diff --git a/src/decentralizepy/sharing/GrowingAlpha.py b/src/decentralizepy/sharing/GrowingAlpha.py
index afe86768ededc073de60518373192513184ec03d..f587cf5f73a667b3d66ad19d0282a7eaaef8c9ef 100644
--- a/src/decentralizepy/sharing/GrowingAlpha.py
+++ b/src/decentralizepy/sharing/GrowingAlpha.py
@@ -60,7 +60,6 @@ class GrowingAlpha(PartialModel):
self.graph,
self.model,
self.dataset,
- self.log_dir,
)
self.base.communication_round = self.communication_round
self.base.step()
diff --git a/src/decentralizepy/sharing/PartialModel.py b/src/decentralizepy/sharing/PartialModel.py
index ed91e39630aa1be2e6bec6874041ac226443c20a..1df11c4871ebeedb0ef4cb805e2c5637e996cb0c 100644
--- a/src/decentralizepy/sharing/PartialModel.py
+++ b/src/decentralizepy/sharing/PartialModel.py
@@ -23,9 +23,31 @@ class PartialModel(Sharing):
dict_ordered=True,
save_shared=False,
):
+ """
+ Constructor
+ Parameters
+ ----------
+ rank : int
+ Local rank
+ machine_id : int
+ Global machine id
+ communication : decentralizepy.communication.Communication
+ Communication module used to send and receive messages
+ mapping : decentralizepy.mappings.Mapping
+ Mapping (rank, machine_id) -> uid
+ graph : decentralizepy.graphs.Graph
+ Graph reprensenting neighbors
+ model : decentralizepy.models.Model
+ Model to train
+ dataset : decentralizepy.datasets.Dataset
+ Dataset for sharing data. Not implemented yer! TODO
+ log_dir : str
+ Location to write shared_params (only writing for 2 procs per machine)
+ """
super().__init__(
- rank, machine_id, communication, mapping, graph, model, dataset, log_dir
+ rank, machine_id, communication, mapping, graph, model, dataset
)
+ self.log_dir = log_dir
self.alpha = alpha
self.dict_ordered = dict_ordered
self.save_shared = save_shared
diff --git a/src/decentralizepy/sharing/Sharing.py b/src/decentralizepy/sharing/Sharing.py
index 91d25d06badb20500e1dd8c797f4c1d84cca1b8b..648e481d9e2439a6048b95aa6b9760cb64f91c1e 100644
--- a/src/decentralizepy/sharing/Sharing.py
+++ b/src/decentralizepy/sharing/Sharing.py
@@ -11,9 +11,28 @@ class Sharing:
API defining who to share with and what, and what to do on receiving
"""
- def __init__(
- self, rank, machine_id, communication, mapping, graph, model, dataset, log_dir
- ):
+ def __init__(self, rank, machine_id, communication, mapping, graph, model, dataset):
+ """
+ Constructor
+ Parameters
+ ----------
+ rank : int
+ Local rank
+ machine_id : int
+ Global machine id
+ communication : decentralizepy.communication.Communication
+ Communication module used to send and receive messages
+ mapping : decentralizepy.mappings.Mapping
+ Mapping (rank, machine_id) -> uid
+ graph : decentralizepy.graphs.Graph
+ Graph reprensenting neighbors
+ model : decentralizepy.models.Model
+ Model to train
+ dataset : decentralizepy.datasets.Dataset
+ Dataset for sharing data. Not implemented yer! TODO
+ log_dir : str
+ Location to write shared_params (only writing for 2 procs per machine)
+ """
self.rank = rank
self.machine_id = machine_id
self.uid = mapping.get_uid(rank, machine_id)
@@ -22,7 +41,6 @@ class Sharing:
self.graph = graph
self.model = model
self.dataset = dataset
- self.log_dir = log_dir
self.communication_round = 0
self.peer_deques = dict()
@@ -31,22 +49,58 @@ class Sharing:
self.peer_deques[n] = deque()
def received_from_all(self):
+ """
+ Check if all neighbors have sent the current iteration
+ Returns
+ -------
+ bool
+ True if required data has been received, False otherwise
+ """
for _, i in self.peer_deques.items():
if len(i) == 0:
return False
return True
def get_neighbors(self, neighbors):
+ """
+ Choose which neighbors to share with
+ Parameters
+ ----------
+ neighbors : list(int)
+ List of all neighbors
+ Returns
+ -------
+ list(int)
+ Neighbors to share with
+ """
# modify neighbors here
return neighbors
def serialized_model(self):
+ """
+ Convert model to json dict. Here we can choose how much to share
+ Returns
+ -------
+ dict
+ Model converted to json dict
+ """
m = dict()
for key, val in self.model.state_dict().items():
m[key] = json.dumps(val.numpy().tolist())
return m
def deserialized_model(self, m):
+ """
+ Convert received json dict to state_dict.
+ Parameters
+ ----------
+ m : dict
+ json dict received
+ Returns
+ -------
+ state_dict
+ state_dict of received
+ """
state_dict = dict()
for key, value in m.items():
state_dict[key] = torch.from_numpy(numpy.array(json.loads(value)))
@@ -67,7 +121,7 @@ class Sharing:
logging.debug("Received model from {}".format(sender))
degree = data["degree"]
del data["degree"]
- self.peer_deques[sender].append((degree, self.deserialized_model(data)))
+ self.peer_deques[sender].append((degree, data))
logging.debug("Deserialized received model from {}".format(sender))
logging.info("Starting model averaging after receiving from all neighbors")
@@ -76,6 +130,7 @@ class Sharing:
for i, n in enumerate(self.peer_deques):
logging.debug("Averaging model from neighbor {}".format(i))
degree, data = self.peer_deques[n].popleft()
+ data = self.deserialized_model(data)
weight = 1 / (max(len(self.peer_deques), degree) + 1) # Metro-Hastings
weight_total += weight
for key, value in data.items():
diff --git a/src/decentralizepy/training/GradientAccumulator.py b/src/decentralizepy/training/GradientAccumulator.py
index 9ea42e62b68c01c1c6a620d7a62c28f46a0dc2eb..66e1e5bf6efdb81126b5edd788ed72e5ba829b48 100644
--- a/src/decentralizepy/training/GradientAccumulator.py
+++ b/src/decentralizepy/training/GradientAccumulator.py
@@ -7,11 +7,29 @@ class GradientAccumulator(Training):
def __init__(
self, model, optimizer, loss, epochs_per_round="", batch_size="", shuffle=""
):
+ """
+ Constructor
+ Parameters
+ ----------
+ model : torch.nn.Module
+ Neural Network for training
+ optimizer : torch.optim
+ Optimizer to learn parameters
+ loss : function
+ Loss function
+ epochs_per_round : int, optional
+ Number of epochs per training call
+ batch_size : int, optional
+ Number of items to learn over, in one batch
+ shuffle : bool
+ True if the dataset should be shuffled before training. Not implemented yet! TODO
+ """
super().__init__(model, optimizer, loss, epochs_per_round, batch_size, shuffle)
def train(self, dataset):
"""
- One training iteration with accumulation of gradients in model.accumulated_gradients
+ One training iteration with accumulation of gradients in model.accumulated_gradients.
+ Goes through the entire dataset.
Parameters
----------
dataset : decentralizepy.datasets.Dataset
diff --git a/src/decentralizepy/training/Training.py b/src/decentralizepy/training/Training.py
index 2b55820b9c6770cfeb6889b9efe30f520fc87c95..6129b34b90e6ae9abbf7e94d7d93dab668cf5b2e 100644
--- a/src/decentralizepy/training/Training.py
+++ b/src/decentralizepy/training/Training.py
@@ -27,6 +27,8 @@ class Training:
Number of epochs per training call
batch_size : int, optional
Number of items to learn over, in one batch
+ shuffle : bool
+ True if the dataset should be shuffled before training. Not implemented yet! TODO
"""
self.model = model
self.optimizer = optimizer
@@ -36,11 +38,18 @@ class Training:
self.shuffle = utils.conditional_value(shuffle, "", False)
def reset_optimizer(self, optimizer):
+ """
+ Replace the current optimizer with a new one
+ Parameters
+ ----------
+ optimizer : torch.optim
+ A new optimizer
+ """
self.optimizer = optimizer
def eval_loss(self, dataset):
"""
- Evaluate the loss
+ Evaluate the loss on the training set
Parameters
----------
dataset : decentralizepy.datasets.Dataset
@@ -61,7 +70,7 @@ class Training:
def train(self, dataset):
"""
- One training iteration
+ One training iteration, goes through the entire dataset
Parameters
----------
dataset : decentralizepy.datasets.Dataset
diff --git a/src/decentralizepy/utils.py b/src/decentralizepy/utils.py
index a6bab0ddf4959363ad597c4ba0a160fa313ebc18..76eec069b0399000a3744931baca8590b3e94fe1 100644
--- a/src/decentralizepy/utils.py
+++ b/src/decentralizepy/utils.py
@@ -5,6 +5,21 @@ import os
def conditional_value(var, nul, default):
+ """
+ Set the value to default if nul.
+ Parameters
+ ----------
+ var : any
+ The value
+ nul : any
+ The null value. Assigns default if var == nul
+ default : any
+ The default value
+ Returns
+ -------
+ type(var)
+ The final value
+ """
if var != nul:
return var
else:
@@ -12,10 +27,30 @@ def conditional_value(var, nul, default):
def remove_keys(d, keys_to_remove):
+ """
+ Removes given keys from the dict. Returns a new list.
+ Parameters
+ ----------
+ d : dict
+ The initial dictionary
+ keys_to_remove : list
+ List of keys to remove from dict
+ Returns
+ -------
+ dict
+ A new dictionary with the given keys removed.
+ """
return {key: d[key] for key in d if key not in keys_to_remove}
def get_args():
+ """
+ Utility to parse arguments.
+ Returns
+ -------
+ args
+ Command line arguments
+ """
parser = argparse.ArgumentParser()
parser.add_argument("-mid", "--machine_id", type=int, default=0)
parser.add_argument("-ps", "--procs_per_machine", type=int, default=1)
@@ -38,6 +73,15 @@ def get_args():
def write_args(args, path):
+ """
+ Write arguments to a json file
+ Parameters
+ ----------
+ args : args
+ Command line args
+ path : str
+ Location of the file to write to
+ """
data = {
"machine_id": args.machine_id,
"procs_per_machine": args.procs_per_machine,