Function(s) expecting a tuple argument don't accept generators

[celynw]

🐛 Bug

From the docs for torch.interpolate, these arguments accept tuples:

• ...
• size (int or Tuple[int] or Tuple[int, int] or Tuple[int, int, int]) – output spatial size.
• scale_factor (float or Tuple[float]) – multiplier for spatial size. If scale_factor is a tuple, its length has to match input.dim().
• ...

However, if size or scale_factor are generators the function fails.

I am not sure if this extends to other similar functions.

To Reproduce

Steps to reproduce the behavior:

size argument

import torch
import torch.nn.functional as F

t = torch.rand([1, 2, 4, 6])
newSize = t.shape[-2:]
F.interpolate(t, size=(d * 2 for d in newSize))

produces error

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/redacted/torch/nn/functional.py", line 3712, in interpolate
    return torch._C._nn.upsample_nearest2d(input, output_size, scale_factors)
TypeError: upsample_nearest2d() received an invalid combination of arguments - got (Tensor, list, NoneType), but expected one of:
 * (Tensor input, tuple of ints output_size, tuple of floats scale_factors)
      didn't match because some of the arguments have invalid types: (Tensor, list, NoneType)
 * (Tensor input, tuple of ints output_size, float scales_h, float scales_w, *, Tensor out)

scale_factor argument

import torch
import torch.nn.functional as F

t = torch.rand([1, 2, 4, 6])
scale = [1, 1]
F.interpolate(t, scale_factor=(i * 2 for i in scale))

produces error

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/redacted/torch/nn/functional.py", line 3678, in interpolate
    if math.floor(scale) != scale:
TypeError: must be real number, not generator

Expected behavior

The generators would at least be attempted to be evaluated to lists/tuples. Even in the case of the size argument, the traceback states that it has received a list rather than a generator!

In both cases, resolving the generator expressions to either tuple or a list works perfectly.

Environment

PyTorch version: 1.10.0
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.3 LTS (x86_64)
GCC version: (Ubuntu 9.3.0-17ubuntu1~20.04) 9.3.0
Clang version: 10.0.0-4ubuntu1 
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.8.12 (default, Oct 12 2021, 13:49:34)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.11.0-41-generic-x86_64-with-glibc2.17
Is CUDA available: True
CUDA runtime version: 11.5.119
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3090
Nvidia driver version: 495.29.05
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.3.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.3.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.3.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.3.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.3.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.3.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.3.1
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] mypy==0.910
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.21.4
[pip3] pytorch-lightning==1.5.1
[pip3] torch==1.10.0
[pip3] torchmetrics==0.5.1
[pip3] torchvision==0.11.1
[conda] blas                      1.0                         mkl  
[conda] cudatoolkit               11.3.1               h2bc3f7f_2  
[conda] ffmpeg                    4.3                  hf484d3e_0    pytorch
[conda] mkl                       2021.3.0           h06a4308_520  
[conda] mkl-service               2.4.0            py38h7f8727e_0  
[conda] mkl_fft                   1.3.1            py38hd3c417c_0  
[conda] mkl_random                1.2.2            py38h51133e4_0  
[conda] numpy                     1.21.2           py38h20f2e39_0  
[conda] numpy-base                1.21.2           py38h79a1101_0  
[conda] pytorch                   1.10.0          py3.8_cuda11.3_cudnn8.2.0_0    pytorch
[conda] pytorch-lightning         1.5.1                    pypi_0    pypi
[conda] pytorch-mutex             1.0                        cuda    pytorch
[conda] torchmetrics              0.5.1                    pypi_0    pypi
[conda] torchvision               0.11.1               py38_cu113    pytorch

Additional context

The reason I was doing this in the first place is that I thought using parentheses would be 'tuple' comprehension rather than list comprehension. I have now learned that this is actually a generator expression.

I could easily use square brackets instead and move on - it's just I also expected this to work and the error message is a bit strange.

cc @albanD @mruberry @jbschlosser @walterddr @kshitij12345

[albanD]

I'm not sure it will be easy to support generators for such code.