Quantize Weight for Gemm/Conv on Quantized Model #22969
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skottmckay
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onnxruntime/core/optimizer/qdq_transformer/weight_bias_quantization.h
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onnxruntime/core/optimizer/qdq_transformer/weight_bias_quantization.cc
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snnn
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Jan 8, 2025
Some quantized models have QDQ around Conv/Gemm but the weight and/or bias are not quantized. This PR adds WeightBiasQuantization optimizer to quantize float weight and/or bias to INT8 and INT32 tensors respectively. We only do this for weight and/or bias initializer so that ConstantFolding will fold the sub-graph to real quantized initializers during the graph optimization next round.
tarekziade
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Jan 10, 2025
Some quantized models have QDQ around Conv/Gemm but the weight and/or bias are not quantized. This PR adds WeightBiasQuantization optimizer to quantize float weight and/or bias to INT8 and INT32 tensors respectively. We only do this for weight and/or bias initializer so that ConstantFolding will fold the sub-graph to real quantized initializers during the graph optimization next round.
guschmue
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Jan 12, 2025
Some quantized models have QDQ around Conv/Gemm but the weight and/or bias are not quantized. This PR adds WeightBiasQuantization optimizer to quantize float weight and/or bias to INT8 and INT32 tensors respectively. We only do this for weight and/or bias initializer so that ConstantFolding will fold the sub-graph to real quantized initializers during the graph optimization next round.
ashrit-ms
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Mar 17, 2025
Some quantized models have QDQ around Conv/Gemm but the weight and/or bias are not quantized. This PR adds WeightBiasQuantization optimizer to quantize float weight and/or bias to INT8 and INT32 tensors respectively. We only do this for weight and/or bias initializer so that ConstantFolding will fold the sub-graph to real quantized initializers during the graph optimization next round.
adrianlizarraga
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Apr 24, 2025
…wnstream 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: #22969
vraspar
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Apr 28, 2025
…wnstream 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: #22969
ankitm3k
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May 12, 2025
…wnstream node is not QuantizeLinear (microsoft#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: microsoft#22969
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Some quantized models have QDQ around Conv/Gemm but the weight and/or bias are not quantized. This PR adds WeightBiasQuantization optimizer to quantize float weight and/or bias to INT8 and INT32 tensors respectively. We only do this for weight and/or bias initializer so that ConstantFolding will fold the sub-graph to real quantized initializers during the graph optimization next round.