Upgrade oneDNN to v2.5.2 #71546
Closed
Upgrade oneDNN to v2.5.2 #71546
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
CI Flow Status⚛️ CI FlowRuleset - Version:
You can add a comment to the PR and tag @pytorchbot with the following commands: # ciflow rerun, "ciflow/default" will always be added automatically
@pytorchbot ciflow rerun
# ciflow rerun with additional labels "-l <ciflow/label_name>", which is equivalent to adding these labels manually and trigger the rerun
@pytorchbot ciflow rerun -l ciflow/scheduled -l ciflow/slowFor more information, please take a look at the CI Flow Wiki. |
🔗 Helpful links
💊 CI failures summary and remediationsAs of commit 65b6966 (more details on the Dr. CI page): ✅ None of the CI failures appear to be your fault 💚
🚧 1 fixed upstream failure:These were probably caused by upstream breakages that were already fixed.
Please rebase on the
|
zou3519
added
the
triaged
XiaobingSuper
added
the
intel priority
|
Hi @VitalyFedyunin , this is the PR for upgrading oneDNN to v2.5.2 with all the needed compatibility updates (due to name changes from MKLDNN to DNNL in oneDNN v2.5.2) for both build and runtime of PyTorch. It combines the build compatibility update from #69957 (which has been closed accordingly). It incorporates the runtime compatibility update in ideep (see intel/ideep@2e103b3). Ideep update was tagged with https://github.com/intel/ideep/tree/pytorch-rls-v2.5.2. Could you please review? Thanks! |
This comment has been minimized.
This comment has been minimized.
yanbing-j
force-pushed
the
yanbing/upgrade_onednn_2_5_2
branch
from
ff8794b to
65b6966
Compare
VitalyFedyunin
approved these changes
Jan 27, 2022
|
@VitalyFedyunin has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
|
@VitalyFedyunin has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
facebook-github-bot
pushed a commit
that referenced
this pull request
Feb 1, 2022
Summary: This PR upgrades oneDNN to v2.5.2, and includes some building support for oneDNN v2.5.2. v2.4 changes: - Improved performance for future Intel Xeon Scalable processor (code name Sapphire Rapids). The functionality is disabled by default and should be enabled via CPU dispatcher control. - Improved binary primitive performance for cases when one of the tensors is broadcasted. - Improved performance of reduction primitive, reorder, shuffle primitives. - Improved performance of depthwise convolution forward propagation for processors with Intel AVX5-12 support - Improved performance of forward inner product primitive for the shapes with minibatch equal to 1 for processors with Intel AVX-512 support - Improved performance of int8 matmul and inner product primitives for processors with Intel AVX2 and Intel DL Boost support v2.5 changes: - Improved performance for future Intel Xeon Scalable processors (code name Sapphire Rapids). The functionality is now enabled by default and requires Linux kernel 5.16. - Improved performance of matmul primitive for processors with Intel AVX-512 support. v2.5.2 changes: - Fixed performance regression in binary primitive with broadcast - Fixed segmentation fault in depthwise convolution primitive for shapes with huge spatial size for processors with Intel AVX-512 support Pull Request resolved: #71546 Reviewed By: george-qi Differential Revision: D33827108 Pulled By: VitalyFedyunin fbshipit-source-id: 8f5a19b331c82af5b0783f081e061e1034a93952
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
this pull request
Feb 3, 2022
Summary: This PR upgrades oneDNN to v2.5.2, and includes some building support for oneDNN v2.5.2. v2.4 changes: - Improved performance for future Intel Xeon Scalable processor (code name Sapphire Rapids). The functionality is disabled by default and should be enabled via CPU dispatcher control. - Improved binary primitive performance for cases when one of the tensors is broadcasted. - Improved performance of reduction primitive, reorder, shuffle primitives. - Improved performance of depthwise convolution forward propagation for processors with Intel AVX5-12 support - Improved performance of forward inner product primitive for the shapes with minibatch equal to 1 for processors with Intel AVX-512 support - Improved performance of int8 matmul and inner product primitives for processors with Intel AVX2 and Intel DL Boost support v2.5 changes: - Improved performance for future Intel Xeon Scalable processors (code name Sapphire Rapids). The functionality is now enabled by default and requires Linux kernel 5.16. - Improved performance of matmul primitive for processors with Intel AVX-512 support. v2.5.2 changes: - Fixed performance regression in binary primitive with broadcast - Fixed segmentation fault in depthwise convolution primitive for shapes with huge spatial size for processors with Intel AVX-512 support Pull Request resolved: pytorch/pytorch#71546 Reviewed By: george-qi Differential Revision: D33827108 Pulled By: VitalyFedyunin fbshipit-source-id: 8f5a19b331c82af5b0783f081e061e1034a93952 (cherry picked from commit 9705212)
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
this pull request
Feb 3, 2022
Summary: This PR upgrades oneDNN to v2.5.2, and includes some building support for oneDNN v2.5.2. v2.4 changes: - Improved performance for future Intel Xeon Scalable processor (code name Sapphire Rapids). The functionality is disabled by default and should be enabled via CPU dispatcher control. - Improved binary primitive performance for cases when one of the tensors is broadcasted. - Improved performance of reduction primitive, reorder, shuffle primitives. - Improved performance of depthwise convolution forward propagation for processors with Intel AVX5-12 support - Improved performance of forward inner product primitive for the shapes with minibatch equal to 1 for processors with Intel AVX-512 support - Improved performance of int8 matmul and inner product primitives for processors with Intel AVX2 and Intel DL Boost support v2.5 changes: - Improved performance for future Intel Xeon Scalable processors (code name Sapphire Rapids). The functionality is now enabled by default and requires Linux kernel 5.16. - Improved performance of matmul primitive for processors with Intel AVX-512 support. v2.5.2 changes: - Fixed performance regression in binary primitive with broadcast - Fixed segmentation fault in depthwise convolution primitive for shapes with huge spatial size for processors with Intel AVX-512 support Pull Request resolved: pytorch/pytorch#71546 Reviewed By: george-qi Differential Revision: D33827108 Pulled By: VitalyFedyunin fbshipit-source-id: 8f5a19b331c82af5b0783f081e061e1034a93952 (cherry picked from commit 9705212)
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
this pull request
Feb 9, 2022
Summary: This PR upgrades oneDNN to v2.5.2, and includes some building support for oneDNN v2.5.2. v2.4 changes: - Improved performance for future Intel Xeon Scalable processor (code name Sapphire Rapids). The functionality is disabled by default and should be enabled via CPU dispatcher control. - Improved binary primitive performance for cases when one of the tensors is broadcasted. - Improved performance of reduction primitive, reorder, shuffle primitives. - Improved performance of depthwise convolution forward propagation for processors with Intel AVX5-12 support - Improved performance of forward inner product primitive for the shapes with minibatch equal to 1 for processors with Intel AVX-512 support - Improved performance of int8 matmul and inner product primitives for processors with Intel AVX2 and Intel DL Boost support v2.5 changes: - Improved performance for future Intel Xeon Scalable processors (code name Sapphire Rapids). The functionality is now enabled by default and requires Linux kernel 5.16. - Improved performance of matmul primitive for processors with Intel AVX-512 support. v2.5.2 changes: - Fixed performance regression in binary primitive with broadcast - Fixed segmentation fault in depthwise convolution primitive for shapes with huge spatial size for processors with Intel AVX-512 support Pull Request resolved: pytorch/pytorch#71546 Reviewed By: george-qi Differential Revision: D33827108 Pulled By: VitalyFedyunin fbshipit-source-id: 8f5a19b331c82af5b0783f081e061e1034a93952 (cherry picked from commit 9705212)
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
this pull request
Feb 9, 2022
Summary: This PR upgrades oneDNN to v2.5.2, and includes some building support for oneDNN v2.5.2. v2.4 changes: - Improved performance for future Intel Xeon Scalable processor (code name Sapphire Rapids). The functionality is disabled by default and should be enabled via CPU dispatcher control. - Improved binary primitive performance for cases when one of the tensors is broadcasted. - Improved performance of reduction primitive, reorder, shuffle primitives. - Improved performance of depthwise convolution forward propagation for processors with Intel AVX5-12 support - Improved performance of forward inner product primitive for the shapes with minibatch equal to 1 for processors with Intel AVX-512 support - Improved performance of int8 matmul and inner product primitives for processors with Intel AVX2 and Intel DL Boost support v2.5 changes: - Improved performance for future Intel Xeon Scalable processors (code name Sapphire Rapids). The functionality is now enabled by default and requires Linux kernel 5.16. - Improved performance of matmul primitive for processors with Intel AVX-512 support. v2.5.2 changes: - Fixed performance regression in binary primitive with broadcast - Fixed segmentation fault in depthwise convolution primitive for shapes with huge spatial size for processors with Intel AVX-512 support Pull Request resolved: pytorch/pytorch#71546 Reviewed By: george-qi Differential Revision: D33827108 Pulled By: VitalyFedyunin fbshipit-source-id: 8f5a19b331c82af5b0783f081e061e1034a93952 (cherry picked from commit 9705212)
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.
This PR upgrades oneDNN to v2.5.2, and includes some building support for oneDNN v2.5.2.
v2.4 changes:
v2.5 changes:
v2.5.2 changes: