[XNNPACK] Serialize weights as fp16 rather than fp32

[mcr229]

Summary

Previously we've used FP32_STATIC_WEIGHTS flag in xnnpack to coerce fp32 weights into fp16 for linear and conv. This allowed us to mimc fp16 computation because the weights would be converted and packed as fp16 at runtime. However, this means we lose the benefit of the smaller .pte file because the weights are serialized as fp32 rather than fp16. Additionally, we still have to load the weights as fp32, since they are converted at runtime. This has some poor effects on performance

Test plan

python -m unittest backends.xnnpack.test.ops.test_linear.TestLinear.test_fp16_linear
python -m unittest backends.xnnpack.test.ops.test_linear.TestLinear
python -m unittest backends.xnnpack.test.ops.test_conv2d.TestConv2d

Llama 3.2 with bf16 weights:
Before:

-rw-r--r--  1 maxren  staff  5468937344 Mar 28 17:00 llama3_2_fp16_direct_convert_runtime.pte

After:

-rw-r--r--  1 maxren  staff  2997443712 Mar 28 16:57 llama3_2_fp16_direct_convert_runtime.pte

[pytorch-bot]

🔗 Helpful Links

🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/9753

• 📄 Preview Python docs built from this PR

Note: Links to docs will display an error until the docs builds have been completed.

✅ No Failures

As of commit cb31420 with merge base ce74f8e ():
💚 Looks good so far! There are no failures yet. 💚

This comment was automatically generated by Dr. CI and updates every 15 minutes.

[digantdesai]

s/fp32_static_weight/force_fp32 - seems a little too vague if you ask me.