| commit | c56bd7ab7934355ed19d4d3ec299b5784bf02379 | [log] [tgz] |
|---|---|---|
| author | Andrzej Warzyński <andrzej.warzynski@arm.com> | Thu Mar 14 20:19:46 2024 +0000 |
| committer | GitHub <noreply@github.com> | Thu Mar 14 20:19:46 2024 +0000 |
| tree | 4c49414aa09e500b4a39dcea15beb6c9ebcb6776 | |
| parent | bba790db477b5b076e587b6c679ac68eb8316ac3 [diff] |
[mlir][linalg] Enable masked vectorisation for depthwise convolutions (#81625)
This patch adds support for masked vectorisation of depthwise 1D WC
convolutions,`linalg.depthwise_conv_1d_nwc_wc`. This is implemented by
adding support for masking.
Two major assumptions are made:
* only the channel dimension can be dynamic/scalable (i.e. the
trailing dim),
* when specifying vector sizes to use in the vectoriser, only the size
corresponding to the channel dim is effectively used (other dims are
inferred from the context).
In terms of scalable vectorisation, this should be sufficient to cover
all practical cases (i.e. making arbitrary dim scalable wouldn't make
much sense). As for more generic cases with dynamic shapes (e.g. W or N
dims being dynamic), more work would be needed. In particular, one would
have to consider the filter and input/output tensors separately.
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