mindspore.boost.boost 源代码

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"""boost"""
from __future__ import absolute_import

import threading
from mindspore.nn.optim import SGD
from mindspore.boost.less_batch_normalization import LessBN
from mindspore.boost.grad_freeze import GradientFreeze
from mindspore.boost.base import OptimizerProcess, ParameterProcess
from mindspore.boost.base import _get_local_pca_mat_path


__all__ = ["AutoBoost"]

_boost_config_mode = ["auto", "manual", "enable_all", "disable_all"]
_boost_config_level = {
    "O0": {
        "less_bn": False,
        "grad_freeze": False,
        "adasum": False,
        "grad_accumulation": False,
        "dim_reduce": False,
        'loss_scale_group': False},
    "O1": {
        "less_bn": True,
        "grad_freeze": True,
        "adasum": False,
        "grad_accumulation": False,
        "dim_reduce": False,
        'loss_scale_group': False},
    "O2": {
        "less_bn": True,
        "grad_freeze": True,
        "adasum": True,
        "grad_accumulation": False,
        "dim_reduce": False,
        'loss_scale_group': False}
    }


[文档]class AutoBoost: r""" Provide auto accelerating for network. Args: level (str): Boost config level. Default: "O0". boost_config_dict (dict): User config hyperparameter dict, recommended config format: .. code-block:: { "boost": { "mode": "auto", "less_bn": False, "grad_freeze": False, "adasum": False, "grad_accumulation": False, "dim_reduce": False, "loss_scale_group": False }, "common": { "gradient_split_groups": [50, 100], "device_number": 8 }, "less_bn": { "fn_flag": True, "gc_flag": True }, "grad_freeze": { "param_groups": 10, "freeze_type": 1, "freeze_p": 0.7, "total_steps": 65536 } "grad_accumulation": { "grad_accumulation_step": 1 }, "dim_reduce": { "rho": 0.55, "gamma": 0.9, "alpha": 0.001, "sigma": 0.4, "n_components": 32, "pca_mat_path": None, "weight_load_dir": None, "timeout": 1800 } } - boost: - mode (str): How to set the boost. Supports ["auto", "manual", "enable_all", "disable_all"]. Default: "auto". - auto: Depend on the argument "boost_level" in class Model. - manual: Depend on "boost_config_dict". - enable_all: Set all boost functions true. - disable_all: Set all boost functions false. - less_bn (bool): Whether to apply less_bn function. Default: False. - grad_freeze: (bool): Whether to apply grad_freeze function. Default: False. - adasum (bool): Whether to apply adasum function. Default: False. - grad_accumulation (bool): Whether to apply grad_accumulation function. Default: False. - dim_reduce (bool): Whether to apply dim_reduce function. Default: False. - loss_scale_group (bool): Whether to apply loss_scale_group function. Default: False. If set dim_reduce true, other functions will be false. If set grad_freeze true and dim_reduce false, other functions will be false. - common: - gradient_split_groups (list): The gradient split point of this network. Default: [50, 100]. - device_number (int): Device number. Default: 8. - less_bn: - fn_flag (bool): Whether changing fc to fn. Default: True. - gc_flag (bool): Whether to apply gc. Default: True. - grad_freeze: - param_groups (int): The number of parameter groups. Default: 10. - freeze_type (int): Gradient freeze grouping strategy, select from [0, 1]. Default: 1. - freeze_p (float): Gradient freezing probability. Default: 0.7. - total_steps (int): Total training steps. Default: 65536. - grad_accumulation: - grad_accumulation_step (int): Steps to accumulate gradients. Default: 1. - dim_reduce: The leading principles of dim_reduce: .. math:: \begin{align} grad\_k &= pca\_mat \cdot grad\\ dk &= - bk \cdot grad\_k\\ sk &= rho ^ m \cdot dk\\ delta\_loss &= sigma \cdot grad\_k.T \cdot sk \end{align} Here: - pca_mat (array): Shape (k*n), k is part of n_components, n is the size of weight. - bk (array): Shape (k*k), is the symmetric positive definite matrix in Quasi-Newton method. we need to find the m satisfy: .. math:: new\_loss < old\_loss + delta\_loss Then, get delta_grad to update the weights for model: .. math:: \begin{align} grad\_k\_proj &= pca\_mat.T \cdot grad\_k\\ new\_grad\_momentum &= gamma \cdot old\_grad\_momentum + grad - grad\_k\_proj\\ delta\_grad &= alpha \cdot new\_grad\_momentum - pca\_mat.T \cdot sk \end{align} - rho (float): Generally, it does not need to be modified. Default: 0.55. - gamma (float): Generally, it does not need to be modified. Default: 0.9. - alpha (float): Generally, it does not need to be modified. Default: 0.001. - sigma (float): Generally, it does not need to be modified. Default: 0.4. - n_components (int): PCA component. Default: 32. - pca_mat_path (str): The path to load pca mat. Default: None. - weight_load_dir (str): The directory to load weight files saved as ckpt. Default: None. - timeout (int): Waiting time to load local pca mat. Default: 1800 (second). User can load the config through the JSON file or use the dictionary directly. The unconfigured parameters will adopt the default values. Raises: ValueError: The boost mode not in ["auto", "manual", "enable_all", "disable_all"]. Supported Platforms: ``Ascend`` Examples: >>> from mindspore.boost import AutoBoost >>> #1) when configuring the dict directly: >>> boost_config_dict = {"boost": {"mode": "auto"}} >>> boost = AutoBoost("O1", boost_config_dict) >>> >>> #2) when loading the dict from a json file: >>> import json >>> boost_json = "/path/boost_config.json" >>> with open(boost_json, 'r') as fp: >>> boost_config_dict = json.load(fp) >>> boost = AutoBoost("O1", boost_config_dict) """ _instance_lock = threading.Lock() _instance = None # pylint: disable=unused-argument def __new__(cls, *args, **kwargs): if AutoBoost._instance is None: with AutoBoost._instance_lock: if AutoBoost._instance is None: AutoBoost._instance = object.__new__(cls) AutoBoost._instance.level = None AutoBoost._instance.boost_config_dict = None return AutoBoost._instance def __init__(self, level="O0", boost_config_dict=""): if level not in _boost_config_level.keys(): level = "O0" if self._instance.level is None: self.level = level self.boost_config_dict = boost_config_dict self._fn_flag = True self._gc_flag = True self._param_groups = 10 self._freeze_type = 1 self._freeze_p = 0.7 self._total_steps = 65536 self.gradient_groups = None self.device_number = 8 self.grad_accumulation_step = 1 self.rho = 0.55 self.gamma = 0.9 self.alpha = 0.001 self.sigma = 0.4 self.n_components = 32 self.pca_mat_path = None self.weight_load_dir = None self.local_pca_mat_path = None self.timeout = 1800 self.boost_config = self._get_configuration(level, self.boost_config_dict) self._param_processer = ParameterProcess()
[文档] def network_auto_process_train(self, network, optimizer): r""" Boost network train. Args: network (Cell): The training network. optimizer (Cell): Optimizer for updating the weights. """ if self.boost_config.get("dim_reduce"): self.local_pca_mat_path = _get_local_pca_mat_path(self.weight_load_dir, self.pca_mat_path, self.n_components, self.device_number, network) optimizer = SGD(network.trainable_params(), learning_rate=1) setattr(optimizer, "dim_reduce", True) return network, optimizer if self.boost_config.get("less_bn"): network = LessBN(network, fn_flag=self._fn_flag) optimizer_process = OptimizerProcess(optimizer) group_params = self._param_processer.assign_parameter_group(network.trainable_params(), self.gradient_groups) optimizer_process.origin_params = \ ParameterProcess.generate_group_params(group_params, optimizer_process.origin_params) if self._gc_flag: optimizer_process.add_grad_centralization(network) optimizer = optimizer_process.generate_new_optimizer() if self.boost_config.get("grad_freeze"): freeze_processer = GradientFreeze(self._param_groups, self._freeze_type, self._freeze_p, self._total_steps) network, optimizer = freeze_processer.freeze_generate(network, optimizer) if self.boost_config.get("adasum"): setattr(optimizer, "adasum", True) return network, optimizer
[文档] def network_auto_process_eval(self, network): r""" Boost network eval. Args: network (Cell): The inference network. """ if self.boost_config.get("dim_reduce"): return network if self.boost_config.get("less_bn"): network = LessBN(network) return network
def _set_fn_flag(self, fn_flag): self._fn_flag = fn_flag def _set_gc_flag(self, gc_flag): self._gc_flag = gc_flag def _set_param_groups(self, param_groups): self._param_groups = param_groups def _set_freeze_type(self, freeze_type): self._freeze_type = freeze_type def _set_freeze_p(self, freeze_p): self._freeze_p = freeze_p def _set_total_steps(self, total_steps): self._total_steps = total_steps def _set_device_number(self, device_number): self.device_number = device_number def _set_grad_accumulation_step(self, grad_accumulation_step): self.grad_accumulation_step = grad_accumulation_step def _set_gradient_split_groups(self, gradient_groups): if not isinstance(gradient_groups, (list, int)): raise ValueError(f"gradient_groups `{gradient_groups}` is not in (list, int)") if isinstance(gradient_groups, int): gradient_groups = list(gradient_groups) self.gradient_groups = gradient_groups def _set_rho(self, rho): self.rho = rho def _set_gamma(self, gamma): self.gamma = gamma def _set_alpha(self, alpha): self.alpha = alpha def _set_sigma(self, sigma): self.sigma = sigma def _set_n_components(self, n_components): self.n_components = n_components def _set_pca_mat_path(self, pca_mat_path): self.pca_mat_path = pca_mat_path def _set_weight_load_dir(self, weight_load_dir): self.weight_load_dir = weight_load_dir def _set_timeout(self, timeout): self.timeout = timeout def _get_configuration(self, level, boost_config_dict): """Get configuration.""" level_config = _boost_config_level.get(level) if not boost_config_dict: return level_config mode = "auto" if 'boost' in boost_config_dict and 'mode' in boost_config_dict['boost']: mode = boost_config_dict['boost']['mode'] if mode not in _boost_config_mode: raise ValueError("The boost mode must be in {}, but got {}".format(_boost_config_mode, mode)) if mode == "manual": for key, value in boost_config_dict["boost"].items(): if key in level_config: level_config[key] = value elif mode == "enable_all": level_config = {key: True for key in level_config} elif mode == "disable_all": level_config = {key: False for key in level_config} self._do_new_config_func(boost_config_dict, level_config) return level_config def _do_new_config_func(self, boost_config_dict, level_config): valid_boost_each_mode_config = [] for key, boost_each_mode_config in boost_config_dict.items(): if key in level_config.keys() and level_config[key] or key == "common": valid_boost_each_mode_config.append(boost_each_mode_config) for boost_each_mode_config in valid_boost_each_mode_config: for key_s in boost_each_mode_config.keys(): if key_s in self._boost_config_func_map: self._boost_config_func_map[key_s](self, boost_each_mode_config[key_s]) _boost_config_func_map = { "fn_flag": _set_fn_flag, "gc_flag": _set_gc_flag, "param_groups": _set_param_groups, "freeze_type": _set_freeze_type, "freeze_p": _set_freeze_p, "total_steps": _set_total_steps, "device_number": _set_device_number, "gradient_split_groups": _set_gradient_split_groups, "grad_accumulation_step": _set_grad_accumulation_step, "rho": _set_rho, "gamma": _set_gamma, "alpha": _set_alpha, "sigma": _set_sigma, "n_components": _set_n_components, "pca_mat_path": _set_pca_mat_path, "weight_load_dir": _set_weight_load_dir, "timeout": _set_timeout }