Module is not an instance of function

fastai
1

If I pip install fastai from github source on Debian/Bullseye using the --no-deps option (ensuring all requirements listed in settings.ini are met using Debian packages - pytorch version 1.7.0) then an import statement such import fastai.data.external leads to an error module is not an instance of function as below. If I remove the --no-deps option the only additional packages that pip installs is torch (version 1.10.1) torchvision (version 0.11.2), and there is no error on using the import statement. I am a bit surprised since setup.ini specifies that torch version >=1.7.0 should be suffecient. I would be grateful if you could point me in the right direction regarding what could be causing this problem.

$ python
Python 3.9.2 (default, Feb 28 2021, 17:03:44) 
[GCC 10.2.1 20210110] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import fastai.data.external
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/lib/python3/dist-packages/fastai/data/external.py", line 6, in <module>
    from ..torch_basics import *
  File "/usr/lib/python3/dist-packages/fastai/torch_basics.py", line 12, in <module>
    from .layers import *
  File "/usr/lib/python3/dist-packages/fastai/layers.py", line 551, in <module>
    def _swish_jit_fwd(x): return x.mul(torch.sigmoid(x))
  File "/usr/lib/python3/dist-packages/torch/jit/_script.py", line 939, in script
    fn = torch._C._jit_script_compile(
TypeError: Object of type 'module' is not an instance of 'function'
2

On further investigation this does not seem to be a problem with Debian’s Python torch package given that the following test script works as expected (shown below).

# test.py
import torch

@torch.jit.script
def foo(x, y):
    if x.max() > y.max():
        r = x
    else:
        r = y
    return r

print(type(foo))  # torch.jit.ScriptFunction

# See the compiled graph as Python code
print(foo.code)

# Call the function using the TorchScript interpreter
foo(torch.ones(2, 2), torch.ones(2, 2))

$ python test.py 
<class 'torch.jit.ScriptFunction'>
def foo(x: Tensor,
    y: Tensor) -> Tensor:
  _0 = bool(torch.gt(torch.max(x), torch.max(y)))
  if _0:
    r = x
  else:
    r = y
  return r
3

I was able to eliminate the error by explicitly importing the sigmoid function from torch as shown below

from torch.jit import script
from torch import sigmoid

@script
def _swish_jit_fwd(x):
    return x.mul(sigmoid(x))

instead of invoking sigmoid as a module attribute as is the case in the fastai codebase, as shown below

import torch
from torch.jit import script

@script
def _swish_jit_fwd(x):
    return x.mul(torch.sigmoid(x))

If FastAI developers have no objection I can create a pull request/patch with all similar changes to support backward compatibility with pytorch 1.7.

4

The included patch (could not attach since txt extension upload is not supported) fixed the issue for me on Debian/Bullseye with Pytorch version 1.7.0. I am still in the process of testing if there are any other issues.

--- a/fastai/layers.py
+++ b/fastai/layers.py
@@ -545,14 +545,15 @@
 
 # Cell
 from torch.jit import script
+from torch import sigmoid, tanh
 
 # Cell
 @script
-def _swish_jit_fwd(x): return x.mul(torch.sigmoid(x))
+def _swish_jit_fwd(x): return x.mul(sigmoid(x))
 
 @script
 def _swish_jit_bwd(x, grad_output):
-    x_sigmoid = torch.sigmoid(x)
+    x_sigmoid = sigmoid(x)
     return grad_output * (x_sigmoid * (1 + x * (1 - x_sigmoid)))
 
 class _SwishJitAutoFn(torch.autograd.Function):
@@ -573,14 +574,15 @@
 class Swish(Module):
     def forward(self, x): return _SwishJitAutoFn.apply(x)
 
+from torch.nn.functional import softplus
 # Cell
 @script
-def _mish_jit_fwd(x): return x.mul(torch.tanh(F.softplus(x)))
+def _mish_jit_fwd(x): return x.mul(tanh(softplus(x)))
 
 @script
 def _mish_jit_bwd(x, grad_output):
-    x_sigmoid = torch.sigmoid(x)
-    x_tanh_sp = F.softplus(x).tanh()
+    x_sigmoid = sigmoid(x)
+    x_tanh_sp = softplus(x).tanh()
     return grad_output.mul(x_tanh_sp + x * x_sigmoid * (1 - x_tanh_sp * x_tanh_sp))
 
 class MishJitAutoFn(torch.autograd.Function):
@@ -654,4 +656,4 @@
     for l in flatten_model(m):
         if getattr(l, 'weight', None) is not None and l.weight.ndim==4:
             return l.weight.shape[1]
-    raise Exception('No weight layer')
\ No newline at end of file
+    raise Exception('No weight layer')