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Do you want to look at the shape/dtype/etc. of every step of you model, but tired of manually writing prints?
Are you bothered by errors like RuntimeError: Expected object of scalar type Double but got scalar type Float, and want to quickly figure out the problem?
TorchSnooper is a PySnooper extension that helps you debugging these errors.
To use TorchSnooper, you just use it like using PySnooper. Remember to replace the pysnooper.snoop with torchsnooper.snoop in your code.
To install:
pip install torchsnooper
We're writing a simple function:
def myfunc(mask, x):
y = torch.zeros(6)
y.masked_scatter_(mask, x)
return yand use it like below
mask = torch.tensor([0, 1, 0, 1, 1, 0], device='cuda')
source = torch.tensor([1.0, 2.0, 3.0], device='cuda')
y = myfunc(mask, source)The above code seems to be correct, but unfortunately, we are getting the following error:
RuntimeError: Expected object of backend CPU but got backend CUDA for argument #2 'mask'
What is the problem? Let's snoop it! Decorate our function with torchsnooper.snoop():
import torch
import torchsnooper
@torchsnooper.snoop()
def myfunc(mask, x):
y = torch.zeros(6)
y.masked_scatter_(mask, x)
return y
mask = torch.tensor([0, 1, 0, 1, 1, 0], device='cuda')
source = torch.tensor([1.0, 2.0, 3.0], device='cuda')
y = myfunc(mask, source)Run our script, and we will see:
Starting var:.. mask = tensor<(6,), int64, cuda:0>
Starting var:.. x = tensor<(3,), float32, cuda:0>
21:41:42.941668 call 5 def myfunc(mask, x):
21:41:42.941834 line 6 y = torch.zeros(6)
New var:....... y = tensor<(6,), float32, cpu>
21:41:42.943443 line 7 y.masked_scatter_(mask, x)
21:41:42.944404 exception 7 y.masked_scatter_(mask, x)
Now pay attention to the devices of tensors, we notice
New var:....... y = tensor<(6,), float32, cpu>
Now, it's clear that, the problem is because y is a tensor on CPU, that is,
we forget to specify the device on y = torch.zeros(6). Changing it to
y = torch.zeros(6, device='cuda'), this problem is solved.
But when running the script again we are getting another error:
RuntimeError: Expected object of scalar type Byte but got scalar type Long for argument #2 'mask'
Look at the trace above again, pay attention to the dtype of variables, we notice
Starting var:.. mask = tensor<(6,), int64, cuda:0>
OK, the problem is that, we didn't make the mask in the input a byte tensor. Changing the line into
mask = torch.tensor([0, 1, 0, 1, 1, 0], device='cuda', dtype=torch.uint8)
Problem solved.
We are building a linear model
class Model(torch.nn.Module):
def __init__(self):
super().__init__()
self.layer = torch.nn.Linear(2, 1)
def forward(self, x):
return self.layer(x)and we want to fit y = x1 + 2 * x2 + 3, so we create a dataset:
x = torch.tensor([[0.0, 0.0], [0.0, 1.0], [1.0, 0.0], [1.0, 1.0]])
y = torch.tensor([3.0, 5.0, 4.0, 6.0])We train our model on this dataset using SGD optimizer:
model = Model()
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
for _ in range(10):
optimizer.zero_grad()
pred = model(x)
squared_diff = (y - pred) ** 2
loss = squared_diff.mean()
print(loss.item())
loss.backward()
optimizer.step()But unfortunately, the loss does not go down to a low enough number.
What's wrong? Let's snoop it! Putting the training loop inside snoop:
with torchsnooper.snoop():
for _ in range(100):
optimizer.zero_grad()
pred = model(x)
squared_diff = (y - pred) ** 2
loss = squared_diff.mean()
print(loss.item())
loss.backward()
optimizer.step()Part of the trace looks like:
New var:....... x = tensor<(4, 2), float32, cpu>
New var:....... y = tensor<(4,), float32, cpu>
New var:....... model = Model( (layer): Linear(in_features=2, out_features=1, bias=True))
New var:....... optimizer = SGD (Parameter Group 0 dampening: 0 lr: 0....omentum: 0 nesterov: False weight_decay: 0)
22:27:01.024233 line 21 for _ in range(100):
New var:....... _ = 0
22:27:01.024439 line 22 optimizer.zero_grad()
22:27:01.024574 line 23 pred = model(x)
New var:....... pred = tensor<(4, 1), float32, cpu, grad>
22:27:01.026442 line 24 squared_diff = (y - pred) ** 2
New var:....... squared_diff = tensor<(4, 4), float32, cpu, grad>
22:27:01.027369 line 25 loss = squared_diff.mean()
New var:....... loss = tensor<(), float32, cpu, grad>
22:27:01.027616 line 26 print(loss.item())
22:27:01.027793 line 27 loss.backward()
22:27:01.050189 line 28 optimizer.step()
We notice that, y has shape (4,), but pred has shape (4, 1). As a result, squared_diff has shape (4, 4) due to broadcasting!
This is not the expected behavior, let's fix it: pred = model(x).squeeze(), now everything looks good:
New var:....... x = tensor<(4, 2), float32, cpu>
New var:....... y = tensor<(4,), float32, cpu>
New var:....... model = Model( (layer): Linear(in_features=2, out_features=1, bias=True))
New var:....... optimizer = SGD (Parameter Group 0 dampening: 0 lr: 0....omentum: 0 nesterov: False weight_decay: 0)
22:28:19.778089 line 21 for _ in range(100):
New var:....... _ = 0
22:28:19.778293 line 22 optimizer.zero_grad()
22:28:19.778436 line 23 pred = model(x).squeeze()
New var:....... pred = tensor<(4,), float32, cpu, grad>
22:28:19.780250 line 24 squared_diff = (y - pred) ** 2
New var:....... squared_diff = tensor<(4,), float32, cpu, grad>
22:28:19.781099 line 25 loss = squared_diff.mean()
New var:....... loss = tensor<(), float32, cpu, grad>
22:28:19.781361 line 26 print(loss.item())
22:28:19.781537 line 27 loss.backward()
22:28:19.798983 line 28 optimizer.step()
And the final model converge to the desired values.