peacock-data-public-datasets-idc-cronscript
/
venv
/lib
/python3.10
/site-packages
/deepspeed
/autotuning
/config.py
| # Copyright (c) Microsoft Corporation. | |
| # SPDX-License-Identifier: Apache-2.0 | |
| # DeepSpeed Team | |
| from deepspeed.runtime.config_utils import get_scalar_param, get_dict_param, DeepSpeedConfigObject | |
| from deepspeed.autotuning.constants import * | |
| class DeepSpeedAutotuningConfig(DeepSpeedConfigObject): | |
| def __init__(self, param_dict): | |
| super(DeepSpeedAutotuningConfig, self).__init__() | |
| self.enabled = None | |
| self.start_step = None | |
| self.end_step = None | |
| self.metric_path = None | |
| self.arg_mappings = None | |
| self.metric = None | |
| self.model_info = None | |
| self.results_dir = None | |
| self.exps_dir = None | |
| self.overwrite = None | |
| if param_dict and AUTOTUNING in param_dict.keys(): | |
| autotuning_dict = param_dict[AUTOTUNING] | |
| else: | |
| autotuning_dict = {} | |
| self._initialize(autotuning_dict) | |
| def _initialize(self, autotuning_dict): | |
| self.enabled = get_scalar_param(autotuning_dict, AUTOTUNING_ENABLED, AUTOTUNING_ENABLED_DEFAULT) | |
| self.fast = get_scalar_param(autotuning_dict, AUTOTUNING_FAST, AUTOTUNING_FAST_DEFAULT) | |
| self.results_dir = get_scalar_param(autotuning_dict, AUTOTUNING_RESULTS_DIR, AUTOTUNING_RESULTS_DIR_DEFAULT) | |
| assert self.results_dir, "results_dir cannot be empty" | |
| self.exps_dir = get_scalar_param(autotuning_dict, AUTOTUNING_EXPS_DIR, AUTOTUNING_EXPS_DIR_DEFAULT) | |
| assert self.exps_dir, "exps_dir cannot be empty" | |
| self.overwrite = get_scalar_param(autotuning_dict, AUTOTUNING_OVERWRITE, AUTOTUNING_OVERWRITE_DEFAULT) | |
| self.start_profile_step = get_scalar_param(autotuning_dict, AUTOTUNING_START_PROFILE_STEP, | |
| AUTOTUNING_START_PROFILE_STEP_DEFAULT) | |
| self.end_profile_step = get_scalar_param(autotuning_dict, AUTOTUNING_END_PROFILE_STEP, | |
| AUTOTUNING_END_PROFILE_STEP_DEFAULT) | |
| self.metric = get_scalar_param(autotuning_dict, AUTOTUNING_METRIC, AUTOTUNING_METRIC_DEFAULT) | |
| self.metric_path = get_scalar_param(autotuning_dict, AUTOTUNING_METRIC_PATH, AUTOTUNING_METRIC_PATH_DEFAULT) | |
| self.tuner_type = get_scalar_param(autotuning_dict, AUTOTUNING_TUNER_TYPE, AUTOTUNING_TUNER_TYPE_DEFAULT) | |
| self.tuner_early_stopping = get_scalar_param(autotuning_dict, AUTOTUNING_TUNER_EARLY_STOPPING, | |
| AUTOTUNING_TUNER_EARLY_STOPPING_DEFAULT) | |
| self.tuner_num_trials = get_scalar_param(autotuning_dict, AUTOTUNING_TUNER_NUM_TRIALS, | |
| AUTOTUNING_TUNER_NUM_TRIALS_DEFAULT) | |
| self.arg_mappings = get_dict_param(autotuning_dict, AUTOTUNING_ARG_MAPPINGS, AUTOTUNING_ARG_MAPPINGS_DEFAULT) | |
| self.model_info = get_model_info_config(autotuning_dict) | |
| self.model_info_path = get_scalar_param(autotuning_dict, AUTOTUNING_MODEL_INFO_PATH, | |
| AUTOTUNING_MODEL_INFO_PATH_DEFAULT) | |
| self.mp_size = get_scalar_param(autotuning_dict, AUTOTUNING_MP_SIZE, AUTOTUNING_MP_SIZE_DEFAULT) | |
| self.max_train_batch_size = get_dict_param(autotuning_dict, AUTOTUNING_MAX_TRAIN_BATCH_SIZE, | |
| AUTOTUNING_MAX_TRAIN_BATCH_SIZE_DEFAULT) | |
| self.min_train_batch_size = get_dict_param(autotuning_dict, AUTOTUNING_MIN_TRAIN_BATCH_SIZE, | |
| AUTOTUNING_MIN_TRAIN_BATCH_SIZE_DEFAULT) | |
| self.max_train_micro_batch_size_per_gpu = get_dict_param( | |
| autotuning_dict, AUTOTUNING_MAX_TRAIN_MICRO_BATCH_SIZE_PER_GPU, | |
| AUTOTUNING_MAX_TRAIN_MICRO_BATCH_SIZE_PER_GPU_DEFAULT) | |
| self.min_train_micro_batch_size_per_gpu = get_dict_param( | |
| autotuning_dict, AUTOTUNING_MIN_TRAIN_MICRO_BATCH_SIZE_PER_GPU, | |
| AUTOTUNING_MIN_TRAIN_MICRO_BATCH_SIZE_PER_GPU_DEFAULT) | |
| self.num_tuning_micro_batch_sizes = get_dict_param(autotuning_dict, AUTOTUNING_NUM_TUNING_MICRO_BATCH_SIZES, | |
| AUTOTUNING_NUM_TUNING_MICRO_BATCH_SIZES_DEFAULT) | |
| def get_model_info_config(param_dict): | |
| if MODEL_INFO in param_dict and param_dict[MODEL_INFO] is not None: | |
| model_info_config = {} | |
| for key, default_value in MODEL_INFO_KEY_DEFAULT_DICT.items(): | |
| model_info_config[key] = get_scalar_param(param_dict[MODEL_INFO], key, default_value) | |
| return model_info_config | |
| return None | |
| def get_default_model_info_config(): | |
| return MODEL_INFO_KEY_DEFAULT_DICT | |