Reduce Overhead in Timers - #2210
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Signed-off-by: Xin Yao <xiny@nvidia.com>
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Signed-off-by: Xin Yao <xiny@nvidia.com>
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| 'optimizer-copy-main-to-model-params', | ||
| 'optimizer', | ||
| ] | ||
| timers_to_log = [] |
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What does this PR do ?
Update: when there're no timers to log (e.g.,
log_level=0), only ~0.02ms overhead.Previously,
timers.logtakes ~5ms, where a lot of PyTorch ops are invoked.Now, it's reduced to ~0.44 ms.
Contribution process
flowchart LR A[Pre-checks] --> B[PR Tests] subgraph Code Review/Approval C1[Expert Review] --> C2[Final Review] end B --> C1 C2 --> D[Merge]Pre-checks
Core 0.8)Code review
The following process is enforced via the CODEOWNERS file for changes into
megatron/core. For changes outside ofmegatron/core, it is up to the PR author whether or not to tag the Final Reviewer team.For MRs into `main` branch
(Step 1): Add PR label
Expert Review(Step 2): Collect the expert reviewers reviews
Expert Reviewlabel when your PR is ready for review.Final Review might get declined if these requirements are not fulfilled.
(Step 3): Final Review
Final Reviewlabel(Optional Step 4): Cherry-pick into release branch
If this PR also needs to be merged into
core_r*release branches, after this PR has been merged, selectCherry-pickto open a new PR into the release branch.For MRs into `dev` branch
The proposed review process for `dev` branch is under active discussion.MRs are mergable after one approval by either
eharper@nvidia.comorzijiey@nvidia.com.Merging your PR
Any member of core-adlr and
core-nemowill be able to merge your PR.