Fix index out of bounds error for 1d convs #15995
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Summary: Unfortunately, we are in a state where conv2d can run either 1d or 2d convs, and the padding/stride/dilation logic is pretty bad. Essentially, if we have a conv1d, we can produce a conv2d where stride becomes [1, value_provided] in the fusion_pass (see get_conv_args). That is why in ops_registrations, we have been indexing at 1. However, if we somehow construct a conv2d_nchw/nchw without going through that fusion_pass, this canonicalization is not done. Ideally, we have conv1d implementations where these arguments are scalars, not lists. But, this is a larger effort, since even the C++ kernels follow this convoluted logic. Temporary fix here is to index at -1, and we'll have to come back to cleaning up conv kernels. Differential Revision: D87943382
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/15995
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Summary:
Unfortunately, we are in a state where conv2d can run either 1d or 2d convs, and the padding/stride/dilation logic is pretty bad.
Essentially, if we have a conv1d, we can produce a conv2d where stride becomes [1, value_provided] in the fusion_pass (see get_conv_args). That is why in ops_registrations, we have been indexing at 1. However, if we somehow construct a conv2d_nchw/nchw without going through that fusion_pass, this canonicalization is not done.
Ideally, we have conv1d implementations where these arguments are scalars, not lists. But, this is a larger effort, since even the C++ kernels follow this convoluted logic.
Temporary fix here is to index at -1, and we'll have to come back to cleaning up conv kernels.
Differential Revision: D87943382