[TOPI] Support grouped conv1d #9832
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tkonolige
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mbrookhart
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Jan 5, 2022
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LGTM. I really like the ND kernel implementation.
Generalize the conv2d compute statement to a generic convNd that supports any layout and groups. Replace some existing conv2d and conv1d compute statements with this generic compute. Also add a topi group_conv1d compute that uses the generic convNd compute. Existing schedules for conv1d work with group_conv1d, so they are reused.
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Thanks, @tkonolige ! |
Raghav-Chakravarthy
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* [TOPI] Support grouped conv1d Generalize the conv2d compute statement to a generic convNd that supports any layout and groups. Replace some existing conv2d and conv1d compute statements with this generic compute. Also add a topi group_conv1d compute that uses the generic convNd compute. Existing schedules for conv1d work with group_conv1d, so they are reused. * permute reduction axis order * formatting
ylc
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* [TOPI] Support grouped conv1d Generalize the conv2d compute statement to a generic convNd that supports any layout and groups. Replace some existing conv2d and conv1d compute statements with this generic compute. Also add a topi group_conv1d compute that uses the generic convNd compute. Existing schedules for conv1d work with group_conv1d, so they are reused. * permute reduction axis order * formatting
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Generalize the conv2d compute statement to a generic convNd that supports any layout and groups. Replace some existing conv2d and conv1d compute statements with this generic compute. Also add a topi group_conv1d compute that uses the generic convNd compute. Existing schedules for conv1d work with group_conv1d, so they are reused.
I've tested this on an internal model and it provides a good speedup over the previous expand -> conv2d -> squeeze hack.
@mbrookhart