def test_can_sparsify_embedding(algo):
config = {"input_info": {"sample_size": [1, 10], "type": "long"}}
sparsity_init = 0.5
config['compression'] = {'algorithm': algo, 'sparsity_init': sparsity_init}
nncf_config = NNCFConfig.from_dict(config)
model = EmbeddingOnlyModel()
model, compression_ctrl = create_compressed_model_and_algo_for_test(
model, nncf_config)
# Should pass
_ = compression_ctrl.statistics()
def nncf_config_with_default_init_args_(mocker):
config = NNCFConfig.from_dict(CONFIG_WITH_ALL_INIT_TYPES)
train_loader = DataLoader(
OnesDatasetMock(INPUT_SAMPLE_SIZE[1:]),
batch_size=1,
num_workers=0, # Workaround for PyTorch MultiprocessingDataLoader issues
shuffle=False)
mocker_criterion = mocker.stub()
mocker_criterion.batch_size = 1
config = register_default_init_args(config, train_loader, mocker_criterion)
return config
def create_sample_config(args, parser) -> SampleConfig:
sample_config = SampleConfig.from_json(args.config)
sample_config.update_from_args(args, parser)
file_path = Path(args.config).resolve()
with safe_open(file_path) as f:
loaded_json = json.load(f)
if sample_config.get("target_device") is not None:
target_device = sample_config.pop("target_device")
loaded_json["target_device"] = target_device
nncf_config = NNCFConfig.from_dict(loaded_json)
sample_config.nncf_config = nncf_config
return sample_config
def add_range_init(config):
for compression in config['compression']:
if compression['algorithm'] == 'quantization':
if 'initializer' not in compression:
compression['initializer'] = {}
compression['initializer'].update(
{'range': {
'num_init_samples': 1
}})
data_loader = create_ones_mock_dataloader(config)
config = NNCFConfig.from_dict(config)
config.register_extra_structs([
QuantizationRangeInitArgs(
wrap_dataloader_for_init(data_loader))
])
return config
