export.md

Export

1. Introduction

2. Supported Framework Model Matrix

3. Examples

4. Appendix

Introduction

Open Neural Network Exchange (ONNX) is an open standard format for representing machine learning models. Exporting FP32 PyTorch/Tensorflow models has become popular and easy to use. However, for Intel Neural Compressor, we hope to export the INT8 model into the ONNX format to achieve higher applicability in multiple frameworks.

Here we briefly introduce our export API for PyTorch FP32/INT8 models. First, the INT8 ONNX model is not directly exported from the INT8 PyTorch model, but quantized after obtaining the FP32 ONNX model using the mature torch.onnx.export API. To ensure the majority of the quantization process of ONNX is consistent with PyTorch, we reuse three key pieces of information from the Neural Compressor model to perform ONNX quantization.

• Quantized operations: Only operations quantized in PyTorch will be quantized in the quantization process of ONNX.
• Scale info: Scale information is collected from the quantization process of PyTorch.
• Weights of quantization aware training(QAT): For quantization aware training, the updated weights are passed to the ONNX model.

Supported Framework Model Matrix

Export	Post-training Dynamic Quantization	Post-training Static Quantization	Quantization Aware Training
FP32 PyTorch Model -> FP32 ONNX Model	✔	✔	✔
INT8 PyTorch Model -> INT8 QDQ ONNX Model	✔	✔	✔
INT8 PyTorch Model -> INT8 QLinear ONNX Model	✔	✔	✔

Examples

FP32 Model Export

from neural_compressor.experimental.common import Model
from neural_compressor.config import Torch2ONNXConfig
inc_model = Model(model)
fp32_onnx_config = Torch2ONNXConfig(
    dtype="fp32",
    example_inputs=torch.randn(1, 3, 224, 224),
    input_names=['input'],
    output_names=['output'],
    dynamic_axes={"input": {0: "batch_size"},
                    "output": {0: "batch_size"}},
)
inc_model.export('fp32-model.onnx', fp32_onnx_config)

INT8 Model Export

# q_model is a Neural Compressor model after performing quantization.
from neural_compressor.config import Torch2ONNXConfig
int8_onnx_config = Torch2ONNXConfig(
    dtype="int8",
    opset_version=14,
    quant_format="QDQ", # or QLinear
    example_inputs=torch.randn(1, 3, 224, 224),
    input_names=['input'],
    output_names=['output'],
    dynamic_axes={"input": {0: "batch_size"},
                    "output": {0: "batch_size"}},
)
q_model.export('int8-model.onnx', int8_onnx_config)

Appendix

Since there is a known quantization gap between PyTorch 'nn.Linear' module and ONNX 'MatMul + Add' subgraph, we provide three recipes.

For different recipes and ONNX INT8 model formats, 'nn.quantized.Linear' will be exported to the following subgraph:

Recipe	QDQ	QLinear
QDQ_OP_FP32_BIAS	QuantizeLinear | DequantizeLinear | MatMul | Add	QuantizeLinear | MatMulIntegerToFloat | Add
QDQ_OP_INT32_BIAS	QuantizeLinear | MatMulInteger | Add | Cast | Mul	QuantizeLinear | MatMulInteger | Add | Cast | Mul
QDQ_OP_FP32_BIAS_QDQ	QuantizeLinear | DequantizeLinear | MatMul | Add | QuantizeLinear | DequantizeLinear	QuantizeLinear | MatMulIntegerToFloat | Add | QuantizeLinear | DequantizeLinear

The default recipe is QDQ_OP_FP32_BIAS. If the accuracy of the exported ONNX INT8 model cannot meet your criterion, we recommend you try recipe QDQ_OP_INT32_BIAS and QDQ_OP_FP32_BIAS_QDQ as follows:

# q_model is a Neural Compressor model after performing quantization.
from neural_compressor.config import Torch2ONNXConfig
int8_onnx_config = Torch2ONNXConfig(
    dtype="int8",
    opset_version=14,
    quant_format="QDQ", # or QLinear
    example_inputs=torch.randn(1, 3, 224, 224),
    input_names=['input'],
    output_names=['output'],
    dynamic_axes={"input": {0: "batch_size"},
                    "output": {0: "batch_size"}},
    recipe='QDQ_OP_INT32_BIAS', # or QDQ_OP_FP32_BIAS_QDQ
)
q_model.export('int8-model.onnx', int8_onnx_config)