def test_compare_model_outputs_linear_static_fx(self):
r"""Compare the output of linear layer in static quantized model and corresponding
output of linear layer in float model
"""
qengine = torch.backends.quantized.engine
qconfig = get_default_qconfig(qengine)
qconfig_dict = {"": qconfig}
float_model = SingleLayerLinearModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
# Run calibration
test_only_eval_fn(prepared_model, self.calib_data)
q_model = convert_fx(prepared_model)
linear_data = self.calib_data[0][0]
expected_act_compare_dict_keys = {"x.stats", "fc1.stats"}
self.compare_and_validate_model_outputs_results_fx(
prepared_float_model, q_model, expected_act_compare_dict_keys,
linear_data)
def test_compare_model_stub_linear_static_fx(self):
r"""Compare the output of static quantized linear layer and its float shadow module"""
qengine = torch.backends.quantized.engine
qconfig = get_default_qconfig(qengine)
qconfig_dict = {"": qconfig}
float_model = SingleLayerLinearModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
# Run calibration
test_only_eval_fn(prepared_model, self.calib_data)
q_model = convert_fx(prepared_model)
linear_data = self.calib_data[0][0]
module_swap_list = [nn.Linear]
expected_ob_dict_keys = {"fc1.stats"}
self.compare_and_validate_model_stub_results_fx(
prepared_float_model,
q_model,
module_swap_list,
expected_ob_dict_keys,
linear_data,
)
def test_compare_weights_linear_static_fx(self):
r"""Compare the weights of float and static quantized linear layer"""
def calibrate(model, calib_data):
model.eval()
with torch.no_grad():
for inp in calib_data:
model(*inp)
def compare_and_validate_results(float_model, q_model):
weight_dict = compare_weights_fx(float_model.state_dict(),
q_model.state_dict())
self.assertEqual(len(weight_dict), 1)
for k, v in weight_dict.items():
self.assertTrue(v["float"].shape == v["quantized"].shape)
float_model = SingleLayerLinearModel()
float_model.eval()
qengine = torch.backends.quantized.engine
qconfig = get_default_qconfig(qengine)
qconfig_dict = {"": qconfig}
prepared_model = prepare_fx(float_model, qconfig_dict)
backup_prepared_model = copy.deepcopy(prepared_model)
backup_prepared_model.eval()
# Run calibration
calibrate(prepared_model, self.calib_data)
q_model = convert_fx(prepared_model)
compare_and_validate_results(backup_prepared_model, q_model)
def test_remove_qconfig_observer_fx(self):
r"""Remove activation_post_process node from fx prepred model"""
float_model = SingleLayerLinearModel()
float_model.eval()
qengine = torch.backends.quantized.engine
qconfig = get_default_qconfig(qengine)
qconfig_dict = {"": qconfig}
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
prepared_float_model.eval()
model = remove_qconfig_observer_fx(prepared_float_model)
modules = dict(model.named_modules())
for node in model.graph.nodes:
if node.op == "call_module":
self.assertFalse(is_activation_post_process(modules[node.target]))
def test_compare_weights_linear_static_fx(self):
r"""Compare the weights of float and static quantized linear layer"""
qengine = torch.backends.quantized.engine
qconfig = get_default_qconfig(qengine)
qconfig_dict = {"": qconfig}
float_model = SingleLayerLinearModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
prepared_float_model.eval()
# Run calibration
test_only_eval_fn(prepared_model, self.calib_data)
q_model = convert_fx(prepared_model)
expected_weight_dict_keys = {"fc1._packed_params._packed_params"}
self.compare_and_validate_model_weights_results_fx(
prepared_float_model, q_model, expected_weight_dict_keys)
