[CI] Test with torch=={1.9.0, 1.12.0} and make tests compatible (development)
#277
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This should fix
`test_network_diag_ggn[<class
'test.converter.converter_cases._Permute'>]`
in `test/converter/test_converter.py`. Between torch 1.11.0 and torch
1.12.0, the GGN-vector products for this case became non-contiguous, and
`torch.nn.utils.convert_parameters.parameters_to_vector` stopped working
as it uses `view`.
Here is a short self-contained snippet to reproduce the issue:
```python
from torch import Tensor, permute, rand, rand_like
from torch.autograd import grad
from torch.nn import Linear, Module
from torch.nn.utils.convert_parameters import parameters_to_vector
from backpack.utils.convert_parameters import tensor_list_to_vector
class Permute(Module):
def __init__(self):
super().__init__()
self.batch_size = 3
self.in_dim = (5, 3)
out_dim = 2
self.linear = Linear(self.in_dim[-1], out_dim)
self.linear2 = Linear(self.in_dim[-2], out_dim)
def forward(self, x):
x = self.linear(x)
x = x.permute(0, 2, 1) # method permute
x = self.linear2(x)
x = permute(x, (0, 2, 1)) # function permute
return x
def input_fn(self) -> Tensor:
return rand(self.batch_size, *self.in_dim)
model = Permute()
inputs = model.input_fn()
outputs = model(inputs)
params = list(model.parameters())
grad_outputs = rand_like(outputs)
v = [rand_like(p) for p in model.parameters()]
vJ_tuple = grad(outputs, params, grad_outputs=grad_outputs)
for p, vJ in zip(params, vJ_tuple):
# all contiguous()
print(p.shape, vJ.shape)
# between 1.11.0 and 1.12.0, the vector-Jacobian product w.r.t. the second
# linear layer's weight is not contiguous anymore
print(p.is_contiguous(), vJ.is_contiguous())
vJ_vector = parameters_to_vector(vJ_tuple)
vJ_vector = tensor_list_to_vector(vJ_tuple)
```
f-dangel
changed the title
torch=={1.9.0, 1.12.0} and make tests compatibletorch=={1.9.0, 1.12.0} and make tests compatible (development)
See #272. Waiting for pytorch/pytorch#88312 before `torch>=1.13` can be supported.
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This PR is the equivalent of #276 for the development branch.