Source code for mxnet.gluon.trainer

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# coding: utf-8
# pylint: disable=
"""Parameter optimizer."""
__all__ = ['Trainer']

from .. import optimizer as opt
from ..model import _create_kvstore
from .parameter import ParameterDict, Parameter

[docs]class Trainer(object): """Applies an `Optimizer` on a set of Parameters. Trainer should be used together with `autograd`. Parameters ---------- params : ParameterDict The set of parameters to optimize. optimizer : str or Optimizer The optimizer to use. See `help `_ on Optimizer for a list of available optimizers. optimizer_params : dict Key-word arguments to be passed to optimizer constructor. For example, `{'learning_rate': 0.1}`. All optimizers accept learning_rate, wd (weight decay), clip_gradient, and lr_scheduler. See each optimizer's constructor for a list of additional supported arguments. kvstore : str or KVStore kvstore type for multi-gpu and distributed training. See help on :any:`mxnet.kvstore.create` for more information. compression_params : dict Specifies type of gradient compression and additional arguments depending on the type of compression being used. For example, 2bit compression requires a threshold. Arguments would then be {'type':'2bit', 'threshold':0.5} See mxnet.KVStore.set_gradient_compression method for more details on gradient compression. Properties ---------- learning_rate: float The current learning rate of the optimizer. Given an Optimizer object optimizer, its learning rate can be accessed as optimizer.learning_rate. """ def __init__(self, params, optimizer, optimizer_params=None, kvstore='device', compression_params=None): if isinstance(params, (dict, ParameterDict)): params = list(params.values()) if not isinstance(params, (list, tuple)): raise ValueError( "First argument must be a list or dict of Parameters, " \ "got %s."%(type(params))) self._params = [] for param in params: if not isinstance(param, Parameter): raise ValueError( "First argument must be a list or dict of Parameters, " \ "got list of %s."%(type(param))) self._params.append(param) self._compression_params = compression_params optimizer_params = optimizer_params if optimizer_params else {} self._scale = optimizer_params.get('rescale_grad', 1.0) self._contexts = self._check_contexts() self._init_optimizer(optimizer, optimizer_params) self._kv_initialized = False self._kvstore = kvstore def _check_contexts(self): contexts = None for param in self._params: ctx = param.list_ctx() assert contexts is None or contexts == ctx, \ "All Parameters must be initialized on the same set of contexts, " \ "but Parameter %s is initialized on %s while previous Parameters " \ "are initialized on %s."%(, str(ctx), str(contexts)) contexts = ctx return contexts def _init_optimizer(self, optimizer, optimizer_params): param_dict = {i: param for i, param in enumerate(self._params)} if isinstance(optimizer, opt.Optimizer): assert not optimizer_params, \ "optimizer_params must be None if optimizer is an instance of " \ "Optimizer instead of str" self._optimizer = optimizer self._optimizer.param_dict = param_dict else: self._optimizer = opt.create(optimizer, param_dict=param_dict, **optimizer_params) self._updaters = [opt.get_updater(self._optimizer) \ for _ in self._contexts] def _init_kvstore(self): arg_arrays = {[0]) for param in self._params} kvstore, update_on_kvstore = _create_kvstore(self._kvstore, len(self._contexts), arg_arrays) if kvstore: if self._compression_params: kvstore.set_gradient_compression(self._compression_params) if 'dist' in kvstore.type: update_on_kvstore = False for i, param in enumerate(self._params): param_arrays = param.list_data() kvstore.init(i, param_arrays[0]) kvstore.pull(i, param_arrays, priority=-i) if update_on_kvstore: kvstore.set_optimizer(self._optimizer) self._kvstore = kvstore self._update_on_kvstore = update_on_kvstore else: self._kvstore = None self._update_on_kvstore = None self._kv_initialized = True @property def learning_rate(self): if not isinstance(self._optimizer, opt.Optimizer): raise UserWarning("Optimizer has to be defined before its learning " "rate can be accessed.") else: return self._optimizer.learning_rate
[docs] def set_learning_rate(self, lr): """Sets a new learning rate of the optimizer. Parameters ---------- lr : float The new learning rate of the optimizer. """ if not isinstance(self._optimizer, opt.Optimizer): raise UserWarning("Optimizer has to be defined before its learning " "rate is mutated.") else: self._optimizer.set_learning_rate(lr)
[docs] def step(self, batch_size, ignore_stale_grad=False): """Makes one step of parameter update. Should be called after `autograd.compute_gradient` and outside of `record()` scope. Parameters ---------- batch_size : int Batch size of data processed. Gradient will be normalized by `1/batch_size`. Set this to 1 if you normalized loss manually with `loss = mean(loss)`. ignore_stale_grad : bool, optional, default=False If true, ignores Parameters with stale gradient (gradient that has not been updated by `backward` after last step) and skip update. """ if not self._kv_initialized: self._init_kvstore() self._optimizer.rescale_grad = self._scale / batch_size for i, param in enumerate(self._params): if param.grad_req == 'null': continue if not ignore_stale_grad: for data in param.list_data(): if not data._fresh_grad: raise UserWarning( "Gradient of Parameter `%s` on context %s has not been updated " "by backward since last `step`. This could mean a bug in your " "model that maked it only use a subset of the Parameters (Blocks) " "for this iteration. If you are intentionally only using a subset, " "call step with ignore_stale_grad=True to suppress this " "warning and skip updating of Parameters with stale gradient" \ %(, str(data.context))) if self._kvstore: self._kvstore.push(i, param.list_grad(), priority=-i) if self._update_on_kvstore: self._kvstore.pull(i, param.list_data(), priority=-i) continue else: self._kvstore.pull(i, param.list_grad(), priority=-i) for upd, arr, grad in zip(self._updaters, param.list_data(), param.list_grad()): if not ignore_stale_grad or arr._fresh_grad: upd(i, grad, arr) arr._fresh_grad = False
[docs] def save_states(self, fname): """Saves trainer states (e.g. optimizer, momentum) to a file. Parameters ---------- fname : str Path to output states file. """ assert self._optimizer is not None if self._update_on_kvstore: self._kvstore.save_optimizer_states(fname, dump_optimizer=True) else: with open(fname, 'wb') as fout: fout.write(self._updaters[0].get_states(dump_optimizer=True))
[docs] def load_states(self, fname): """Loads trainer states (e.g. optimizer, momentum) from a file. Parameters ---------- fname : str Path to input states file. """ if self._update_on_kvstore: self._kvstore.load_optimizer_states(fname) self._optimizer = self._kvstore._updater.optimizer else: with open(fname, 'rb') as f: states = for updater in self._updaters: updater.set_states(states) updater.optimizer = self._updaters[0].optimizer self._optimizer = self._updaters[0].optimizer