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Update QCOM llama hardware support #15965
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/15965
Note: Links to docs will display an error until the docs builds have been completed. ❗ 1 Active SEVsThere are 1 currently active SEVs. If your PR is affected, please view them below: ✅ No FailuresAs of commit 8fe731e with merge base 12d17ef ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This PR needs a
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shewu-quic
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Thanks to make document better!
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| We’ve validated this flow on the **Samsung Galaxy S23**, **Samsung Galaxy S24**, and **OnePlus 12**. | ||
| Support on other hardware depends on the **HTP architecture (HtpArch)**. | ||
| The **16a4w_block** format and **weight sharing between prefill and decode** are supported on **V73 and newer**. |
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Minimum requirement of LPBQ is v69.
Minimum requirement of weight sharing is v73
Minimum requirement of 16bit activation and 16 bit weight for matmul is V73. (16 bit kv)
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Updated, what do you think?
| For older devices, you may need to **retune the quantization recipe**. A good starting point is: | ||
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| - Use **16a4w** | ||
| - Optionally apply **SpinQuant** for better stability and accuracy |
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Partially layers apply 16a8w to get better accuracy.
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Updated
shewu-quic
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LGTM. Thanks!
Added hardware support details and memory limit error handling instructions.
Updated hardware support section to include Samsung Galaxy S25.
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Added hardware support details and memory limit error handling instructions.
It seems like lots of users try to use the llama flow on other hardware other than phones. Let's try to document it first