Optional Utilities#

Exporting PyTorch Models to ONNX#

Note

Skip this step if you already have the ONNX format model.

For PyTorch models, it is recommended to use the TorchScript-based ONNX exporter for exporting ONNX models. Please refer to the PyTorch documentation for guidance.

Tips:#

Before exporting, please perform model.eval().
Models with opset 17 are recommended.
For NPU_CNN platforms, dynamic input shapes are currently not supported, and only a batch size of 1 is allowed. Please ensure that the shape of the input is fixed, and the batch dimension is set to 1.

Example code:

torch.onnx.export(
    model,
    input,
    model_output_path,
    opset_version=17,
    input_names=['input'],
    output_names=['output'],
)

Opset Versions: Models with opset 17 are recommended. Models must be opset 10 or higher to be quantized. Models with opset lower than 10 should be reconverted to ONNX from their original framework using a later opset. Alternatively, you can refer to the usage of the version converter for ONNX Version Converter. Opset 10 does not support some node fusions and may not achieve the best performance. We recommend updating the model to opset 17 for better performance. Moreover, per-channel quantization is supported for opset 13 or higher versions.
Large Models > 2GB: Due to the 2GB file size limit of Protobuf, for ONNX models exceeding 2GB, additional data will be stored separately. Please ensure that the .onnx file and the data file are placed in the same directory. Also, please set the use_external_data_format parameter to True for large models when quantizing.

Pre-processing on the Float Model#

Pre-processing is the transformation of a float model to prepare it for quantization. It consists of the following three optional steps:

Symbolic shape inference: This is best suited for transformer models.
Model Optimization: This step uses the ONNX Runtime native library to rewrite the computation graph, including merging computation nodes and eliminating redundancies to improve runtime efficiency.
ONNX shape inference.

The goal of these steps is to improve quantization quality. The ONNX Runtime quantization tool works best when the tensor’s shape is known. Both symbolic shape inference and ONNX shape inference help determine tensor shapes. Symbolic shape inference works best with transformer-based models, and ONNX shape inference works with other models.

Model optimization performs certain operator fusions that make the quantization tool’s job easier. For instance, a Convolution operator followed by BatchNormalization can be fused into one during optimization, which can be quantized very efficiently.

Unfortunately, a known issue in ONNX Runtime is that model optimization cannot output a model size greater than 2GB. So for large models, optimization must be skipped.

The pre-processing API is in the Python module onnxruntime.quantization.shape_inference, function quant_pre_process().

from onnxruntime.quantization import shape_inference

shape_inference.quant_pre_process(
    input_model_path: str,
    output_model_path: str,
    skip_optimization: bool = False,
    skip_onnx_shape: bool = False,
    skip_symbolic_shape: bool = False,
    auto_merge: bool = False,
    int_max: int = 2**31 - 1,
    guess_output_rank: bool = False,
    verbose: int = 0,
    save_as_external_data: bool = False,
    all_tensors_to_one_file: bool = False,
    external_data_location: str = "./",
    external_data_size_threshold: int = 1024,)

Arguments

input_model_path: (String) This parameter specifies the file path of the input model that is to be pre-processed for quantization.
output_model_path: (String) This parameter specifies the file path where the pre-processed model will be saved.
skip_optimization: (Boolean) This flag indicates whether to skip the model optimization step. If set to True, model optimization will be skipped, which may cause ONNX shape inference failure for some models. The default value is False.
skip_onnx_shape: (Boolean) This flag indicates whether to skip the ONNX shape inference step. The symbolic shape inference is most effective with transformer-based models. Skipping all shape inferences may reduce the effectiveness of quantization, as a tensor with an unknown shape cannot be quantized. The default value is False.
skip_symbolic_shape: (Boolean) This flag indicates whether to skip the symbolic shape inference step. Symbolic shape inference is most effective with transformer-based models. Skipping all shape inferences may reduce the effectiveness of quantization, as a tensor with an unknown shape cannot be quantized. The default value is False.
auto_merge: (Boolean) This flag determines whether to automatically merge symbolic dimensions when a conflict occurs during symbolic shape inference. The default value is False.
int_max: (Integer) This parameter specifies the maximum integer value that is to be considered as boundless for operations like slice during symbolic shape inference. The default value is 2**31 - 1.
guess_output_rank: (Boolean) This flag indicates whether to guess the output rank to be the same as input 0 for unknown operations. The default value is False.
verbose: (Integer) This parameter controls the level of detailed information logged during inference. A value of 0 turns off logging, 1 logs warnings, and 3 logs detailed information. The default value is 0.
save_as_external_data: (Boolean) This flag determines whether to save the ONNX model to external data. The default value is False.
all_tensors_to_one_file: (Boolean) This flag indicates whether to save all the external data to one file. The default value is False.
external_data_location: (String) This parameter specifies the file location where the external file is saved. The default value is “./”.
external_data_size_threshold: (Integer) This parameter specifies the size threshold for external data. The default value is 1024.

Evaluating the Quantized Model#

If you have scripts to evaluate float models, you can replace the float model file with the quantized model for evaluation.

Note that if BFP/BF16/FP16/int32 data types are used in the quantized model, it is necessary to register the custom operations library to the ONNX Runtime inference session before evaluation. For example:

import onnxruntime as ort

so = ort.SessionOptions()
so.register_custom_ops_library(quark.onnx.get_library_path())
session = ort.InferenceSession(quantized_model, so)

Dumping the Simulation Results#

Sometimes after deploying the quantized model, it is necessary to compare the simulation results on the CPU/GPU and the output values on the DPU. You can use the dump_model API of Quark ONNX to dump the simulation results with the quantized_model. Currently, only models containing FixNeuron nodes support this feature. For models using QuantFormat.QDQ, you can set dump_float to True to save float data for all nodes’ results.

# This function dumps the simulation results of the quantized model,
# including weights and activation results.
quark.onnx.dump_model(
    model,
    dump_data_reader=None,
    random_data_reader_input_shape={},
    dump_float=False,
    output_dir='./dump_results',)

Arguments

model: (String) This parameter specifies the file path of the quantized model whose simulation results are to be dumped.
dump_data_reader: (CalibrationDataReader or None) This parameter is a data reader that is used for the dumping process. The first batch will be taken as input. If you wish to use random data for a quick test, you can set dump_data_reader to None. The default value is None.
random_data_reader_input_shape: (Dict) It is required to use a dict {name: shape} to specify a certain input. For example, RandomDataReaderInputShape={“image”: [1, 3, 224, 224]} for the input named “image”. The default value is an empty dict {}.
dump_float: (Boolean) This flag determines whether to dump the floating-point value of nodes’ results. If set to True, the float values will be dumped. Note that this may require a lot of storage space. The default value is False.
output_dir: (String) This parameter specifies the directory where the dumped simulation results will be saved. After successful execution of the function, dump results are generated in this specified directory. The default value is ‘./dump_results’.

Note: The batch_size of the dump_data_reader will be better set to 1 for DPU debugging.

Dump results of each FixNeuron node (including weights and activation) are generated in output_dir after the command has been successfully executed.

For each quantized node, results are saved in *.bin and *.txt formats (* represents the output name of the node). If “dump_float” is set to True, the output of all nodes is saved in *_float.bin and *_float.txt (* represents the output name of the node), please note that this may require a lot of storage space.

Examples of dumping results are shown in the following table. Due to considerations for the storage path, the ‘/’ in the node name will be replaced with ‘_’.

Table 2. Example of Dumping Results

Quantized	Node Name	Saved Weights or Activations
Yes	/conv1/Conv_out	{output_dir}/dump_results/_conv1_Conv_output_0_DequantizeLinear_Output.bin	{output_dir}/dump_results/_conv1_Conv_output_0_DequantizeLinear_Output.txt
Yes	onnx::Conv_501_DequantizeLinear	{output_dir}/dump_results/onnx::Conv_501_DequantizeLinear_Output.bin	{output_dir}/dump_results/onnx::Conv_501_DequantizeLinear_Output.txt
No	/avgpool/GlobalAveragePool	{output_dir}/dump_results/_avgpool_GlobalAveragePool_output_0_float.bin	{output_dir}/dump_results/_avgpool_GlobalAveragePool_output_0_float.txt

Optional Utilities

Contents

Optional Utilities#

Exporting PyTorch Models to ONNX#

Tips:#

Pre-processing on the Float Model#

Evaluating the Quantized Model#

Dumping the Simulation Results#