When we have hardware support, we can use it. When we don't have hardware support, we can still do better than vec_base.h. I'm not sure to what extent we're set up to properly test both `defined(__ARM_FEATURE_BF16)` and `!defined(__ARM_FEATURE_BF16)` builds, feedback especially welcome there.
Testing: vec_test_all_types should cover correctness. For perf, seems clear that using vectorized intrinsics should be better than vec_base?
Differential Revision: [D64997747](https://our.internmc.facebook.com/intern/diff/D64997747/)
Pull Request resolved: pytorch#139090
Approved by: https://github.com/jgong5, https://github.com/malfet
ghstack dependencies: pytorch#139084

…torch#139558)
Discovered this bug when working on Vectorized<BFloat16>; apparently we have no automated testing for aarch64 without FP16.
Testing: Manually disable FP16 feature for local vec_test_all_types run on Mac; see pass.
Differential Revision: [D65385267](https://our.internmc.facebook.com/intern/diff/D65385267/)
Pull Request resolved: pytorch#139558
Approved by: https://github.com/malfet
ghstack dependencies: pytorch#139084, pytorch#139090

…rch#139081)
Following the previous move of fp16_gemv_trans.
Testing: Checked for performance regression with llm_benchmarks' `python benchmarks/benchmark_torch_mm.py llm`, didn't find one
Differential Revision: [D64930872](https://our.internmc.facebook.com/intern/diff/D64930872/)
Pull Request resolved: pytorch#139081
Approved by: https://github.com/malfet
ghstack dependencies: pytorch#139084, pytorch#139090, pytorch#139558

pytorch#139208)
Very similar to pytorch#137917, but for bf16.
Differential Revision: [D65155971](https://our.internmc.facebook.com/intern/diff/D65155971/)
Pull Request resolved: pytorch#139208
Approved by: https://github.com/malfet
ghstack dependencies: pytorch#139084, pytorch#139090, pytorch#139558, pytorch#139081

This is the big milestone for bf16 and should enable us to close pytorch/torchchat#1253 .
Testing: ran python torchchat.py generate llama3.2-1b --dtype bf16 --device cpu on x86 machine with AVX512-bf16. observed similar tokens/sec with and without MKL path hand-disabled. Also observed speedup from ~2.1 tok/sec to 7.4 tok/sec on x86 machine with only AVX2.
Differential Revision: [D65170967](https://our.internmc.facebook.com/intern/diff/D65170967/)
Pull Request resolved: pytorch#139220
Approved by: https://github.com/malfet
ghstack dependencies: pytorch#139084, pytorch#139090, pytorch#139558, pytorch#139081, pytorch#139208

Fix typo causing compilation error on aarch64 architecture with BF16 support. (#139090)
tag: @swolchok
Pull Request resolved: #142370
Approved by: https://github.com/Skylion007, https://github.com/malfet