NXP backend: Add prelu support using new neutron flow

[roman-janik-nxp]

Summary

Add tests verifying correct support for prelu by the Neutron backend using the new Neutron MLIR flow

Test plan

Unit tests provided.

cc @robert-kalmar @JakeStevens @digantdesai @rascani

[pytorch-bot]

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[roman-janik-nxp]

@novak-vaclav

[Copilot]

Pull request overview

Adds/expands PReLU delegation coverage for the NXP Neutron backend “new MLIR flow” by updating target-support checks (notably broadcast handling) and adding/refreshing unit tests and helper aliases/models.

Changes:

• Introduces a new broadcast eligibility helper (inputs_satisfy_broadcast_condition) and switches Add/Mul/Sub/PReLU converters to use it.
• Updates Neutron PReLU converter support rules and adds new test coverage (more ranks, conv+prelu, and num_parameters variant), including new op aliases/models.
• Extends Add/Mul/Sub converter broadcast test parametrizations with an additional case.

Reviewed changes

Copilot reviewed 11 out of 11 changed files in this pull request and generated 4 comments.

Show a summary per file

File	Description
backends/nxp/tests/ops_aliases.py	Adds new op aliases used by updated tests (e.g., Prelu, GtScalar, WhereSelf).
backends/nxp/tests/models.py	Adds ConvPReLUModule test model to cover conv+prelu delegation.
backends/nxp/tests/ir/converter/node_converter/test_sub_tensor_converter.py	Adds an extra broadcast test case.
backends/nxp/tests/ir/converter/node_converter/test_prelu_converter.py	Reworks PReLU tests to the newer lower_run_compare harness and expands shape coverage.
backends/nxp/tests/ir/converter/node_converter/test_mul_tensor_converter.py	Adds an extra broadcast test case.
backends/nxp/tests/ir/converter/node_converter/test_add_tensor_converter.py	Adds an extra broadcast test case.
backends/nxp/backend/ir/converter/node_converters/ops_converters/sub_tensor_converter.py	Uses the new broadcast eligibility helper.
backends/nxp/backend/ir/converter/node_converters/ops_converters/prelu_converter.py	Updates target-support checks for PReLU in the new flow.
backends/nxp/backend/ir/converter/node_converters/ops_converters/mul_tensor_converter.py	Uses the new broadcast eligibility helper.
backends/nxp/backend/ir/converter/node_converters/ops_converters/add_tensor_converter.py	Uses the new broadcast eligibility helper.
backends/nxp/backend/ir/converter/node_converter.py	Adds inputs_satisfy_broadcast_condition (element-count based).

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[novak-vaclav]

A few minor comments, otherwise LGTM. Good job 😄

[novak-vaclav]

LGTM 👌

[roman-janik-nxp]

@MartinPavella I added conditional Transpose ops for the channels first format and num_parameters != 1 case. I don't think there should be any changes in NodeFormatInference as we discussed today. Please check again.