Extract value_type-generic NEON Vectorized<Half> functions to CRTP base class #139084
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…to CRTP base class This is in prepraration for adding NEON Vectorized<BFloat16>, which will be simplified by sharing this stuff. Differential Revision: [D64997744](https://our.internmc.facebook.com/intern/diff/D64997744/) [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/139084
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This was referenced Oct 28, 2024
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This pull request was exported from Phabricator. Differential Revision: D64997744 |
This was referenced Oct 28, 2024
… functions to CRTP base class" This is in prepraration for adding NEON Vectorized<BFloat16>, which will be simplified by sharing this stuff. Differential Revision: [D64997744](https://our.internmc.facebook.com/intern/diff/D64997744/) [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D64997744 |
… functions to CRTP base class" This is in prepraration for adding NEON Vectorized<BFloat16>, which will be simplified by sharing this stuff. Differential Revision: [D64997744](https://our.internmc.facebook.com/intern/diff/D64997744/) [ghstack-poisoned]
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This pull request was exported from Phabricator. Differential Revision: D64997744 |
… functions to CRTP base class" This is in prepraration for adding NEON Vectorized<BFloat16>, which will be simplified by sharing this stuff. Differential Revision: [D64997744](https://our.internmc.facebook.com/intern/diff/D64997744/) [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D64997744 |
… functions to CRTP base class" This is in prepraration for adding NEON Vectorized<BFloat16>, which will be simplified by sharing this stuff. Differential Revision: [D64997744](https://our.internmc.facebook.com/intern/diff/D64997744/) [ghstack-poisoned]
|
This pull request was exported from Phabricator. Differential Revision: D64997744 |
atalman
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Nov 11, 2024
…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
zero000064
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Nov 14, 2024
…se class (pytorch#139084) This is in prepraration for adding NEON Vectorized<BFloat16>, which will be simplified by sharing this stuff. Differential Revision: [D64997744](https://our.internmc.facebook.com/intern/diff/D64997744/) Pull Request resolved: pytorch#139084 Approved by: https://github.com/malfet
zero000064
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Nov 14, 2024
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
zero000064
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Nov 14, 2024
…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
zero000064
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Nov 14, 2024
…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
zero000064
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Nov 14, 2024
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
zero000064
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Nov 14, 2024
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
pobin6
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Dec 5, 2024
…se class (pytorch#139084) This is in prepraration for adding NEON Vectorized<BFloat16>, which will be simplified by sharing this stuff. Differential Revision: [D64997744](https://our.internmc.facebook.com/intern/diff/D64997744/) Pull Request resolved: pytorch#139084 Approved by: https://github.com/malfet
pobin6
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Dec 5, 2024
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
pobin6
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Dec 5, 2024
…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
pobin6
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Dec 5, 2024
…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
pobin6
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Dec 5, 2024
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
pobin6
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Dec 5, 2024
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
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Stack from ghstack (oldest at bottom):
This is in prepraration for adding NEON Vectorized, which will be simplified by sharing this stuff.
Differential Revision: D64997744
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10