[pytorchbot]

Fixes found while exporting models to ExecuTorch backends.

Fixes

• exir: constant_prop_pass keeps tensors that in-place ops write, also through views or in no_grad/autocast blocks. to_backend keeps the output node last. to_edge lowers torch._check_tensor_all and torch.sym_ite. GroupBasedPartitioner no longer makes partitions that depend on each other, unless caller node groups already do. Tensor constants get static shapes; deduplicated constants keep their data.
• AOTI (AOTInductor) and CUDA: the missing-kernel check catches scatter and index_put fallbacks. detach lowers, also inside torch.cond. Strided outputs are copied. Transposed convolutions (1-D, 2-D, no dilation) and torch._check_tensor_all lower.
• XNNPACK: the quantizer skips bfloat16 tensors, and cat respects scoped configs. ELU is delegated when XNNPACK can run it.
• Qualcomm: autocast regions are inlined correctly. Unquantized bfloat16 nodes, data-dependent shapes and HTP partitions without runtime inputs stay on the CPU. Fixes for stacks, 0-d selects, RMSNorm and int64 constants.
• OpenVINO: model data stays alive while OpenVINO 2026.4 reads it. Partitions are converted with their input types.
• Core ML: bfloat16 ops, and ops above rank 5 outside static layout chains, stay on the CPU.
• MLX, Arm, Cortex-M, Vulkan, NXP, portable: dtype checks for MLX tensor lists, one constant slot per weight, slice updates on a constant base; TOSA keeps unused delegate inputs for VGF; MatmulToBmmPass takes a constant operand; Vulkan constants above 4 dims; non-strict programs in the NXP quantizer; cummin.out.

Behavior changes

• A CUDA .pte loads only the weights it was exported with. A .ptd with other weights, for example after fine-tuning, needs a newly exported .pte.
• CUDA graphs turn off when outputs do not match the ETensor layout. If only the capture call sees such a layout, that call returns an error.
• GroupBasedPartitioner is about 2x slower on large graphs, and some graphs get a different number of partitions. to_backend keeps the original order of top-level nodes.
• XNNPACK: weight-only quantized linears, and 8da4w or 8da8w linears (8-bit dynamic activations, 4-bit or 8-bit weights) lowered with to_backend, fail at export instead of giving wrong results. Delegated ELU flushes subnormal values to zero. Cats outside a scoped quantizer config are no longer quantized.
• Ethos-U raises an error for a delegate with unused inputs instead of returning wrong values.

We would like to cherry-pick this PR to release/1.6 after it merges. It needs #23915 there first.

Testing

Each fix has a test that fails without it. Run on an A100 (CUDA, Vulkan), an x86 CPU (OpenVINO, QNN SDK), macOS (Core ML, MLX), the TOSA reference model and VGF emulation (Arm), and the eIQ Neutron SDK (NXP). Not run: Metal, ROCm, Windows CUDA.

cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils @Sebastian-Larsson @robell @rascani

[pytorch-bot]

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❗ 1 Active SEVs

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• PRs based before #202327 fail all Linux builds: can't find build-external-packages action

❌ 2 New Failures, 81 Pending, 1 Unrelated Failure, 1 Unclassified Failure

As of commit 6207bdf with merge base 4db778e ():

NEW FAILURES - The following jobs have failed:

• pull / test-llama-runner-linux (fp32, xnnpack+custom+qe, mt-l-arm64g4-16-62, executorch-ubuntu-22.04-gcc... / linux-job (gh)
  executorch
• pull / unittest-editable / linux / linux-job (gh)
  exir/tests/test_pass_infra.py::TestPassBaseSymbolicInputs::test_export_pass_tracks_replayed_unbacked_symbols

UNCLASSIFIED FAILURE - DrCI could not classify the following job because the workflow did not run on the merge base. The failure may be pre-existing on trunk or introduced by this PR:

• Test Metal Backend / test-model-metal-e2e (openai, whisper-small, quantized-int4-metal) / macos-job (gh) (this job did not run on the merge base, so DrCI cannot tell whether the failure is pre-existing)
  executorch

FLAKY - The following job failed but was likely due to flakiness present on trunk:

• trunk / unittest-release / linux / linux-job (gh) (detected as infra flaky with no log or failing log classifier)

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