[Topi, x86] Using MKL blas for quantized dense #6115
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
|
@eric-haibin-lin Yes, the MXNet+MKLDNN baseline is also in |
|
Better to show the performance of TVM before using MKL s8u8s32 GEMM. |
|
@TaoLv Good point, I added the latency numbers for TVM alone. Thanks for pointing it out! |
|
@icemelon9 Can you please manage this PR? |
junrushao
approved these changes
Jul 26, 2020
|
Ping @icemelon9 |
|
While it is OK to make use of the mkldnn in this case, we should always work hard to get good integer schedules and learn from the insights, just as the case we did for the CUDA softmax and other cases. |
|
@tqchen Agreed. For now, my reasoning was to just extend MKL to int8. But I agree that it will be better to focus on TVM schedules. This one will require more work as even FP32 schedules for dense are not well optimized. |
trevor-m
pushed a commit
to trevor-m/tvm
that referenced
this pull request
Aug 26, 2020
* [Topi, x86] Using MKL blas for quantized dense * Typo * CBLAS_OFFSET only available for MKL * Skipping tests as GPU CI uses Openblas * Retrigger Co-authored-by: Ubuntu <[email protected]>
trevor-m
pushed a commit
to trevor-m/tvm
that referenced
this pull request
Aug 26, 2020
* [Topi, x86] Using MKL blas for quantized dense * Typo * CBLAS_OFFSET only available for MKL * Skipping tests as GPU CI uses Openblas * Retrigger Co-authored-by: Ubuntu <[email protected]>
trevor-m
pushed a commit
to trevor-m/tvm
that referenced
this pull request
Aug 26, 2020
* [Topi, x86] Using MKL blas for quantized dense * Typo * CBLAS_OFFSET only available for MKL * Skipping tests as GPU CI uses Openblas * Retrigger Co-authored-by: Ubuntu <[email protected]>
trevor-m
pushed a commit
to trevor-m/tvm
that referenced
this pull request
Sep 2, 2020
* [Topi, x86] Using MKL blas for quantized dense * Typo * CBLAS_OFFSET only available for MKL * Skipping tests as GPU CI uses Openblas * Retrigger Co-authored-by: Ubuntu <[email protected]>
trevor-m
pushed a commit
to neo-ai/tvm
that referenced
this pull request
Sep 3, 2020
* [Topi, x86] Using MKL blas for quantized dense * Typo * CBLAS_OFFSET only available for MKL * Skipping tests as GPU CI uses Openblas * Retrigger Co-authored-by: Ubuntu <[email protected]>
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.
Using MKL for quantized dense, following the MKL fallback for FP32 dense.
On C5.12x large cascade lake with VNNI support, results for BERT base are as follows (latency in ms)
@icemelon9 @eric-haibin-lin @shoubhik
TVM Alone has bad performance because we don't have a good integer dense schedule.