/usr/lib/python2.7/dist-packages/mdp/parallel/parallelflows.py is in python-mdp 3.5-1.
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Module for parallel flows that can handle the parallel training / execution.
Corresponding classes for task callables and ResultContainer are defined here
as well.
"""
from __future__ import print_function
from builtins import zip
from builtins import str
from builtins import next
from builtins import range
import mdp
from mdp import numx as n
from .parallelnodes import NotForkableParallelException
from .scheduling import (
TaskCallable, ResultContainer, OrderedResultContainer, Scheduler
)
from mdp.hinet import FlowNode
### Helper code for node purging before transport. ###
class _DummyNode(mdp.Node):
"""Dummy node class for empty nodes."""
@staticmethod
def is_trainable():
return False
def _execute(self, x):
err = "This is only a dummy created by 'parallel._purge_flownode'."
raise mdp.NodeException(err)
_DUMMY_NODE = _DummyNode()
def _purge_flownode(flownode):
"""Replace nodes that are """
for i_node, node in enumerate(flownode._flow):
if not (node._train_phase_started or node.use_execute_fork()):
flownode._flow.flow[i_node] = _DUMMY_NODE
### Train task classes ###
class FlowTaskCallable(TaskCallable):
"""Base class for all flow callables.
It deals activating the required extensions.
"""
def __init__(self):
"""Store the currently active extensions."""
self._used_extensions = mdp.get_active_extensions()
super(FlowTaskCallable, self).__init__()
def setup_environment(self):
"""Activate the used extensions."""
# deactivate all active extensions for safety
mdp.deactivate_extensions(mdp.get_active_extensions())
mdp.activate_extensions(self._used_extensions)
class FlowTrainCallable(FlowTaskCallable):
"""Implements a single training phase in a flow for a data block.
A FlowNode is used to simplify the forking process and to
encapsulate the flow.
You can also derive from this class to define your own callable class.
"""
def __init__(self, flownode, purge_nodes=True):
"""Store everything for the training.
keyword arguments:
flownode -- FlowNode containing the flow to be trained.
purge_nodes -- If True nodes not needed for the join will be replaced
with dummy nodes to reduce the footprint.
"""
self._flownode = flownode
self._purge_nodes = purge_nodes
super(FlowTrainCallable, self).__init__()
def __call__(self, data):
"""Do the training and return only the trained node.
data -- training data block (array or list if additional arguments are
required)
"""
if type(data) is n.ndarray:
self._flownode.train(data)
else:
self._flownode.train(*data)
# note the local training in ParallelFlow relies on the flownode
# being preserved, so derived classes should preserve it as well
if self._purge_nodes:
_purge_flownode(self._flownode)
return self._flownode
def fork(self):
return self.__class__(self._flownode.fork(),
purge_nodes=self._purge_nodes)
class TrainResultContainer(ResultContainer):
"""Container for parallel nodes.
Expects flownodes as results and joins them to save memory.
A list containing one flownode is returned, so this container can replace
the standard list container without any changes elsewhere.
"""
def __init__(self):
super(TrainResultContainer, self).__init__()
self._flownode = None
def add_result(self, result, task_index):
if not self._flownode:
self._flownode = result
else:
self._flownode.join(result)
def get_results(self):
flownode = self._flownode
self._flownode = None
return [flownode,]
### Execute task classes ###
class FlowExecuteCallable(FlowTaskCallable):
"""Implements data execution through a Flow.
A FlowNode is used to simplify the forking process and to
encapsulate the flow.
"""
def __init__(self, flownode, nodenr=None, purge_nodes=True):
"""Store everything for the execution.
flownode -- FlowNode for the execution
nodenr -- optional nodenr argument for the flow execute method
purge_nodes -- If True nodes not needed for the join will be replaced
with dummy nodes to reduce the footprint.
"""
self._flownode = flownode
self._nodenr = nodenr
self._purge_nodes = purge_nodes
super(FlowExecuteCallable, self).__init__()
def __call__(self, x):
"""Return the execution result.
x -- data chunk
If use_fork_execute is True for the flownode then it is returned
in the result tuple.
"""
y = self._flownode.execute(x, nodenr=self._nodenr)
if self._flownode.use_execute_fork():
if self._purge_nodes:
_purge_flownode(self._flownode)
return (y, self._flownode)
else:
return (y, None)
def fork(self):
return self.__class__(self._flownode.fork(), nodenr=self._nodenr,
purge_nodes=self._purge_nodes)
class ExecuteResultContainer(OrderedResultContainer):
"""Default result container with automatic restoring of the result order.
This result container should be used together with BiFlowExecuteCallable.
Both the execute result (x and possibly msg) and the forked BiFlowNode
are stored.
"""
def __init__(self):
"""Initialize attributes."""
super(ExecuteResultContainer, self).__init__()
self._flownode = None
def add_result(self, result, task_index):
"""Remove the forked BiFlowNode from the result and join it."""
excecute_result, forked_flownode = result
super(ExecuteResultContainer, self).add_result(excecute_result,
task_index)
if forked_flownode is not None:
if self._flownode is None:
self._flownode = forked_flownode
else:
self._flownode.join(forked_flownode)
def get_results(self):
"""Return the ordered results.
The joined BiFlowNode is returned in the first result list entry,
for the following result entries BiFlowNode is set to None.
This reduces memory consumption while staying transparent for the
ParallelBiFlow.
"""
excecute_results = super(ExecuteResultContainer, self).get_results()
flownode_results = ([self._flownode,]
+ ([None] * (len(excecute_results)-1)))
return list(zip(excecute_results, flownode_results))
### ParallelFlow Class ###
class ParallelFlowException(mdp.FlowException):
"""Standard exception for problems with ParallelFlow."""
pass
class NoTaskException(ParallelFlowException):
"""Exception for problems with the task creation."""
pass
class ParallelFlow(mdp.Flow):
"""A parallel flow provides the methods for parallel training / execution.
Nodes in the flow which are not derived from ParallelNode are trained in
the normal way. The training is also done normally if fork() raises a
TrainingPhaseNotParallelException. This can be intentionally used by the
node to request local training without forking.
Parallel execution on the other hand should work for all nodes, since it
only relies on the copy method of nodes.
The stop_training method is always called locally, with no forking or
copying involved.
Both parallel training and execution can be done conveniently by providing
a scheduler instance to the train or execute method.
It is also possible to manage the tasks manually. This is done via the
methods setup_parallel_training (or execution), get_task and use_results.
The code of the train / execute method can serve as an example how to use
these methods and process the tasks by a scheduler.
"""
def __init__(self, flow, verbose=False, **kwargs):
"""Initialize the internal variables.
Note that the crash_recovery flag is is not supported, so it is
disabled.
"""
kwargs["crash_recovery"] = False
super(ParallelFlow, self).__init__(flow, verbose=verbose,
**kwargs)
self._train_data_iterables = None # all training data
self._train_data_iterator = None # iterator for current training
# index of currently trained node, also used as flag for training
# takes value None for not training
self._i_train_node = None
self._flownode = None # used during training
# iterable for execution data
# also signals if parallel execution is underway
self._exec_data_iterator = None
self._next_task = None # buffer for next task
self._train_callable_class = None
self._execute_callable_class = None
@mdp.with_extension("parallel")
def train(self, data_iterables, scheduler=None,
train_callable_class=None,
overwrite_result_container=True,
**kwargs):
"""Train all trainable nodes in the flow.
If a scheduler is provided the training will be done in parallel on the
scheduler.
data_iterables -- A list of iterables, one for each node in the flow.
The iterators returned by the iterables must
return data arrays that are then used for the node training.
See Flow.train for more details.
If a custom train_callable_class is used to preprocess the data
then other data types can be used as well.
scheduler -- Value can be either None for normal training (default
value) or a Scheduler instance for parallel training with the
scheduler.
If the scheduler value is an iterable or iterator then it is
assumed that it contains a scheduler for each training phase.
After a node has been trained the scheduler is shutdown. Note that
you can e.g. use a generator to create the schedulers just in time.
For nodes which are not trained the scheduler can be None.
train_callable_class -- Class used to create training callables for the
scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the train_callable_class is only used if a scheduler was
provided. By default NodeResultContainer is used.
overwrite_result_container -- If set to True (default value) then
the result container in the scheduler will be overwritten with an
instance of NodeResultContainer (unless it is already an instance
of NodeResultContainer). This improves the memory efficiency.
"""
# Warning: If this method is updated you also have to update train
# in ParallelCheckpointFlow.
if self.is_parallel_training:
raise ParallelFlowException("Parallel training is underway.")
if scheduler is None:
if train_callable_class is not None:
err = ("A train_callable_class was specified but no scheduler "
"was given, so the train_callable_class has no effect.")
raise ParallelFlowException(err)
super(ParallelFlow, self).train(data_iterables, **kwargs)
else:
if train_callable_class is None:
train_callable_class = FlowTrainCallable
schedulers = None
# do parallel training
try:
self.setup_parallel_training(
data_iterables,
train_callable_class=train_callable_class,
**kwargs)
# prepare scheduler
if not isinstance(scheduler, Scheduler):
# scheduler contains an iterable with the schedulers
# self._i_train_node was set in setup_parallel_training
schedulers = iter(scheduler)
scheduler = next(schedulers)
if self._i_train_node > 0:
# dispose schedulers for pretrained nodes
for _ in range(self._i_train_node):
if scheduler is not None:
scheduler.shutdown()
scheduler = next(schedulers)
elif self._i_train_node is None:
# all nodes are already trained, dispose schedulers
for _ in range(len(self.flow) - 1):
if scheduler is not None:
scheduler.shutdown()
# the last scheduler will be shutdown in finally
scheduler = next(schedulers)
last_trained_node = self._i_train_node
else:
schedulers = None
# check that the scheduler is compatible
if ((scheduler is not None) and
overwrite_result_container and
(not isinstance(scheduler.result_container,
TrainResultContainer))):
scheduler.result_container = TrainResultContainer()
## train all nodes
while self.is_parallel_training:
while self.task_available:
task = self.get_task()
scheduler.add_task(*task)
results = scheduler.get_results()
if results == []:
err = ("Could not get any training tasks or results "
"for the current training phase.")
raise Exception(err)
else:
self.use_results(results)
# check if we have to switch to next scheduler
if ((schedulers is not None) and
(self._i_train_node is not None) and
(self._i_train_node > last_trained_node)):
# dispose unused schedulers
for _ in range(self._i_train_node - last_trained_node):
if scheduler is not None:
scheduler.shutdown()
scheduler = next(schedulers)
last_trained_node = self._i_train_node
# check that the scheduler is compatible
if ((scheduler is not None) and
overwrite_result_container and
(not isinstance(scheduler.result_container,
TrainResultContainer))):
scheduler.result_container = TrainResultContainer()
finally:
# reset iterable references, which cannot be pickled
self._train_data_iterables = None
self._train_data_iterator = None
if (schedulers is not None) and (scheduler is not None):
scheduler.shutdown()
def setup_parallel_training(self, data_iterables,
train_callable_class=FlowTrainCallable):
"""Prepare the flow for handing out tasks to do the training.
After calling setup_parallel_training one has to pick up the
tasks with get_task, run them and finally return the results via
use_results. tasks are available as long as task_available returns
True. Training may require multiple phases, which are each closed by
calling use_results.
data_iterables -- A list of iterables, one for each node in the flow.
The iterators returned by the iterables must
return data arrays that are then used for the node training.
See Flow.train for more details.
If a custom train_callable_class is used to preprocess the data
then other data types can be used as well.
train_callable_class -- Class used to create training callables for the
scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
"""
if self.is_parallel_training:
err = "Parallel training is already underway."
raise ParallelFlowException(err)
self._train_callable_class = train_callable_class
self._train_data_iterables = self._train_check_iterables(data_iterables)
self._i_train_node = 0
self._flownode = FlowNode(mdp.Flow(self.flow))
self._next_train_phase()
def _next_train_phase(self):
"""Find the next phase or node for parallel training.
When it is found the corresponding internal variables are set.
Nodes which are not derived from ParallelNode are trained locally.
If a fork() fails due to a TrainingPhaseNotParallelException
in a certain train phase, then the training is done locally as well
(but fork() is tested again for the next phase).
"""
# find next node that can be forked, if required do local training
while self._i_train_node < len(self.flow):
current_node = self.flow[self._i_train_node]
if not current_node.is_training():
self._i_train_node += 1
continue
data_iterable = self._train_data_iterables[self._i_train_node]
try:
self._flownode.fork()
# fork successful, prepare parallel training
if self.verbose:
print ("start parallel training phase of " +
"node no. %d in parallel flow" %
(self._i_train_node+1))
self._train_data_iterator = iter(data_iterable)
first_task = self._create_train_task()
# make sure that the iterator is not empty
if first_task is None:
if current_node.get_current_train_phase() == 1:
err_str = ("The training data iteration for node "
"no. %d could not be repeated for the "
"second training phase, you probably "
"provided an iterator instead of an "
"iterable." % (self._i_train_node+1))
raise mdp.FlowException(err_str)
else:
err_str = ("The training data iterator for node "
"no. %d is empty." % (self._i_train_node+1))
raise mdp.FlowException(err_str)
task_data_chunk = first_task[0]
# Only first task contains the new callable (enable caching).
# A fork is not required here, since the callable is always
# forked in the scheduler.
self._next_task = (task_data_chunk,
self._train_callable_class(self._flownode))
break
except NotForkableParallelException as exception:
if self.verbose:
print ("could not fork node no. %d: %s" %
(self._i_train_node+1, str(exception)))
print ("start nonparallel training phase of " +
"node no. %d in parallel flow" %
(self._i_train_node+1))
self._local_train_phase(data_iterable)
if self.verbose:
print ("finished nonparallel training phase of " +
"node no. %d in parallel flow" %
(self._i_train_node+1))
self._stop_training_hook()
self._flownode.stop_training()
self._post_stop_training_hook()
if not self.flow[self._i_train_node].is_training():
self._i_train_node += 1
else:
# training is finished
self._i_train_node = None
def _local_train_phase(self, data_iterable):
"""Perform a single training phase locally.
The internal _train_callable_class is used for the training.
"""
current_node = self.flow[self._i_train_node]
task_callable = self._train_callable_class(self._flownode,
purge_nodes=False)
empty_iterator = True
for i_task, data in enumerate(data_iterable):
empty_iterator = False
# Note: if x contains additional args assume that the
# callable can handle this
task_callable(data)
if self.verbose:
print (" finished nonparallel task no. %d" % (i_task+1))
if empty_iterator:
if current_node.get_current_train_phase() == 1:
err_str = ("The training data iteration for node "
"no. %d could not be repeated for the "
"second training phase, you probably "
"provided an iterator instead of an "
"iterable." % (self._i_train_node+1))
raise mdp.FlowException(err_str)
else:
err_str = ("The training data iterator for node "
"no. %d is empty." % (self._i_train_node+1))
raise mdp.FlowException(err_str)
def _post_stop_training_hook(self):
"""Hook method that is called after stop_training is called."""
pass
def _create_train_task(self):
"""Create and return a single training task without callable.
Returns None if data iterator end is reached.
"""
try:
return (next(self._train_data_iterator), None)
except StopIteration:
return None
@mdp.with_extension("parallel")
def execute(self, iterable, nodenr=None, scheduler=None,
execute_callable_class=None,
overwrite_result_container=True):
"""Train all trainable nodes in the flow.
If a scheduler is provided the execution will be done in parallel on
the scheduler.
iterable -- An iterable or iterator that returns data arrays that are
used as input to the flow. Alternatively, one can specify one
data array as input.
If a custom execute_callable_class is used to preprocess the data
then other data types can be used as well.
nodenr -- Same as in normal flow, the flow is only executed up to the
nodenr.
scheduler -- Value can be either None for normal execution (default
value) or a Scheduler instance for parallel execution with the
scheduler.
execute_callable_class -- Class used to create execution callables for
the scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
Note that the execute_callable_class is only used if a scheduler was
provided. If a scheduler is provided the default class used is
NodeResultContainer.
overwrite_result_container -- If set to True (default value) then
the result container in the scheduler will be overwritten with an
instance of OrderedResultContainer (unless it is already an
instance of OrderedResultContainer). Otherwise the results might
have a different order than the data chunks, which could mess up
any subsequent analysis.
"""
if self.is_parallel_training:
raise ParallelFlowException("Parallel training is underway.")
if scheduler is None:
if execute_callable_class is not None:
err = ("A execute_callable_class was specified but no "
"scheduler was given, so the execute_callable_class "
"has no effect.")
raise ParallelFlowException(err)
return super(ParallelFlow, self).execute(iterable, nodenr)
if execute_callable_class is None:
execute_callable_class = FlowExecuteCallable
# check that the scheduler is compatible
if overwrite_result_container:
if not isinstance(scheduler.result_container,
ExecuteResultContainer):
scheduler.result_container = ExecuteResultContainer()
# do parallel execution
self._flownode = FlowNode(mdp.Flow(self.flow))
try:
self.setup_parallel_execution(
iterable,
nodenr=nodenr,
execute_callable_class=execute_callable_class)
while self.task_available:
task = self.get_task()
scheduler.add_task(*task)
result = self.use_results(scheduler.get_results())
finally:
# reset remaining iterator references, which cannot be pickled
self._exec_data_iterator = None
return result
def setup_parallel_execution(self, iterable, nodenr=None,
execute_callable_class=FlowExecuteCallable):
"""Prepare the flow for handing out tasks to do the execution.
After calling setup_parallel_execution one has to pick up the
tasks with get_task, run them and finally return the results via
use_results. use_results will then return the result as if the flow was
executed in the normal way.
iterable -- An iterable or iterator that returns data arrays that are
used as input to the flow. Alternatively, one can specify one
data array as input.
If a custom execute_callable_class is used to preprocess the data
then other data types can be used as well.
nodenr -- Same as in normal flow, the flow is only executed up to the
nodenr.
execute_callable_class -- Class used to create execution callables for
the scheduler. By specifying your own class you can implement data
transformations before the data is actually fed into the flow
(e.g. from 8 bit image to 64 bit double precision).
"""
if self.is_parallel_training:
raise ParallelFlowException("Parallel training is underway.")
self._execute_callable_class = execute_callable_class
if isinstance(iterable, n.ndarray):
iterable = [iterable]
self._exec_data_iterator = iter(iterable)
first_task = self._create_execute_task()
if first_task is None:
errstr = ("The execute data iterator is empty.")
raise mdp.FlowException(errstr)
task_data_chunk = first_task[0]
# Only first task contains the new callable (enable caching).
# A fork is not required here, since the callable is always
# forked in the scheduler.
self._next_task = (task_data_chunk,
self._execute_callable_class(self._flownode,
purge_nodes=True))
def _create_execute_task(self):
"""Create and return a single execution task.
Returns None if data iterator end is reached.
"""
try:
# TODO: check if forked task is forkable before enforcing caching
return (next(self._exec_data_iterator), None)
except StopIteration:
return None
def get_task(self):
"""Return a task either for either training or execution.
A a one task buffer is used to make task_available work.
tasks are available as long as need_result returns False or all the
training / execution is done. If no tasks are available a
NoTaskException is raised.
"""
if self._next_task is not None:
task = self._next_task
if self._i_train_node is not None:
self._next_task = self._create_train_task()
elif self._exec_data_iterator is not None:
self._next_task = self._create_execute_task()
else:
raise NoTaskException("No data available for execution task.")
return task
else:
raise NoTaskException("No task available for execution.")
@property
def is_parallel_training(self):
"""Return True if parallel training is underway."""
return self._i_train_node is not None
@property
def is_parallel_executing(self):
"""Return True if parallel execution is underway."""
return self._exec_data_iterator is not None
@property
def task_available(self):
"""Return True if tasks are available, otherwise False.
If False is returned this can indicate that results are needed to
continue training.
"""
return self._next_task is not None
def use_results(self, results):
"""Use the result from the scheduler.
During parallel training this will start the next training phase.
For parallel execution this will return the result, like a normal
execute would.
results -- Iterable containing the results, normally the return value
of scheduler.ResultContainer.get_results().
The individual results can be the return values of the tasks.
"""
if self.is_parallel_training:
for result in results:
# the flownode contains the original nodes
self._flownode.join(result)
if self.verbose:
print ("finished parallel training phase of node no. " +
"%d in parallel flow" % (self._i_train_node+1))
self._stop_training_hook()
self._flownode.stop_training()
self._post_stop_training_hook()
if not self.flow[self._i_train_node].is_training():
self._i_train_node += 1
self._next_train_phase()
elif self.is_parallel_executing:
self._exec_data_iterator = None
ys = [result[0] for result in results]
if self._flownode.use_execute_fork():
flownodes = [result[1] for result in results]
for flownode in flownodes:
if flownode is not None:
self._flownode.join(flownode)
return n.concatenate(ys)
class ParallelCheckpointFlow(ParallelFlow, mdp.CheckpointFlow):
"""Parallel version of CheckpointFlow.
Note that train phases are always closed, so e.g. CheckpointSaveFunction
should not expect open train phases. This is necessary since otherwise
stop_training() would be called remotely.
"""
def __init__(self, flow, verbose=False, **kwargs):
"""Initialize the internal variables."""
self._checkpoints = None
super(ParallelCheckpointFlow, self).__init__(flow=flow,
verbose=verbose,
**kwargs)
def train(self, data_iterables, checkpoints, scheduler=None,
train_callable_class=FlowTrainCallable,
overwrite_result_container=True,
**kwargs):
"""Train all trainable nodes in the flow.
Same as the train method in ParallelFlow, but with additional support
of checkpoint functions as in CheckpointFlow.
"""
super(ParallelCheckpointFlow, self).train(
data_iterables=data_iterables,
scheduler=scheduler,
train_callable_class=train_callable_class,
overwrite_result_container=overwrite_result_container,
checkpoints=checkpoints,
**kwargs)
def setup_parallel_training(self, data_iterables, checkpoints,
train_callable_class=FlowTrainCallable,
**kwargs):
"""Checkpoint version of parallel training."""
self._checkpoints = self._train_check_checkpoints(checkpoints)
super(ParallelCheckpointFlow, self).setup_parallel_training(
data_iterables,
train_callable_class=train_callable_class,
**kwargs)
def _post_stop_training_hook(self):
"""Check if we reached a checkpoint."""
super(ParallelCheckpointFlow, self)._post_stop_training_hook()
i_node = self._i_train_node
if self.flow[i_node].get_remaining_train_phase() == 0:
if ((i_node <= len(self._checkpoints))
and self._checkpoints[i_node]):
dict = self._checkpoints[i_node](self.flow[i_node])
# store result, just like in the original CheckpointFlow
if dict:
self.__dict__.update(dict)
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