def test_compare_model_stub_linear_dynamic_fx(self):
r"""Compare the output of dynamic quantized linear layer and its float shadow module"""
qconfig_dict = {"object_type": [(nn.Linear, default_dynamic_qconfig)]}
float_model = SingleLayerLinearDynamicModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
prepared_float_model.eval()
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_dynamic_fx(self):
r"""Compare the weights of float and dynamic quantized linear layer"""
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(len(v["float"]) == len(v["quantized"]))
for i, val in enumerate(v["quantized"]):
self.assertTrue(
v["float"][i].shape == v["quantized"][i].shape)
float_model = SingleLayerLinearDynamicModel()
float_model.eval()
qconfig = torch.quantization.qconfig.default_dynamic_qconfig
qconfig_dict = {"": qconfig}
prepared_model = prepare_fx(float_model, qconfig_dict)
backup_prepared_model = copy.deepcopy(prepared_model)
backup_prepared_model.eval()
q_model = convert_fx(prepared_model)
compare_and_validate_results(backup_prepared_model, q_model)
def test_compare_weights_linear_dynamic_fx(self):
r"""Compare the weights of float and dynamic quantized linear layer"""
qconfig_dict = {"object_type": [(nn.Linear, default_dynamic_qconfig)]}
float_model = SingleLayerLinearDynamicModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
prepared_float_model.eval()
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)
def test_compare_weights_linear_dynamic_fx(self):
r"""Compare the weights of float and dynamic quantized linear layer"""
qconfig = torch.quantization.qconfig.default_dynamic_qconfig
qconfig_dict = {"": qconfig}
float_model = SingleLayerLinearDynamicModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
backup_prepared_model = copy.deepcopy(prepared_model)
backup_prepared_model.eval()
q_model = convert_fx(prepared_model)
expected_weight_dict_keys = {"fc1._packed_params._packed_params"}
self.compare_and_validate_model_weights_results_fx(
backup_prepared_model, q_model, expected_weight_dict_keys
)
def test_compare_model_outputs_linear_dynamic_fx(self):
r"""Compare the output of linear layer in dynamic quantized model and corresponding
output of linear layer in float model
"""
qconfig_dict = {"object_type": [(nn.Linear, default_dynamic_qconfig)]}
float_model = SingleLayerLinearDynamicModel()
float_model.eval()
prepared_model = prepare_fx(float_model, qconfig_dict)
prepared_float_model = copy.deepcopy(prepared_model)
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)