per-group and per-channel quantization #14340
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Differential Revision: D13166591 fbshipit-source-id: 667e08448ae2546bf49693d66b16f517f0e4fe7e
Differential Revision: D13167073 fbshipit-source-id: 7926bc42856fc1119d9e6e1b893ef8856e792bac
Summary: Pull Request resolved: pytorch#14340 Pull Request resolved: pytorch/FBGEMM#25 Per-group and per-channel quantization in fbgemm This diff also cleans up explicit template instantiation using macro expansion This diff also changes randFill interface which was easy to make mistakes of generating integer random numbers for floating point vectors. Using this in DNNLOWP operators will be done in a separate diff. Differential Revision: D13176386 fbshipit-source-id: 3137039d2822e42a16881638d54897d9c8bc75f4
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Summary: Pull Request resolved: pytorch/pytorch#14340 Pull Request resolved: pytorch#25 Per-group and per-channel quantization in fbgemm This diff also cleans up explicit template instantiation using macro expansion This diff also changes randFill interface which was easy to make mistakes of generating integer random numbers for floating point vectors. Using this in DNNLOWP operators will be done in a separate diff. Differential Revision: D13176386 fbshipit-source-id: e08c676b6b9cf301f76b87cdb901ecc51c4cc8a4
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Summary: Pull Request resolved: #14340 Pull Request resolved: pytorch/FBGEMM#25 Per-group and per-channel quantization in fbgemm This diff also cleans up explicit template instantiation using macro expansion This diff also changes randFill interface which was easy to make mistakes of generating integer random numbers for floating point vectors. Using this in DNNLOWP operators will be done in a separate diff. Reviewed By: dskhudia Differential Revision: D13176386 fbshipit-source-id: e46c53e31e21520bded71b8ed86e8b19e010e2dd
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Summary:
Per-group and per-channel quantization in fbgemm
This diff also cleans up explicit template instantiation using macro expansion
Using this in DNNLOWP operators will be done in a separate diff.
Differential Revision: D13176386