diff --git a/README.rst b/README.rst
index 38cba02c7e0a8ce478cd67641a3ce0f34fdc55ec..5acb1c43daadf40a8d6e0e06a61d0bcfa354b0b5 100644
--- a/README.rst
+++ b/README.rst
@@ -15,6 +15,7 @@ Setting up decentralizepy
pip3 install --upgrade pip
pip install --upgrade pip
+* On Mac M1, installing ``pyzmq`` fails with `pip`. Use ``conda``.
* Install decentralizepy for development. ::
pip3 install --editable .\[dev\]
diff --git a/eval/main.ipynb b/eval/main.ipynb
index db66e893b9325a7fffbbcaa421197b7cbc33899b..80daae672a600f6358cfa9f559b50ecb47cf2261 100644
--- a/eval/main.ipynb
+++ b/eval/main.ipynb
@@ -268,7 +268,7 @@
},
{
"cell_type": "code",
- "execution_count": 24,
+ "execution_count": 1,
"metadata": {},
"outputs": [
{
@@ -300,6 +300,26 @@
"\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<generator object Module.parameters at 0x111ec1ba0>"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "m1.parameters()"
+ ]
+ },
{
"cell_type": "code",
"execution_count": 25,
@@ -5715,7 +5735,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.8.11"
},
"orig_nbformat": 4
},
diff --git a/src/decentralizepy/node/Node.py b/src/decentralizepy/node/Node.py
index ef66d1752dae14a341b8c04042ee42961544f91a..24c316818feb5698531e90eb498ced15253e03f5 100644
--- a/src/decentralizepy/node/Node.py
+++ b/src/decentralizepy/node/Node.py
@@ -177,7 +177,7 @@ class Node:
],
)
self.trainer = train_class(
- self.model, self.optimizer, self.loss, **train_params
+ self.model, self.optimizer, self.loss, self.log_dir, **train_params
)
def init_comm(self, comm_configs):
diff --git a/src/decentralizepy/training/ChangeAccumulator.py b/src/decentralizepy/training/ChangeAccumulator.py
new file mode 100644
index 0000000000000000000000000000000000000000..cf9641fc8da1b117671f384650615ec7b4e8c77e
--- /dev/null
+++ b/src/decentralizepy/training/ChangeAccumulator.py
@@ -0,0 +1,148 @@
+import json
+import os
+from pathlib import Path
+
+import torch
+
+from decentralizepy.training.Training import Training
+from decentralizepy.utils import conditional_value
+
+
+class ChangeAccumulator(Training):
+ """
+ This class implements the training module which also accumulates model change in a list.
+
+ """
+
+ def __init__(
+ self,
+ model,
+ optimizer,
+ loss,
+ log_dir,
+ rounds="",
+ full_epochs="",
+ batch_size="",
+ shuffle="",
+ save_accumulated="",
+ ):
+ """
+ Constructor
+
+ Parameters
+ ----------
+ model : torch.nn.Module
+ Neural Network for training
+ optimizer : torch.optim
+ Optimizer to learn parameters
+ loss : function
+ Loss function
+ log_dir : str
+ Directory to log the model change.
+ rounds : int, optional
+ Number of steps/epochs per training call
+ full_epochs: bool, optional
+ True if 1 round = 1 epoch. False if 1 round = 1 minibatch
+ batch_size : int, optional
+ Number of items to learn over, in one batch
+ shuffle : bool
+ True if the dataset should be shuffled before training.
+ save_accumulated : bool
+ True if accumulated weight change should be written to file
+
+ """
+ super().__init__(
+ model, optimizer, loss, log_dir, rounds, full_epochs, batch_size, shuffle
+ )
+ self.save_accumulated = conditional_value(save_accumulated, "", True)
+ self.communication_round = 0
+ if self.save_accumulated:
+ self.model_change_path = os.path.join(
+ self.log_dir, "model_change/{}".format(self.rank)
+ )
+ Path(self.model_change_path).mkdir(parents=True, exist_ok=True)
+
+ self.model_val_path = os.path.join(
+ self.log_dir, "model_val/{}".format(self.rank)
+ )
+ Path(self.model_val_path).mkdir(parents=True, exist_ok=True)
+
+ def save_vector(self, v, s):
+ """
+ Saves the given vector to the file.
+
+ Parameters
+ ----------
+ v : torch.tensor
+ The torch tensor to write to file
+ s : str
+ Path to folder to write to
+
+ """
+ output_dict = dict()
+ output_dict["order"] = list(self.model.state_dict().keys())
+ shapes = dict()
+ for k, v in self.model.state_dict().items():
+ shapes[k] = list(v.shape)
+ output_dict["shapes"] = shapes
+
+ output_dict[self.communication_round] = v.tolist()
+
+ with open(
+ os.path.join(
+ s,
+ "{}.json".format(self.communication_round + 1),
+ ),
+ "w",
+ ) as of:
+ json.dump(output_dict, of)
+
+ def save_change(self):
+ """
+ Saves the change and the gradient values for every iteration
+
+ """
+ tensors_to_cat = [
+ v.data.flatten() for _, v in self.model.accumulated_gradients[0].items()
+ ]
+ change = torch.abs(torch.cat(tensors_to_cat, dim=0))
+ self.save_vector(change, self.model_change_path)
+
+ def save_model_params(self):
+ """
+ Saves the change and the gradient values for every iteration
+
+ """
+ tensors_to_cat = [v.data.flatten() for _, v in self.model.items()]
+ params = torch.abs(torch.cat(tensors_to_cat, dim=0))
+ self.save_vector(params, self.model_val_path)
+
+ def train(self, dataset):
+ """
+ One training iteration with accumulation of model change in model.accumulated_gradients.
+ Goes through the entire dataset.
+
+ Parameters
+ ----------
+ dataset : decentralizepy.datasets.Dataset
+ The training dataset. Should implement get_trainset(batch_size, shuffle)
+
+ """
+ self.model.accumulated_gradients = []
+ self.init_model = {
+ k: v.data.clone().detach()
+ for k, v in zip(self.model.state_dict(), self.model.parameters())
+ }
+ super().train(dataset)
+ with torch.no_grad():
+ change = {
+ k: v.data.clone().detach() - self.init_model[k]
+ for k, v in zip(self.model.state_dict(), self.model.parameters())
+ }
+ self.model.accumulated_gradients.append(change)
+
+ if self.save_accumulated:
+ self.save_change()
+ self.save_model_params()
+
+ self.communication_round += 1
diff --git a/src/decentralizepy/training/GradientAccumulator.py b/src/decentralizepy/training/GradientAccumulator.py
index 3c160594d74e6f50ae107cc0f486077e5428c6bc..3171019f7397cb49b757e3a41315ec967fa5d27f 100644
--- a/src/decentralizepy/training/GradientAccumulator.py
+++ b/src/decentralizepy/training/GradientAccumulator.py
@@ -14,6 +14,7 @@ class GradientAccumulator(Training):
model,
optimizer,
loss,
+ log_dir,
rounds="",
full_epochs="",
batch_size="",
@@ -30,6 +31,8 @@ class GradientAccumulator(Training):
Optimizer to learn parameters
loss : function
Loss function
+ log_dir : str
+ Directory to log the model change.
rounds : int, optional
Number of steps/epochs per training call
full_epochs: bool, optional
@@ -41,7 +44,7 @@ class GradientAccumulator(Training):
"""
super().__init__(
- model, optimizer, loss, rounds, full_epochs, batch_size, shuffle
+ model, optimizer, loss, log_dir, rounds, full_epochs, batch_size, shuffle
)
def trainstep(self, data, target):
diff --git a/src/decentralizepy/training/Training.py b/src/decentralizepy/training/Training.py
index 6696f357bbd631151be5a5f6a9eaa747a8b01493..47c8f778fb38761f766504d7461b72c9b2005618 100644
--- a/src/decentralizepy/training/Training.py
+++ b/src/decentralizepy/training/Training.py
@@ -16,6 +16,7 @@ class Training:
model,
optimizer,
loss,
+ log_dir,
rounds="",
full_epochs="",
batch_size="",
@@ -32,6 +33,8 @@ class Training:
Optimizer to learn parameters
loss : function
Loss function
+ log_dir : str
+ Directory to log the model change.
rounds : int, optional
Number of steps/epochs per training call
full_epochs: bool, optional
@@ -45,6 +48,7 @@ class Training:
self.model = model
self.optimizer = optimizer
self.loss = loss
+ self.log_dir = log_dir
self.rounds = utils.conditional_value(rounds, "", int(1))
self.full_epochs = utils.conditional_value(full_epochs, "", False)
self.batch_size = utils.conditional_value(batch_size, "", int(1))