SYCL: Rename oneMKL to oneMath #12192

Rbiessy · 2025-03-05T09:29:04Z

oneMKL Interface was moved to uxlfoundation and renamed to oneMath. This PR updates the SYCL backend to use the new library.

So far the Intel backend has been using the Intel oneMKL directly. After the renaming Intel oneMKL and oneMath have different namespaces so it is much easier to ensure all SYCL backends use oneMath. As a consequence, INTEL_CPU and INTEL_GPU targets have been introduced to make it possible to use oneMath compile-time dispatcher. The INTEL target can still be used but will fallback to the runtime dispatcher which can introduce a small overhead.
Unlike Intel oneMKL, oneMath is not available as a pre-built package. To ensure it is still as easy to use as before oneMath can be fetched and compiled automatically if not provided with oneMath_DIR.

The new version of oneMath had CMake improvements which make it possible to properly integrate oneMath with CMake rather than relying on environment variables such as LIBRARY_PATH or CPLUS_INCLUDE_DIR.

Using get_onemath_backend lets us avoid duplicating paths when using oneMath with different backends. The changes will help us ensure we avoid compilation issues when using APIs only available in Intel oneMKL.

I believe the changes will also fix the issues mentioned in #10851 in a cleaner way.

Rbiessy · 2025-03-05T10:54:16Z

This change should not affect performance for Intel backends as long as the compile-time dispatcher is used. Some performance results running on PVC before my changes:

model	size	params	backend	ngl	threads	sm	test	t/s
llama 70B Q4_K - Small	37.57 GiB	70.55 B	SYCL	99	8	none	pp512	512.45 ± 1.26
llama 70B Q4_K - Small	37.57 GiB	70.55 B	SYCL	99	8	none	tg128	4.90 ± 0.00
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	8	none	pp512	3411.37 ± 14.70
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	8	none	tg128	34.82 ± 0.18
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	8	none	pp512	3230.48 ± 21.10
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	8	none	tg128	21.61 ± 0.10

with these changes:

model	size	params	backend	ngl	threads	sm	test	t/s
llama 70B Q4_K - Small	37.57 GiB	70.55 B	SYCL	99	8	none	pp512	514.92 ± 3.08
llama 70B Q4_K - Small	37.57 GiB	70.55 B	SYCL	99	8	none	tg128	4.89 ± 0.00
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	8	none	pp512	3399.59 ± 13.79
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	8	none	tg128	34.74 ± 0.16
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	8	none	pp512	3259.86 ± 10.15
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	8	none	tg128	21.53 ± 0.08

s-Nick · 2025-03-06T09:16:31Z

Changes look good to me, but when I build it from scratch there are a lot of warnings from oneMath.
Is there a way to hide them? I think it would provide a better user experience and also it would helps developers to monitor warnings only from llama.cpp

docs/backend/SYCL.md

ggml/src/ggml-sycl/CMakeLists.txt

docs/backend/SYCL.md

NeoZhangJianyu

oneMath is new & good product for multiple platform.
It's good to be used to support multiple GPUs.

Because SYCL backend has been used in some commercial products for Intel GPU, we must provide stable solution based on stable comments.

So, I think for Intel GPU, we still continue using components provided by official oneAPI: compiler, oneMKL, oneDNN.

If you want to use oneMath right now, please use Macro to define new branch for non Intel GPU code path.
When it's done, please build and verify on pure oneAPI environment for Intel GPU.

Additionally, please consider if need to support Intel CPU in SYCL backend.
I don't think it bring benefit to Intel CPU user than CPU backend.

Thank you!

docs/backend/SYCL.md

examples/sycl/README.md

ggml/src/ggml-sycl/CMakeLists.txt

ggml/src/ggml-sycl/dpct/helper.hpp

NeoZhangJianyu · 2025-03-07T03:17:38Z

ggml/src/ggml-sycl/dpct/helper.hpp

-                                                  beta_value, data_c, ldc);
-#endif
-        }
+    template <class Ta, class Tb, class Tc, class Ts>


I suggest warp gemm() functions of oneMKL and oneMath in one unify API() in dpct::helper, so that make the ggml code is clear & simple.

I don't understand the suggestion. I am still planning to make the switch to always use oneMath so I don't think it is relevant in that case?

For Intel path, I suggest using oneMKL which comes from oneAPI official package, instead of oneMath as 3rd party.

If define a unified API, like dpct::gemm(dcpt::transpose a_trans, xxxx, int ldc), this new function will hide the difference of oneMath and oneMKL.

That means ggml-sycl.cpp won't call oneMKL or oneMKL directly, only dpct::helper call oneMKL or oneMath.
So when we change low level code, ggml-sycl.cpp won't be impacted directly.

I see. I still prefer the approach of using oneMath only which already hides the differences between Intel oneMKL and other vendors libraries.

qnixsynapse

Tested on an Intel A750 with intel-oneapi-base-toolkit 2025.0.1, works okay..

LGTM.

Edit: Also, we should remove the mention of Intel CPU from the docs as this backend only targets GPUs.

NeoZhangJianyu · 2025-03-07T06:28:40Z

I tested the code in pure oneAPI. It's passed. It's good!

I don't oppose to add new packages for non-intel GPU. I understand you.
I hope SYCL backend for Intel GPU use stable official packages.

I suggest divide the code of intel GPU and non Intel GPU in SYCL backend if you want to add oneMath and more packages:

In code level, I have two alternatives:

Design a new class to provide unified API for gemm() functions based on oneMKL, or oneMatch or oneDNN. (recommend)
for intel, call oneMKL or oneDNN like base.
for non-intel, call oneMath.

In CMakefile.txt, I suggest design two branches for intel and non-intel.
So you only update the non-intel branch.
Divide SYCL backend into two: SYCL backend, Multiple SYCL backend.

SYCL backend support Intel device only.
Multiple SYCL backend support Intel/NV/AMD devices.
These two backends are maintained separately and less conflict.
They could refer to each other's code and design.
The code could be clean and compact. In current code, there are branches for intel or non-intel. And they will be more with the more optimization in the future.

Thank you!

NeoZhangJianyu · 2025-03-07T08:07:20Z

Another concern is the CMake will download the oneMath during building.
That will impact the build speed.

Is it possible to move the download from CMake to prepare work like install oneAPI?

Thank you!

ggml/src/ggml-sycl/CMakeLists.txt

Rbiessy · 2025-03-12T09:52:56Z

@NeoZhangJianyu @airMeng there are a few comments regarding concerns of introducing oneMath for Intel devices.

Pasting my same reply from one of the comment: The SYCL backend is meant to be a generic backend that can support multiple HW so we should avoid splitting for different paths as long as we can maintain performance. oneMath is designed to solve this issue exactly.

Another concern is the CMake will download the oneMath during building.

Using FetchContent will download oneMath during CMake configuration time already, see https://cmake.org/cmake/help/latest/module/FetchContent.html. A user can also choose not to use FetchContent by building oneMath separately and installing it or setting -DoneMath_DIR.

Rbiessy · 2025-03-13T18:41:32Z

@NeoZhangJianyu @airMeng I believe I have addressed all the comments. Do let me know if you have more concerns with the approach that I haven't answered. If not please do not keep the PR blocked, thanks!

NeoZhangJianyu · 2025-03-15T03:43:20Z

Thank your patience to answer the comments! :)

I think oneMath is immature product now.
I don't oppose to use it in non-Intel GPU case.
If the oneMath is only used in non-Intel GPU case, I agree to merge it.

Here is the reason:
For 1:
oneMath's latest release is 0.6 on No.7 Nov. 2024.
There are still warning in building oneMath log in CI.
oneMath can't cover all APIs of oneMKL by now. That means we can't use some existed API of oneMKL by oneMath API.

For 2:

The only benefit I learn is reduce the code branch of oneMKL in Intel and non-Intel case.
Side-effect:

a. need more building time:
       It need to build from source code when using it in llama.cpp.
      Including download and build it, the building time is increased from 21s to 42s in linux.

b. create more lib files:
     In windows, it create 3 dll files in build/bin:
         557056      onemath.dll
        121745920 onemath_blas_mklcpu.dll
        121745920 onemath_blas_mklgpu.dll

         It take more time when link to new dll files, but I didnt record.

     In liunx, it create 3 files in `build/lib`:
         2703600  libonemath_blas_mklcpu.so.0
         2703600  libonemath_blas_mklgpu.so.0
         914544    libonemath.so.0


  I don't know if they need to be included in llama.cpp release package.
  If yes, it means more disk cost.

c. break the offline build style of SYCL backend.
   Through oneMath is downloaded during make automatically, it still add dependence.
   Some developer access github unstable by internet.
   Additional download will increase the risk of CI and build time.

d. Add new warnings of oneMath.

I think oneMath is good product to reduce the multiple APIs cross platforms.
But it's not good enough now.
SYCL backend is used by some commercial developers for Intel GPU, instead of non-Intel GPU.
That's why I always high light to use the stable official packages (like oneAPI toolkit).

For non-Intel GPU, oneMath is OK. Maybe nobody care above side effect.

Above is all I want to say.

If you still like to merge this PR, I could approve.
But could I will create another PR to remove the side effort above of Intel GPU by skip using oneMath for Intel GPU?

By the way, when we create SYCL backend, Intel GPU is only target device. It's same of mine now.
I contribute to llama.cpp as personal contributor in my spare time.
It's due to my interesting and I hope more Intel GPU users can enjoy llama.cpp.

Thank you!

NeoZhangJianyu · 2025-03-16T15:09:41Z

@zunigasllc why spam join this review?
Is it so hot PR? :)

qnixsynapse · 2025-03-16T16:00:36Z

@NeoZhangJianyu please ignore, it's a spam account.

Rbiessy · 2025-03-18T18:43:17Z

@NeoZhangJianyu, thank you for the feedback.

Regarding your point 1 the CI only see a few warnings in the tests on linux related to the random number generation. They are harmless and not enough to consider the library not stable. oneMath is only used for GEMM and batched GEMM. As far as I know all the features provided by Intel oneMKL for these operations are also available in oneMath. If that's not the case we can help integrate the missing features in oneMath.

Regarding the side effects in your point 2, the main benefit is indeed to reduce the code branching. I believe this helps SYCL developers in general. It would also help avoid issued such as this one where a namespace used is only available in Intel oneMKL but not oneMath.
a. & b. I agree there is a cost for the building time and disk space (under 4MB in total). Note that with the recent changes the libraries libonemath_blas_mklcpu.so should not be built anymore. With ccache enabled the compilation time will be barely measurable.
c. As documented here users can still download, build and install oneMath separately if it is important for them. Yes it is one more dependency, that is the solution that has been designed by Intel to provide generic SYCL packages. Note that we have also tried to push for oneMath to be available as a pre-built packages in the discussions here. This is not a priority at the moment but integrating oneMath in llama would be one more argument to enable this. It is a chicken-egg problem, we can't improve oneMath if it is not used anywhere and we can't use it in projects if it is not "good enough". From our experience with oneMath it is indeed mature enough, it is being used in GROMACS.
d. I have fixed the warnings showing up. Llama enable strict warnings globally, these have been disabled for oneMath. As mentioned above, the Intel CI does not show warnings with the default warnings for the library itself. See for instance this job from this GitHub CI running.

If you still like to merge this PR, I could approve.

Yes it would really help us if you don't block such PRs, thanks. Your feedback is welcomed though!

But could I will create another PR to remove the side effort above of Intel GPU by skip using oneMath for Intel GPU?

If customers face issues I think we should first look into a solution to fix them in oneMath. If it is not possible we could consider reverting to using Intel oneMKL directly. Until then I would like to try using oneMath.

I hope this answers all of the remaining concerns.

airMeng · 2025-03-19T00:47:45Z

how about the performance on BMG/LNL? The performance number you show is only on PVC.

NeoZhangJianyu · 2025-03-19T01:58:02Z

@Rbiessy
I still think Intel GPU won't be impacted by the PR (oneMath).
In this year, the optimization gate will opened, but Intel, CUDA/Hip can't walk on same path:
It's impossible to make SYCL backend get good performance on Intel, NV & AMD GPU by same code.

SYCL backend is more important to Intel user and ISV on Intel GPU.
We must make sure this path is always stable and fast.

I hope allow me create another PR to skip oneMath for Intel GPU.

Rbiessy · 2025-03-19T13:44:34Z

how about the performance on BMG/LNL? The performance number you show is only on PVC.

@airMeng I did not run them before as I was confident enough it would not show any measurable difference either. The results are below for a couple of small models. The differences are just noise.

master results on BMG

model	size	params	backend	ngl	sm	test	t/s
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	none	pp512	2074.35 ± 2.41
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	none	tg128	31.75 ± 0.16
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	none	pp512	1921.31 ± 335.13
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	none	tg128	10.55 ± 0.02

PR results on BMG

model	size	params	backend	ngl	sm	test	t/s
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	none	pp512	2075.38 ± 4.72
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	none	tg128	32.80 ± 0.18
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	none	pp512	1897.90 ± 277.29
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	none	tg128	10.63 ± 0.01

master results on LNL

model	size	params	backend	ngl	threads	sm	test	t/s
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	8	none	pp512	534.03 ± 1.25
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	8	none	tg128	12.10 ± 0.85
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	8	none	pp512	548.45 ± 4.34
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	8	none	tg128	3.86 ± 0.01

PR results on LNL

model	size	params	backend	ngl	threads	sm	test	t/s
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	8	none	pp512	535.93 ± 2.77
llama 8B Q4_K - Medium	4.58 GiB	8.03 B	SYCL	99	8	none	tg128	11.84 ± 1.00
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	8	none	pp512	574.42 ± 2.25
llama 8B Q8_0	7.95 GiB	8.03 B	SYCL	99	8	none	tg128	3.99 ± 0.19

Rbiessy · 2025-03-19T13:55:13Z

Intel, CUDA/Hip can't walk on same path: It's impossible to make SYCL backend get good performance on Intel, NV & AMD GPU by same code.

SYCL backend is more important to Intel user and ISV on Intel GPU. We must make sure this path is always stable and fast.

@NeoZhangJianyu We agree on this. We are not trying to make all backends use the same paths for every operation. For the common fp16 or fp32 gemm it is possible because all vendors provide native libraries with similar APIs. We also want to make sure the performance is optimal on Intel devices. This is just helping us clean some code.
From what we have seen so far oneMath operations are not that much used, in particular if oneDNN is enabled. Hence I think you are making this a bigger problem than what is really. The biggest challenge to improve the SYCL backend performance today is to optimize the quantized matmul variants which do not rely on oneMath nor oneDNN.

I hope allow me create another PR to skip oneMath for Intel GPU.

As I said above, if there are important issues first let us know what they are and we can discuss the solutions then. We could find a solution to allow using Intel oneMKL again if needed.

NeoZhangJianyu · 2025-03-20T01:16:51Z

I have listed the side-effect of this PR.

airMeng

Looks good on BMG/LNL, waiting for more feedback from the user side

Rbiessy · 2025-03-20T15:35:14Z

For context @NeoZhangJianyu found that oneMath is a large library on Windows of around 100MB. This is due to static linking, I created an issue about it here: uxlfoundation/oneMath#654

I agree this is a valid concern so I'll revert to using Intel oneMKL directly for Intel devices.

Rbiessy · 2025-03-28T16:45:33Z

@NeoZhangJianyu I've switched back to using Intel oneMKL for Intel devices in 995aea3

Let me know if you have any concern with this approach, thanks.

NeoZhangJianyu · 2025-03-31T01:33:51Z

docs/backend/SYCL.md

@@ -300,6 +280,8 @@ For AMD GPUs we should expect at least one SYCL-HIP device [`hip:gpu`]:

 ### II. Build llama.cpp

+The SYCL backend depends on [oneMath](https://github.com./uxlfoundation/oneMath). By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`.


This description is not mandatory for Intel GPU.
Suggest move to NV/AMD GPU chapers.

Moved in 06fe2ca

NeoZhangJianyu

I have added several small comments.
There is a common issue in all changed CPP files:
There are still calling oneapi::math for Intel GPU.
Should it rollback to "mkl" for Intel GPU?

ggml/src/ggml-sycl/CMakeLists.txt

NeoZhangJianyu · 2025-03-31T02:08:21Z

ggml/src/ggml-sycl/CMakeLists.txt

+        target_compile_definitions(ggml-sycl PRIVATE GGML_SYCL_AMD)
+    else()
+        # Fallback to oneMath runtime dispatcher
+        target_link_libraries(ggml-sycl PRIVATE ONEMATH::onemath)


This path is for Intel in fact.
Please update to use oneMKL .

Intel devices will not go through this path. This whole section is in the else() branch from https://github.com./ggml-org/llama.cpp/pull/12192/files/0b6f9a90978e79684de0fb5d9129e8f7e2bddcfb#diff-123d14cf628b04694c9022d4210d80c14a29409095635150b944f8dfd1c70b37R105

ggml/src/ggml-sycl/dpct/helper.hpp

NeoZhangJianyu · 2025-03-31T02:14:39Z

ggml/src/ggml-sycl/dpct/helper.hpp

+        auto data_a      = get_memory<const Ta>(a);
+        auto data_b      = get_memory<const Tb>(b);
+        auto data_c      = get_memory<Tc>(c);
+        oneapi::math::blas::column_major::gemm(get_onemath_backend(q), a_trans, b_trans, m, n, k, alpha_value, data_a,


Should it rollback to "mkl" for Intel GPU?

ggml/src/ggml-sycl/dpct/helper.hpp

NeoZhangJianyu

It's great work!
Thank you!

Rbiessy · 2025-04-01T08:45:37Z

Thank you :)

Rbiessy added 5 commits February 28, 2025 13:54

Rename oneMKL Interface to oneMath

a856351

Use oneMath for Intel vendor

dccc9f8

Rename occurences to mkl

3577bc0

clang-format

4482516

Silence verbose warnings

2c79721

github-actions bot added documentation Improvements or additions to documentation examples ggml changes relating to the ggml tensor library for machine learning SYCL https://en.wikipedia.org/wiki/SYCL - GPU programming language labels Mar 5, 2025

Rbiessy requested a review from Alcpz March 5, 2025 10:52

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Rbiessy added 3 commits March 6, 2025 15:18

Set oneMath HIP_TARGETS

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Fix silence warnings

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Remove step to build oneMath from build instructions

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Rbiessy mentioned this pull request Mar 6, 2025

SYCL: Fixes for building SYCL backend for AMD GPUs #10851

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Rbiessy added 3 commits March 13, 2025 15:18

Use fixed oneMath version

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Remove INTEL_CPU

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Fold CMake oneDNN conditions

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Merge branch 'master' into romain/rename_onemath

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Use Intel oneMKL for Intel devices

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Merge branch 'master' into romain/rename_onemath

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Link against MKL::MKL_SYCL::BLAS only

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Move oneMath documentation to Nvidia and AMD sections

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SYCL: Rename oneMKL to oneMath #12192

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		@@ -300,6 +280,8 @@ For AMD GPUs we should expect at least one SYCL-HIP device [`hip:gpu`]:

		### II. Build llama.cpp

		The SYCL backend depends on [oneMath](https://github.com./uxlfoundation/oneMath). By default it is automatically built along with the project. A specific build can be provided by setting the CMake flag `-DoneMath_DIR=/path/to/oneMath/install/lib/cmake/oneMath`.

SYCL: Rename oneMKL to oneMath #12192

SYCL: Rename oneMKL to oneMath #12192

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NeoZhangJianyu Mar 31, 2025 •

edited

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