Fix subgraph quantization regression in onnxruntime 1.17 #19421
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Is it possible to add a unit test to make sure it does not break again? |
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Sure will do! |
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@xadupre I added a test. |
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/azp run Linux CPU CI Pipeline, Linux CPU Minimal Build E2E CI Pipeline, Linux GPU CI Pipeline, Linux GPU TensorRT CI Pipeline, Linux OpenVINO CI Pipeline, MacOS CI Pipeline, ONNX Runtime Web CI Pipeline, onnxruntime-binary-size-checks-ci-pipeline, Linux QNN CI Pipeline |
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/azp run Windows CPU CI Pipeline, Windows GPU CI Pipeline, Windows GPU TensorRT CI Pipeline, Windows ARM64 QNN CI Pipeline, orttraining-linux-ci-pipeline, orttraining-linux-gpu-ci-pipeline, orttraining-ortmodule-distributed, ONNX Runtime React Native CI Pipeline, Windows x64 QNN CI Pipeline |
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/azp run Linux MIGraphX CI Pipeline, orttraining-amd-gpu-ci-pipeline |
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@fxmarty, could you setup lintrunner and run |
| for attr in node.attribute: | ||
| if attr.type == onnx.AttributeProto.GRAPH: | ||
| for initializer in attr.g.initializer: | ||
| self.assertTrue("shared.weight" not in initializer.name) |
Check notice
Code scanning / CodeQL
Imprecise assert Note test
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/azp run Linux CPU CI Pipeline, Linux CPU Minimal Build E2E CI Pipeline, Linux GPU CI Pipeline, Linux GPU TensorRT CI Pipeline, Linux OpenVINO CI Pipeline, MacOS CI Pipeline, ONNX Runtime Web CI Pipeline, onnxruntime-binary-size-checks-ci-pipeline, Linux QNN CI Pipeline |
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/azp run Windows CPU CI Pipeline, Windows GPU CI Pipeline, Windows GPU TensorRT CI Pipeline, Windows ARM64 QNN CI Pipeline, orttraining-linux-ci-pipeline, orttraining-linux-gpu-ci-pipeline, orttraining-ortmodule-distributed, ONNX Runtime React Native CI Pipeline, Windows x64 QNN CI Pipeline |
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/azp run Linux MIGraphX CI Pipeline, orttraining-amd-gpu-ci-pipeline, Big Models |
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Azure Pipelines successfully started running 9 pipeline(s). |
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Azure Pipelines successfully started running 3 pipeline(s). |
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Azure Pipelines successfully started running 9 pipeline(s). |
As per title, fixes #19418 ONNX Runtime 1.17 broke the quantization of ONNX models with subgraphs where initializers are placed on the top-level graph, while different subgraphs use the same initializer.
As per title, fixes #19418
ONNX Runtime 1.17 broke the quantization of ONNX models with subgraphs where initializers are placed on the top-level graph, while different subgraphs use the same initializer.