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Validate kernel_shape and output_padding lengths in DML kernel setup - #31999
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Akshay Sonawane (apsonawane) merged 2 commits intoAug 13, 2026
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InitializeKernel copied the trailing spatialDimensionCount elements of kernel_shape and the leading spatialDimensionCount elements of output_padding without checking either vector was that long, so a shorter attribute made the iterator arithmetic run off the ends of the buffers. The output_padding check required only two elements, which is short for 3D. The adjacent strides, dilations and pads attributes already validate their lengths the same way; extend that to these two, and to the filter tensor shape used when kernel_shape is absent. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
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Pull request overview
This PR hardens the DML convolution kernel-argument initialization path by validating attribute vector lengths before slicing/copying, preventing out-of-bounds iterator arithmetic when ONNX attributes are shorter than the operator’s spatial rank.
Changes:
- Validate
kernel_shapelength againstspatialDimensionCountbefore copying trailing spatial elements intoargs.windowSize. - Validate
filterTensorShapelength when inferringkernel_shapefrom the filter tensor, preventing invalidend() - spatialDimensionCountarithmetic. - Validate
output_paddinglength againstspatialDimensionCount(instead of a hard-coded>= 2) before copying.
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Akshay Sonawane (apsonawane)
enabled auto-merge (squash)
August 12, 2026 04:00
Ti-Tai Wang (titaiwangms)
approved these changes
Aug 13, 2026
Akshay Sonawane (apsonawane)
deleted the
fix/dml-conv-kernel-attr-length
branch
August 13, 2026 17:54
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InitializeKernel copied the trailing spatialDimensionCount elements of kernel_shape and the leading spatialDimensionCount elements of output_padding without checking either vector was that long, so a shorter attribute made the iterator arithmetic run off the ends of the buffers. The output_padding check required only two elements, which is short for 3D. The adjacent strides, dilations and pads attributes already validate their lengths the same way; extend that to these two, and to the filter tensor shape used when kernel_shape is absent.