def test_create_rb_algo_with_per_layer_loss():
config = get_empty_config()
config['compression'] = {'algorithm': 'rb_sparsity', "params": {"sparsity_level_setting_mode": 'local'}}
_, compression_ctrl = create_compressed_model_and_algo_for_test(MockModel(), config)
# pylint: disable=protected-access
assert isinstance(compression_ctrl._loss, SparseLossForPerLayerSparsity)
def test_can_not_create_magnitude_algo__without_steps():
config = get_basic_magnitude_sparsity_config()
config['compression']['params'] = {
'schedule': 'multistep',
'multistep_sparsity_levels': [0.1]
}
with pytest.raises(AttributeError):
_, _ = create_compressed_model_and_algo_for_test(MockModel(), config)
def test_can_create_quant_loss_and_scheduler():
config = get_quantization_config_without_range_init()
_, compression_ctrl = create_compressed_model_and_algo_for_test(MockModel(), config)
loss = compression_ctrl.loss
assert isinstance(loss, CompressionLoss)
scheduler = compression_ctrl.scheduler
assert isinstance(scheduler, CompressionScheduler)
def test_can_create_magnitude_algo__without_levels():
config = get_basic_magnitude_sparsity_config()
config['compression']['params'] = {
'schedule': 'multistep',
'multistep_steps': [1]
}
_, compression_ctrl = create_compressed_model_and_algo_for_test(
MockModel(), config)
assert compression_ctrl.scheduler.sparsity_level == approx(0.1)
def test_can_not_create_magnitude_algo__with_adaptive_scheduler():
config = get_empty_config()
config['compression'] = {
'algorithm': 'magnitude_sparsity',
"params": {
"schedule": 'adaptive'
}
}
with pytest.raises(TypeError):
_, _ = create_compressed_model_and_algo_for_test(MockModel(), config)
def test_rb_sparsity__can_set_sparsity_level_for_module():
config = get_empty_config()
config['compression'] = {'algorithm': 'rb_sparsity', "params": {"sparsity_level_setting_mode": 'local'}}
_, compression_ctrl = create_compressed_model_and_algo_for_test(MockModel(), config)
# pylint: disable=protected-access
assert list(compression_ctrl._loss.per_layer_target.values())[0] == 1
compression_ctrl.set_sparsity_level(0.7, compression_ctrl.sparsified_module_info[0])
assert list(compression_ctrl._loss.per_layer_target.values())[0] == pytest.approx(0.3)
def test_can_create_rb_algo__with_adaptive_scheduler():
config = get_empty_config()
config['compression'] = {
'algorithm': 'rb_sparsity',
"params": {
"schedule": 'adaptive'
}
}
_, compression_ctrl = create_compressed_model_and_algo_for_test(
MockModel(), config)
assert isinstance(compression_ctrl.scheduler, AdaptiveSparsityScheduler)
def test_can_choose_scheduler(algo, schedule_type, scheduler_class):
config = get_empty_config()
config['compression'] = {
'algorithm': algo,
"params": {
"schedule": schedule_type
}
}
_, compression_ctrl = create_compressed_model_and_algo_for_test(
MockModel(), config)
assert isinstance(compression_ctrl.scheduler, scheduler_class)
def test_hawq_raises_error_if_method_returns_none(mocker, method_name):
config = create_hawq_test_config()
model = MockModel()
config = register_default_init_args(config, mocker.stub(), mocker.stub())
mocker.patch('nncf.quantization.algo.QuantizationController._do_range_init')
mocker.patch('nncf.quantization.init_precision.HAWQPrecisionInitializer._calc_traces')
mocked_trace = mocker.patch('nncf.quantization.init_precision.HAWQPrecisionInitializer.' + method_name)
mocked_trace.return_value = None
with pytest.raises(RuntimeError):
create_compressed_model_and_algo_for_test(model, config)
def test_create_rb_algo_with_stub_scheduler():
config = get_empty_config()
config['compression'] = {
'algorithm': 'rb_sparsity',
"params": {
"sparsity_level_setting_mode": 'local'
}
}
_, compression_ctrl = create_compressed_model_and_algo_for_test(
MockModel(), config)
# pylint: disable=protected-access
assert isinstance(compression_ctrl.scheduler, StubCompressionScheduler)
def test_hawq_behaviour__if_method_returns_none(mocker, method_name, expected_behavior):
config = HAWQConfigBuilder().build()
config['quantizer_setup_type'] = 'pattern_based'
model = MockModel()
config = register_default_init_args(config, mocker.stub(), mocker.stub())
mocker.patch('nncf.quantization.algo.QuantizationController._do_range_init')
mocker.patch('nncf.quantization.precision_init.hawq_init.HAWQPrecisionInitializer._calc_traces')
mocked_trace = mocker.patch('nncf.quantization.precision_init.hawq_init.HAWQPrecisionInitializer.' + method_name)
mocked_trace.return_value = None
with expected_behavior:
create_compressed_model_and_algo_for_test(model, config)
def test_can_create_sparse_scheduler__with_defaults(self, algo):
config = get_empty_config()
config['compression'] = {
'algorithm': algo,
"params": {
"schedule": 'polynomial'
}
}
_, compression_ctrl = create_compressed_model_and_algo_for_test(
MockModel(), config)
scheduler = compression_ctrl.scheduler
assert scheduler.initial_sparsity == 0
assert scheduler.sparsity_target == 0.5
assert scheduler.sparsity_target_epoch == 90
assert scheduler.sparsity_freeze_epoch == 100
def test_input_wrapper_wrap_inputs(
mocker, inputs_test_struct: InputWrappingTestStruct):
input_infos = inputs_test_struct.input_infos
model_args = inputs_test_struct.model_args
model_kwargs = inputs_test_struct.model_kwargs
ref_wrapping_sequence = inputs_test_struct.ref_wrapping_sequence
stub_cpu_model = MockModel()
mocker.patch('nncf.dynamic_graph.input_wrapping.nncf_model_input')
from nncf.dynamic_graph.input_wrapping import nncf_model_input
mgr = InputInfoWrapManager(input_infos, inspect.signature(forward),
stub_cpu_model)
mgr.wrap_inputs(model_args, model_kwargs)
test_wrapping_sequence = [
cl[0][0] for cl in nncf_model_input.call_args_list
]
test_identical_to_ref = all(
map(torch.equal, ref_wrapping_sequence, test_wrapping_sequence))
assert test_identical_to_ref
def test_range_init_is_called(config_cutter, range_init_call_count,
precision_init_call_count,
bn_adaptation_call_count, mocker):
config = create_hawq_test_config()
config['compression']['initializer'].update(
{'batchnorm_adaptation': {
'num_bn_adaptation_steps': 5
}})
model = MockModel()
config = register_default_init_args(config, mocker.stub(), mocker.stub())
range_init_spy = mocker.patch(
'nncf.quantization.algo.QuantizationController._do_range_init')
precision_init_spy = mocker.patch(
'nncf.quantization.init_precision.HAWQPrecisionInitializer.apply_init')
bn_adaptation_spy = mocker.patch(
'nncf.initialization.DataLoaderBNAdaptationRunner.run')
config_cutter(config['compression'])
create_compressed_model_and_algo_for_test(model, config)
assert range_init_spy.call_count == range_init_call_count
assert precision_init_spy.call_count == precision_init_call_count
assert bn_adaptation_spy.call_count == bn_adaptation_call_count