Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/zasdfgbnm/TorchSnooper
Debug PyTorch code using PySnooper
https://github.com/zasdfgbnm/TorchSnooper
Last synced: 3 months ago
JSON representation
Debug PyTorch code using PySnooper
- Host: GitHub
- URL: https://github.com/zasdfgbnm/TorchSnooper
- Owner: zasdfgbnm
- License: mit
- Created: 2019-05-28T17:09:09.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-04-28T22:36:52.000Z (about 3 years ago)
- Last Synced: 2024-03-17T21:23:41.278Z (3 months ago)
- Language: Python
- Homepage:
- Size: 306 KB
- Stars: 786
- Watchers: 15
- Forks: 44
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-stars - TorchSnooper
README
# TorchSnooper
Status:
[](https://github.com/zasdfgbnm/TorchSnooper/actions)
[](https://github.com/zasdfgbnm/TorchSnooper/actions)
Deploy (only run on release):
[](https://github.com/zasdfgbnm/TorchSnooper/actions)
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](https://github.com/cool-RR/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
```
TorchSnooper also support [snoop](https://github.com/alexmojaki/snoop). To use TorchSnooper with snoop, simply execute:
```python
torchsnooper.register_snoop()
```
or
```python
torchsnooper.register_snoop(verbose=True)
```
at the beginning, and use snoop normally.
# Example 1: Monitoring device and dtype
We're writing a simple function:
```python
def myfunc(mask, x):
y = torch.zeros(6)
y.masked_scatter_(mask, x)
return y
```
and use it like below
```python
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()`:
```python
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.
# Example 1.5: Using Snoop instead of PySnooper
We could also choose to use [snoop](https://github.com/alexmojaki/snoop) instead of [PySnooper](https://github.com/cool-RR/PySnooper).
Remember to install `snoop` manually since it is not a dependency of TorchSnooper:
```
pip install snoop
```
The code in example 1 using snoop looks like:
```python
import torch
import torchsnooper
import snoop
torchsnooper.register_snoop()
@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)
```
and the screenshot looks like:
# Example 2: Monitoring shape
We are building a linear model
```python
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:
```python
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:
```python
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:
```python
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.
# Example 3: Monitoring nan and inf
Let's say we have a model that output the likelihood of something. For this example, we will just use a mock:
```python
class MockModel(torch.nn.Module):
def __init__(self):
super(MockModel, self).__init__()
self.unused = torch.nn.Linear(6, 4)
def forward(self, x):
return torch.tensor([0.0, 0.25, 0.9, 0.75]) + self.unused(x) * 0.0
model = MockModel()
```
During training, we want to minimize the negative log likelihood, we have code:
```python
for epoch in range(100):
batch_input = torch.randn(6, 6)
likelihood = model(batch_input)
log_likelihood = likelihood.log()
target = -log_likelihood.mean()
print(target.item())
optimizer.zero_grad()
target.backward()
optimizer.step()
```
Unfortunately, we first get `inf` then `nan` for our target during training. What's wrong? Let's snoop it:
```python
with torchsnooper.snoop():
for epoch in range(100):
batch_input = torch.randn(6, 6)
likelihood = model(batch_input)
log_likelihood = likelihood.log()
target = -log_likelihood.mean()
print(target.item())
optimizer.zero_grad()
target.backward()
optimizer.step()
```
We will see the part of the output of the snoop looks like:
```
19:30:20.928316 line 18 for epoch in range(100):
New var:....... epoch = 0
19:30:20.928575 line 19 batch_input = torch.randn(6, 6)
New var:....... batch_input = tensor<(6, 6), float32, cpu>
19:30:20.929671 line 20 likelihood = model(batch_input)
New var:....... likelihood = tensor<(6, 4), float32, cpu, grad>
19:30:20.930284 line 21 log_likelihood = likelihood.log()
New var:....... log_likelihood = tensor<(6, 4), float32, cpu, grad, has_inf>
19:30:20.930672 line 22 target = -log_likelihood.mean()
New var:....... target = tensor<(), float32, cpu, grad, has_inf>
19:30:20.931136 line 23 print(target.item())
19:30:20.931508 line 25 optimizer.zero_grad()
19:30:20.931871 line 26 target.backward()
inf
19:30:20.960028 line 27 optimizer.step()
19:30:20.960673 line 18 for epoch in range(100):
Modified var:.. epoch = 1
19:30:20.961043 line 19 batch_input = torch.randn(6, 6)
19:30:20.961423 line 20 likelihood = model(batch_input)
Modified var:.. likelihood = tensor<(6, 4), float32, cpu, grad, has_nan>
19:30:20.961910 line 21 log_likelihood = likelihood.log()
Modified var:.. log_likelihood = tensor<(6, 4), float32, cpu, grad, has_nan>
19:30:20.962302 line 22 target = -log_likelihood.mean()
Modified var:.. target = tensor<(), float32, cpu, grad, has_nan>
19:30:20.962715 line 23 print(target.item())
19:30:20.963089 line 25 optimizer.zero_grad()
19:30:20.963464 line 26 target.backward()
19:30:20.964051 line 27 optimizer.step()
```
Reading the output, we find that, at the first epoch (`epoch = 0`), the `log_likelihood` has `has_inf` flag.
The `has_inf` flag means, your tensor contains `inf` in its value. The same flag appears for `target`.
And at the second epoch, starting from `likelihood`, tensors all have a `has_nan` flag.
From our experience with deep learning, we would guess this is because the first epoch has `inf`, which causes
the gradient to be `nan`, and when parameters are updated, these `nan` propagate to parameters and causing all
future steps to have `nan` result.
Taking a deeper look, we figure out that the `likelihood` contains 0 in it, which leads to `log(0) = -inf`. Changing
the line
```python
log_likelihood = likelihood.log()
```
into
```python
log_likelihood = likelihood.clamp(min=1e-8).log()
```
Problem solved.