def test_input_info_specification_from_config(mocker, input_info_test_struct):
stub_fn = mocker.stub()
mock_model = MockModel(stub_fn)
config = get_basic_quantization_config("symmetric")
input_info_config_entry = input_info_test_struct[0]
target_argument_info = input_info_test_struct[
1] # type: List[ModelInputInfo]
config["input_info"] = input_info_config_entry
_, _ = create_compressed_model_and_algo_for_test(mock_model, config)
forward_call_args = stub_fn.call_args[0]
forward_call_kwargs = stub_fn.call_args[1]
ref_args_info = list(
filter(lambda x: x.keyword is None, target_argument_info))
ref_kw_vs_arg_info = {
x.keyword: x
for x in target_argument_info if x.keyword is not None
}
def check_arg(arg: torch.Tensor, ref_arg_info: ModelInputInfo):
assert list(arg.shape) == ref_arg_info.shape
assert arg.dtype == ref_arg_info.type
assert len(forward_call_args) == len(ref_args_info)
assert len(forward_call_kwargs) == len(ref_kw_vs_arg_info)
assert set(forward_call_kwargs.keys()) == set(ref_kw_vs_arg_info.keys())
for idx, arg in enumerate(forward_call_args):
check_arg(arg, ref_args_info[idx])
for keyword, arg in forward_call_kwargs.items():
check_arg(arg, ref_kw_vs_arg_info[keyword])
def test_context_independence(model_name, model_builder, input_size, _case_config):
config = get_basic_quantization_config(_case_config.quant_type, input_sample_sizes=input_size[0])
compressed_models = [create_compressed_model_and_algo_for_test(model_builder[0](), config)[0],
create_compressed_model_and_algo_for_test(model_builder[1](), config)[0]]
for i, compressed_model in enumerate(compressed_models):
check_model_graph(compressed_model, model_name[i], _case_config.graph_dir)