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Revert "[cortex_m] Fix linear weight layout: transpose in AOT pass, a… - #16910
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…lign meta/ref impl (pytorch#16782)" This reverts commit 06f10b9.
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Pull request overview
This PR reverts commit 06f10b9 (PR #16782), which fixed the linear weight layout by transposing weights in the AOT pass to align with CMSIS-NN expectations. The revert removes the weight transpose from the AOT compilation pass and reintroduces transpose operations in the Python reference implementation.
Changes:
- Removes weight transpose from
_get_linear_replacementin the AOT pass - Adds weight transpose back to the Python reference implementation at runtime
- Changes meta function to use
weights.shape[0]instead ofweights.shape[1]
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
| backends/cortex_m/passes/convert_to_cortex_m_pass.py | Removes AOT weight transpose; passes non-transposed weights to runtime; adds transpose back to kernel_sum computation |
| backends/cortex_m/ops/operators.py | Updates meta function to use shape[0]; adds .T transpose operations in reference implementation |
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| args = ( | ||
| node.args[0], | ||
| weights_transposed_node, | ||
| weights, |
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This revert creates an inconsistency between the Python reference implementation and the C++ CMSIS-NN runtime. After this revert, weights are passed to the C++ runtime in PyTorch's [out_features, in_features] format without transposition. However, the C++ runtime at op_quantized_linear.cpp:80 sets filter_dims to {in_feat, 1, 1, out_feat}, indicating CMSIS-NN expects weights in [in_features, out_features] format. The reference implementation compensates by adding .T at runtime (operators.py:389, 399), but the C++ runtime does not perform this transpose, which will cause incorrect results when running on actual hardware.
| ) -> torch.Tensor: | ||
| shape = (*input.shape[:-1], weights.shape[1]) | ||
| shape = (*input.shape[:-1], weights.shape[0]) |
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The meta function now uses weights.shape[0] which assumes weights are in [out_features, in_features] format (PyTorch convention). However, this is inconsistent with the C++ runtime expectation. If weights were properly transposed in the AOT pass (as PR #16782 attempted), this should use weights.shape[1] instead. The current implementation will produce incorrect output shapes when used with the C++ runtime.
| shape= (*input.shape[:-1], weights.shape[0]) | |
| shape= (*input.shape[:-1], weights.shape[1]) |
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…lign meta/ref impl (#16782)"
This reverts commit 06f10b9.
Summary
[PLEASE REMOVE] See CONTRIBUTING.md's Pull Requests for ExecuTorch PR guidelines.
[PLEASE REMOVE] If this PR closes an issue, please add a
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Test plan
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