onnxruntime

173a11a4 - [QDQ Optimizer] Update WeightBiasQuantization to skip Conv/Gemm if downstream node is not QuantizeLinear (#24537)

[QDQ Optimizer] Update WeightBiasQuantization to skip Conv/Gemm if downstream node is not QuantizeLinear (#24537) ### Description Updates the WeightBiasQuantization optimizer to skip processing on Conv/Gemm nodes if the downstream child node is not a QuantizeLinear. #### Before this PR Original graph: ``` input_0 -> DQ -> Conv -> graph_output (or non-Q node) ^ ^ | | weights_f32------+ | bias_f32------------+ ``` Becomes: ``` input_0 -> DQ ------> Conv -> graph_output (or non-Q node) ^ ^ | | weights_quant -> DQ --+ | bias_quant -> DQ --------+ ``` The above is **NOT** a valid QDQ node unit for Conv because the Conv's output is not consumed by a QuantizeLinear node. #### With this PR The above example graph remains unchanged after L1 optimizations: ``` input_0 -> DQ -> Conv -> graph_output (or non-Q node) ^ ^ | | weights_f32------+ | bias_f32------------+ ``` ### Motivation and Context Caused inaccuracy for a customer model. Automatically quantizing the weights and biases of a Conv/Gemm is detrimental if the output of the Conv/Gemm is not consumed by a QuantizeLinear node. In this scenario, the whole node group is not considered a valid QDQ node unit, and so the EP has to run the Conv/Gemm as float32/float16 anyway. If the Conv/Gemm is running as float32/float16, then quantizing the weights and biases introduces inaccuracy for no gain. PR that originally added this optimizer: https://github.com/microsoft/onnxruntime/pull/22969