[XNNPACK Quantizer] Select between TConvs and Convs #11776
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/11776
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…ups==1 Pull Request resolved: #11730 Supporting Quantized Transposed Convs with Groups being 1. Previously, There was some added support for Quantized Transposed Convolutions but only when the channel axis is 1 and when the groups is 1. The current Quantizer didn't support this because it only allows quantizaing along the zero dim, which is generally the output channels. However for TransposedConvs, the dimension of the weights are: ``` [in_channels, out_channels/groups, h, w] ``` Since we want to keep quantization along the output channels, we now need to quantize along axis = 1. The reason we require groups to be one is because XNNPACK takes in filters of the dimension: ``` [out_channels, H, W, in_channels/groups] ``` Since we are quantizing along the output channels, in pytorch we expect to have out_channels/groups scales, but in xnnpack we have out_channels scales! Realistically we would need to support this with some affine quantization, where we provide a scale for every group, every out_channel. However for now, we just ensure the constraint where groups == 1. ghstack-source-id: 291033630 @exported-using-ghexport Differential Revision: [D76631781](https://our.internmc.facebook.com/intern/diff/D76631781/)
…groups ==1 Pull Request resolved: #11731 Here we support dynamically quantized Deconvolutions. There is some refactoring of the previous diff, but in general, we just remove the constraint in the Dynamism check that the convolution isn't transposed. For the same reasons as before, this only supports channel_axis = 1 and groups = 1. ghstack-source-id: 291033632 @exported-using-ghexport Differential Revision: [D76638904](https://our.internmc.facebook.com/intern/diff/D76638904/)
Pull Request resolved: #11732 Allow selection of Difference between transposed convs and regular convs. Previously, we grouped all conv targets together (transposed and regular convs), but now we enable better per-operator selection ghstack-source-id: 291033631 Differential Revision: [D76641838](https://our.internmc.facebook.com/intern/diff/D76641838/)
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This PR was created by the merge bot to help merge the original PR into the main branch. ghstack PR number: pytorch#11732 by @mcr229 ^ Please use this as the source of truth for the PR details, comments, and reviews ghstack PR base: https://github.com/pytorch/executorch/tree/gh/mcr229/33/base ghstack PR head: https://github.com/pytorch/executorch/tree/gh/mcr229/33/head Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/mcr229/32/orig Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/mcr229/33/orig @diff-train-skip-merge --------- Co-authored-by: Max Ren <maxren@meta.com>
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This PR was created by the merge bot to help merge the original PR into the main branch.
ghstack PR number: #11732 by @mcr229
^ Please use this as the source of truth for the PR details, comments, and reviews
ghstack PR base: https://github.com/pytorch/executorch/tree/gh/mcr229/33/base
ghstack PR head: https://github.com/pytorch/executorch/tree/gh/mcr229/33/head
Merge bot PR base: https://github.com/pytorch/executorch/tree/gh/mcr229/32/orig
Merge bot PR head: https://github.com/pytorch/executorch/tree/gh/mcr229/33/orig
@diff-train-skip-merge