[EZ][BE] Add missing acosh op to vec256_float_neon.h
#122513
Closed
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/122513
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit 6aafeea with merge base 52e9049 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
pytorch-bot
bot
added
the
module: cpu
Skylion007
approved these changes
Mar 22, 2024
Contributor
Author
|
@pytorchbot merge |
Collaborator
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
pytorchmergebot
pushed a commit
that referenced
this pull request
Mar 26, 2024
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
pytorch-bot bot
pushed a commit
that referenced
this pull request
Apr 22, 2024
As base class has it https://github.com/pytorch/pytorch/blob/ed15370aabf951eec2ba0140de5ff71634868791/aten/src/ATen/cpu/vec/vec_base.h#L367-L369 Discovered while attempting to enabling Inductor vectorization on ARM platform Pull Request resolved: #122513 Approved by: https://github.com/Skylion007
pytorch-bot bot
pushed a commit
that referenced
this pull request
Apr 22, 2024
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
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
As base class has it
pytorch/aten/src/ATen/cpu/vec/vec_base.h
Lines 367 to 369 in ed15370
Discovered while attempting to enabling Inductor vectorization on ARM platform
cc @jgong5 @mingfeima @XiaobingSuper @sanchitintel @ashokei @jingxu10