Optimum roberta base quantization model recall drop 10%

We use roberta base model for a binary classification problem, and I tried quantization using torch, onnxruntime and Optimum, both torch and onnxruntime quantization model recall drop around 5%, but Optimum quantization model recall drop around 10%, for all these models, precision is same as the original model.


ort_model = ORTModelForSequenceClassification.from_pretrained(model_path, export=True)
quantizer = ORTQuantizer.from_pretrained(ort_model)
dqconfig = AutoQuantizationConfig.arm64(
model_quantized_path = quantizer.quantize(

I tried different params in AutoQuantizationConfig.arm64 function, but the result is similar.

By the way, how can I determine which nodes to exclude in quantization, is there a tool?

Hi @Ivan1999,

To check out which nodes are sensitive to quantization, you can create a quantization preprocessor (as done here), to iteratively exclude operations which can result in significant drop in accuracy when quantized (such as GELU, LayerNorm, softmax)

@echarlaix , I tried, not work,

# Create a quantization preprocessor to determine the nodes to exclude when applying static quantization
quantization_preprocessor = QuantizationPreprocessor()
# Exclude the nodes constituting LayerNorm
# Exclude the nodes constituting GELU
# Exclude the residual connection Add nodes
quantization_preprocessor.register_pass(ExcludeNodeAfter("Add", "Add"))
# Exclude the Add nodes following the Gather operator
quantization_preprocessor.register_pass(ExcludeNodeAfter("Gather", "Add"))
# Exclude the Add nodes followed by the Softmax operator
quantization_preprocessor.register_pass(ExcludeNodeFollowedBy("Add", "Softmax"))

Can you show the script you used to quantize the model with ONNNXRuntime (i.e. without Optimum) please?

It’s been a long time, after some configuration I made both onnx and optimum quantization recall drop around 6%.