(3/n) Support 2D Parallelism - Efficient loading of full-state checkpoints #19870
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awaelchli
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awaelchli
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May 15, 2024
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## master #19870 +/- ##
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- Coverage 84% 59% -25%
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Files 425 420 -5
Lines 35010 34925 -85
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- Hits 29369 20527 -8842
- Misses 5641 14398 +8757 |
justusschock
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May 15, 2024
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What does this PR do?
Follow-up to #19852.
I found that loading large full-state-dict checkpoints into a distributed model can lead to OOM (e.g. Llama 3 70B) because PyTorch's approach of loading on rank-0, then broadcasting and redistributing is applied to the entire checkpoint at once, instead of on a per-parameter or per-module basis (see comment).
In this PR, I load the checkpoint per-parameter, which seems to work as it should.
Mini inference benchmark on Llama 3 8B (8xA100)
Before:
4.64 GB (peak memory usage), 13.43 seconds to load
Now:
3.20 GB (peak memory usage), 13.72 seconds to load
Llama 3 70B (8xA100)
Before:
OOM
Now:
20.01 GB (peak memory usage), 40.73 seconds to load
Benchmarks done with this LitGPT branch.
cc @justusschock @awaelchli @carmocca @Borda