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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."%(param.name, 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 = {param.name: param.data(self._contexts[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" \
%(param.name, 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 = f.read()
for updater in self._updaters:
updater.set_states(states)
updater.optimizer = self._updaters[0].optimizer
self._optimizer = self._updaters[0].optimizer
