Onednn

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2.3.3

This is a patch release containing the following changes to v2.3.2:
* Reverted check for memory descriptor stride validity for unit dimensions (861c6252a5957bd908a5183fcaa4cd7e29b61192)
* Fixed build errors on Fucshia OS (753b5310317938d37379a34c7d817fb94f61efec)
* Fixed implicit conversion in GPU GEMM implementation (30dee23e2d60e255ecc0b0dc199cdccb284c66b1)
* Addressed issues detected by clang TSan (888ab523863e59433da70e72a14927a590673f89, 7555fd839fe04320fd0a470019f798b0b958c45e, 4ffdb3cd09f46df7b7d544c76f0b63d8cb4801ca, 57b8ffd8ec2bea4c15687e52cf906c7c5cb1202a, b52b2c09a54968b5bc8739034f2bcb925af0b9ce, 84b200f6493a094f46b48503e4519d457ad7d0b5, 67deb8eae31cecb42f8339c2baf9cb7b9ff73962)
* Fixed undefined access issues detected by clang UBSan (5bab17c967e4550f7168943605cdd8b93833d564, 3494b1e973f2db02dac09c70ad6292ac07fa881a, 688536052e9395f8c8e8ecddc9cc28d42ff19272, 8cbe861995c27ee1be24b22c13067f011e919c7b, b13a2156857a16bb0677469b80eba30b56f6f91f, 859622df60226b3de38169ec9cd40a7b3715d6b9, 5813c99f69d8033289cbfff47a97df4cb0420abd)
* Fixed memory leak in CPU GEMM implementation (45e3039fbed8407cc3bc09db5228f6e620ba3286, fd6d14caa1085ead840b6f5470d6bc69f66336f8)
* Fixed int8 convolution correctnes issues on Intel Integrated Graphics (b7d40a0ec245bb33960e8dc5571d1e0c462c3c3b, 72e48568014a22cfc0886794bcae0d36527c339b)
* Fixed access violation issue in GEMM implementation on Windows (aac6b2325379c3954ff773c8eca9a903958ba90a)

2.3.2

This is a patch release containing the following changes to v2.3.1:
* Fixed performance regression in fp32 inner product primitive for processors with Intel AVX512 support (3e379b8c51a2fc2e72be6c49c9e6855f003af9e6)
* Removed assert related to Winograd convolution algorithm dispatching on GEN9 GPUs (2b4f73adf89a3804dd5018014596ad2354309d40)

2.3.1

This is a patch release containing the following changes to v2.3:
* Improved int8 GEMM performance for processors with Intel AVX2 and Intel DL Boost support (f5c071bc371c26cac30bb68cda3ab1224ed697c1)
* Fixed integer overflow for inner product implementation on CPUs (66971b57889d1246c643d736e50195c1bcd46a60)
* Fixed out of bounds access in GEMM implementation for Intel SSE 4.1 (4e81df0a26e520c161527d52ce63d55734e9dabb)
* Fixed correctness issue for depthwise convolution post-op with non-default scales on CPUs (783e1d6f035d20915cc1c8722d1b512888111beb, 066c832f7a2f6892a79c3f1b5a04b1a5f236e874)
* Fixed crash for s8 binary primitive on Windows (d9fd397e2f130dddffbd2ced37edb300a2ba7649)
* Fixed performance regression in fp32 to u8 reorder for Intel AMX specific memory formats (97f40cf0efef17361e948423a0b4fc2db04a903c, 532648adff4fe8590838f1f90409463b9237e358)
* Fixed correctness issue for bfloat16 convolution weight gradient on processors with Intel AMX support (053406d0fd5a91f3e64adb81828be1632b74f9a5, 6649b759a5e801ad095c3c44d74c1dc27ab82617)
* Fixed correctness issue for bfloat16 inner product backpropagation on processors with Intel AMX support (a2e6c55261bb3c353a295b7e2e57d403e5d73696)
* Fixed correctness issue for bfloat16 convolution with padded memory formats on GEN9 GPUs (c0aea07a7e5b21829e4d484e232b9eccf49128d4)
* Fixed correctness issue for int8 matmul primitive with zero points on processors with Intel AMX support (55cb716084cc625bc97e5f90b4f82bb2fcd72962)
* Fixed segfault in depthwise convolution post-op on CPUs (ad466354b3108c4cacb1b85a6f93f8bdfe9d4e59)

2.3

Performance Optimizations
* Extended primitive cache to improve primitive descriptor creation performance.
* Improved primitive cache performance in multithreaded configurations.
* Intel Architecture Processors
* Introduced initial optimizations for bfloat16 compute functionality for future Intel Xeon Scalable processor (code name Sapphire Rapids). The functionality is disabled by default and should be enabled via [CPU dispatcher control](https://oneapi-src.github.io/oneDNN/dev_guide_cpu_dispatcher_control.html).
* Improved performance of binary primitive and binary post-op for cases with broadcast and mixed source and destination formats.
* Improved performance of reduction primitive.
* Improved performance of depthwise convolution primitive with NHWC activations for training cases
* Intel Graphics Products
* Improved fp32 and fp16 Winograd convolution performance.
* Introduced support for automatic selection between direct and Winograd convolution algorithms.
* Improved int8 depthwise convolution performance.
* Improved performance of reorder, shuffle, concat, binary, and batch normalization primitives
* Improved layer normalization performance for blocked formats.
* AArch64-based Processors
* Improved reorder primitive performance for systems with SVE 128 and SVE 256 support.
* Improved eltwise primitive performance for systems with SVE 512 support.

Functionality
* Extended [batch normalization](https://oneapi-src.github.io/oneDNN/dev_guide_batch_normalization.html) and [layer normalization](https://oneapi-src.github.io/oneDNN/dev_guide_layer_normalization.html) primitives API to take separate scale and shift arguments.
* Extended [resampling](https://oneapi-src.github.io/oneDNN/dev_guide_resampling.html) primitive with post-ops support and mixed source and destination data types.

Usability
* Introduced binary distribution in [conda-forge](https://github.com/conda-forge/onednn-feedstock). Supported configurations cover Linux, Windows, and macOS operating systems and Intel64/AMD64, Aarch64, and PPC64 architectures.
* Introduced support for GPU-only build. This configuration helps to reduce binary footprint for applications targeting GPU.
* Introduced an option to use GNU OpenMP as CPU runtime for DPC++ configuration.
* Introduced [verbose log converter](https://github.com/oneapi-src/oneDNN/tree/master/scripts/verbose_converter). This tool processes [oneDNN verbose logs](https://oneapi-src.github.io/oneDNN/dev_guide_verbose.html) and generates test cases for benchdnn.

Breaking Changes
* Updated minimal supported CMake version from to 2.8.12 (was 2.8.11).
* Updated minimal supported ACL version from 21.05 (was 21.02).

Thanks to the Contributors
This release contains contributions from the project core team as well as Alexandre Truong aletru01, Arthur Mitrano aaraujom, fitchbe fitchbe, Isuru Fernando isuruf, Joe Ramsay joeramsay, Kentaro Kawakami kawakami-k, leizheng1 leizheng1, Nomoto Kazuhiro NomotoKazuhiro, Peter Caday petercad, Pablo Romero pablocum, Takumi-H Takumi-Honda, Uwe L. Korn xhochy, Vasily Rubtsov vasilyru. We would also like to thank everyone who asked questions and reported issues.

2.3rc2

This is a release candidate for oneDNN v2.3. Please provide feedback and submit defect reports via [Github issues](https://github.com/oneapi-src/oneDNN/issues/new/choose).

2.3rc

This is a release candidate for oneDNN v2.3. Please provide feedback and submit defect reports via [Github issues](https://github.com/oneapi-src/oneDNN/issues/new/choose).

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