def test_load_config_from_json_2():
device = "cuda"
model = ortmodule.ORTModule(Net().to(device))
# load from json once.
path_to_json = os.path.join(
os.getcwd(),
"orttraining_test_ortmodule_experimental_json_config_1.json")
load_from_json(model, path_to_json)
# load from json another time
path_to_json = os.path.join(
os.getcwd(),
"orttraining_test_ortmodule_experimental_json_config_2.json")
load_from_json(model, path_to_json)
for training_mode in [True, False]:
ort_model_attributes = model._torch_module._execution_manager(
training_mode)
# test propagate cast ops
assert ort_model_attributes._propagate_cast_ops_strategy == C.PropagateCastOpsStrategy.INSERT_AND_REDUCE
assert ort_model_attributes._propagate_cast_ops_level == 5
assert ort_model_attributes._propagate_cast_ops_allow == ["XYZ", "PQR"]
# test use external gpu allocator
assert ort_model_attributes._use_external_gpu_allocator == True
# test enable custom autograd function
assert ort_model_attributes._enable_custom_autograd_function == False
# test allow layer norm mod precision
assert ort_model_attributes._allow_layer_norm_mod_precision == False
# test use static shape
assert ort_model_attributes._use_static_shape == False
# test run symbolic shape inference
assert ort_model_attributes._run_symbolic_shape_infer == True
# test enable grad acc optimization
assert ort_model_attributes._enable_grad_acc_optimization == False
# test skip check
assert ort_model_attributes._skip_check.value == 10
# test debug options
assert ort_model_attributes._debug_options.save_onnx_models.save == True
assert ort_model_attributes._debug_options.save_onnx_models.name_prefix == "my_other_model"
assert ort_model_attributes._debug_options.logging.log_level.name == "INFO"
# test use memory aware gradient builder.
assert ort_model_attributes._use_memory_efficient_gradient == True
# test fallback policy
assert ort_model_attributes._fallback_manager.policy.value == 250
# assert onnx opset version
assert ortmodule.ONNX_OPSET_VERSION == 12
def test_load_config_from_json_1():
device = 'cuda'
model = ORTModule(Net().to(device))
# load from json once.
path_to_json = os.path.join(os.getcwd(), 'orttraining_test_ortmodule_experimental_json_config_2.json')
load_from_json(model, path_to_json)
# load from json another time
path_to_json = os.path.join(os.getcwd(), 'orttraining_test_ortmodule_experimental_json_config_1.json')
load_from_json(model, path_to_json)
for training_mode in [True, False]:
ort_model_attributes = model._torch_module._execution_manager(training_mode)
# test propagate cast ops
assert ort_model_attributes._propagate_cast_ops_strategy == C.PropagateCastOpsStrategy.FLOOD_FILL
assert ort_model_attributes._propagate_cast_ops_level == 3
assert ort_model_attributes._propagate_cast_ops_allow == ["ABC", "DEF"]
# test use external gpu allocator
assert ort_model_attributes._use_external_gpu_allocator == False
# test enable custom autograd function
assert ort_model_attributes._enable_custom_autograd_function == True
# test allow layer norm mod precision
assert ort_model_attributes._allow_layer_norm_mod_precision == True
# test use static shape
assert ort_model_attributes._use_static_shape == True
# test run symbolic shape inference
assert ort_model_attributes._run_symbolic_shape_infer == False
# test enable grad acc optimization
assert ort_model_attributes._enable_grad_acc_optimization == True
# test skip check
assert ort_model_attributes._skip_check.value == 14
# test debug options
assert ort_model_attributes._debug_options.save_onnx_models.save == True
assert ort_model_attributes._debug_options.save_onnx_models.name_prefix == 'my_model'
assert ort_model_attributes._debug_options.logging.log_level.name == "VERBOSE"
# test use memory aware gradient builder.
assert ort_model_attributes._use_memory_efficient_gradient == False
# test fallback policy
assert ort_model_attributes._fallback_manager.policy.value == 1
def test_load_config_from_json_2():
device = 'cuda'
model = ORTModule(Net().to(device))
# load from json once.
path_to_json = os.path.join(
os.getcwd(),
'orttraining_test_ortmodule_experimental_json_config_1.json')
load_from_json(model, path_to_json)
# load from json another time
path_to_json = os.path.join(
os.getcwd(),
'orttraining_test_ortmodule_experimental_json_config_2.json')
load_from_json(model, path_to_json)
for training_mode in [True, False]:
ort_model_attributes = model._torch_module._execution_manager(
training_mode)
# test propagate cast ops
assert ort_model_attributes._propagate_cast_ops_strategy == C.PropagateCastOpsStrategy.REMOVE_INPUT_OUTPUT_UP_DOWN_CASTS
assert ort_model_attributes._propagate_cast_ops_level == 5
assert ort_model_attributes._propagate_cast_ops_allow == ["XYZ", "PQR"]
# test use external gpu allocator
assert ort_model_attributes._use_external_gpu_allocator == True
# test enable custom autograd function
assert ort_model_attributes._enable_custom_autograd_function == False
# test allow layer norm mod precision
assert ort_model_attributes._allow_layer_norm_mod_precision == False
# test use static shape
assert ort_model_attributes._use_static_shape == False
# test run symbolic shape inference
assert ort_model_attributes._run_symbolic_shape_infer == True
# test enable grad acc optimization
assert ort_model_attributes._enable_grad_acc_optimization == False
# test skip check
assert ort_model_attributes._skip_check.value == 10
# test debug options
assert ort_model_attributes._debug_options.save_onnx_models.save == True
assert ort_model_attributes._debug_options.save_onnx_models.name_prefix == 'my_other_model'
assert ort_model_attributes._debug_options.logging.log_level.name == "INFO"