[CpuInductor] Implement masked_load for integral types #122608
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Discovered while looking at `test_comprehensive_fft_ihfft2_cpu_int64` on non-AVX512 capable CPUs where (5, 6, 7) shape were big enough to start a vectorized loop Added `test_pad_cast` regression test Fixes #122606 [ghstack-poisoned]
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/122608
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jansel
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Discovered while looking at `test_comprehensive_fft_ihfft2_cpu_int64` on non-AVX512 capable CPUs where (5, 6, 7) shape were big enough to start a vectorized loop Added `test_pad_cast` regression test Fixes #122606 cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx peterbell10 ipiszy yf225 chenyang78 kadeng muchulee8 aakhundov ColinPeppler amjames desertfire chauhang [ghstack-poisoned]
Discovered while looking at `test_comprehensive_fft_ihfft2_cpu_int64` on non-AVX512 capable CPUs where (5, 6, 7) shape were big enough to start a vectorized loop Added `test_pad_cast` regression test Fixes #122606 cc voznesenskym penguinwu EikanWang jgong5 Guobing-Chen XiaobingSuper zhuhaozhe blzheng wenzhe-nrv jiayisunx peterbell10 ipiszy yf225 chenyang78 kadeng muchulee8 aakhundov ColinPeppler amjames desertfire chauhang [ghstack-poisoned]
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This started as a re-land of #105590 but focusing on enabling it on MacOS, but quickly turned into landing very limited platform-specific acceleration at this time (I.e. this PR does not add any NEON accelerated code at all, just enables vectorized compilation for the existing abstractions) Enabling the test harness, uncovered number of latent issues in CPU inductor that were fixed in the following PRS: - #122511 - #122513 - #122580 - #122608 Following was added/changed to enable vectorization code to work on MacOS - Added VecNEON class to `_inductor/codecache.py` that is supported on all AppleSilicon Macs - Added `Vectorized::loadu_one_fourth` to `vec_base.h`, and limit it to 8-bit types - Change 64-bit integral types mapping to `int64_t`/`uint64_t` to align with the rest of the code, as on MacOS, `int64_t` is a `long long` rather than `long` (see #118149 for more details) See table below for perf changes with and without torch.compile using [gpt-fast](https://github.com/pytorch-labs/gpt-fast) running `stories15M` on M2 Pro: | dtype | Eager | Compile (before) | Compile (after) | | ------ | ------ | --------- | --------- | | bfloat16 | 120 tokens/sec | 130 tokens/sec | 156 tokens/sec | | float32 | 158 tokens/sec | 140 tokens/sec | 236 tokens/sec | | float16 | 235 tokens/sec | 81 tokens/sec | 58 tokens/sec | Pull Request resolved: #122217 Approved by: https://github.com/jansel
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Use `if constexpr` to separate float vs integral masked load for avx512 Discovered while looking at `test_comprehensive_fft_ihfft2_cpu_int64` on non-AVX512 capable CPUs where (5, 6, 7) shape were big enough to start a vectorized loop Added `test_pad_cast` regression test Fixes #122606 Pull Request resolved: #122608 Approved by: https://github.com/jansel ghstack dependencies: #122607
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This started as a re-land of #105590 but focusing on enabling it on MacOS, but quickly turned into landing very limited platform-specific acceleration at this time (I.e. this PR does not add any NEON accelerated code at all, just enables vectorized compilation for the existing abstractions) Enabling the test harness, uncovered number of latent issues in CPU inductor that were fixed in the following PRS: - #122511 - #122513 - #122580 - #122608 Following was added/changed to enable vectorization code to work on MacOS - Added VecNEON class to `_inductor/codecache.py` that is supported on all AppleSilicon Macs - Added `Vectorized::loadu_one_fourth` to `vec_base.h`, and limit it to 8-bit types - Change 64-bit integral types mapping to `int64_t`/`uint64_t` to align with the rest of the code, as on MacOS, `int64_t` is a `long long` rather than `long` (see #118149 for more details) See table below for perf changes with and without torch.compile using [gpt-fast](https://github.com/pytorch-labs/gpt-fast) running `stories15M` on M2 Pro: | dtype | Eager | Compile (before) | Compile (after) | | ------ | ------ | --------- | --------- | | bfloat16 | 120 tokens/sec | 130 tokens/sec | 156 tokens/sec | | float32 | 158 tokens/sec | 140 tokens/sec | 236 tokens/sec | | float16 | 235 tokens/sec | 81 tokens/sec | 58 tokens/sec | Pull Request resolved: #122217 Approved by: https://github.com/jansel
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Stack from ghstack (oldest at bottom):
Use
if constexprto separate float vs integral masked load for avx512Discovered while looking at
test_comprehensive_fft_ihfft2_cpu_int64onnon-AVX512 capable CPUs where (5, 6, 7) shape were big enough to start a vectorized loop
Added
test_pad_castregression testFixes #122606
cc @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov @ColinPeppler @amjames @desertfire @chauhang