[Don't merge]Upgrade submodule oneDNN to v3.7 (#147498)(Zi) #147917
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/147917
Note: Links to docs will display an error until the docs builds have been completed. ❌ 13 New Failures, 1 Unrelated FailureAs of commit 85ed6ad with merge base ab81ca5 ( NEW FAILURES - The following jobs have failed:
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This PR is to upgrade submodule oneDNN to v3.7. ## Improvements - Improved performance of convolution and matmul primitives on Intel Xeon processors with Intel AMX instruction set support (formerly Sapphire Rapids and Granite Rapids). - Improved performance of int8 and fp32 forward convolution primitive on processors with Intel AVX2 instruction set support. - Improved performance of fp8 matmul primitives with bf16 and fp16 bias data type on Intel Xeon processors with Intel AMX instruction set support (formerly Sapphire Rapids and Granite Rapids). - Introduced initial optimizations for Intel GPUs based on Xe3 architecture. - Added bfloat16 support for SDPA, implemented fp16 and bf16 gemm kernel in SDPA. - Fixed f16 matmul accuracy, the issue of SDPA cannot dispatched to ukernel, bf16/fp16/fp32 conv performance, INT8 Kernel trigger page fault, deconvolution precision issue on complex128 and fp64 and gemm correctness issue in float16 issues. - Improved bf16 matmul performance with fp32 destination with Arm Compute Library (ACL). - Improved bf16 to fp32 reorder performance. - Improved bf16 reorder performance. - Improved bf16 convolution with ACL. Fixes pytorch#136348. ## Validation results on CPU 1. NLP models accuracy/inference/training   2. Torchbench cpu userbenchmark inference & training  3. Inductor quantization  4. Dynamo benchmarks         ## Validation results on XPU Accuracy is same as baseline. Performance is shown below.  ## Validation results on ARM   Pull Request resolved: pytorch#147498 Approved by: https://github.com/fadara01, https://github.com/mingfeima, https://github.com/atalman
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This PR is to upgrade submodule oneDNN to v3.7.
Improvements
Fixes #136348.
Validation results on CPU
Validation results on XPU
Accuracy is same as baseline. Performance is shown below.
Validation results on ARM
Pull Request resolved: #147498
Approved by: https://github.com/fadara01, https://github.com/mingfeima, https://github.com/atalman
Fixes #ISSUE_NUMBER
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