[Inductor] Add NEON ISA support on arm64 Macs #122217
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/122217
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This is a re-land of #105590 but this time enbaling it only for Darwin platform where those instructions are available by default
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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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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:
acoshop to vec256_float_neon.h #122513Following was added/changed to enable vectorization code to work on MacOS
_inductor/codecache.pythat is supported on all AppleSilicon MacsVectorized::loadu_one_fourthtovec_base.h, and limit it to 8-bit typesint64_t/uint64_tto align with the rest of the code, as on MacOS,int64_tis along longrather thanlong(see [C10] Make Scalar constructable from longs #118149 for more details)See table below for perf changes with and without torch.compile using gpt-fast running
stories15Mon M2 Pro:cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10 @voznesenskym @penguinwu @EikanWang @Guobing-Chen @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @peterbell10 @ipiszy @yf225 @chenyang78 @kadeng @muchulee8 @aakhundov @ColinPeppler @amjames @desertfire @chauhang