VAIQ_WARN - 3.5 English

Vitis AI User Guide (UG1414)

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3.5 English
Vai_q_pytorch displays a warning message if an issue could lead to problems or incompleteness in the quantization result (r to the message text for details). The message format is [VAIQ_WARN][MESSAGE_ID]: message text. The quantization process can still proceed to completion despite the warning.

List important warning messages in the following table:

Table 1. Vai_q_pytorch Warning Message Table
Message ID Description
QUANTIZER_TORCH_BATCHNORM_AFFINE BatchNorm OP attribute affine=False has been replaced by affine=True when parsing the model.
QUANTIZER_TORCH_BITWIDTH_MISMATCH Bit width setting in the configuration file. If it conflicts with that from torch_quantizer API, the setting in the configuration file is used.
QUANTIZER_TORCH_CONVERT_XMODEL Convert to XMODEL failed. Check the message text to locate the reason.
QUANTIZER_TORCH_CUDA_UNAVAILABLE CUDA (HIP) is not available. Change the device to CPU.
QUANTIZER_TORCH_DATA_PARALLEL Data parallel is not supported. The wrapper 'torch.nn.DataParallel' has been removed in vai_q_pytorch.
QUANTIZER_TORCH_DEPLOY_MODEL Only quantization aware training process has a deployable model.
QUANTIZER_TORCH_DEVICE_MISMATCH The input arguments device mismatches with the quantizer device type.
QUANTIZER_TORCH_EXPORT_XMODEL Failed to generate XMODEL due to some reasons. Refer to the message text.
QUANTIZER_TORCH_FINETUNE_IGNORED Fast fine-tuning function is ignored in test mode.
QUANTIZER_TORCH_FLOAT_OP vai_q_pytorch recognizes the list OP as a float operator by default.
QUANTIZER_TORCH_INSPECTOR_PATTERN The OP might not be fused by the compiler and is assigned to DPU.
QUANTIZER_TORCH_LEAKYRELU Force to change the negative_slope of LeakyReLU to 0.1015625 because DPU only supports this value. It is recommended to change all negative_slope of LeakyReLU to 0.1015625 and re-train the float model for better-deployed model accuracy.
QUANTIZER_TORCH_MATPLOTLIB matplotlib is needed for visualization but not found. It needs to be installed.
QUANTIZER_TORCH_MEMORY_SHORTAGE There is not enough memory for fast finetune, and this process is ignored. Try to use a smaller calibration dataset.
QUANTIZER_TORCH_NO_XIR Cannot find the XIR package in the environment. It needs to be installed.
QUANTIZER_TORCH_REPLACE_RELU6 ReLU6 has been replaced by ReLU.
QUANTIZER_TORCH_REPLACE_SIGMOID Sigmoid has been replaced by Hardsigmoid.
QUANTIZER_TORCH_REPLACE_SILU SiLU has been replaced by Hardswish.
QUANTIZER_TORCH_SHIFT_CHECK Quantization scale is too large or too small.
QUANTIZER_TORCH_TENSOR_NOT_QUANTIZED Some tensors are not quantized. Check their particularity.
QUANTIZER_TORCH_TENSOR_TYPE_NOT_QUANTIZABLE The tensor type of the node cannot be quantized. Only support float32/double/float16 quantization.
QUANTIZER_TORCH_TENSOR_VALUE_INVALID The tensor has an "inf" or "nan" value. Quantization for this tensor is ignored.
QUANTIZER_TORCH_TORCH_VERSION Only support exporting TorchScript with PyTorch 1.10 and later versions.
QUANTIZER_TORCH_XIR_MISMATCH XIR version does not match the current vai_q_pytorch.
QUANTIZER_TORCH_XMODEL_DEVICE Not support to dump XMODEL when the target device is not DPU.
QUANTIZER_TORCH_REUSED_MODULE Reused module might lead to low accuracy of QAT. Ensure this is what you expect. Refer to the message text to locate the module with the issue.
QUANTIZER_TORCH_DEPRECATED_ARGUMENT The argument device is no longer needed. Device information is obtained from the model directly.
QUANTIZER_TORCH_SCALE_VALUE Exported scale values are not trained.