Source code for horovod.torch.elastic.state

# Copyright 2020 Uber Technologies, Inc. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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# ==============================================================================

import copy

import torch

from horovod.common.elastic import ObjectState
from horovod.torch.elastic.sampler import ElasticSampler
from horovod.torch.functions import allgather_object, \
    broadcast_object, broadcast_optimizer_state, broadcast_parameters
from horovod.torch.mpi_ops import rank

[docs]class TorchState(ObjectState): """State representation of a PyTorch training process. Multiple models and optimizers are supported by providing them as kwargs. During initialization, `TorchState` will assign attributes for every keyword argument, and handle its state synchronization. Args: model: Optional PyTorch model. optimizer: Optional PyTorch optimizer. kwargs: Attributes sync, will be exposed as attributes of the object. If a handler exists for the attribute type, it will be used to sync the object, otherwise it will be handled an ordinary Python object. """ def __init__(self, model=None, optimizer=None, **kwargs): kwargs.update(dict(model=model, optimizer=optimizer)) self._handlers, kwargs = _get_handlers(kwargs) for name, handler in self._handlers.items(): setattr(self, name, handler.value) super(TorchState, self).__init__(bcast_object=broadcast_object, get_rank=rank, **kwargs)
[docs] def save(self): for handler in self._handlers.values(): super(TorchState, self).save()
[docs] def restore(self): for handler in self._handlers.values(): handler.restore() super(TorchState, self).restore()
[docs] def sync(self): for handler in self._handlers.values(): handler.sync() super(TorchState, self).sync()
def __setattr__(self, name, value): if hasattr(self, name) and name in self._handlers: self._handlers[name].set_value(value) super().__setattr__(name, value)
class StateHandler(object): def __init__(self, value): self.value = value def save(self): raise NotImplementedError() def restore(self): raise NotImplementedError() def sync(self): raise NotImplementedError() def set_value(self, value): self.value = value class ModelStateHandler(StateHandler): def __init__(self, model): super().__init__(model) self._saved_model_state = copy.deepcopy(self.value.state_dict()) def save(self): self._saved_model_state = copy.deepcopy(self.value.state_dict()) def restore(self): self.value.load_state_dict(self._saved_model_state) def sync(self): broadcast_parameters(self.value.state_dict(), root_rank=0) class OptimizerStateHandler(StateHandler): def __init__(self, optimizer): super().__init__(optimizer) self._saved_optimizer_state = copy.deepcopy(self.value.state_dict()) def save(self): self._saved_optimizer_state = copy.deepcopy(self.value.state_dict()) def restore(self): self.value.load_state_dict(self._saved_optimizer_state) def sync(self): broadcast_optimizer_state(self.value, root_rank=0) class SamplerStateHandler(StateHandler): def __init__(self, sampler): super().__init__(sampler) self._saved_sampler_state = copy.deepcopy(self.value.state_dict()) def save(self): self._saved_sampler_state = copy.deepcopy(self.value.state_dict()) def restore(self): self.value.load_state_dict(self._saved_sampler_state) def sync(self): # Get the set of processed indices from all workers world_processed_indices = _union(allgather_object(self.value.processed_indices)) # Replace local processed indices with global indices state_dict = self.value.state_dict() state_dict['processed_indices'] = world_processed_indices # Broadcast and load the state to make sure we're all in sync self.value.load_state_dict(broadcast_object(state_dict)) def _union(sets): # Union a list of sets into a single set return set().union(*sets) _handler_registry = [ (torch.nn.Module, ModelStateHandler), (torch.optim.Optimizer, OptimizerStateHandler), (ElasticSampler, SamplerStateHandler), ] def get_handler_registry(): return _handler_registry def set_handler_registry(registry): global _handler_registry _handler_registry = registry def _get_handler(v): for handler_type, handler_cls in _handler_registry: if isinstance(v, handler_type): return handler_cls(v) return None def _get_handlers(kwargs): handlers = {} remainder = {} for k, v in kwargs.items(): handler = _get_handler(v) if handler: handlers[k] = handler else: remainder[k] = v return handlers, remainder