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Summary:
The vectorized code was rounding to even in halfway cases with _mm256_round_ps + (_MM_FROUND_TO_NEAREST_INT |_MM_FROUND_NO_EXC) (see more details in https://software.intel.com/en-us/node/523819), but we were still using std::round in a couple of places which does rounding away from zero in halfway cases.
With this diff, we use std::nearbyint in all scalar code (except a few cases where we don't care exact rounding mode and uses rint which is the fastest in general) to be more consistent. nearbyint is the same as what the vectorized code does only when the current rounding mode is FE_TONEAREST but in practice this is OK because we almost always use the default rounding mode FE_TONEAREST.

This is inspired by Marat's diff for mobile quantization.

Reviewed By: dskhudia

Differential Revision: D13017719

Summary:
The vectorized code was rounding to even in halfway cases with _mm256_round_ps + (_MM_FROUND_TO_NEAREST_INT |_MM_FROUND_NO_EXC) (see more details in https://software.intel.com/en-us/node/523819), but we were still using std::round in a couple of places which does rounding away from zero in halfway cases.
With this diff, we use std::nearbyint in all scalar code (except a few cases where we don't care exact rounding mode and uses rint which is the fastest in general) to be more consistent. nearbyint is the same as what the vectorized code does only when the current rounding mode is FE_TONEAREST but in practice this is OK because we almost always use the default rounding mode FE_TONEAREST.

This is inspired by Marat's diff for mobile quantization.

Reviewed By: dskhudia

Differential Revision: D13017719

fbshipit-source-id: dec9c27e9693e727e6616a94606c902aadb61cab
@jspark1105
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@pytorchbot retest this please

facebook-github-bot pushed a commit to pytorch/FBGEMM that referenced this pull request Nov 14, 2018
Summary:
Pull Request resolved: #9

Pull Request resolved: pytorch/pytorch#13960

The vectorized code was rounding to even in halfway cases with _mm256_round_ps + (_MM_FROUND_TO_NEAREST_INT |_MM_FROUND_NO_EXC) (see more details in https://software.intel.com/en-us/node/523819), but we were still using std::round in a couple of places which does rounding away from zero in halfway cases.
With this diff, we use std::nearbyint in all scalar code (except a few cases where we don't care exact rounding mode and uses rint which is the fastest in general) to be more consistent. nearbyint is the same as what the vectorized code does only when the current rounding mode is FE_TONEAREST but in practice this is OK because we almost always use the default rounding mode FE_TONEAREST.

This is inspired by Marat's diff for mobile quantization.

Reviewed By: dskhudia

Differential Revision: D13017719

fbshipit-source-id: 6b8f99db7ea2e233aa2e3bd2adf622e03ed6258e
@ezyang ezyang added the merged label Jun 25, 2019
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2 participants