def __init__()

in optimum_benchmark/backends/base.py [0:0]

• 38 lines of code
• 4 McCabe index (conditional complexity)

    def __init__(self, config: BackendConfigT):
        self.config = config

        self.logger = getLogger(self.NAME)
        self.logger.info(f"Allocating {self.NAME} backend")

        self.logger.info(f"\t+ Seeding backend with {self.config.seed}")
        self.seed()

        if self.config.library == "diffusers":
            self.logger.info("\t+ Benchmarking a Diffusers pipeline")
            self.pretrained_config = get_diffusers_pretrained_config(self.config.model, **self.config.model_kwargs)
            self.automodel_loader = get_diffusers_auto_pipeline_class_for_task(self.config.task)
            self.model_shapes = extract_diffusers_shapes_from_model()
            self.pretrained_processor = None
            self.generation_config = None

        elif self.config.library == "timm":
            self.logger.info("\t+ Benchmarking a Timm model")
            self.pretrained_config = get_timm_pretrained_config(self.config.model)
            self.model_shapes = extract_timm_shapes_from_config(self.pretrained_config)
            self.automodel_loader = get_timm_model_creator()
            self.pretrained_processor = None
            self.generation_config = None

        elif self.config.library == "llama_cpp":
            self.logger.info("\t+ Benchmarking a LlamaCpp model")
            self.pretrained_processor = None
            self.pretrained_config = None
            self.generation_config = None
            self.automodel_loader = None
            self.model_shapes = {}

        else:
            self.logger.info("\t+ Benchmarking a Transformers model")
            self.automodel_loader = get_transformers_auto_model_class_for_task(self.config.task, self.config.model_type)
            self.generation_config = get_transformers_generation_config(self.config.model, **self.config.model_kwargs)
            self.pretrained_config = get_transformers_pretrained_config(self.config.model, **self.config.model_kwargs)
            self.pretrained_processor = get_transformers_pretrained_processor(
                self.config.processor, **self.config.processor_kwargs
            )
            self.model_shapes = extract_transformers_shapes_from_artifacts(
                self.pretrained_config, self.pretrained_processor
            )