I’m currently trying with the STM32N6570-DK board using the ST Edge-AI v2.2.0
I would like to know whether a lightweight Transformer model (for example, a small Vision Transformer or a minimal Transformer encoder) can be successfully converted and executed on the STM32N6. I checked that batch_matmul, transpose, etc. are supported on the link (https://stm32ai-cs.st.com/assets/embedded-docs/stneuralart_operator_support.html )
Currently, after converting the lightweight ViT model to tflite or onnx, I am trying to convert it using stedgeai and stm32cubeide, but only the following error messages appear and are not being converted
Code, model file, and error text: https://github.com/minchoCoin/lightweight_vit
These model was successfully analyzed with STM32Cube.AI MCU runtime on STM32CubeIDE
Following error messages appear when I try to convert these model(tflite and onnx) with STM32Cube.AI Neural-ART runtime in STM32CubeIDE or with stedgeai in command line
1. tflite
stedgeai generate --model custom_vit_int8.tflite --target stm32n6 --st-neural-art default@user_neuralart_STM32N6570-DK.json
ST Edge AI Core v2.2.0-20266 2adc00962
WARNING: nl_8 is not quantized
...
STEDGEAI_BuildAtonnExe_Win/git/onnx_backend/platform_passes/transform_gemm_fc_into_conv.cc:203: runTransform: Assertion `(b_shape.size() == 1) || ((b_shape[0].dim == M) || b_shape[0].dim == N)` failed.
Warning: Missing Quantization info for Pow_35_exp; will consider Pow_35_exp as a native Float
...
Warning: Lowering of node=Transpose_52 kind=Transpose not yet supported. the generated code will not compile
terminate called after throwing an instance of 'std::runtime_error'
what(): SW mapping failed:
Node Transpose_88 not mapped
Internal compiler error (signo=6), please report it
2. onnx
ST Edge AI Core v2.2.0-20266 2adc00962
INTERNAL ERROR: Exported ONNX could be malformed since ONNX shape inference fails
thank you for your support in advance!
