huggingface / optimum-graphcore Conditional Complexity

Intro

Conditional complexity (also called cyclomatic complexity) is a term used to measure the complexity of software. The term refers to the number of possible paths through a program function. A higher value ofter means higher maintenance and testing costs (infosecinstitute.com).
Conditional complexity is calculated by counting all conditions in the program that can affect the execution path (e.g. if statement, loops, switches, and/or operators, try and catch blocks...).
Conditional complexity is measured at the unit level (methods, functions...).
Units are classified in four categories based on the measured McCabe index: 1-5 (simple units), 6-10 (medium complex units), 11-25 (complex units), 26+ (very complex units).

Learn more...

Conditional Complexity Overall

There are 601 units with 6,967 lines of code in units (15.1% of code).

0 very complex units (0 lines of code)
7 complex units (612 lines of code)
22 medium complex units (914 lines of code)
49 simple units (1,144 lines of code)
523 very simple units (4,297 lines of code)

Legend:

51+

26-50

11-25

6-10

1-5

Alternative Visuals

3D view of all units

Conditional Complexity per Extension

51+

26-50

11-25

6-10

1-5

Conditional Complexity per Logical Component

primary logical decomposition

51+

26-50

11-25

6-10

1-5

Most Complex Units

Top 50 most complex units

Unit	# lines	McCabe index	# params
def __post_init__() in optimum/graphcore/training_args.py	115	43	1
def parallelize() in optimum/graphcore/models/mt5/modeling_mt5.py	119	39	2
def create() in notebooks/packed_bert/utils/packing/dataset_creator.py	100	38	1
def _to_options() in optimum/graphcore/ipu_configuration.py	74	34	3
def create_optimizer() in optimum/graphcore/trainer.py	69	28	1
def get_ipu_config() in notebooks/text_embeddings_models/config.py	66	27	6
def parallelize() in optimum/graphcore/models/t5/modeling_t5.py	69	27	5
def prepare_train_features() in notebooks/squad_preprocessing.py	45	22	1
def parallelize() in optimum/graphcore/models/whisper/modeling_whisper.py	79	22	5
def LPFHP() in notebooks/packed_bert/utils/packing/algorithms.py	88	20	4
def _validate_ipu_config() in optimum/graphcore/ipu_configuration.py	41	18	1
def _prepare_inputs_for_on_device_generation() in optimum/graphcore/generation/utils.py	34	16	4
def new_forward() in optimum/graphcore/pipelines/__init__.py	20	15	4
def _validate_kv_cache() in optimum/graphcore/generation/utils.py	46	15	4
def _push_from_checkpoint() in optimum/graphcore/trainer.py	29	15	2
def prepare_validation_features() in notebooks/squad_preprocessing.py	24	14	1
def SPFHP() in notebooks/packed_bert/utils/packing/algorithms.py	70	14	3
def extract_hyperparameters_from_trainer() in optimum/graphcore/modelcard.py	33	14	1
def _forward() in optimum/graphcore/pipelines/automatic_speech_recognition.py	40	14	4
def forward() in optimum/graphcore/generation/on_device_generation.py	91	14	4
def parallelize() in optimum/graphcore/models/bart/modeling_bart.py	57	14	4
def _load_from_checkpoint() in optimum/graphcore/trainer.py	43	13	3
def _get_train_sampler() in optimum/graphcore/trainer.py	32	12	1
def _save_checkpoint() in optimum/graphcore/trainer.py	37	12	3
def parallelize() in optimum/graphcore/models/t5/modeling_t5.py	28	12	1
def num_parameters() in optimum/graphcore/modeling_utils.py	11	12	3
def update_from_string() in optimum/graphcore/ipu_configuration.py	24	11	2
def compute_loss() in optimum/graphcore/trainer.py	18	11	4
def push_to_hub() in optimum/graphcore/trainer.py	24	11	4
def load_generation_config() in optimum/graphcore/trainer_seq2seq.py	14	10	2
def init_git_repo() in optimum/graphcore/trainer.py	35	10	2
def change_modules_for_ipu() in optimum/graphcore/models/deberta/modeling_deberta.py	22	10	2
def __getitem__() in notebooks/packed_bert/utils/packing/dataset_templates.py	22	9	2
def check_model_type() in optimum/graphcore/pipelines/__init__.py	20	9	3
def forward() in optimum/graphcore/generation/utils.py	23	9	4
def _save() in optimum/graphcore/trainer.py	23	9	3
def _rotate_checkpoints() in optimum/graphcore/trainer.py	18	9	3
def deparallelize() in optimum/graphcore/models/mt5/modeling_mt5.py	29	9	1
def disentangled_att_bias() in optimum/graphcore/models/deberta/modeling_deberta.py	44	9	6
def add_to_kv_cache() in optimum/graphcore/generation/attention_mixin.py	31	8	3
def to_dict() in optimum/graphcore/training_args.py	10	8	1
def _check_dataset_can_fill_batch() in optimum/graphcore/trainer.py	27	8	3
def _attn() in optimum/graphcore/models/gpt2/optimized_gpt2_attn.py	21	8	6
def parallelize() in optimum/graphcore/models/gpt2/modeling_gpt2.py	30	8	1
def parallelize() in optimum/graphcore/models/deberta/modeling_deberta.py	33	8	1
def to_pipelined() in optimum/graphcore/modeling_utils.py	20	8	3
def predict() in notebooks/packed_bert/pipeline/packed_bert.py	51	7	3
def _parse_and_tokenize() in optimum/graphcore/pipelines/text2text_generation.py	25	7	4
def _sanitize_parameters() in optimum/graphcore/pipelines/zero_shot_classification.py	20	7	2
def _wrap_and_compile_model_for_evaluation() in optimum/graphcore/trainer_seq2seq.py	7	7	3