rfc: prototype integration with ArmCL #795
Merged
Conversation
This file contains 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
vpirogov
reviewed
Aug 3, 2020
| - Step 1: introduce CMake changes to allow inclusion of ArmCL as a dependency; | ||
| - Step 2: changes to `src/cpu/platform.hpp` to support new AARCH64 macros; | ||
| - Step 3: addition of ArmCL based implementation into `src/cpu/aarch64` and | ||
| integration into `src/cpu/cpu_convolution_list.cpp`. |
There was a problem hiding this comment.
Please add changes to documentation (README.md and doc/*) to cover new dependencies and build options.
There was a problem hiding this comment.
Yes, no problem. We will include a docs update in the implementation plan.
|
Hijacked @kawakami-k's comment from a neighbor thread:
I expected there will be some intersection between the RFCs (e.g. the directory structure, maybe some compiler knobs, etc). So, @kawakami-k, please review this RFC too. |
|
I don't see any part of this RFC #795 that would cause inconsistency with Fujitsu's RFC, which will be posted soon. |
emfomenk
approved these changes
Aug 17, 2020
There was a problem hiding this comment.
Thank you for the very informative RFC and reference to the PoC implementation!
The proposal looks good to me. There is a minor issue with the implementation you described below, specifically keeping the modifiable object (acl_data_) in immutable primitive descriptor or primitive objects (what we've already discussed offline). But since this is an implementation detail, I think this doesn't affect the RFC per se.
4 tasks
5 tasks
2 tasks
nSircombe
added a commit
to nSircombe/pytorch
that referenced
this pull request
Jan 11, 2021
Since version 1.6, oneDNN has provided limited support for AArch64 builds. This minor change is to detect an AArch64 CPU and permit the use of `USE_MKLDNN` in that case. Build flags for oneDNN are also modified accordingly. Note: oneDNN on AArch64, by default, will use oneDNN's reference C++ kernels. These are not optimised for AArch64, but oneDNN v1.7 onwards provides support for a limited set of primitives based Arm Compute Library. See: oneapi-src/oneDNN#795 and: oneapi-src/oneDNN#820 for more details. Support for ACL-based oneDNN primitives in PyTorch will require some further modification,
facebook-github-bot
pushed a commit
to pytorch/pytorch
that referenced
this pull request
Jan 13, 2021
Summary: Since version 1.6, oneDNN has provided limited support for AArch64 builds. This minor change is to detect an AArch64 CPU and permit the use of `USE_MKLDNN` in that case. Build flags for oneDNN are also modified accordingly. Note: oneDNN on AArch64, by default, will use oneDNN's reference C++ kernels. These are not optimised for AArch64, but oneDNN v1.7 onwards provides support for a limited set of primitives based Arm Compute Library. See: oneapi-src/oneDNN#795 and: oneapi-src/oneDNN#820 for more details. Support for ACL-based oneDNN primitives in PyTorch will require some further modification, Fixes #{issue number} Pull Request resolved: #50400 Reviewed By: izdeby Differential Revision: D25886589 Pulled By: malfet fbshipit-source-id: 2c81277a28ad4528c2d2211381e7c6692d952bc1
3 tasks
3 tasks
4 tasks
This was referenced Jul 29, 2021
This was referenced Sep 20, 2021
3 tasks
lb991
added a commit
to lb991/oneDNN
that referenced
this pull request
Apr 26, 2022
This PR adds a PReLU primitive which makes use of Compute Library for the Arm® architecture (ACL), optimised for AArch64 targets. The datatype support is just for f32 and can only be used in forward mode. Implementation follows a similar approach to oneapi-src#1281, including measures to avoid the parallelisation of small workloads. It builds upon the approach originally introduced in the PR oneapi-src#820 and RFC oneapi-src#795. The primitive offers a minimum of ~ x10 speedup for large tensors (~1million f32s) and is comparable for very small tensors. Co-authored-by: Louis Kaplan <louis.kaplan@arm.com> - [X] Do all unit and benchdnn tests (`make test` and `make test_benchdnn_*`) pass locally for each commit? - [X] Have you formatted the code using clang-format? - [X] Have you submitted performance data that demonstrates performance improvements?
3 tasks
lb991
added a commit
to lb991/oneDNN
that referenced
this pull request
May 3, 2022
This PR adds a PReLU primitive which makes use of Compute Library for the Arm® architecture (ACL), optimised for AArch64 targets. The datatype support is just for f32 and can only be used in forward mode. Implementation follows a similar approach to oneapi-src#1281, including measures to avoid the parallelisation of small workloads. It builds upon the approach originally introduced in the PR oneapi-src#820 and RFC oneapi-src#795. The primitive offers a minimum of ~ x10 speedup for large tensors (~1million f32s) and is comparable for very small tensors. Co-authored-by: Louis Kaplan <louis.kaplan@arm.com> - [X] Do all unit and benchdnn tests (`make test` and `make test_benchdnn_*`) pass locally for each commit? - [X] Have you formatted the code using clang-format? - [X] Have you submitted performance data that demonstrates performance improvements?
lb991
added a commit
to lb991/oneDNN
that referenced
this pull request
May 13, 2022
This PR adds a PReLU primitive which makes use of Compute Library for the Arm® architecture (ACL), optimised for AArch64 targets. The datatype support is just for f32 and can only be used in forward mode. Implementation follows a similar approach to oneapi-src#1281, including measures to avoid the parallelisation of small workloads. It builds upon the approach originally introduced in the PR oneapi-src#820 and RFC oneapi-src#795. The primitive offers a minimum of ~ x10 speedup for large tensors (~1million f32s) and is comparable for very small tensors. Co-authored-by: Louis Kaplan <louis.kaplan@arm.com> - [X] Do all unit and benchdnn tests (`make test` and `make test_benchdnn_*`) pass locally for each commit? - [X] Have you formatted the code using clang-format? - [X] Have you submitted performance data that demonstrates performance improvements?
mgouicem
pushed a commit
that referenced
this pull request
May 31, 2022
This PR adds a PReLU primitive which makes use of Compute Library for the Arm® architecture (ACL), optimised for AArch64 targets. The datatype support is just for f32 and can only be used in forward mode. Implementation follows a similar approach to #1281, including measures to avoid the parallelisation of small workloads. It builds upon the approach originally introduced in the PR #820 and RFC #795. The primitive offers a minimum of ~ x10 speedup for large tensors (~1million f32s) and is comparable for very small tensors. Co-authored-by: Louis Kaplan <louis.kaplan@arm.com> - [X] Do all unit and benchdnn tests (`make test` and `make test_benchdnn_*`) pass locally for each commit? - [X] Have you formatted the code using clang-format? - [X] Have you submitted performance data that demonstrates performance improvements?
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.
The goal for this RFC is to demonstrate an approach for providing optimized implementations of machine learning operators in oneDNN, on AArch64, based on the open-source Arm Compute Library.
Rendered document
Others involved: @nSircombe @cfRod @alenik01