[fix][FSDP] fix weight init when using apply() (fixes #490 and #444) #543
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Doesn't work with SyncBN, will fix |
min-xu-ai
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Mar 20, 2021
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The test failures might be related to the cast_buffer change? |
It's very weird, tests only seem to fail on 1.6, but pass in 1.7.1 and 1.8 (and on my local machine)... will dig a bit |
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Summary: Before this PR (facebookresearch/fairscale#543) was merged, we used to need the extra cuda() calls. Now, they are not needed. Unfortunately, this doesn't solve the long model init time issue we have. A FSDP model init still take >20 mins for me. This is really bad for debugging the regnet128 conv layer crash problem I am debugging. The following debugging output shows that most delays are in FSDP wrapping, some in BN wrapping and some in the layer wrapping. ``` INFO 2021-04-14 12:18:35,883 regnet_2.py: 159: block created INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:18:35,884 regnet_2.py: 161: cpu INFO 2021-04-14 12:19:07,388 regnet_2.py: 163: block bn wrapped INFO 2021-04-14 12:19:18,388 regnet_2.py: 166: block wrapped ``` In any case, this PR is pretty safe and should go in so that we don't need to do an extra `cuda()` call before wrapping. Pull Request resolved: fairinternal/ssl_scaling#75 Reviewed By: prigoyal Differential Revision: D27776285 Pulled By: min-xu-ai fbshipit-source-id: 3e43c6fe750fd6ee35933400b03a069d62040d8a
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compute_deviceso that we can usesummon_full_paramsimmediately afterFSDP.__init__, even if the params are still on CPU (this also fixes [FSDP] improve robustness to mismatch between torch.cuda.current_device and model's device #444)FSDP.applyso that it callssummon_full_paramsfirst. This makes it possible to do weight inits viamodel.apply(custom_weight_init_fn)without segfaulting, and should give identical results to not using FSDPThis also required reworking
_all_buffers_toto no longer useapply, since it is called from within_lazy_initand created some circular logic:apply -> summon_full_params -> _lazy_init -> _all_buffers_to -> apply.