Troubleshooting Guide for BigDL-Nano

Refer to this section for common issues faced while using BigDL-Nano.

Installation

Why I fail to install openvino==2022.2 when pip install bgdl-nano[inference]?

Please check your system first as openvino 2022.x does not support centos anymore. Refer OpenVINO release notes for more details.

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 will be fixed when next stable version releases.

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 "avx512". If there is no avx512_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.