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In pytorch#9466 I got rid of storage views and eliminated all places where they were used... OR SO I THOUGHT. In actuality, under certain conditions (specifically, if you trained a CUDA multiprocessing model shared over CUDA IPC and then serialized your parameters), you could also serialize storage slices to the saved model format. In pytorch#9466, I "fixed" the case when you loaded the legacy model format (really, just unshared the storages--not strictly kosher but if you aren't updating the parameters, shouldn't matter), but NOT the modern model format, so such models would fail. So, I could have applied the legacy model format fix too, but hyperfraise remarked that he had applied a fix that was effectively the same as unsharing the storages, but it had caused his model to behave differently. So I looked into it again, and realized that using a custom deleter, I could simulate the same behavior as old storage slices. So back they come. In principle, I could also reimplement storage views entirely using our allocators, but I'm not going to do that unless someone really really wants it. Fixes pytorch#10120. Signed-off-by: Edward Z. Yang <ezyang@fb.com>
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LGTM! Thanks for the fix!
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Summary: In pytorch#9466 I got rid of storage views and eliminated all places where they were used... OR SO I THOUGHT. In actuality, under certain conditions (specifically, if you trained a CUDA multiprocessing model shared over CUDA IPC and then serialized your parameters), you could also serialize storage slices to the saved model format. In pytorch#9466, I "fixed" the case when you loaded the legacy model format (really, just unshared the storages--not strictly kosher but if you aren't updating the parameters, shouldn't matter), but NOT the modern model format, so such models would fail. So, I could have applied the legacy model format fix too, but hyperfraise remarked that he had applied a fix that was effectively the same as unsharing the storages, but it had caused his model to behave differently. So I looked into it again, and realized that using a custom deleter, I could simulate the same behavior as old storage slices. So back they come. In principle, I could also reimplement storage views entirely using our allocators, but I'm not going to do that unless someone really really wants it. Fixes pytorch#10120. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Pull Request resolved: pytorch#11314 Reviewed By: ailzhang Differential Revision: D9671966 Pulled By: ezyang fbshipit-source-id: fd863783d03b6a6421d6b9ae21ce2f0e44a0dcce
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In #9466 I got rid of storage views and eliminated all places where
they were used... OR SO I THOUGHT. In actuality, under certain
conditions (specifically, if you trained a CUDA multiprocessing model
shared over CUDA IPC and then serialized your parameters), you could
also serialize storage slices to the saved model format. In #9466,
I "fixed" the case when you loaded the legacy model format (really,
just unshared the storages--not strictly kosher but if you aren't
updating the parameters, shouldn't matter), but NOT the modern model format, so
such models would fail.
So, I could have applied the legacy model format fix too, but
hyperfraise remarked that he had applied a fix that was effectively
the same as unsharing the storages, but it had caused his model to
behave differently. So I looked into it again, and realized that
using a custom deleter, I could simulate the same behavior as old
storage slices. So back they come.
In principle, I could also reimplement storage views entirely using
our allocators, but I'm not going to do that unless someone really
really wants it.
Fixes #10120.
Signed-off-by: Edward Z. Yang ezyang@fb.com