Quantize Bias for Conv/Gemm on Quantized Model #22889
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skottmckay
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onnxruntime/core/optimizer/qdq_transformer/bias_quantization.cc
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guschmue
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Some quantized models don't have Conv/Gemm node's bias quantized but still leave them in float. This PR is to create a sub-graph to quantize the bias for Conv/Gemm nodes with scale = scale_input_0 * scale_input_1 and zp = 0. We only do this for bias initializer so that ConstantFolding will fold the sub-graph to a real quantized int32 bias initializer during the graph optimization next round.
ankitm3k
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Some quantized models don't have Conv/Gemm node's bias quantized but still leave them in float. This PR is to create a sub-graph to quantize the bias for Conv/Gemm nodes with scale = scale_input_0 * scale_input_1 and zp = 0. We only do this for bias initializer so that ConstantFolding will fold the sub-graph to a real quantized int32 bias initializer during the graph optimization next round.
ankitm3k
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to intel/onnxruntime
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Dec 11, 2024
Some quantized models don't have Conv/Gemm node's bias quantized but still leave them in float. This PR is to create a sub-graph to quantize the bias for Conv/Gemm nodes with scale = scale_input_0 * scale_input_1 and zp = 0. We only do this for bias initializer so that ConstantFolding will fold the sub-graph to a real quantized int32 bias initializer during the graph optimization next round.
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Some quantized models don't have Conv/Gemm node's bias quantized but still leave them in float. This PR is to create a sub-graph to quantize the bias for Conv/Gemm nodes with scale = scale_input_0 * scale_input_1 and zp = 0. We only do this for bias initializer so that ConstantFolding will fold the sub-graph to a real quantized int32 bias initializer during the graph optimization next round.