add: detect Q/DQ with int16/uint16 initializers for GPU Scale Transform Pass #768
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ankitm3k
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@mklimenk Please test, review & merge |
mklimenk
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Aug 4, 2025
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| auto is_16bit_tensor = [](const onnxruntime::NodeArg* node_arg) -> bool { | ||
| if (!node_arg) return false; | ||
| const auto* type_proto = node_arg->TypeAsProto(); | ||
| if (type_proto && type_proto->has_tensor_type()) { | ||
| auto elem_type = type_proto->tensor_type().elem_type(); | ||
| return (elem_type == ONNX_NAMESPACE::TensorProto_DataType_UINT16 || | ||
| elem_type == ONNX_NAMESPACE::TensorProto_DataType_INT16); | ||
| } | ||
| return false; | ||
| }; |
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Please move it to a separate function, there's no need for long multi-line lambdas
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| // QuantizeLinear: [float_input, scale, zero_point] -> [quantized_output] | ||
| // The quantized output tensor determines the quantization type |
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| // Zero point (index 2) must match quantized tensor type per ONNX spec | ||
| // It's optional - absent for INT32 and some float8 types | ||
| if (input_defs.size() >= 3 && is_16bit_tensor(input_defs[2])) { |
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Should it be output_defs[2]? It seems like the portion in the previous condition
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yes its the zero_point dtype that is tested to check the INT16/UINT16 dtype
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Description
This PR enables the GPU Scale Transform Pass by detecting the UINT16 & INT16 Initializers type in the Q/DQ Nodes in the graph. This isolates the dependency on using the enable_qdq_optimizer provider option pass in the legacy OVEP code.