This PR adds support for submatrices in offline tuning for:
- GEMM
- GEMM and bias
- ScaledGEMM
- Batch Strided GEMM
New UTs to cover submatrices. Submatrices for strided batch API is not part of this PR and will be done seperately.
There is also a bug fix for offline tuning for full matrix for GEMM and bias in the `NT` case. Offline and online UTs were updated to cover this corner case.
To improve code readability, swapped definition of transA and transB.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/151138
Approved by: https://github.com/jeffdaily
Improvements to unit tests and warnings for unsupported cases in offline tuning. Here are more details:
- Previously we only compared the OpSig for the untuned vs. tuned entries. This was not strict enough so we now compare OpSig+ParamSig.
- The main offline and online UTs are now stricter to make sure we exercise the code paths for the four combinations of transA and transB.
- Offline tuning does not support some tensor shapes. Emit warning and skip tuning.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/150142
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
The main purpose of this PR is to fix offline tuning for ScaledGEMM. The previous UT passed because it was not strict enough. Additionally:
- All the offline tuning tests now do a comparison with the online results to ensure that ParamSignature match.
- We raise an error if submatrices are encountered as this is only supported in online tuning mode.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149677
Approved by: https://github.com/jeffdaily
This PR includes additional enhancements to TF32 support in TunableOp.
- OpSignature now differentiates between float32 and tf32 data types.
- Offline tuning now supports TF32.
- Unit tests for online and offline tuning of TF32.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/149088
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
This PR adds support for rowwise scaling versus tensorwise scaling on scaled GEMM.
There are few other items included in this PR as well:
- Fixes for offline tuning of scaled GEMM
- Simplification of existing offline UT
- Update existing online UT to also test rowwise versus tensorwise scaled GEMM
- New UT for offline scaled GEMM
Pull Request resolved: https://github.com/pytorch/pytorch/pull/148238
Approved by: https://github.com/jeffdaily
Fixes#141652
This PR contains:
- Fix for `matmul_offline_mgpu_tunableop`
- Modifications to _checking_tuning_assertions to enable TunableOp if it is disabled. Also moved it into the concurrent futures initializer.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143507
Approved by: https://github.com/jeffdaily
TunableOp's rotating buffer feature cannot be properly tested because the environment variable that controls this feature is sticky. A Python API is introduced to modify this value.
Additional items in this PR:
* UT for rotating buffer API
* Clean up UTs that were setting the rotating buffer via the environment variable
* Align behavior of environment variable and Python API when a negative value (< 0) is set.
* Update documentation.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/143172
Approved by: https://github.com/jeffdaily
Fixes#141652
This PR fixes (at least in part) the unit test failure. However, we may also need to do a separate flush of the untuned results-- if this test continues to be flaky, another PR would be needed to flush the untuned results as well.
Tested locally and it seems to be working.
Also fixing code that was accidentally commented out code in the unit test from the prior multi-gpu offline tuning PR https://github.com/pytorch/pytorch/pull/139673
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142269
Approved by: https://github.com/jeffdaily
This PR enhances offline tuning to support multi-GPUs.
High-level description of algorithm:
- Duplicate GEMMs are first eliminated
- GEMMs are distributed to multi-GPUs for tuning
- Results are gathered into a file with `_full` in the filename
Also adding support for GemmAndBias and ScaledGemm
Pull Request resolved: https://github.com/pytorch/pytorch/pull/139673
Approved by: https://github.com/jeffdaily, https://github.com/hongxiayang
