Exporting model to TFlite fails with quantized engine FBGEMM is not supported

[sbocconi]

Describe the bug
Trying to export the model with config configs/pfld/pfld_mbv2n_112.py fails with RuntimeError: quantized engine FBGEMM is not supported
Environment
Environment you use when bug appears:

1. Python version: 3.10
2. PyTorch Version: torch==2.0.1
3. MMCV Version: 2.0.1
4. EdgeLab Version: na
5. Code you run
   python3 tools/export.py configs/pfld/pfld_mbv2n_112.py work_dirs/pfld_mbv2n_112/epoch_1.pth --target tflite --cfg-options data_root=datasets/meter/
6. The detailed error

Traceback (most recent call last):
  File "/Users/SB/Projects/Software/Zephyros/Courses/Microcontrollers/ModelAssistant/tools/export.py", line 509, in <module>
    main()
  File "/Users/SB/Projects/Software/Zephyros/Courses/Microcontrollers/ModelAssistant/tools/export.py", line 501, in main
    export_tflite(args, model, loader)
  File "/Users/SB/Projects/Software/Zephyros/Courses/Microcontrollers/ModelAssistant/tools/export.py", line 375, in export_tflite
    ptq_model = quantizer.quantize()
  File "/opt/homebrew/Caskroom/miniconda/base/envs/sscma/lib/python3.10/site-packages/tinynn/graph/quantization/quantizer.py", line 530, in quantize
    qat_model = self.prepare_qat(rewritten_graph, self.is_input_quantized, self.backend, self.fuse_only)
  File "/opt/homebrew/Caskroom/miniconda/base/envs/sscma/lib/python3.10/site-packages/tinynn/graph/quantization/quantizer.py", line 3664, in prepare_qat
    self.prepare_qat_prep(graph, is_input_quantized, backend)
  File "/opt/homebrew/Caskroom/miniconda/base/envs/sscma/lib/python3.10/site-packages/tinynn/graph/quantization/quantizer.py", line 714, in prepare_qat_prep
    self.prepare_qconfig(graph, backend)
  File "/opt/homebrew/Caskroom/miniconda/base/envs/sscma/lib/python3.10/site-packages/tinynn/graph/quantization/quantizer.py", line 3598, in prepare_qconfig
    torch.backends.quantized.engine = backend
  File "/opt/homebrew/Caskroom/miniconda/base/envs/sscma/lib/python3.10/site-packages/torch/backends/quantized/__init__.py", line 33, in __set__
    torch._C._set_qengine(_get_qengine_id(val))
RuntimeError: quantized engine FBGEMM is not supported

Additional context
Running on Mac M2, torch cpu-only, mmcv compiled from source

[MILK-BIOS]

Hi @sbocconi! We wondered your torch installed from pip or conda? We strongly recommend use pip to install torch.

[sbocconi]

Hi @MILK-BIOS, the error is because on ARM architectures such as MacOS M2 FBGEMM is not supported, so apparently you need to use python tools/export.py --backend qnnpack.

BTW, I have used pip to install torch.

[MILK-BIOS]

Oh, glad to see you have solved the question! We need to make our code more compatiable.

[sbocconi]

Unfortunately the Mac M2 ARM is not well supported yet due to the fact that it is a new architecture. I had to do the following two changes to make it work:

1. export OMP_NUM_THREADS=1 && python tools/train.py <params> Otherwise code hangs
2. Change is_mps_available() in mmengine/device/utils.py to return always False otherwise I get the following error:

TypeError: Cannot convert a MPS Tensor to float64 dtype as the MPS framework doesn't support float64. Please use float32 instead.

Maybe you can mention this in the documentation?