def test_staged_scheduler_with_empty_quantization():
config = get_squeezenet_quantization_config()
config['compression'].update({
'params': {
"activations_quant_start_epoch": 1,
"weights_quant_start_epoch": 2,
}
})
model = squeezenet1_1_custom(num_classes=10, pretrained=False, dropout=0)
model, algo = create_compressed_model_and_algo_for_test(model, config)
scheduler = algo.scheduler
for module in algo.all_quantizations.values():
assert not module.is_enabled_quantization()
scheduler.epoch_step()
for module in algo.all_quantizations.values():
if module.is_weights:
assert not module.is_enabled_quantization()
else:
assert module.is_enabled_quantization()
scheduler.epoch_step()
for module in algo.all_quantizations.values():
assert module.is_enabled_quantization()
def test_hawq_on_single_conv_without_quantizers(_seed, dataset_dir, tmp_path,
params: HAWQTestParams):
config = get_squeezenet_quantization_config(batch_size=params.batch_size)
iter_number = params.iter_number
tolerance = 4e-4
model = squeezenet1_1_custom(num_classes=10, pretrained=False, dropout=0)
from examples.common.models.classification.squeezenet import model_urls
load_state(model, model_zoo.load_url(model_urls['squeezenet1_1']))
model = model.cuda()
criterion = nn.CrossEntropyLoss().cuda()
if not dataset_dir:
dataset_dir = str(tmp_path)
data_loader, _ = create_test_dataloaders(config.get("model_size"),
dataset_dir, params.batch_size)
device = next(model.parameters()).device
for _, param in model.named_parameters():
param.requires_grad = False
first_conv = next(iter(get_all_modules_by_type(model, 'Conv2d').values()))
first_conv.weight.requires_grad = True
trace_estimator = HessianTraceEstimator(model, criterion, device,
data_loader,
params.num_data_points)
actual_state = trace_estimator.get_average_traces(max_iter=iter_number,
tolerance=tolerance)
assert math.isclose(actual_state.item(), params.ref_trace, rel_tol=1e-09)
def test_staged_scheduler_with_hawq():
config = get_squeezenet_quantization_config()
config['compression'].update({
'params': {
"activations_quant_start_epoch": 1,
"weights_quant_start_epoch": 2,
},
'initializer': {
'range': {
'num_init_samples': 1
},
'precision': {
"type": "hawq",
"num_data_points": 1,
"iter_number": 1,
"tolerance": 1
}
}
})
num_classes = 10
model = squeezenet1_1(num_classes=num_classes, dropout=0)
input_infos_list = create_input_infos(config)
input_sample_size = input_infos_list[0].shape
data_loader = DataLoader(
HawqDatasetMock(input_sample_size[1:], num_classes),
batch_size=1,
num_workers=0, # Workaround for PyTorch MultiprocessingDataLoader issues
shuffle=False)
criterion = nn.CrossEntropyLoss().cuda()
config = register_default_init_args(config, data_loader, criterion)
model, algo = create_compressed_model_and_algo_for_test(model, config)
scheduler = algo.scheduler
for module in algo.all_quantizations.values():
assert not module.is_enabled_quantization()
scheduler.epoch_step()
for module in algo.all_quantizations.values():
assert not module.is_enabled_quantization()
scheduler.epoch_step()
for module in algo.all_quantizations.values():
if module.is_weights:
assert not module.is_enabled_quantization()
else:
assert module.is_enabled_quantization()
scheduler.epoch_step()
for module in algo.all_quantizations.values():
assert module.is_enabled_quantization()
def test_staged_scheduler_with_range_init():
config = get_squeezenet_quantization_config()
config['compression'].update({
'params': {
"activations_quant_start_epoch": 1,
"weights_quant_start_epoch": 2,
},
'initializer': {
'range': {
'num_init_samples': 1
}
}
})
model = squeezenet1_1(num_classes=10, dropout=0)
input_infos_list = create_input_infos(config)
input_sample_size = input_infos_list[0].shape
data_loader = DataLoader(
OnesDatasetMock(input_sample_size[1:]),
batch_size=1,
num_workers=0, # Workaround for PyTorch MultiprocessingDataLoader issues
shuffle=False)
config.register_extra_structs([QuantizationRangeInitArgs(data_loader)])
model, algo = create_compressed_model_and_algo_for_test(model, config)
scheduler = algo.scheduler
for module in algo.all_quantizations.values():
assert not module.is_enabled_quantization()
scheduler.epoch_step()
for module in algo.all_quantizations.values():
assert not module.is_enabled_quantization()
scheduler.epoch_step()
for module in algo.all_quantizations.values():
if module.is_weights:
assert not module.is_enabled_quantization()
else:
assert module.is_enabled_quantization()
scheduler.epoch_step()
for module in algo.all_quantizations.values():
assert module.is_enabled_quantization()
def create_hawq_test_config(batch_size, num_data_points):
config = get_squeezenet_quantization_config()
config['batch_size'] = batch_size
config['compression'].update({
'initializer': {
'precision': {
"type": "hawq",
"bits": [
4,
8,
6,
7,
5
],
"num_data_points": num_data_points,
"iter_number": 1,
"tolerance": 1e-2
},
'range': {
'num_init_steps': 1
}
}})
return config
