Mixing
Using nn.ModuleList over Python list dramatically slows down training
0
I'm training a very simple model that takes the number of hidden layers as a parameter. I originally stored these hidden layers in a vanilla python list , however when converting this list to a nn.ModuleList, training slows down dramatically by at least one order of magnitude!
AdderNet
class AdderNet(nn.Module):
def __init__(self, num_hidden, hidden_width):
super(AdderNet, self).__init__()
self.relu = nn.ReLU()
self.hiddenLayers =
self.inputLayer = nn.Linear(2, hidden_width)
self.outputLayer = nn.Linear(hidden_width, 1)
for i in range(num_hidden):
self.hiddenLayers.append(nn.Linear(hidden_width, hidden_width))
self.hiddenLayers = nn.ModuleList(self.hiddenLayers) # <--- causes DRAMATIC slowdown!
def forward(self, x):
out = self.inputLayer(x)
out = self.relu(out)
for layer in self.hiddenLayers:
out = layer(out)
out = self.relu(out)
return self.outputLayer(out)
Training
for epoch in range(num_epochs):
for i in range(0,len(data)):
out = model.forward(data[i].x)
loss = lossFunction(out, data[i].y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
python neural-network pytorch
edited Nov 21 '18 at 5:27
Milo Lu
1,61511427
asked Nov 21 '18 at 0:52
Stephen LaskyStephen Lasky
140112
add a comment |
0
I'm training a very simple model that takes the number of hidden layers as a parameter. I originally stored these hidden layers in a vanilla python list , however when converting this list to a nn.ModuleList, training slows down dramatically by at least one order of magnitude!
AdderNet
class AdderNet(nn.Module):
def __init__(self, num_hidden, hidden_width):
super(AdderNet, self).__init__()
self.relu = nn.ReLU()
self.hiddenLayers =
self.inputLayer = nn.Linear(2, hidden_width)
self.outputLayer = nn.Linear(hidden_width, 1)
for i in range(num_hidden):
self.hiddenLayers.append(nn.Linear(hidden_width, hidden_width))
self.hiddenLayers = nn.ModuleList(self.hiddenLayers) # <--- causes DRAMATIC slowdown!
def forward(self, x):
out = self.inputLayer(x)
out = self.relu(out)
for layer in self.hiddenLayers:
out = layer(out)
out = self.relu(out)
return self.outputLayer(out)
Training
for epoch in range(num_epochs):
for i in range(0,len(data)):
out = model.forward(data[i].x)
loss = lossFunction(out, data[i].y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
python neural-network pytorch
edited Nov 21 '18 at 5:27
Milo Lu
1,61511427
asked Nov 21 '18 at 0:52
Stephen LaskyStephen Lasky
140112
add a comment |
0
0
0
1
I'm training a very simple model that takes the number of hidden layers as a parameter. I originally stored these hidden layers in a vanilla python list , however when converting this list to a nn.ModuleList, training slows down dramatically by at least one order of magnitude!
AdderNet
class AdderNet(nn.Module):
def __init__(self, num_hidden, hidden_width):
super(AdderNet, self).__init__()
self.relu = nn.ReLU()
self.hiddenLayers =
self.inputLayer = nn.Linear(2, hidden_width)
self.outputLayer = nn.Linear(hidden_width, 1)
for i in range(num_hidden):
self.hiddenLayers.append(nn.Linear(hidden_width, hidden_width))
self.hiddenLayers = nn.ModuleList(self.hiddenLayers) # <--- causes DRAMATIC slowdown!
def forward(self, x):
out = self.inputLayer(x)
out = self.relu(out)
for layer in self.hiddenLayers:
out = layer(out)
out = self.relu(out)
return self.outputLayer(out)
Training
for epoch in range(num_epochs):
for i in range(0,len(data)):
out = model.forward(data[i].x)
loss = lossFunction(out, data[i].y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
python neural-network pytorch
edited Nov 21 '18 at 5:27
Milo Lu
1,61511427
asked Nov 21 '18 at 0:52
Stephen LaskyStephen Lasky
140112
I'm training a very simple model that takes the number of hidden layers as a parameter. I originally stored these hidden layers in a vanilla python list , however when converting this list to a nn.ModuleList, training slows down dramatically by at least one order of magnitude!
AdderNet
class AdderNet(nn.Module):
def __init__(self, num_hidden, hidden_width):
super(AdderNet, self).__init__()
self.relu = nn.ReLU()
self.hiddenLayers =
self.inputLayer = nn.Linear(2, hidden_width)
self.outputLayer = nn.Linear(hidden_width, 1)
for i in range(num_hidden):
self.hiddenLayers.append(nn.Linear(hidden_width, hidden_width))
self.hiddenLayers = nn.ModuleList(self.hiddenLayers) # <--- causes DRAMATIC slowdown!
def forward(self, x):
out = self.inputLayer(x)
out = self.relu(out)
for layer in self.hiddenLayers:
out = layer(out)
out = self.relu(out)
return self.outputLayer(out)
Training
for epoch in range(num_epochs):
for i in range(0,len(data)):
out = model.forward(data[i].x)
loss = lossFunction(out, data[i].y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
python neural-network pytorch
python neural-network pytorch
edited Nov 21 '18 at 5:27
Milo Lu
1,61511427
asked Nov 21 '18 at 0:52
Stephen LaskyStephen Lasky
140112
edited Nov 21 '18 at 5:27
Milo Lu
1,61511427
asked Nov 21 '18 at 0:52
Stephen LaskyStephen Lasky
140112
edited Nov 21 '18 at 5:27
Milo Lu
1,61511427
edited Nov 21 '18 at 5:27
Milo Lu
1,61511427
edited Nov 21 '18 at 5:27
Milo Lu
1,61511427
1,61511427
asked Nov 21 '18 at 0:52
Stephen LaskyStephen Lasky
140112
asked Nov 21 '18 at 0:52
Stephen LaskyStephen Lasky
140112
asked Nov 21 '18 at 0:52
Stephen LaskyStephen Lasky
140112
140112
add a comment |
add a comment |
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