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Fix llama_qnn Buck target import failures - #24100
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Summary: `//executorch/examples/qualcomm/oss_scripts/llama:llama_qnn` currently fails at import time for every model, before any argument is parsed, with `ModuleNotFoundError: No module named 'executorch.examples.models.gemma4'` and then `... granite_speech`. The Buck targets drifted from the sources synced from upstream: the Gemma4 enablement (pytorch#20979) added an unconditional `executorch.examples.models.gemma4` import with no owning target for `gemma4/__init__.py`, Granite-Speech added `model/audio_encoder.py` plus a `granite_speech` import, and QAD (pytorch#21036) added `quantize/qat.py`, `dataset/{constants,targets}.py` and the `train/` package, none of which are in any target. This adds a `gemma4` library owning `gemma4/__init__.py`, a new `train` library (including its `train/config/*.yaml` resources), the missing sources in `static_llama`, `dataset` and `quantize`, and the `gemma4` and `granite_speech` deps on `llama_lib`. The `fbcode/` and `xplat/` copies are kept byte-identical. Known limitation, not addressed here: under Buck `QNN_SDK_ROOT` is a placeholder, so `is_qnn_sdk_version_less_than("2.35")` cannot detect the SDK version and masked softmax is disabled during export even though the pinned SDK (2.50) supports it. Authored with Claude Code. Differential Revision: D127006045
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/24100
Note: Links to docs will display an error until the docs builds have been completed. ❌ 36 Pending, 2 Unclassified FailuresAs of commit b704557 with merge base 71a713c ( UNCLASSIFIED FAILURES - DrCI could not classify the following jobs because the workflow did not run on the merge base. The failures may be pre-existing on trunk or introduced by this PR:
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Summary:
//executorch/examples/qualcomm/oss_scripts/llama:llama_qnncurrently fails at import time for every model, before any argument is parsed, withModuleNotFoundError: No module named 'executorch.examples.models.gemma4'and then... granite_speech. The Buck targets drifted from the sources synced from upstream: the Gemma4 enablement (#20979) added an unconditionalexecutorch.examples.models.gemma4import with no owning target forgemma4/__init__.py, Granite-Speech addedmodel/audio_encoder.pyplus agranite_speechimport, and QAD (#21036) addedquantize/qat.py,dataset/{constants,targets}.pyand thetrain/package, none of which are in any target.This adds a
gemma4library owninggemma4/__init__.py, a newtrainlibrary (including itstrain/config/*.yamlresources), the missing sources instatic_llama,datasetandquantize, and thegemma4andgranite_speechdeps onllama_lib. Thefbcode/andxplat/copies are kept byte-identical.Known limitation, not addressed here: under Buck
QNN_SDK_ROOTis a placeholder, sois_qnn_sdk_version_less_than("2.35")cannot detect the SDK version and masked softmax is disabled during export even though the pinned SDK (2.50) supports it.Authored with Claude Code.
Differential Revision: D127006045
cc @cbilgin @psiddh