support gpu spmd #5
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wbmc
reviewed
Dec 20, 2023
| @@ -2881,6 +2881,7 @@ PjRtStreamExecutorLoadedExecutable::Execute( | |||
| auto& statusor = results[i]; | |||
| if (!statusor.ok()) { | |||
| if (returned_futures.has_value()) { | |||
| VLOG(0) << "returned_futures clear"; | |||
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Should remove the LOG or use VLOG(3) for debug?
| num_partitions()); | ||
| } | ||
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||
| VLOG(1) << "Executing computation " << name() |
| << " num_partitions=" << num_partitions() | ||
| << " num_addressable_devices=" << num_addressable_devices; | ||
| TF_ASSIGN_OR_RETURN( | ||
| auto result, |
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During gpu training, each gpu corresponds to a process, so each gpu can only get its own device information for executing subgraphs, so it is necessary to modify the underlying logic of open xla to support local execute.
Using the original logic would result in a coredump when the result of the computation is retrieved.