NXP backend: Fix bug when a partition output is also used by nodes inside the partition.#20423
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…side the partition.
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20423
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@novak-vaclav @irtrukhina please feel free to have a look. |
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Summary
[De]quantize nodes that form QDQ clusters are handled by turning float tensors to int8 and assigning to them the quantization parameters in the Neutron IR. But these operators cannot just disappear because the graph would be disconnected (there would be "holes" in it). Usually we can redirect the outputs of these operators to their inputs, and if this is not possible, we can convert the operators to identity operators, which are optimized out later.
Up until a recent PR,
dequantizenodes were always being turned into identity ops (not skipped), which caused issues in optimization passes. This is because the QDQ nodes are converted before the compute nodes. So for example adequantizethat's near the end of the model would be converted to a noop Transpose and inserted into theoperatorslist before the compute ops which topologically come before it. So the topological ordering of the graph was broken.The recent PR made it so that only the
dequantizenodes that consume model inputs are turned to identity, and the rest is skipped (fixing the topological order issue). That PR however failed to identify an edge case where if a node is both the output of a partition and it is also used as input to other nodes inside that partition, thedequantizenode that comes after it cannot be skipped, and it must be turned into identity. Therefore, we cannot avoid breaking the topological order.For this reason, after all edge operators are converted to Neutron IR (and before any IR optimizations), the operators of the graph need to be topologically sorted.
This PR fixes the bug with the intermediate output, and it introduces the topological operator sorting.
Test plan
Unit test provided.
cc @robert-kalmar @JakeStevens @digantdesai @rascani