log_softmax

[copyrightly]

Summary: Use the following property to implement log_softmax

log(exp(x_i) / sum(exp(x_j))) = x_i - log(sum(exp(x_j)))

• numerically stable, since we can avoid log(small_number) -> -\infty
• reusing existing softmax shader to generate variants

Differential Revision: D56688093

[pytorch-bot[bot]]

:link: Helpful Links

:test_tube: See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/3411

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:x: 2 New Failures

As of commit 5de743fa3bbd066d4d55ecd947ad687a6134fc25 with merge base 2c1e2836e321d9205eeb7662ffcfe51aaece6f98 ():

NEW FAILURES - The following jobs have failed:

• pull / unittest / linux (buck2) / linux-job (gh) backends/arm/test/ops/test_view.py::TestSimpleView::test_view_tosa_BI_2
• pull / unittest / macos (buck2) / macos-job (gh) backends/arm/test/ops/test_view.py::TestSimpleView::test_view_tosa_BI_3

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This pull request was exported from Phabricator. Differential Revision: D56688093

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This pull request was exported from Phabricator. Differential Revision: D56688093

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This pull request was exported from Phabricator. Differential Revision: D56688093

[facebook-github-bot]

This pull request was exported from Phabricator. Differential Revision: D56688093

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This pull request was exported from Phabricator. Differential Revision: D56688093

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This pull request was exported from Phabricator. Differential Revision: D56688093

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This pull request has been merged in pytorch/executorch@5b65163c37f77cd98073783745396dae847864d7.