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This repository has been archived by the owner on Nov 17, 2023. It is now read-only.
and then add all mutable inputs to the write_vars array on line 294 (here read_vars is the same as use_vars, and write_vars is the same as mutate_vars in the graph executor above).
This means that its possible that write dependencies would not be tracked correctly in the symbol/module flows for operators that use the FMutateInputs attribute. Currently it looks like only BatchNorm uses this.
Test case
This can be tested using BatchNorm, by creating a symbol graph that only contains batch norm and run with the same running mean and variance many times
The text was updated successfully, but these errors were encountered:
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Description
In the Symbol/Module flow, the graph executor does not track mutable input dependencies when setting up the use_vars and mutate_vars arrays here:
https://github.com/apache/incubator-mxnet/blob/master/src/executor/graph_executor.cc#L1222-L1234
Notice that inputs are only added to use_vars on line 1224, and mutate_vars only contains outputs.
Compare this to the CachedOp flow in Gluon where we get the mutable inputs here:
https://github.com/apache/incubator-mxnet/blob/master/src/imperative/imperative_utils.h#L245
and then add all mutable inputs to the write_vars array on line 294 (here read_vars is the same as use_vars, and write_vars is the same as mutate_vars in the graph executor above).
This means that its possible that write dependencies would not be tracked correctly in the symbol/module flows for operators that use the FMutateInputs attribute. Currently it looks like only BatchNorm uses this.
Test case
This can be tested using BatchNorm, by creating a symbol graph that only contains batch norm and run with the same running mean and variance many times
The text was updated successfully, but these errors were encountered: