Comments (1)
Format any is designed to allow compute intensive layers like convolution and inner product to choose the layout which will result in the best performance. The rest of the layers, including relu, lrn, batch norm and concats/splits will work with all the layouts that come out of the convolutions or inner products. When creating these primitives you should use previous layer output descriptor directly. Please refer to examples or documentation.
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Related Issues (20)
- Is this based opencl or intel dpc++ HOT 6
- undefined symbol: void __cdecl __itt_pt_mark_event(unsigned char) and error : undefined symbol: void __cdecl __itt_pt_mark(unsigned char) HOT 2
- How to check whether the SYCL version oneDNN depends on is backward compatible? HOT 7
- [nvidia] int8 convolution with s8 dst primitive and a sum post op fails correctness check
- windows build faile with "FAILED: cmTC_478c5.exe" HOT 2
- test the example of ocl, it reports "onednn_verbose,primitive,error,ocl,errcode -30,CL_INVALID_VALUE,src\gpu\ocl\ocl_utils.cpp:509" HOT 9
- X64: "Error in M_tail_block index, not within range" raised in brgemm_matmul HOT 3
- Understanding Injectors and evaluating their performance. HOT 6
- Security.md: replace incorrect email address HOT 1
- Build failure on AArch64 due to brgemm_matmul_t HOT 3
- which case can report "No configurations found." HOT 9
- Why is the convolution performance of bf16 using opencl very low? HOT 3
- Bad speed for f32:s8:f32 matmul HOT 11
- How can I create a matmul primitive with A16W8 (active 16bits, weight 8bits) configuration? HOT 2
- [Proposal] Add cpu alloc/free callback to support customlize memory alloctor APIs. HOT 2
- Assertion `dynamic_cast<derived_type>(base) == base' failed HOT 3
- Why do the "reorder" operations of the same operator take very different times on the CPU and GPU platforms? HOT 3
- [ACL] 3D convolution kernel `NEConv3D` is not integrated
- INT8 Performance difference between OneDNN v2.6.3 and v3.4.1 HOT 1
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