[TorchOnnxToTorch] Fix DepthToSpace conversion in CRD mode#4351
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angelzzzzz wants to merge 1 commit intollvm:mainfrom
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[TorchOnnxToTorch] Fix DepthToSpace conversion in CRD mode#4351angelzzzzz wants to merge 1 commit intollvm:mainfrom
angelzzzzz wants to merge 1 commit intollvm:mainfrom
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According to the ONNX spec, in CRD mode the input should be reshaped to [b, c / (blocksize^2), blocksize, blocksize, h, w] instead of [b, blocksize, blocksize, c / (blocksize^2), h, w].
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@sahas3 Could you please review this PR and share feedback or suggestions when you have time? Thanks a lot! |
sahas3
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Oct 29, 2025
| Value transposedInput; | ||
| Value reshapeSizesList; | ||
| if (mode == "DCR") { | ||
| reshapeSizesList = rewriter.create<Torch::PrimListConstructOp>( |
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I am not familiar with the ONNX part of the codebase. Are there e2e tests that will lock down the numeric for DCR/CRD mode? If not, can you add some please?
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According to the ONNX spec, in the CRD mode, elements along the depth dimension from the input tensor are rearranged in the following order: column, row, and the depth. The output y is computed from the input x as below:
So, the input should be reshaped to [b, c / (blocksize^2), blocksize, blocksize, h, w] instead of [b, blocksize, blocksize, c / (blocksize^2), h, w].