v3.6.1

This is a patch release containing the following changes to v3.6:

• Fixed convolution correctness issue in some scenarios involving persistent cache on Intel GPUs (e595e59)
• Fixed potential page faults in reduction primitive implementation for Intel GPUs (7740c75, a4fcef9, 32d8660)
• Implemented a workaround for GCC 13 bug that resulted in matmul hangs on some Intel Arc graphics SKUs (a30d526)
• Updated execution units (EU) number detection logic for Intel GPUs based on Xe2 architecture to accommodate for behavior changes in Linux driver (04e7eac, 97b04bd)
• Fixed build issue for static library with ONEDNN_VERBOSE=OFF (7f476cb)
• Fixed correctness issue in SYCL deconvolution implementation with post-ops (8f600a3)
• Fixed memory formats checks in SYCL softmax implementation (6ae73e4)
• Fixed correctness issue in SYCL resampling implementation with post-ops (9845057)
• Aligned accessor types in SYCL kernels with SYCL specification (0d9b3bd)
• Improved scales argument checks in generic SYCL kernels (9f73bf1, 7d85c75)
• Fixed correctness issue in int8 convolution with sum post-op on NVIDIA GPUs (7486ed8)
• Relaxed accuracy test threshold for bf16 softmax on NVIDIA GPUs (e9d0fdb)
• Added support for bf16 and fp16 bias for fp8 matmul on Intel CPUs (188ae7f)
• Fixed a bug that prevented dispatching Intel AVX-512 with Intel DL Boost implementation in int8 RNN primitive (bf58e72)
• Fixed a runtime fail with CL_OUT_OF_RESOURCES error in fp16 convolution on Intel Arc graphics (39a5f67, 7e1663f)

v3.6

Performance Optimizations

Intel Architecture Processors

• Improved performance for 4th generation Intel Xeon Scalable processors (formerly Sapphire Rapids).
• Improved performance for Intel Xeon 6 processors (formerly Granite Rapids).
• Improved performance of group normalization primitive.
• Improved bf16 matmul performance with int4 compressed weights on processors with Intel AMX instruction set support.
• Improved performance of fp8 matmul, pooling, and eltwise primitives on processors with Intel AMX instruction set support.
• Improved fp32 RNN primitive performance on processors with Intel AVX2 instruction set support.
• Improved performance of the following subgraphs with Graph API:
  • convolution and binary operation fusions with better layout selection in Graph API.
  • fp8 convolution and unary or binary on processors with Intel AMX instruction set support.
  • Scaled Dot Product Attention (SDPA) without scale, Multi-Query Attention (MQA), and Grouped Query Attention (GQA) patterns.
  • LayerNorm, GroupNorm, and SoftMax with int8 quantized output and zero-points.

Intel Graphics Products

• Improved performance for the Intel Data Center GPU Max Series (formerly Ponte Vecchio).
• Introduced broad production quality optimizations for Intel Arc Graphics for Intel Core Ultra processors (Series 2) (formerly Lunar Lake).
• Introduced broad production quality optimizations for future discrete GPU based on Xe2 architecture (code name Battlemage).
• Introduced support for Intel Arc Graphics for future Intel Core Ultra processor (code name Arrow Lake-H).
• Improved performance of fp8_e5m2 primitives on Intel Data Center GPU Max Series (formerly Ponte Vecchio).
• Improved matmul and inner product primitives performance for shapes relevant to large language models (LLMs) on GPUs with Intel XMX support.
• Improved int8 convolution performance with weight zero-points.
• Reduced primitive creation time for softmax, layer normalization, and concat primitives via kernel reuse.
• Improved performance of the following subgraphs with Graph API:
  • SDPA without scale, MQA, and GQA patterns. f16 variants of these patterns significantly benefit from Intel(R) Xe Matrix Extensions (Intel(R) XMX) support.
  • fp8, convolution, and unary or binary on the Intel Data Center GPU Max Series.
  • LayerNorm, GroupNorm, and SoftMax with int8 quantized output and zero-points.

AArch64-based Processors

• Improved fp32 convolution backpropagation performance on processors with SVE support.
• Improved reorder performance for blocked format on processors with SVE support.
• Improved bf16 softmax performance on processors with SVE support.
• Improved batch normalization performance on processors with SVE support.
• Improved matmul performance on processors with SVE support.
• Improved fp16 convolution with Arm Compute Library (ACL).
• Improved matmul performance with ACL.
• Switched matmul and convolution implementation with ACL to stateless API significantly improving primitive creation time and increasing caching efficiency and performance for these operators.

Functionality

• Introduced generic GPU support. This implementation relies on portable SYCL kernels and can be used as a starting point to enable new devices in oneDNN.
• Extended functionality supported on NVIDIA GPUs and AMD GPUs with SYCL-based implementations.
• Enabled support for int8 activations with grouped scales and int8 or int4 compressed weights in matmul primitive. This functionality is implemented on Intel GPUs.
• Introduces support for stochastic rounding for fp8 data type functionality.
• [experimental] Extended microkernel API:
  • Introduced int8 quantization support.
  • Extended transform microkernel with transposition support and support for arbitrary strides.
  • Introduced verbose diagnostics support.
• [experimental] Extended sparse API:
  • Introduced support for sparse memory with coordinate (COO) storage format.
  • Extended matmul primitive to work with sparse memory in COO format. This functionality is implemented on CPUs and Intel GPUs.
• Introduced int8 support in eltwise primitive with 'clip' algorithm. This functionality is implemented on CPUs.
• Graph API:
  • Introduced GroupNorm operation and fusions in Graph API.
  • Introduced support for standalone StaticReshape and StaticTranspose operations.

Usability

• Added examples for SDPA, MQA, and GQA patterns implementation with Graph API.
• Added an example for deconvolution primitive.
• Added examples for Vanilla RNN and LBR GRU RNN cells.
• Introduced support for Intel oneAPI DPC++/C++ Compiler 2025.0.
• Introduced interoperability with SYCL Graph record/replay mode.
• Removed dependency on OpenCL runtime for NVIDIA and AMD GPUs.
• [experimental] Introduced logging mechanism based on spdlog library.
• Introduced support for ONEDNN_ENABLE_WORKLOAD build knob for Graph API.
• Improved performance of get_partitions() function in Graph API.

Validation

• Introduced protection from out-of-memory scenarios in benchdnn Graph API driver.

Deprecated Functionality

• Experimental Graph Compiler is deprecated and will be removed in future releases.

Breaking Changes

• Experimental microkernel API in this release is not compatible with the version available in oneDNN v3.5.
• Updated minimal supported ACL version to 24.08.1 (was 24.04).

Thanks to these Contributors

This release contains contributions from the project core team as well as Abdel @quickwritereader, Adam Jackson @nwnk, Aleksandr Voron @alvoron, Alexey Makarevich @amakarev, Annop Wongwathanarat @annop-w, Daniel Kuts @apach301, @deepeshfujitsu, Fadi Arafeh @fadara01, Fritz Heckel @fwph, Gorokhov Dmitriy @dmitry-gorokhov, Deeksha Kasture @kasturedeeksha, Kentaro Kawakami @kawakami-k, Marek Michalowski @michalowski-arm, @matthias-bonne, @Menooker, Michael Froelich @MichaelFroelich,
Nicolas Miller @npmiller, Nikhil Sharma @nikhilfujitsu, @nishith-fujitsu, Permanence AI Coder @Permanence-AI-Coder, Radu Salavat @Radu2k, Renato Barros Arantes @renato-arantes, Robert Cohn @rscohn2, Robert Hardwick @robert-hardwick, Ryo Suzuki @Ryo-not-rio, Shreyas-fuj @Shreyas-fuj, Shu Chen @shu1chen, Siddhartha Menon @Sqvid, Song Jiaming @Litchilitchy, Vladimir Paramuzov @vladimir-paramuzov, Yifei Zhang @yifeizh2. We would also like to thank everyone who asked questions and reported issues.

v3.6-rc

Performance Optimizations

Intel Architecture Processors

• Improved performance for 4th generation Intel Xeon Scalable processors (formerly Sapphire Rapids).
• Improved performance for Intel Xeon 6 processors (formerly Granite Rapids).
• Improved performance of group normalization primitive.
• Improved bf16 matmul performance with int4 compressed weights on processors with Intel AMX instruction set support.
• Improved performance of fp8 matmul, pooling, and eltwise primitives on processors with Intel AMX instruction set support.
• Improved fp32 RNN primitive performance on processors with Intel AVX2 instruction set support.
• Improved performance of the following subgraphs with Graph API:
  • convolution and binary operation fusions with better layout selection in Graph API.
  • fp8 convolution and unary or binary on processors with Intel AMX instruction set.
  • Scaled Dot Product Attention (SDPA) without scale, Multi-Query Attention (MQA), and Grouped Query Attention (GQA) patterns.
  • LayerNorm, GroupNorm, and SoftMax with int8 quantized output and zero-points.

Intel Graphics Products

• Improved performance for the Intel Data Center GPU Max Series (formerly Ponte Vecchio).
• Introduced broad production quality optimizations for Intel Arc Graphics for Intel Core Ultra Processors (Series 2) (formerly Lunar Lake).
• Introduced broad production quality optimizations for future discrete GPU based on Xe2 architecture (code name Battlemage).
• Introduced support for Intel Arc Graphics for future Intel Core Ultra Processor (code name Arrow Lake-H).
• Improved performance of fp8_e5m2 primitives on Intel Data Center GPU Max Series (formerly Ponte Vecchio).
• Improved matmul and inner product primitives performance for shapes relevant to large language models (LLMs) on GPUs with Intel XMX support.
• Improved int8 convolution performance with weight zero points.
• Reduced primitive creation time for softmax, layer normalization, and concat primitives via kernel reuse.
• Improved performance of the following subgraphs with Graph API:
  • SDPA without scale, MQA, and GQA patterns. f16 variants of these patterns significantly benefit from Intel(R) Xe Matrix Extensions (Intel(R) XMX) support.
  • fp8 convolution and unary or binary on Intel Data Center GPU Max Series.
  • LayerNorm, GroupNorm, and SoftMax with int8 quantized output and zero-points.

AArch64-based Processors

• Improved fp32 convolution backpropagation performance on processors with SVE support.
• Improved reorder performance for blocked format on processors with SVE support.
• Improved bf16 softmax performance on processors with SVE support.
• Improved batch normalization performance on processors with SVE support.
• Improved matmul performance on processors with SVE support.
• Improved fp16 convolution with Arm Compute Library (ACL).
• Improved matmul performance with ACL.
• Switched matmul and convolution implementation with ACL to stateless API significantly improving primitive creation time and increasing caching efficiency and performance for these operators.

Functionality

• Introduced generic GPU support. This implementation relies on portable SYCL kernels and can be used as a starting point to enable new devices in oneDNN.
• Extended functionality supported on NVIDIA GPUs and AMD GPUs with SYCL based implementations.
• Enabled support for int8 activations with grouped scales and int8 or int4 compressed weights in matmul primitive. This functionality is implemented on Intel GPUs.
• Introduces support for stochastic rounding for fp8 data type functionality.
• [experimental] Extended microkernel API:
  • Introduced int8 quantization support.
  • Extended transform microkernel with transposition support and support for arbitrary strides.
  • Introduced verbose diagnostics support.
• [experimental] Extended sparse API:
  • Introduced support for sparse memory with coordinate (COO) storage format.
  • Extended matmul primitive to work with sparse memory in COO format. This functionality is implemented on CPUs and Intel GPUs.
• Introduced int8 support in eltwise primitive with 'clip' algorithm. This functionality is implemented on CPUs.
• Graph API:
  • Introduced GroupNorm operation and fusions in Graph API.
  • Introduced support for standalone StaticReshape and StaticTranspose operations.

Usability

• Added examples for SDPA, MQA, and GQA patterns implementation with Graph API.
• Added an example for deconvolution primitive.
• Added examples for Vanilla RNN and LBR GRU RNN cells.
• Introduced support for Intel DPC++/C++ Compiler 2025.0.
• Introduced interoperability with SYCL Graph record/replay mode.
• Removed dependency on OpenCL runtime for NVIDIA and AMD GPUs.
• [experimental] Introduced logging mechanism based on spdlog library.
• Introduced support for ONEDNN_ENABLE_WORKLOAD build knob for Graph API.
• Improved performance of get_partitions() function in Graph API.

Validation

• Introduced protection from out of memory scenarios in benchdnn Graph API driver.

Breaking Changes

• Experimental microkernel API in this release is not compatible with the version available in oneDNN v3.5.
• Updated minimal supported ACL version to 24.08.1 (was 24.04).

Thanks to these Contributors

This release contains contributions from the project core team as well as Abdel @quickwritereader, Adam Jackson @nwnk, Aleksandr Voron @alvoron, Alexey Makarevich @amakarev, Annop Wongwathanarat @annop-w, Daniel Kuts @apach301, @deepeshfujitsu, Fadi Arafeh @fadara01, Fritz Heckel @fwph, Gorokhov Dmitriy @dmitry-gorokhov, Deeksha Kasture @kasturedeeksha, Kentaro Kawakami @kawakami-k, Marek Michalowski @michalowski-arm, @matthias-bonne, @Menooker, Michael Froelich @MichaelFroelich, Nicolas Miller @npmiller, Nikhil Sharma @nikhilfujitsu, @nishith-fujitsu, Permanence AI Coder @Permanence-AI-Coder, Radu Salavat @Radu2k, Renato Barros Arantes @renato-arantes, Robert Cohn @rscohn2, Robert Hardwick @robert-hardwick, Ryo Suzuki @Ryo-not-rio, Shreyas-fuj @Shreyas-fuj, Shu Chen @shu1chen, Siddhartha Menon @Sqvid, Song Jiaming @Litchilitchy, Vladimir Paramuzov @vladimir-paramuzov, Yifei Zhang @yifeizh2. We would also like to thank everyone who asked questions and reported issues.

v3.5.3

This is a patch release containing the following changes to v3.5.2:

• Fixed correctness issue in convolution weight gradient for small shapes on Intel GPUs (49eee6a, 281dd3b)
• Extended MLP patterns supported by experimental Graph Compiler to cover cases relevant to ChatGLM model (ff680fc)
• Fixed performance regression in bf16 depthwise convolution on Intel CPUs (d6c216a)

v3.5.2

This is a patch release containing the following changes to v3.5.1:

• Fixed performance regression for some Graph API subgraphs with LayerNorm operation (82f629c)
• Fixed runtime error for Graph API subgraphs including 6D LayerNorm operation (f704f09)
• Fixed an issue with host compiler version detection in SYCL configurations (730b976)
• Fixed an issue with missing DNNL_TARGET_ARCH define for builds not relying on CMake (87848b9)
• Fixed a test issue for matmul with low-precision scales and/or zero-points (91c35d8)
• Fixed segfault issue in bfloat16 shuffle on AArch64 processors (9116681)
• Fixed runtime issue in quantized layer normalization pattern with Graph API (0013e8c)

v3.4.4

This is a patch release containing the following changes to v3.4.3:

• Fixed an issue with host compiler version detection in SYCL configurations (fcaa1b4)

v3.5.1

This is a patch release containing the following changes to v3.5:

• Fixed potential page fault in matmul on Intel Datacenter Max Series GPUs (a9c525d)
• Fixed potential stack overflow issue in convolution implementation for Intel GPUs (0fb7e6e)
• Added test cases for matmul with compressed weights (015ccb1)
• Extended Graph API LayerNorm operation with zero points support (dc2701a)
• Fixed primitive creation error for depthwise convolution backpropagation on Intel GPUs (4a045e4, b529d22)

v3.5

Performance Optimizations

Intel Architecture Processors

• Improved performance for 4th generation Intel Xeon Scalable processors (formerly Sapphire Rapids).
• Improved performance for the future Intel Xeon Scalable processors (code-named Sierra Forest and Granite Rapids).
• Improved performance of group normalization primitive.
• Improved performance of matmul primitive with sum post-op for batched cases on processors with Intel AMX instruction set support.
• Improved performance of the following subgraphs with Graph API:
  • Multi-Query Attention (MQA).
  • Scaled Dot Product Attention (SDPA), including the variant with select operation.
  • LayerNorm + Multiply + Quantize produced by SmoothQuant algorithm.
  • Convolution + Sigmoid + Multiply with mixed precisions.

Intel Graphics Products

• Improved performance for Processor Graphics based on Xe2 architecture.
• Improved performance for the Intel Data Center GPU Max Series (formerly Ponte Vecchio).
• Improved performance for Intel Arc graphics (formerly Alchemist and DG2) and the Intel Data Center GPU Flex Series (formerly Arctic Sound).
• Improved RNN primitive performance for LSTM cell case.
• Improved performance of f8_e4m3 data type emulation on Intel Data Center GPU Max Series (formerly Ponte Vecchio).

AArch64-based Processors

• Improved convolution forward propagation, matmul, and softmax performance for processors with SVE support.
• Improved bf16 matmul, convolution, and reorder primitives performance with Arm Compute Library (ACL).
• Improved eltwise primitive performance with gelu_erf algorithm with ACL.

Functionality

• Introduced sum and binary post-ops support for layer normalization primitive. This functionality is currently implemented on CPUs only.
• Introduced support for int4 data type and extended quantization model with support for grouped scales and zero points.
• Introduced fp64 matmul support. This functionality is currently implemented on Intel GPUs with hardware acceleration for fp64 math only.
• Extended floating point math mode API to support weight decompression scenarios. See matmul weights decompression example to get started. New floating mode is supported in the following configurations:
  • bfloat16 matmul with int8 weights on Intel CPUs.
  • float16 and bfloat16 matmul with int8 or int4 weights on Intel GPUs.
• [experimental] Introduced microkernel API for Intel Architecture Processors. This API exposes internal mechanisms used in matmul and convolution implementation to expert users.

Usability

• Extended error messages for engine and memory objects creation errors.
• Extended verbose mode diagnostics with information on dispatching decisions for all primitives.
• Introduced support for clang++ host compiler in SYCL builds.
• Introduced API for tensor serialization and deserialization.
• Extended verbose mode diagnostics for Graph API with information on pattern matcher decisions.
• Introduced OpenCL runtime support for Graph API.
• Added support for building oneDNN with installed Arm Compute Library (ACL).

Validation

• Extended benchdnn with support for tensor tags in RNN primitive validation.

Breaking Changes

• Updated minimal supported ACL version to 24.04 (was 23.11).

Thanks to these Contributors

This release contains contributions from the project core team as well as Abdel @quickwritereader, @AngryLoki, Crefeda Rodrigues @cfRod, Daniel Richard G. @iskunk, David Svantesson @davsva01, @deepeshfujitsu, Dylan Angus @dylan-angus-codeplay, Emanuele Rocca @ema, Fadi Arafeh @fadara01, Hernan Martinez @hmartinez82, John Osorio @kala855, Jonathan Deakin @jondea, @kasturedeeksha, Kentaro Kawakami @kawakami-k, Nikita Shulga @malfet, Radu Salavat @Radu2k, Renato Barros Arantes @renato-arantes, Roman Zhukov @rozhukov, Ryo Suzuki @Ryo-not-rio, @Shreyas-fuj, Sunita Nadampalli @snadampal, Tadej Ciglarič @t4c1, Vineel Abhinav @vineelabhinav, @vishwascm. We would also like to thank everyone who asked questions and reported issues.

v3.4.3

This is a patch release containing the following changes to v3.4.2:

• Fixed GPU detection issues on systems with several different Intel GPUs (0fb7e6e)

v3.5-rc

This is a release candidate for oneDNN v3.5. Please provide feedback and submit defect reports via Github issues.

Performance Optimizations

• Intel Architecture Processors:

  • Improved performance for 4th generation Intel Xeon Scalable processors (formerly Sapphire Rapids).
  • Improved performance for the future Intel Xeon Scalable processors (code-named Sierra Forest and Granite Rapids).
  • Improved performance of group normalization primitive.
  • Improved performance of matmul primitive with sum post-op for batched cases on processors with Intel AMX instruction set support.
  • Improved performance of the following subgraphs with Graph API:
    • Multi-Query Attention (MQA).
    • Scaled Dot Product Attention (SDPA), including the variant with select operation.
    • LayerNorm + Multiply + Quantize produced by SmoothQuant algorithm.
    • Convolution + Sigmoid + Multiply with mixed precisions.

• Intel Graphics Products:

  • Improved performance for Processor Graphics based on Xe2 architecture.
  • Improved performance for the Intel Data Center GPU Max Series (formerly Ponte Vecchio).
  • Improved performance for Intel Arc graphics (formerly Alchemist and DG2) and the Intel Data Center GPU Flex Series (formerly Arctic Sound).
  • Improved RNN primitive performance for LSTM cell case.
  • Improved performance of f8_e4m3 data type emulation on Intel Data Center GPU Max Series (formerly Ponte Vecchio).

• AArch64-based Processors:

  • Improved convolution forward propagation, matmul, and softmax performance for processors with SVE support.
  • Improved bf16 matmul performance with Arm Compute Library (ACL).
  • Improved eltwise primitive performance with gelu_erf algorithm with ACL.

Functionality

• Introduced sum and binary post-ops support for layer normalization primitive. This functionality is currently implemented on CPUs only.
• Introduced support for int4 data type and extended quantization model with support for grouped scales and zero points.
• Introduced fp64 matmul support. This functionality is currently implemented on Intel GPUs only.
• Extended floating point math mode API to support weight decompression scenarios. See matmul weights decompression example to get started. New floating mode is supported in the following configurations:
  • bfloat16 matmul with int8 weights on Intel CPUs.
  • float16 and bfloat16 matmul with int8 or int4 weights on Intel GPUs.
• [experimental] Introduced microkernel API for Intel Architecture Processors. This API exposes internal mechanisms used in matmul and convolution implementation to expert users.

Usability

• Extended error messages for engine and memory objects creation errors.
• Extended verbose mode diagnostics with information on dispatching decisions for all primitives.
• Introduced support for clang++ host compiler in SYCL builds.
• Introduced API for tensor serialization and deserialization.
• Extended verbose mode diagnostics for Graph API with information on pattern matcher decisions.
• Introduced OpenCL runtime support for Graph API.
• Added support for building oneDNN with installed Arm Compute Library (ACL).

Validation

• Extended benchdnn with support for tensor tags in RNN primitive validation.

Thanks to these Contributors

This release contains contributions from the project core team as well as @AngryLoki, Crefeda Rodrigues @cfRod, Daniel Richard G. @iskunk, @deepeshfujitsu, Dylan Angus @dylan-angus-codeplay, Emanuele Rocca @ema, Hernan Martinez @hmartinez82, John Osorio @kala855, Jonathan Deakin @jondea, @kasturedeeksha, Kentaro Kawakami @kawakami-k, Nikita Shulga @malfet, Radu Salavat @Radu2k, Renato Barros Arantes @renato-arantes, Roman Zhukov @rozhukov, Shreyas-fuj @Shreyas-fuj, Sunita Nadampalli @snadampal, Tadej Ciglarič @t4c1, Vineel Abhinav @vineelabhinav, @vishwascm. We would also like to thank everyone who asked questions and reported issues.