[inductor] Fix convolution autotune check when groups != 1#163094
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[inductor] Fix convolution autotune check when groups != 1#163094
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…63094) When generating the triton template for convolution, we check `V.graph.sizevars.statically_known_equals(in_chan * groups, x.get_size()[1]) `. Note that in this check, we should consider the groups. This check verifies, at compile time, that the total number of input channels expected by the convolution weights (in_chan * groups) exactly matches the number of channels in the input tensor (x.get_size()[1]). This fix is good in general as it allows for conv triton template to be generated when `groups> 1`. It's also required for unified runtime to use AOTI as a backend delegate, because unified runtime is libtorch-free, so we cannot use the ATEN fallback of conv2d. ``` python test/inductor/test_select_algorithm.py -k test_convolution2_group ``` Pull Request resolved: pytorch#163094 Approved by: https://github.com/SherlockNoMad
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…63094) When generating the triton template for convolution, we check `V.graph.sizevars.statically_known_equals(in_chan * groups, x.get_size()[1]) `. Note that in this check, we should consider the groups. This check verifies, at compile time, that the total number of input channels expected by the convolution weights (in_chan * groups) exactly matches the number of channels in the input tensor (x.get_size()[1]). This fix is good in general as it allows for conv triton template to be generated when `groups> 1`. It's also required for unified runtime to use AOTI as a backend delegate, because unified runtime is libtorch-free, so we cannot use the ATEN fallback of conv2d. ``` python test/inductor/test_select_algorithm.py -k test_convolution2_group ``` Pull Request resolved: pytorch#163094 Approved by: https://github.com/SherlockNoMad
dsashidh
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…63094) When generating the triton template for convolution, we check `V.graph.sizevars.statically_known_equals(in_chan * groups, x.get_size()[1]) `. Note that in this check, we should consider the groups. This check verifies, at compile time, that the total number of input channels expected by the convolution weights (in_chan * groups) exactly matches the number of channels in the input tensor (x.get_size()[1]). This fix is good in general as it allows for conv triton template to be generated when `groups> 1`. It's also required for unified runtime to use AOTI as a backend delegate, because unified runtime is libtorch-free, so we cannot use the ATEN fallback of conv2d. ``` python test/inductor/test_select_algorithm.py -k test_convolution2_group ``` Pull Request resolved: pytorch#163094 Approved by: https://github.com/SherlockNoMad
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When generating the triton template for convolution, we check
V.graph.sizevars.statically_known_equals(in_chan * groups, x.get_size()[1]). Note that in this check, we should consider the groups.This check verifies, at compile time, that the total number of input channels expected by the convolution weights (in_chan * groups) exactly matches the number of channels in the input tensor (x.get_size()[1]).
This fix is good in general as it allows for conv triton template to be generated when
groups> 1. It's also required for unified runtime to use AOTI as a backend delegate, because unified runtime is libtorch-free, so we cannot use the ATEN fallback of conv2d.cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @ipiszy @chenyang78 @kadeng @muchulee8 @amjames @chauhang @aakhundov @coconutruben