Empty SparseTensor convolution segfault

[francois-drielsma]

Describe the bug When passing an empty SparseTensor object through a MinkowskiConvolution, MinkowskiEngine throws a segfault.

---

To Reproduce Docker image: deeplearnphysics/larcv2:ub20.04-cuda12.1-pytorch2.2.1-larndsim

Code snippet

import torch
import MinkowskiEngine as ME

input = ME.SparseTensor(coordinates=torch.empty(0,4).to(dtype=torch.int), features=torch.empty(0,1))
conv = ME.MinkowskiConvolution(in_channels=1, out_channels=3, dimension=3, kernel_size=3)
conv(input)

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Expected behavior Would hope for an empty output with the appropriate number of features, e.g.:

SparseTensor(
  coordinates=tensor([], size=(0, 4), dtype=torch.int32)
  features=tensor([], size=(0, 3), grad_fn=<MinkowskiConvolutionFunctionBackward>)
  coordinate_map_key=coordinate map key:[1, 1, 1]
  coordinate_manager=CoordinateMapManagerCPU(
    [1, 1, 1, ]:  CoordinateMapCPU:0x4
    algorithm=MinkowskiAlgorithm.DEFAULT
  )
  spatial dimension=3)

---

Desktop (please complete the following information):

• OS: Ubuntu 20.04
• Python version: 3.8.10
• Pytorch version: 2.2.1
• CUDA version: 12.1
• NVIDIA Driver version: 535.161.07
• Minkowski Engine version: 0.5.4
• Output of the following command. (If you installed the latest MinkowskiEngine, paste the output of python -c "import MinkowskiEngine as ME; ME.print_diagnostics()". Otherwise, paste the output of the following command.)

  
  /usr/local/lib/python3.8/dist-packages/MinkowskiEngine/__init__.py:36: UserWarning: The environment variable `OMP_NUM_THREADS` not set. MinkowskiEngine will automatically set `OMP_NUM_THREADS=16`. If you want to set `OMP_NUM_THREADS` manually, please export it on the command line before running a python script. e.g. `export OMP_NUM_THREADS=12; python your_program.py`. It is recommended to set it below 24.
  warnings.warn(
  ==========System==========
  Linux-4.18.0-372.32.1.el8_6.x86_64-x86_64-with-glibc2.29
  DISTRIB_ID=Ubuntu
  DISTRIB_RELEASE=20.04
  DISTRIB_CODENAME=focal
  DISTRIB_DESCRIPTION="Ubuntu 20.04.6 LTS"
  3.8.10 (default, Nov 22 2023, 10:22:35) 
  [GCC 9.4.0]
  ==========Pytorch==========
  2.2.1+cu121
  torch.cuda.is_available(): True
  ==========NVIDIA-SMI==========
  /usr/bin/nvidia-smi
  Driver Version 535.161.07
  CUDA Version 12.2
  VBIOS Version 92.00.19.00.13
  Image Version G506.0202.00.02
  GSP Firmware Version 535.161.07
  ==========NVCC==========
  /usr/local/cuda/bin/nvcc
  nvcc: NVIDIA (R) Cuda compiler driver
  Copyright (c) 2005-2023 NVIDIA Corporation
  Built on Mon_Apr__3_17:16:06_PDT_2023
  Cuda compilation tools, release 12.1, V12.1.105
  Build cuda_12.1.r12.1/compiler.32688072_0
  ==========CC==========
  /usr/bin/c++
  c++ (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0
  Copyright (C) 2019 Free Software Foundation, Inc.
  This is free software; see the source for copying conditions.  There is NO
  warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

==========MinkowskiEngine========== 0.5.4 MinkowskiEngine compiled with CUDA Support: True NVCC version MinkowskiEngine is compiled: 12010 CUDART version MinkowskiEngine is compiled: 12010

************************************************************************************
**Additional context**
I did not do extensive version testing, but this seems to work for torch 1.13 in an older container: deeplearnphysics/larcv2:ub20.04-cuda11.6-pytorch1.13-larndsim

[francois-drielsma]

Having a similar issue with MinkowskiConvolutionTranspose, even with the older version of PyTorch. This:

import torch
import MinkowskiEngine as ME

input = ME.SparseTensor(coordinates=torch.empty(0,4).to(dtype=torch.int), features=torch.empty(0,1))
conv = ME.MinkowskiConvolution(in_channels=1, out_channels=4, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1], dimension=3)
conv_t = ME.MinkowskiConvolutionTranspose(in_channels=4, out_channels=4, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1], dimension=3)
x = conv(input)
x = conv_t(x)

throws a segfault on the last line.