%s\n" % key
for key in model.activation_quantizers.keys())
def test_can_create_quant_loss_and_scheduler():
model = BasicConvTestModel()
config = get_basic_quantization_config()
reset_context('orig')
reset_context('quantized_graphs')
compression_algo = create_compression_algorithm(model, config)
loss = compression_algo.loss
assert isinstance(loss, CompressionLoss)
scheduler = compression_algo.scheduler
assert isinstance(scheduler, CompressionScheduler)
def create_model(config):
ssd_net = build_ssd(config.model, config.ssd_params,
config.input_sample_size[-1], config.num_classes,
config)
ssd_net.to(config.device)
compression_algo = create_compression_algorithm(ssd_net, config)
ssd_net = compression_algo.model
weights = config.get('weights')
if weights:
sd = torch.load(weights, map_location='cpu')
load_state(ssd_net, sd)
ssd_net.train()
model, _ = prepare_model_for_execution(ssd_net, config)
return compression_algo, model
def test_magnitude_scheduler_can_do_epoch_step__with_last():
model = MagnitudeTestModel()
config = get_multistep_normed_abs_config()
compression_algo = create_compression_algorithm(model, config)
scheduler = compression_algo.scheduler
scheduler.epoch_step(3)
assert scheduler.prev_ind == 2
assert compression_algo.sparsity_level == 0.9
assert compression_algo.threshold == pytest.approx(0.371, 0.01)
scheduler.epoch_step()
assert scheduler.prev_ind == 2
assert compression_algo.sparsity_level == 0.9
assert compression_algo.threshold == pytest.approx(0.371, 0.01)
def test_magnitude_algo_set_binary_mask_on_forward():
model = MagnitudeTestModel()
config = get_basic_magnitude_sparsity_config()
config['compression']['weight_importance'] = 'abs'
compression_algo = create_compression_algorithm(model, config)
compression_algo.set_sparsity_level(0.3)
model = compression_algo.model
with torch.no_grad():
model(torch.ones([1, 1, 10, 10]))
op = model.conv1.pre_ops['0']
check_equal(ref_mask_1, op.operand.binary_mask)
op = model.conv2.pre_ops['0']
check_equal(ref_mask_2, op.operand.binary_mask)
def test_sparse_network(self, model_name, model_builder, input_size, algo, params):
model = model_builder()
from nncf.layers import NNCF_MODULES_MAP
sparsifiable_modules = list(NNCF_MODULES_MAP.values())
ref_num_sparsed = len(get_all_modules_by_type(model, sparsifiable_modules))
ctx = reset_context('test')
config = get_empty_config(input_sample_size=input_size)
config["compression"] = {"algorithm": algo, "params": params}
compression_algo = create_compression_algorithm(model, config)
assert ref_num_sparsed == len(compression_algo.sparsified_module_info)
model = compression_algo.model
with context('test') as c:
_ = model(torch.zeros(input_size))
c.reset_scope_operator_call_counters()
_ = model(torch.zeros(input_size))
check_graph(to_networkx(ctx), model_name, algo)
def test_scheduler_can_do_epoch_step(self, algo, schedule, get_params, ref_levels):
model = BasicConvTestModel()
config = get_empty_config()
config['compression']['algorithm'] = algo
config['compression']["params"] = get_params()
config['compression']["params"]["schedule"] = schedule
compression_algo = create_compression_algorithm(model, config)
scheduler = compression_algo.scheduler
assert pytest.approx(scheduler.current_sparsity_level) == ref_levels[0]
for ref_level in ref_levels[1:]:
scheduler.epoch_step()
assert pytest.approx(scheduler.current_sparsity_level) == ref_level
for m in compression_algo.sparsified_module_info:
assert not m.operand.sparsify
