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Troubleshooting Guide for BigDL-Nano
Refer to this section for common issues faced while using BigDL-Nano.
Installation
Why I fail to install openvino-dev==2022.2 when pip install bigdl-nano[inference]?
Please check your system first as openvino-dev 2022.2 does not support centos. Refer this for more details. You can install bigdl-nano[inference] >= 2.2 instead, as bigdl-nano[inference] >= 2.2 use openvino-dev >= 2022.3 which supports centos again.
How to solve SYNK issue caused by onnx when pip install bigdl-nano[inference]?
We are trying to solve this issue by upgrading onnx to 1.13. But there exists conflict between onnx 1.13 and other dependencies (such as intel-tensorflow and pytorch-lightning), mainly because of protobuf version. If you are concerned about security of onnx, we recommend to pip install onnx==1.13 after pip install bigdl-nano[inference].
Inference
could not create a primitive descriptor iterator when using bf16 related methods
Please make sure you use context manager provided by InferenceOptimizer.get_context, you can refer this howto guide for context manager for more details.
assert precision in list(self.cur_config['ops'].keys()) when using ipex quantization with inc on machine with BF16 instruction set
It's known issue for Intel® Neural Compressor that they don't deal with BF16 op well at version 1.13.1 . This has been fixed in version 2.0. You can install bigdl-nano[inference] >= 2.2 to fix this problem.
Why my output is not bf16 dtype when using bf16+ipex related methods?
Please check your torch version and ipex version first. Now we only have CI/CD for ipex>=1.12, and we can't guarantee 100% normal operation when the version is lower than this.
TypeError: cannot serialize xxx object when InferenceOptimizer.optimize() calling all ipex related methods or when InferenceOptimizer.trace(use_ipex=True) / InferenceOptimizer.quantize(use_ipex=True)
In InferenceOptimizer.optimize(), actually we use ipex.optimize(model, dtype=torch.bfloat16, inplace=False) to make sure not change original model. If your model can't be deepcopy, you should use InferenceOptimizer.trace(use_ipex=True, xxx, inplace=True) or InferenceOptimizer.quatize(use_ipex=True, xxx, inplace=True) instead and make sure setting inplace=True.
error message like set strict=False when InferenceOptimizer.trace(accelerator='jit') or InferenceOptimizer.quantize(accelerator='jit')
You can set strict=False for torch.jit.trace by setting jit_strict=False in InferenceOptimizer.trace(accelerator='jit', xxx, jit_strict=False) or InferenceOptimizer.quantize(accelerator='jit', xxx, jit_strict=False).
Refer API usage of torch.jit.trace for more details.
Why channels_last option fails for my computer vision model?
Please check the shape of your input data first, we don't support channels_last for 3D input now. If your model is a 3D model or your input data is not 4D Tensor, normally channels_last option will fail.
Why InferenceOptimizer.load(dir) fails to load my model saved by InferenceOptimizer.save(model, dir)
If you accelerate the model with accelerator=None by InferenceOptimizer.trace/InferenceOptimizer.quantize or it's just a normal torch.nn.Module, you have to pass original FP32 model to load pytorch model by InferenceOptimizer.load(dir, model=model).
Why my bf16 model is slower than fp32 model?
You can first check whether your machine supports the bf16 instruction set first by lscpu | grep "bf16". If there is no avx512_bf16 or amx_bf16 in the output, then, without instruction set support, the performance of bf16 cannot be guaranteed, and generally, its performance will deteriorate.
INVALID_ARGUMENT : Got invalid dimensions for input or [ PARAMETER_MISMATCH ] Can not clone with new dims. when do inference with OpenVINO / ONNXRuntime accelerated model
This error usually occurs when your dataset produces data with dynamic shape, and such case needs you to manually set dynamic_axes parameter and pass dynamic_axes to trace/quantize.
For examples, if your forward function looks like def forward(x: torch.Tensor):, and it recieves 4d Tensor as input. However, your input data's shape will vary during inference, it will be (1, 3, 224, 224) or (1, 3, 256, 256), then in such case, you should:
dynamic_axes['x'] = {0: 'batch_size', 2: 'width', 3: 'height'} # this means the 0/2/3 dim of your input data may vary during inference
input_sample = torch.randn(1, 3, 224, 224)
acce_model = trace(model=model, input_sample=x, dynamic_axes=dynamic_axes)
You can refer to API usage of torch.onnx.export for more details.
Why jit didn't work on my model?
Please check first if you use patch_cuda(disable_jit=True) command of Nano, if you have used it to disable cuda operation, it will disable jit at the same time by torch.jit._state.disable(), so jit has no effect now.
How to cope with out-of-memory during workload with Intel® Extension for PyTorch*
If you found the workload runs with Intel® Extension for PyTorch* occupies a remarkably large amount of memory, you can try to reduce the occupied memory size by setting weights_prepack=False when calling InferenceOptimizer.trace \ InferenceOptimizer.quantize.
RuntimeError: Check 'false' failed at src/frontends/common/src/frontend.cpp
You may see this error when you do inference with accelerator=OpenVINO in keras. It only occurs when you use intel-tensorflow >= 2.8 and you forget source bigdl-nano-init. The way to solve this problem is just source bigdl-nano-init or source bigdl-nano-init -j.
TypeError: deprecated() got an unexpected keyword argument 'name'
If a version problem caused by too low cryptography version. You can fix it by just pip install cryptography==38.0.0 .